By 2026, technology strategy transforms fundamentally: companies shift from isolated innovations to building systems that are faster, safer, and structurally more resilient. AI becomes a natural part of product workflows, but it is only one element of a broader shift toward trust-by-design architectures, continuous security, and stricter regulatory expectations.

Infrastructure moves toward hybrid and region-specific models, shaped by data sovereignty and the global expansion of data centers. At the same time, the talent landscape changes: strong engineering expertise, AI literacy, and operational maturity become essentials, not differentiators.

Companies that succeed in 2026 will combine AI-enabled workflows, built-in security, modern infrastructure choices, and globally distributed talent into a unified execution model.

Why 2026 Is a Technological Turning Point

In 2023–2025, the adoption of artificial intelligence (AI) stops being “innovation for the sake of hype” and becomes a critical element of business operations. Since 2023, the share of companies that not only experiment but regularly use generative AI has been steadily growing.

According to a McKinsey & Company report, by 2024, 65% of organizations reported using GenAI in at least one business function almost twice as many as the year before. These are no longer pilots these are real operational workflows.

At the same time, regulatory and operational requirements intensify: security, compliance, explainability, and resilience become mandatory. Combined with accelerated demand for digital transformation, this creates a context where 2026 is viewed as a point of “setting one’s own standards,” rather than “catching up with competitors.”

The world is rapidly approaching a moment when AI moves from experimentation to full-scale systemic business transformation. According to McKinsey & Company’s “The State of AI in 2023: Generative AI’s Breakout Year”, about one-third of organizations reported they already use generative AI regularly in at least one business function. This means AI is no longer a niche add-on it is becoming part of the operating model. Companies that want to remain competitive now have to adapt.

Key Drivers Behind This Shift

1. Speed of development and time-to-market

   With advanced AI tools becoming widely accessible, traditional development cycles start to feel too slow. Markets increasingly expect new features, products, and updates to be delivered faster. AI helps accelerate development without compromising quality.
2. Transparency and trust

   As AI tools permeate business environments, the demand for explainability and clarity grows. Both developers and stakeholders, including regulators, expect AI-driven decisions to be understandable and traceable.
3. Integration of AI into every layer of the business

   AI is no longer treated as a separate experiment. It is becoming a foundational capability across product development, support, R&D, analytics, and DevOps.

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Agentic AI: from a “tool” to a “digital workforce”

Agentic AI marks a shift from traditional generative models to systems that operate with a higher level of autonomy and contextual awareness. These agents can plan actions, execute multi-step workflows through APIs and tools, adapt their behavior in real time, and make task-level decisions independently. By 2024–2025, many organizations began viewing AI agents not just as assistants but as digital employees integrated directly into operational workflows.

What Agentic AI actually does

• Plans and sequences actions instead of producing isolated outputs.
• Executes tasks across tools, services, and APIs without manual intervention.
• Adjusts decisions and behavior based on context and feedback.
• Operates with autonomy at the task level, similar to a junior analyst or operator.

Where Agentic AI already creates value

• Operational workflows

  Automation of routine tasks, incident triage, metric monitoring, and initial data processing, resulting in lower workload and faster response times.
• Business functions

  Measurable gains in marketing, customer support, and back-office operations; Deloitte’s 2024 report highlights early ROI even when many implementations are still in pilot stages.
• Enterprise platforms

  Integrated into decision support, content generation, analytics, and document management rather than used as isolated features.

What this means for engineering teams and outsourcing

• Automation of routine work

  Parts of PM, QA, and DevOps activities can be delegated to agents, reducing dependency on headcount and increasing overall efficiency.
• Shift in outsourcing expectations

  Clients increasingly want orchestrated ecosystems that combine people, agents, and processes—not just “hours and developers.”
• New talent requirements

  Engineers must understand AI-driven workflows, demonstrate architectural thinking, and manage change in systems where AI is part of the operational loop.

DevSecOps as a Must-Have Capability

As AI-driven systems grow more complex, DevSecOps shifts from a “best practice” to a non-negotiable baseline. Modern organizations can no longer afford security as a late-stage activity; instead, protection, compliance, and auditability must be integrated directly into the delivery pipeline. This shift is reinforced by global standards and regulatory expectations, including ISO/IEC 27001, NIST guidelines, and the EU AI Act, which collectively require provable security, continuous monitoring, and full traceability across software systems.

Automated Security Checks as the New Normal

Modern DevSecOps pipelines rely on automation to reduce human error and accelerate detection. As recommended in industry best practices from OWASP and NIST, every commit, build, and deployment is expected to pass through multiple layers of automated checks.

Typical pipelines now include:

• SAST/DAST on every commit, static and dynamic analysis to detect vulnerabilities early.
• Automatic ticket creation when issues are found, eliminating the risk of “lost” or ignored findings.
• Continuous log and alert monitoring, often enhanced by AI agents capable of triaging incidents in real time.
• Dependency and container vulnerability scanning integrated into each build.

This is not a toolbox; it is a continuous security system. Automation shortens incident response time, reduces operational risk, and establishes a verifiable record of security compliance — a requirement increasingly enforced by regulators, auditors, and enterprise customers.

Compliance-by-Default Architecture

By 2026, software systems are expected to be built so that compliance is not an add-on but a structural property — “secure by default, compliant by design.” This aligns with international standards such as ISO/IEC 27001 and guidance from ENISA and NIST, which emphasize embedding governance controls directly into engineering workflows.

A compliance-by-default architecture typically includes:

• Secure pipeline templates, ensuring every project begins with correct access control, encryption, and audit settings.
• Automated security policies applied at build and deployment stages.
• Least-privilege access control enforced by default, not manually configured per team.
• CI/CD gates that halt deployment if any security checks fail.

This approach prevents the accumulation of “security debt,” stops risky exceptions from slipping into production, and ensures that every release is both audit-ready and regulation-aligned.

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Trust by Design: why modern products must prove trust, not just claim it

“Trust by Design” has shifted from a trend to a requirement: security, transparency, ethics, and governance must be embedded from day one, not patched in later. Modern systems are expected to anticipate risks, explain decisions, maintain audit trails, and enforce strict access control. It’s no longer enough to say “our product is secure”—companies must prove it through certifications, transparent processes, and observable module behavior.

This applies directly to AI. The EU AI Act (2024) requires explainability and complete traceability for high-risk systems, making AI components auditable and accountable.
Source: European Parliament, 2024 

Access management now follows the principle of least privilege: everything is forbidden unless explicitly required. These expectations are reinforced across ISO/IEC 27001:2022 and ISO/IEC 27701:2019, which define strict requirements for security controls, logging, and governance.

Trust must be engineered directly into the SDLC. Automated SAST/DAST checks, continuous logging, anomaly detection, and secure-by-default development patterns are what enable teams to deliver fast without compromising safety. This is the emerging “trust-speed paradox”: clients want immediate delivery, but also airtight security, compliance, certifications, and evidence before the first deployment.

Reports from McKinsey highlight this shift: organizations increasingly treat trust, security, and explainability as core differentiators rather than operational overhead.

While many consulting frameworks describe idealized automated trust pipelines, few organizations have implemented them fully. Yet demand continues to grow: businesses expect systems that correlate alerts, create tasks automatically, escalate anomalies, and use AI-driven logic to distinguish real issues from noise.

This isn’t a small process update, it’s a new industrial shift in how software is built. Companies that adopt Trust by Design early will gain a competitive advantage, while those relying on outdated practices will struggle to meet rising expectations.

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Regional Hubs as the Strategic Choice for 2026

Why regional hubs matter

By 2026, IT outsourcing is shifting from cost-driven offshore models to value-driven, collaboration-first Regional Hub partnerships. The rise of AI-driven product development, accelerated release cycles, and the need for tightly integrated distributed teams make them not just an alternative but the strategic default.

The rise of regional engineering hubs

The defining trend for 2025–2026 is the strengthening of regional engineering ecosystems — hubs with deep expertise, senior talent density, and mature delivery practices.

Eastern Europe
As noted in the Future of IT 2025 report (Alcor BPO), Eastern Europe remains one of the strongest regions for businesses due to its high engineering maturity, availability of strong middle/senior developers, and Western-aligned work culture.

Countries leading the cluster include Poland, Ukraine, Romania, Czech Republic and Lithuania.

Türkiye and the Middle East
These regions emerge as attractive options for European companies — combining geographic proximity, cost efficiency, and growing technical depth.

This global diversification allows businesses to assemble distributed engineering teams capable of delivering 18–20 hours of productive development per day without sacrificing coordination quality.

What this means for businesses in 2026

• Speed

  Hubs support rapid iteration, real-time communication, and faster delivery cycles.
• Quality & stability

  Mature engineering cultures, strong mid/senior talent pools, and solid DevSecOps practices reduce delivery risks.
• Scalability

  Companies can expand teams quickly across regional hubs without compromising communication or governance.
• Cost-to-value balance

  Regional hubs offer a mature, high-performance delivery ecosystem, optimized for predictability, speed, and consistently strong engineering output.

For organizations building AI-heavy, compliance-sensitive, or mission-critical digital products, regional hubs are not merely an outsourcing option — they are a core part of modern engineering strategy.

The Road to 2026: How Businesses Should Shape Their Strategies Today

1. Treat AI adoption as a systemic transformation, not a “pilot ± luck” experiment

Companies that succeed with generative AI typically:

• Integrate AI across multiple functions simultaneously (marketing, IT, operations)
• Build their own or customized models instead of relying exclusively on off-the-shelf solutions
• Design governance, security, and control frameworks in parallel with technical implementation.

2. Implement DevSecOps and embrace “security by default”

Security, transparency, and control are becoming essential competitive advantages. Architectures that lack logging, auditability, and human-in-the-loop oversight lose credibility already at the negotiation stage.

3. Use engineering hubs for flexibility and cost-effective scaling

Regional engineering hubs have become a strategic way to scale capabilities quickly, accelerate product delivery, and maintain operational control without compromising quality or compliance. Their time-zone proximity, senior talent density, and alignment with industry standards make them especially valuable for European organizations that require fast iteration cycles, predictable execution, and culturally compatible collaboration.

4. Invest in talent capable of operating in the new AI-driven environment

Strong engineers who combine technical expertise with business thinking and AI literacy are becoming invaluable. HR strategies, value propositions, and retention models must evolve to attract and retain such specialists.

Conclusion

By 2026, AI-driven transformation will no longer be optional — it becomes a defining feature of how competitive organizations operate. Companies that move beyond pilots and embrace AI as a structural capability will pull ahead, while those who delay will struggle to keep pace with rising expectations for speed, transparency, and operational excellence.

Agentic AI fundamentally reshapes work itself: tasks once handled manually now flow through autonomous digital agents that support engineering, operations, and business teams. This shift elevates the importance of governance, trust, and robust security practices. As adoption increases, clients, regulators, and investors expect clear auditability, responsible AI policies, and architectures that can be explained, not just executed.

The global talent landscape is also changing. Access to skilled engineers becomes a core competitive factor, driving organizations toward development hub models that offer proximity, scalability, and compliance without compromising on quality. Meanwhile, internal teams must evolve: the most valuable specialists are those who combine engineering expertise with business reasoning and AI fluency.

Ultimately, the winners of 2026 will be the companies that:

• Embed AI across multiple business functions
• Design secure, transparent systems from day one,
• Scale through flexible local hubs ecosystems
• Invest in people capable of navigating an AI-augmented environment.

The transformation is already underway. The question for every organization is no longer whether to adopt AI at scale — but how quickly and strategically they can make it a core part of their operating model.

Scrum myths: we have no risks, we are flexible!

“Scrum is about speed and flexibility, not about any risk registers!” – some may say. Let’s figure it out: Is there risk management in Scrum?

Spoiler: Yes. It doesn’t appear to be the case in the classics, and it’s not written in all caps in the manifesto.

TOP-3 Scrum Myths

Let’s separate fact from fiction: here are the top Scrum myths—and what’s really true.

Myth #1: There are No Risks in Scrum—Iterations are a Magic Pill

Reality: There are always risks. Iterations only reduce the damage from encountering them.

Is the CI broken? Is the developer sick? Is the client suddenly “changing his mind”?

Even the most flexible process won’t save you if the team doesn’t think about what could go wrong.

Myth #2: Scrum Magically Handles Risks on Its Own

Reality: Scrum gives you tools, but it doesn’t do the work for you.

• Planning — to discuss potential problems before they hit you.
• Daily checks — to catch warning signs: “We can’t launch a feature for the second day.”
• Retrospective — to understand why a bug popped up 2 hours before the release, and what to do about it.

But if you’re going through the motions and not talking about the matter, there won’t be any magic.

Myth #3: Talking About Risks is Not Agile

Reality: Talking about risks is a sign of maturity.

If you know that:

• Two people are going on vacation
• The new library is an experiment
• The client changes requirements every 3 days

… it’s better not to hide your head in the Scrum guide, but to say out loud: “We have a risk here.”

And then ask: what can be done about it?

Real Scrum is Not Naivety, but Awareness

Scrum does not require you to start a 500-line spreadsheet with probability assessment. But it does imply that the team thinks, observes, and acts.

Risk management in Scrum is a built-in behavior: to notice a problem before it explodes.

Summary

✔ Scrum does not cancel risks – it teaches you to live with them

✔ Iterations, dailies, planning, and retrospectives are built-in risk management mechanisms

✔ The main thing is not to ignore, but to discuss and act

Because if you don’t manage risk, it manages you. Even if you call it “flexibility.”

The Internet of Things (IoT) is a concept of systems developed by connecting a large number of smart devices into a network. IoT-enabled systems help establish innovative warehousing and increase the level of automation. In essence, they help streamline inventory management and reduce operating costs.

According to recent statistics, technology adoption in warehouse automation is rising rapidly. Moreover, the number of responders who will adopt IoT in warehouse management will double by 2030.

How IoT Warehousing Works

The use of intelligent devices connected to one system promotes the fourth industrial revolution development.

The four core components of an IoT system are:

• Smart devices and sensors — IoT devices are the essential component of an intelligent warehouse. Smart sensors and IoT devices gather data and send it to processing units. 
• Network connectivity — Wired or wireless networks are used to connect IoT devices into one system. Network connectivity helps transfer all the data acquired by IoT devices to a server.
• Data processing and storing — A processing unit or server that is used to analyze all the data received. Also, it can store data for further usage.
• User interface — An application that helps users access raw data or get useful insights. Usually, the layer has the form of a dashboard that conveniently displays the required information. Also, the user interface should offer the opportunity to send commands to establish centralized IoT warehouse management.

An IoT system foresees the opportunity to gather data automatically and send requests to intelligent devices. The architecture of an IoT-enabled warehouse management system is the following:

1. IoT Devices

All types of smart sensors and IoT devices gather raw data, so it get processed by IoT-based systems. Usually, intelligent devices track the placement of assets and help monitor warehouse environments. Also, they help track the number of items left in a warehouse and new packages that arrive at a warehouse.

2. Gateway

A gateway connects IoT warehouse devices to the Internet or local network to establish an IoT in warehouse management system. Smart devices can be connected via:

• WiFi network
• Ethernet connection
• Bluetooth connection
• Cellular network
• Near Field Communication (NFC)

3. Server

A dedicated computer receives raw data from IoT devices. All the data is processed to get valuable insights or saved for further usage. In order to automate the warehouse management process, a server can send commands to smart devices following developed algorithms.

The two types of servers that can be used are:

• Cloud-based — A dedicated or shared server installed and maintained by a third-party company. It offers the opportunity to scale the disc space and computing performance upon demand. Access to a cloud server is provided via the Internet.
• On-premises — A physical server installed in a warehouse to create a local network of IoT devices. An on-premises server uses physical hard drives to store data and has limited computing power.

Let’s compare the two types of servers in more detail.

	Cloud-based	On-premises
Data control	All the data is transferred to a cloud server via the Internet.	All the information is transferred to an on-premises server using a local network or Internet connection.
Scalability	It can be easily scaled up upon demand manually or automatically. An external vendor provides additional computing power.	Additional hardware needs to be installed for vertical scaling.
Launch time	Fast and stress-free launch.	A business needs to purchase the required hardware and install it.
Cost	A business needs to cover the subscription fee only. It depends on the amount of computing power assigned.	A business needs to cover the following expenses: installation, maintenance, scalability, security, storage, and downtime losses.
Maintenance	Maintenance is provided by a vendor.	It’s required to have dedicated specialists for on-premises server maintenance.
Security	High level of security. However, data is transferred via the Internet.	A company fully manages security. There is an option to build a local network.
Hardware control	All hardware is chosen and managed by an external vendor.	A company is responsible for installing and updating hardware.

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4. Application

A mobile or desktop application with a well-thought-out user interface offers the opportunity to consume information and send commands. An application should be integrated into an inventory management system to track all the items in a warehouse. 

The IoT-driven warehouse system has a two-way connection. Hence, a user or server can send commands to IoT devices using a gateway. 

5. Edge Computing — Additional Layer

Large warehouses with rich IoT systems use an additional layer. The edge computing layer is located between the gateway and data storage layers. 

It implies extra edge nodes that can analyze data from smart devices and issue commands without sending information to the central server. 

The benefits of adding the edge computing layer are the following:

Improved speed

Usually, data processing units are installed near IoT devices and are designed to analyze data acquired from specific sensors only.

Consequently, it improves the speed of data processing and command issuing, which is vital for real-time tracking and sensors that help monitor safety. The quick response time is crucial for fire alarms, collision detectors, or gas leak prevention actuators.

Lower amount of data transferred

Large systems that utilize the Internet of Things in warehouse management and large-scale warehouse IoT solutions must transfer enormous amounts of data to the central server. Consequently, it increases the load on the gateway layer.

The integration of edge processing helps minimize the amount of data transferred to the central server. 

Reduced amount of data processed by the main server

The increased amount of data that needs to be analyzed affects the required computing power, which causes additional expenses. In the case when units in the edge computing layer process most data, the main server receives the minimum amount of information. 

In essence, a lower amount of computing power is required. It causes decreased cloud server bills or cheaper maintenance of on-premises servers.

Optimized data storage

A large amount of disk space is required when transferring all the data from IoT devices to the central server. However, the edge computing layer can optimize the amount of data needed to be stored. 

Additional computing units can analyze large amounts of raw data from smart devices and sensors to form valuable insights. They transmit the insights or condensed data to the main server. 

The optimized amount of data needed to be stored reduces the demand for free disc space.

Enhanced scalability

One of the core advantages of the edge computing layer is the ability to scale up the use of IoT in warehouse management for seamless administration of supply chains. 

The load on the central server and network does not increase significantly when new devices are added. Consequently, businesses can quickly scale up their warehouses by adding many new intelligent devices, smart sensors, and additional data processing units to the system.

Iot Devices and Use Cases

The is a large assortment of smart devices used for building smart warehouses. The most popular types of IoT devices are:

RFID Tags

Radio-frequency identification (RFID) tags are the most widespread technologies used in inventory management. Every tag is unique and holds information about a product. A tag can transmit data using radio waves. 

An RFID tag can hold many types of information. The most common data types are:

• Tag’s serial number
• SKU number 
• Name
• Location
• Weight
• Production date

There are two types of RFID tags:

• Active — A tag can access an energy source to generate radio waves transmitting data. Active RFID tags include an integrated circuit, antenna, battery, and an onboard transmitter. This type of tag has a more extended range. 
• Passive — Radio waves transmitted by an antenna generate current in an RFID tag so that it can share data. Passive tags have a short range. They consist of two parts: an integrated circuit and an antenna.

The core advantages of this technology are:

• Fast scanning — The technology foresees the opportunity to scan RFID tags in bulk. A scanner can read hundreds of tags at once. Therefore, the technology is beneficial for high-load warehouses. 
• No light requirements — RFID tags transmit data using radio waves. Therefore, there is no need to pour light on a tag to scan it.
• Ability to read and write data — Most types of RFID tags offer the opportunity to read and write data. Consequently, information can be updated, and tags can be reused. The serial number of tags is the only data type that cannot be updated. 
• Durable design — RFID tags can be used in harsh environments and last long. 
• Option to encrypt data — The technology offers the opportunity to write encrypted data to tags to enhance the security of inventory management. 
• Barcode system integration — Barcodes can be printed on RFID tags to integrate them with barcode scanning systems.

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Beacons

Beacons are small Bluetooth-powered devices that help facilitate inventory management. They come in different sizes and shapes. Besides, they may have various technical specifications. 

A beacon has an in-built battery to maintain continuous Bluetooth connectivity. Beacon tags can help track the location of assets precisely, which is the primary aim of the technology. Meanwhile, they can also contain data about products they are attached to.

In order to share information about an item’s location and properties, beacons need to be connected to another device via Bluetooth. A connected device becomes a point of network for a beacon when connected. 

Here is how beacons work in more detail:

1. A beacon device emits a Bluetooth signal
2. The Bluetooth signal is picked by a device connected to a network
3. Data is transferred from a beacon to a server for further processing

The key points to know about using beacons for IoT in a warehouse are:

• Bluetooth signal is emitted constantly — Any device can pick it simultaneously when reaching the required range. It facilitates live-time tracking of items in a warehouse. 
• Data storage option — Beacons can hold from 60 to 450 bytes of data. 
• Precise indoor positioning — Bluetooth Low Energy (BLE) technology helps detect the distance to an asset indoors if one device is connected. If several devices are in a beacon’s Bluetooth range, the technology can also determine the direction for precise positioning. The feature offers a competitive advantage over GPS trackers. 
• Extensive range — Unlike Near Field Communication (NFC) technology, beacons can emit Bluetooth signals that have up to a 230-feet range.
• Option to install additional sensors — Beacons offer the opportunity to install additional sensors for measuring the temperature, light level, humidity, movements, etc. 
• Long-lasting battery life — Since beacons use the BLE technology, they can work from 18 to 24 months on one battery. The battery life depends on the number of extra features. Beacons can work from 6 to 8 months in case additional sensors are installed. Energy-saving models of beacons can work for up to 5 years without the need to replace a battery. 

Beacons can be attached to: 

• Assets — The most widespread use of beacons. They help monitor items in a warehouse and locate them fast. 
• Vehicles — With the help of additional sensors installed, beacons can help monitor the location of cars, performance, idle time, etc. Also, they can notify about collision risks. 
• Tools — Help track tools used in a warehouse and help identify their location fast.
• Workers — Wearable beacons can track workers’ performance and identify idle time. 

Smart Sensors

An IoT warehouse can be easily managed and maintained from one place if intelligent sensors are installed, and modern warehousing techniques are applied. 

Smart sensors have inbuilt processing units that help detect certain items and analyze data collected before transferring it using a network connection. Moreover, they can help automate a lot of processes and enhance security.

The types of smart sensors used for IoT warehouse management are the following:

• Acoustic — Detect audio vibrations to define dangerous environments for workers and meet local regulations. 
• Chemical — Analyze the composition of fluids stored in a warehouse to detect dangerous cargo or meet quality standards. 
• Electrical — Measure the voltage, current, and power in a grid to help keep the electrical network of a warehouse safe. 
• Environmental — Monitor physical conditions in a warehouse. Environmental sensors can detect and measure temperature, gas leaks, humidity, air pressure, light, etc. 
• Image — Capture video using a camera, infrared, or ultraviolet (UV) sensors. Most smart image sensors can detect motion. Usually, image sensors are installed for security purposes. 
• Motion and force — Smart sensors that measure motion, weight, size, vibration level, position, rotational, etc. Motion and force sensors facilitate inventory management by tracking assets and their properties automatically. 
• Touch — Detect physical contact with an object. Tech sensors can measure the pressure applied or utilize the human body’s natural conductivity to detect touches.

The most widespread smart sensors on the IoT in the warehouse management market are temperature and humidity sensors. They help maintain the required conditions to store items properly. In case any unacceptable environmental conditions are detected, managers get notified immediately.

GPS Trackers

GPS trackers are widely used in fleet management. Smart trackers can help facilitate warehouse assets, tools, and vehicle tracking. For instance, GPS trackers can be attached to palettes, storage racks, forklifts, and other devices for precise positioning. 

Unfortunately, low signal and accuracy make GPS trackers less reliable indoors. However, they work accurately outdoors. Therefore, if assets are stored on large outdoor sites, GPS trackers can help find the required items fast.

The smart features of GPS trackers help enhance the application of IoT in warehouse management. The core features of smart GPS trackers are:

• Live time tracking — Being connected to the Internet, GPS positioning data is sent to a server for live time traceability. 
• Geofencing — Users can set operating and storing zones for assets, tools, and vehicles. In case any tracking items leave the permitted working zone or get misplaced, a manager gets a notification simultaneously.

AI Cameras

The use of cameras in an IoT warehouse isn’t limited to security as the main role of technology in warehousing. AI-powered cameras use artificial intelligence to monitor all the processes. They are capable of identifying objects captured by using smart algorithms. Also, they can define patterns and notify managers about accidents.

The main advantages that drive businesses to start using smart cameras when adopting the Internet of Things in a warehouse are:

• Easy installation — Smart cameras can be connected to a network using WiFi or Ethernet. Also, they may have in-built batteries. Wireless devices are easy to install in various locations. Nevertheless, a wired power connection is the most popular option because it erases the need to replace batteries.
• Object identification — Artificial Intelligence (AI) and Machine Learning (ML) offer the opportunity to identify objects and their properties. For instance, a camera can count the number of storage racks or boxes passed by to control all items in a warehouse meticulously.
• Remote access to cameras — With the help of an Internet connection, cameras can broadcast captured video in live time. Consequently, managers can use applications to access the video from any location to monitor all the processes.
• Motion detection — Video captured in high resolution may require a lot of storage space. In order to minimize expense, smart cameras have in-built motion-detection sensors, so they film when objects move within their focus distance.

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Smart HVAC Systems

Smart heating, ventilation, and air conditioning (HVAC) systems help streamline the usage of IoT in warehouses. Automation helps minimize expenses and keep the required environment in a warehouse stress-free. 

In most cases, intelligent HVAC systems have the form of detectors and accelerators connected into one system to maintain the optimal environments automatically. 

For instance, a smart thermostat measures the temperature and sends commands to keep the required condition. Also, the application of the Internet of Things in warehouses can help build HVAC systems run by certain algorithms.

The use of smart HVAC systems for IoT in warehouse management and maintenance helps achieve the following:

• Reduced utility bills
• Optimized energy system load
• Automatic maintenance

Also, smart HVAC systems have beneficial advantages that are:

• Predictive maintenance
• Real-time alerts and monitoring
• Detailed analytics 
• Guaranteed regulatory compliance

IoT Warehousing Trends

The Internet of things in the warehouse management market keeps rising at a high pace. Many companies develop new devices that help leverage the power of technologies to enhance warehouse management. 

The top three technologies used to streamline the IoT in warehouse management and inventory tracking are:

AR Glasses

The use of smart glasses is an upcoming trend in IoT warehouse management. Augmented reality (AR) glasses can add additional content to lenses, facilitating the work of personnel. 

For instance, AR glasses can show workers a list of items they need to pick and directions. 

The top features of AR glasses that help boost the performance of workers are:

• Speech recognition — It allows sending commands by voice and communicating with others hands-free. 
• Barcode scanning — In-built barcode scanner erases the need to use external devices. Workers can quickly scan barcodes of items by pointing glasses at them. Information about items can be shown on lenses or sent to a server via a gateway.
• Replaceable batteries — Smart glasses consume a lot of energy, so they need a reliable energy source. Replaceable batteries can provide the full-shift working capability for AR glasses.
• Video capturing and streaming — Some devices have in-built cameras to capture all the activities on video. Moreover, this IoT warehouse device can help stream video in live time and hands-free.

Automated Guided Vehicles

Automated guided vehicles (AGVs) are small robots that help adopt the autonomous inventory management approach. AGVs are small robots that move assets in a warehouse automatically. They navigate in an IoT warehouse using floor stickers, LiFi technology, vision cameras, or wires.

The core advantages of this technology are:

• Automated assets moving
• 24/7 work
• Great performance in monotonous tasks
• Automated route picking
• Automated charing 
• Reduced risk of human error
• Easy to scale up by adding new AGVs

These days, many enterprise-grade companies use automated guided vehicles to enhance IoT in warehouse management and increase automation.

Drones

Drones can facilitate inventory tracking in an IoT warehouse. They can operate indoors and outdoors without any issues. Drones that float indoors have protection cases installed to keep blades safe in case of a collision. 

With the help of drones, workers can easily make large distances and explore the upper shelves of a rack without the need to use a ladder. They can find the required items and scan codes remotely. Also, drones can be used to examine the condition of other tools and constructions without reaching them physically.

Some advanced drone models can be used to transport packages in a warehouse.

Warehouse Automation System — Top Solutions and Trends

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Application Of IoT In Warehouse Management

There are many other ways businesses adopt IoT in warehouse management because of the large assortment of smart devices and the ability to connect them to a system. 

Let’s check the most widespread examples of IoT in warehouse applications for inventory management and monitoring.

Automated Items Tracking

Developing IoT in warehouse management is tough without thorough tracking of items. Automated item tracking helps track every item entering or leaving a facility. An inventory management system must know all the details about objects, their current location, and storage places. 

Automated items tracking has the form of devices that detect codes, scan them, and send information about assets to a server. 

For instance, automated item tracking can be adopted in an IoT warehouse with the help of devices that scan barcodes or signs on racks moved by AGVs. Also, an RFID tag scanner can be installed on a belt to track all items that pass by.

Storage Racks Moved By Robots

Automated guided vehicles that move storage racks are one of the most widespread types of IoT in warehouse management. Robots can take shelves with items to the picking and packing zone automatically. 

After picking up the required products by workers, AGVs move shelves to the defined storage place in a warehouse. Robots can also automatically take empty shelves to the putaway zone and move them to the storage zone. Hence, workers can store incoming products without the need to move into a warehouse. 

Amazon is one of the youngest adopters of this technology. The company has been using automated guided vehicles to move assets in its warehouses for over ten years.

Smart Warehouse Maintenance

The application of the Internet of Things in warehouses helps make the maintenance process less burdening. Smart warehouse maintenance requires businesses to install many sensors, actuators, and HVAC systems connected to the Internet.

For example, an average smart warehouse needs to imply thermostat and humidity sensors that are connected to one system. Smart sensors track the environment in a warehouse and send commands to HVAC systems to maintain the required conditions.

Businesses can easily scale up IoT in warehouse management by installing various sensors and actuators like smart locks, gas leakage sensors, or smart bulbs.

CodeIt IoT Warehousing Expertise

The CodeIT team has rich experience in building IoT-integrated tools for centralized monitoring and control.

The most recent solutions we delivered to our clients have been developed by the CodeIT AI Lab—to streamline warehouse management using the latest tech.

Learn more about the tools we’ve created and problems solved for our clients below.

1. AI-Driven Maintenance

The tool is fully integrated with the client’s IoT devices. It collects all the data and analyzes it using AI, delivering real-time insights and predictive maintenance recommendations. Also, it comprises a smart assistant for the tech team. 

Problem

The client experienced unplanned downtime and high maintenance costs. The problems were caused by the extensive labor involvement and poorly planned maintenance schedules.

Solution

The software development process is composed of analysis, planning, and development (MVP + new features each sprint). Also, our specialists created custom integrations with the client’s IoT sensors and prepared technical documentation.

The core components of the AI-driven predictive maintenance solution are:

• Real-time data. All the data is collected from the IoT sensors installed in a warehouse and is transferred to the central server.
• Self-hosted architecture. The AI tool is installed on on-site services—no information is shared with third parties.
• Failure prediction. A machine learning (ML) model was trained on the client’s data. It helps predict when certain equipment is likely to fail and analyzes the root causes, using the collected data.
• Automated maintenance scheduling. The integration with the client’s maintenance planning workflow enabled the creation of the smart planning feature. Technicians automatically receive instructions and alerts thanks to the computerized maintenance management system (CMMS) interface integration. 
• Dashboard & real-time alerts. All data about warehouse operations can be accessed conveniently on various devices through a custom dashboard.

AI technician assistant. The tech team is supported by an AI agent that is trained on the internal knowledge base and an extensive number of manuals.

2. Computer Vision and Control

The “digital eye” that comprises an IoT-integrated AI vision system helps automatically monitor warehouse operations, responding quickly to unforeseen challenges.

Problem

Manual warehouse inspections are prone to inefficiencies, with key problems frequently going unnoticed.

Solution

The CodeIT team developed a custom machine learning (ML) model and trained it using the warehouse operations photos provided by the client. The solution was deployed gradually with fine-tuning sessions, improving the performance.

The key components of the AI vision and warehouse inspection system are:

• IoT integration. Data from smart cameras and sensors is gathered on the central server in real time, enabling centralized operations monitoring.
• AI vision. Images are processed in milliseconds, detecting anomalies in a warehouse, equipment failure, improper item placement, etc.

Benefits Of Applying Iot In Warehouse Management

The adoption of IoT positively impacts warehouse management because of automation evolvement.

Reduced Operating Costs

Inventory and warehouse management automation provided by IoT devices helps minimize operating and labor costs. Also, intelligent devices help increase performance and deliver valuable insights to reduce expenses on warehouse management.

Real-Time Tracking

IoT devices offer the opportunity to track all the assets in real time. Managers can easily access live-time data and send commands that will be performed simultaneously. It streamlines inventory management and helps reduce the risk of failure. 

For instance, RFID scanners installed on a belt can scan tags on items and send data to a warehouse management system in live time. Also, it can update the information on RFID tags or write additional details.

Improved Inventory Management

With the help of IoT devices like beacons, businesses can track the precise location of assets. A warehouse management system can suggest the most optimal item storage layouts, having a large amount of data on the item’s location. Consequently, a company will be required to spend fewer resources on the assets moving and delivering them faster.

Increased Performance

Automation helps perform routine tasks fast and without any interruptions. Real-time tracking and data-driven analysis deliver wise suggestions for improving workers’ performance and optimizing warehousing processes.

Enhanced Forecasts Accuracy

IoT devices and intelligent sensors help collect large amounts of data. It can be analyzed to increase the accuracy of demand forecasts. Also, smart devices make the maintenance of a warehouse more transparent so that a business can predict utility expenses more accurately. 

Automated Maintenance

IoT devices help reduce the demand in the labor force to maintain a warehouse. Smart sensors can collect information about temperature, humidity, leaks, etc. A server can send commands to adjust HVAC systems or engage actuators automatically, being connected to one system.

Reduced Risks

The wide usage of technologies helps minimize the risk of human error in warehouse management. Smart sensors can notify workers about the excess load on racks or a chance of a forklift collision, keeping a business safe from unforeseen accidents. Also, smart locks, cameras, and motion sensors can minimize the risk of fraud or product theft. 

Detailed Analytics

Smart devices help get detailed analytics and helpful insights in one place because they can collect and transmit large amounts of raw data via different networks.

Summing Up

IoT in warehouse management is a new trend in the logistics industry. Using smart technologies united into one system helps facilitate inventory management and automate many processes. Essentially, it helps reduce operational costs, lowers risks, and boosts performance. Faster delivery of goods also helps increase customer satisfaction. 

The share of IoT in the warehouse market is rising continuously because smart devices also help fight upcoming challenges in supply chain management. Labor force shortage, overstock, and asset misplacement are the main challenges that the use of IoT in warehouse management helps overcome.

FAQ

Enterprise resource planning (ERP) software is a type of digital solution that helps manage various business processes and operations using a centralized service. It automates processes and serves as a shared database. Using cross-system pipelines, an ERP system with the baseline functionality can fetch financial and operational information.

Manufacturing ERP modules exchange data and commands with the core ERP solution. The integration of new modules helps enrich the baseline functionality of a system.

Each module is designed to perform individual actions. The composition of manufacturing modules in an ERP system is custom for each solution and is defined by business needs and processes. 

As per statistics, 97% of manufacturing businesses use custom-built ERP systems. They are composed of diverse modules that are aligned with business needs.

The most widespread ERP modules for the manufacturing industry include the following.

Basic ERP Modules	Advanced ERP Modules
Inventory management	Quality management
Production planning and management	Supply chain management
Procurement	Customer relationship management (CRM)
Sales and order management	Maintenance management
Human resources (HR)	Business intelligence
Financial management	Workflow management
Warehouse management	Marketing

Basic ERP Manufacturing Modules

The baseline manufacturing ERP software modules are the most commonly integrated. They help provide visibility of the most crucial processes and operations.

1. Inventory Management

The module integration enables raw materials and manufactured product tracking. Real-time inventory management is enabled by automated barcode and UDI scanning. It helps monitor inventory levels, get real-time updates on work-in-progress, and get alerts of out-of-stock materials.

Module key features:

• UDI scanning
• stock replenishment alerts
• item tracing
• inventory processes analysis

Module implementation impact: Inventory visibility and accurate demand forecasting. Also, the module helps reduce stockout and excess inventory by enabling analyzing processes, monitoring, and manufacturing process optimization.

2. Production Planning and Management

Production process design and activities scheduling. Besides, the manufacturing ERP module helps monitor the resource consumption and production processes. The module enables the opportunity to check, set, and update machine programs.

Module key features:

• machine program administration
• production scheduling
• manufacturing capacity planning
• batch management

Module implementation impact: Enables the opportunity to coordinate manufacturing processes by controlling machine programs. The module implication helps achieve reduced expenses by optimizing production processes and resource utilization.

3. Procurement

Fully control and automate the raw material and product acquisition. The module offers centralized access to information about vendors, quotes, resource availability, delivery time, and other crucial details. Create automated material replenishment orders creation and manage them.

Module key features:

• vendor management
• order processing and update
• contract management
• automation replenishment agents
• analytics

Module implementation impact: Lowered material costs thanks to efficient supplier management. Also, the module implementation helps reduce administrative burden by enabling automated replenishment and vendor-managed inventory management.

4. Sales and Order Management

Top-to-bottom management of sales administration using the eCommerce ERP module and sales order module. Track payments, issue invoices, handle returns/refunds, and monitor work orders and order fulfillment progress for a make-to-order business. The automation implication, driven by the supply chain manager, streamlines sales and item distribution processes with demand planning.

Module key features:

• leads and transactions tracking with serial number and lot number tracking
• pricing and discount management for global, multi-currency, and multi-entity modules
• promotion code issuing and management
• inventory availability check with bill of materials (BOM) integration
• order fulfillment and tracking
• invoice and billing management aligned with production schedule

Module implementation impact: Fast and error-free order fulfillment, management, and tracking. Enhanced order completion visibility with supply chain manager integration with shipping companies. Operational efficiency is enabled by full sales and order fulfillment transparency.

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5. Human Resources (HR)

Manage digital records on employee characteristics and hiring processes to orchestrate operations and production management using ERP & MRP systems. The human resources ERP modules manage roles, skills requirements, compensation, and time off.

Module key features:

• employee characteristics information update
• payroll modules and tax data management
• schedule arrangement and update for workforce management
• time off management
• compensation administration with labor cost tracking

Module implementation impact: Helps automate HR and financial management processes and synchronize data with seamless integration across systems. It results in improved employee satisfaction and reduced administrative burden achieved through workforce management optimization.

6. Financial Management

The module is responsible for managing financial operations, transactions, quotes, invoices, etc. Moreover, it helps analyze all the financial data, summarize it, and compose customized reports for supporting decision-making.

Module key features:

• accounting and charting
• invoice review
• cash flow management
• financial planning and forecasting
• custom report creation

Module implementation impact: The module enabled real-time visibility of the financial processes. It helps consolidate data from multiple departments and automates burdening administrative processes. The implications of top-tier technologies help enable data-baked forecasting.

7. Warehouse Management

Control operations for efficient utilization of storage space. Stakeholders can enable centralized facility maintenance and item handling using the manufacturing ERP module.

Module key features:

• inventory tracking and picking
• storage space management
• picking and packing control
• storage environment management

Module implementation impact: The module usage helps achieve smart resource allocation and workflow optimization. It results in enhanced order fulfillment. The implementation of smart devices helps reduce the amount of manual input required for warehouse management.

Read also:

IoT In Warehouse Management — Extensive Guide

Advanced ERP Modules For Manufacturing

Advanced manufacturing modules in ERP help enable additional functionality. They help automate workflows and reduce administrative burden.

1. Quality Management

The quality control module enables continuous monitoring of the quality of produced items using automated quality control & testing. It integrates quality assurance & control practices into existing production workflows with shop floor control module and PDM module support. It ensures clients receive defect-free products and match established quality standards through formula & recipe management.

Module key features:

• automated defect identification
• standard compliance check
• continuous product quality assessment

Module implementation impact: Continuous quality assurance & control monitoring helps achieve improved product quality. It minimizes recalls and returns. The quality control module enables tracking defects and root causes of production flaws with advanced software features.

2. Supply Chain Management

The comprehensive control and monitoring of the flow of materials and manufactured products. The information includes data about suppliers, contractors, distributors, retailers, and consumers. The manufacturing ERP module helps develop and optimize supply chain strategies, track inventory levels, forecast demand, trace shipping, etc.

Module key features:

• supplier management
• procurement strategy development
• demand planning
• inventory and warehouse management
• shipment tracking
• distributor management
• supply chain analytics.

Module implementation impact: The module enables full visibility and control of materials sourcing, inventory holding, and product distribution. It helps analyze supply chain workflows and optimize processes. Also, the module enables data analysis and demand forecasting to avoid inventory stockouts and overstocking.

3. Customer Relationship Management (CRM)

Manage interactions with customers and prospective clients using a CRM system. Build trusted relationships with customers and make custom offers to increase sales. Gather feedback to improve marketing strategies and sales funnels through campaign results analysis.

Module key features:

• customer service management
• custom pricing strategies
• sales and marketing activities analysis
• automation of lead generation and management for prospect organization
• customer contact tracking and analysis
• ERP integration for a single source of truth
• cross-department collaboration for streamlined processes

Module implementation impact: Manufacturers can enable personalized customer interactions using the CRM system module. Besides, optimizing lead generation and prospect organization strategies helps increase return on investments (ROI).

4. Maintenance Management

Optimize manufacturing efficiency by enabling predictive maintenance and reducing equipment downtime. Create well-thought-out machine repair and maintenance plans to minimize production downtime.

Module key features:

• machine maintenance scheduling
• equipment data analysis
• asset management
• spare parts and expendable materials management

Module implementation impact: Scheduled equipment maintenance helps reduce line downtime. The analysis of data collected by sensors helps identify anomalies and predict possible equipment failures in advance.

5. Business Intelligence

The module within the business intelligence (BI) manufacturing ERP analyzes large amounts of design data and extracts insights. It provides access to custom tools for visualizing product specifications and generating custom reports with product data management (PDM).

Module key features:

• design data collection and analysis
• charting and visualization of bills of material
• report generation for engineering change orders
• predictive analytics and data forecasting for product lifecycle management
• dashboard creation with centralized control
• revision control for engineering change management

Module implementation impact: The module helps stakeholders make data-backed decisions. It enables access to insights and real-time product specifications using a dashboard. The BI optimizes processes and cuts expenses by defining bottlenecks, problem root causes, and anomalies in product lifecycle management.

6. Workflow Management

The module, integrated with Tipalti finance automation software, manages financial operations, accounts payable, accounts receivable, global payments, and invoices. It analyzes financial data, summarizes it with general ledger consolidation, and composes customized reports for supporting decision-making.

Module key features:

• accounting and chart of accounts management
• paperless invoice processing and review
• cash flow management with real-time spend analysis
• financial planning and forecasting with automatic revenue recognition
• custom report creation for automated global regulatory compliance

Module implementation impact: The module enables real-time visibility of financial processes through digital transformation. It consolidates data from multiple departments with general ledger consolidation and automates administrative processes. The implications of top-tier technologies enable data-backed forecasting.

7. Marketing

This manufacturing ERP module is useful for running and optimizing marketing campaigns. It helps collect and analyze the outcomes of marketing activities. Hence, businesses spend their budgets more effectively, targeting the most relevant audiences.

Module key features:

• marketing campaign management
• lead management
• email marketing
• social media management

Module implementation impact: Detailed analysis of marketing activities helps cut costs and improve lead generation. The centralized data access helps manage multiple marketing campaigns, applying diverse strategies to find the most optimal ones.

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Benefits Of Manufacturing ERP Modules

The top five ERP modules benefits for manufacturing companies are listed below.

As per the research, the average answer confidence rate for the benefits listed below is 75.45%, which is higher than the minimum required rate of 60%.

Reduced Operational Costs

The implication of manufacturing ERP software modules is business management to optimize processes, enable automation, and increase performance. It results in reduced operational costs, including:

• traveling and communication cost
• staff/employee cost
• product delivery cost
• inventory cost
• maintenance cost

Optimized Production Cycling

Using basic and advanced manufacturing modules in ERP software, businesses manage to enable full workflow visibility. It enables the opportunity to identify points of growth and bottlenecks to eliminate. 

The top processes optimized by EPR manufacturing modules implementation include the following:

• production
• stock procurement 
• report generation
• data preparation
• order checking
• debt payment

Improved Data Quality

Automated data collection and analysis help improve the quality of information. Using established data-sharing pipelines helps reduce loss or unobserved data transformation. As per interviewed stakeholders, the usage of ERP manufacturing modules helps:

• reduce the risk of price miscalculation
• prevent data loss
• eliminate the risk of inputting incorrect data
• increase the accuracy of data

Enhanced Customer Service

Optimized processes help deliver top-tier customer services. The usage of ERP modules for manufacturing helps eliminate the risk of selling out-of-stock products. Also, they enable the opportunity to offer custom-centric services, including personalized pricing strategies and custom order delivery. 

The core outcomes of enhanced customer service are defined as the following:

• reduced order cancellation
• minimized the number of order issues
• improved customer satisfaction
• leveling with customers’ needs

Continuous Business Improvement

Using manufacturing ERP modules helps businesses achieve continuous business process improvement by unlocking new opportunities. ERP modules also help improve awareness and collaboration between departments.

The top four continuous business improvements highlighted by stakeholders are:

• job function restructuring
• improved knowledge sharing
• ease of data analysis
• increased employee satisfaction

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ERP Module Implementation Challenges

The implementation of manufacturing modules in ERP is related to challenges that may occur. The three most widespread challenges and solutions are as follows.

Challenge	Solution
Compatibility issues	Run a technical review and determine the specifications for new ERP modules.
Workflow adjustments	Analyze all the workflows and map them out. Create a detailed transition plan, including roles and responsibilities description for new module integration.
Data inconsistency	Establish data integrity requirements. Build additional data transformation and validation tools upon a need.
Employee training	Create learning materials and run workshops. Implement a custom learning management system to track the progress and assess grasped skills.
Vendor lock-in	Build custom solutions and choose open-source pre-built components.
Lack of real-time visibility	Implement IoT for seamless data exchange and optimize the existing data handling algorithms.

Compatibility Issues

The boundaries set by legacy software may affect the ability to implement ERP manufacturing modules. Hence, implementing selected modules may require additional ERP software adjustments or middleware development.

How to overcome this challenge: Conduct a technical review of the existing ERP software for manufacturing to determine the specifications that should be met when selecting a new ERP module.

Workflow Adjustment

Integrating new manufacturing ERP modules requires stakeholders to reengineer existing processes and workflows. It results in the disruption of workflows and unplanned downtime caused by the lack of new instructions.

How to overcome this challenge: Analyze existing business workflows and outline all the changes caused by new module implementation. Prepare a detailed transition plan that includes roles and responsibilities during the ERP model integration. Also, prepare and present new workflows to your team in advance.

Data Inconsistency

Machines and systems generate data in different formats, causing unification challenges when implementing new manufacturing business modules. Hence, businesses need to develop and integrate additional data mapping tools for integrating new modules and building data-sharing pipelines.

How to overcome this challenge: Analyze the existing ERP software for a manufacturing company and define data integrity requirements. Establish strict data validation, transformation, and synchronization protocols before implementing new manufacturing modules in ERP software.

Employee Training

User training is a crucial part of the new ERP module implementation processes. Therefore, it’s advisable to build the UI of new models similar to the legacy solutions to simplify the transition and new software adoption.

How to overcome this challenge: Configure a learning management system for proper training and skill assessment plan is required because of the possible employee resistance for grasping new skills. Also, create step-by-step instructions and run workshops, helping workers understand how to use new software.

Vendor Lock-In

The usage of pre-built components and integrations with third-party vendors may cause strict dependency on other providers. As a consequence, you may experience issues with scaling up your software or adding new functionality. 

How to overcome this challenge: Prioritize using custom-built ERP modules with an architecture that fully aligns with your business needs. Also, it’s advisable to choose open-source solutions when using pre-built components.

Lack of Real-Time Visibility

Unoptimized and legacy technologies usage may lead to a significant delay in data transfer. Hence, you may lack real-time insights about the ongoing shop floor operations. Moreover, slow data transformation and transfer may cause production disruptions and shop floor machine idling.

How to overcome this challenge: Implement IoT integrations and optimize algorithms, achieving the maximum data transfer speed. For instance, the implementation of algorithms that transfer data only when the collected value changes helps reduce the load on the central server and network. Also, you can optimize data mapping and validation algorithms for better performance.
 

Featured Information For C-levels

The implementation of manufacturing ERP modules helps boost operational efficiency and enable AI-driven analytics. The key insights include the following.

• 83% have already implemented enterprise resource and production planning modules
• 97% of businesses use custom-built or customized ERP manufacturing solutions
• 81% say that an ERP system is an essential component for running business operations
• 48% of manufacturers already use cloud-based ERP solutions
• 32% experienced challenges with making EPR systems live
• 43% noticed improved reporting and analytics
• 16.5% is the average costs cut

Case Study

Curious about how CodeIT tackles real-world challenges in the manufacturing industry? Dive into the details of one of our successful projects below.

Problem

A manufacturing company with a 30-year experience is needed to modernize the production. Stakeholders experienced a lack of real-time visibility of all the manufacturing processes. The client requested our team to develop a machine uptime monitoring system with custom features. Moreover, we had to integrate the developed solution into the existing manufacturing execution software.

Solution

We’ve analyzed the technical requirements provided by the client and created a detailed solution implementation plan. Our team has developed the front-end and back-end machine uptime monitoring software comprising the following components:

• Dashboard. A web application that offers access to crucial information about the shop floor. All the data is updated in real time.
• Data visualization. The data collected from machines is pre-processed and displayed in the form of charts.
• Report generation. Users can generate custom reports to track defined metrics upon demand.
• Machine status viewer. Authorized users can track the status and programs of selected machines.
• System permission configuration. The admin users can set custom data access permission for selected roles.

The developed software has been integrated into the existing manufacturing execution system.

Intrigued by the journey behind the product? Dive into the case study.

Case study:

Machines Uptime Monitoring Software

Conclusion

The integration of manufacturing ERP modules helps enable additional functionality of enterprise resource planning systems. There is a wide variety of industry-specific ERP modules that can be integrated.

Basic ERP manufacturing modules:

• Inventory Management
• Production Planning and Management
• Procurement
• Sales and Order Management
• Human Resources (HR)
• Financial Management
• Warehouse Management

Advanced ERP manufacturing modules:

• Quality management
• Supply chain management
• Customer relationship management (CRM)
• Maintenance management
• Business intelligence
• Workflow management
• Marketing

FAQ

A manufacturing execution system (MES) is a digitized solution that comprises software and hardware components that enable full visibility and production control, connecting the business planning and operations control layers.

A 45% reduction in production time, a 50% decrease in paperwork, and a 15% drop in machine downtime are the foremost gains reported by manufacturers using MES.

But none of these results are achievable without one essential element: a solid MES architecture.

In the article below, you will learn more about the key components, architecture types, key considerations, and more.

The functionality of an MES can be extended by connecting external modules for analyzing data, making reports, developing forecasts, etc.

Key components: hardware and software

The MES architecture comprises hardware and software components united into one system for monitoring and controlling manufacturing processes.

Hardware Components

The core hardware components that help interconnect production line machines and integrate them into an MES are as follows.

1. Servers and databases.

   On-premise or cloud-based servers host manufacturing execution software. Databases store the manufacturing data collected from machines.
2. Sensors and actuators.

   Gather information about crucial parameters like temperature, weight, speed, etc. Actuators perform machinery production processes.
3. Barcode scanners and RFID readers.

   Automatically collect data from scanned codes and tags to monitor inventory in real time.
4. Programmable logic controllers.

   Collect data and issue commands serving as a communication link between MES and shop floor machines.
5. Human-machine interface (HMI) devices.

   Touch screens and control panels used by staff to operate production machines.
6. Networking hardware.

   Routers, switches, wires, hotspots, and other devices create a local network and connect all the components.
7. Desktops and mobile devices.

   Equipment that is used for accessing collected data and using MES software.

Software Components

The core software components help enable the functionality of the manufacturing execution system.

1. MES software.

   Performs the manufacturing processes and operations management.
2. MES modules.

   Software components that enable additional functionality. The most popular modules are analysis, reporting, quality management, downtime management, maintenance, etc.
3. Database management system.

   Stores data retrieved from production floor machines. 
4. Third-party integration tools.

   Additional software from third-party vendors that are connected via APIs.
5. User interface.

   The visual representation of MES software features tailored to the specific needs of users.

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Types of MES architecture

The hardware and software components of a manufacturing execution system can be combined in different ways to achieve the maximum output. The most popular MES system architectures are listed below.

Monolithic

The manufacturing execution software is developed as a single component comprising all the required features. It is perfectly tailored to the needs of a specific production.

However, due to the monolithic approach, scaling up the existing monolith application and implementing new features upon demand may be challenging.

Module-Based

The modular MES architecture comprises diverse components interconnected into a single system. Each component of the system may use software from different vendors and can be developed using diverse technologies.

The module-based architecture enables top customization opportunities. The functionality of a MES can be enhanced by connecting additional modules, including the following:

• scheduling 
• work-in-progress and inventory tracking
• quality management
• business intelligence
• machine utilization monitoring

Cloud-Based

The manufacturing software is hosted on remote servers that a third-party vendor manages. Cloud servers enable the opportunity to seamlessly scale up storage capacity and computing power upon a need. The software installed on a remote server can only be accessed via the Internet.

Hybrid

The hybrid MES architecture incorporates software components installed on cloud and local-based servers. It enables the opportunity to achieve the benefits of cloud-based infrastructure. Furthermore, this architecture of a manufacturing execution system helps maintain top system resilience and offline accessibility of its core components.

Edge-Based

The implementation of edge computing in an MES system architecture helps decrease the amount of data to be processed by the main server and transferred over a network. Edge nodes are small data processing units installed close to the manufacturing floor machines.

They collect and pre-process data. Hence, only summarized or critical information is transferred to the main server. Also, edge nodes can transform and validate collected data. 

Integration with ERP systems and PLC

As per the ISA-95 framework, the MES architecture is a part of Level 3 (MES) of the industrial automation pyramid. It connects the machine control and business logic layers.

Level 0 (Production process) describes the overall manufacturing processes, materials, equipment, etc.

Levels 1,2 (Sensing, monitoring, and control) are represented by:

• programmable logic controllers (PLCs)
• sensors (scales, optic readers, scanners, thermometers, etc.)
• actuators and automatic guided vehicles (AGVs)
• transmitters and network devices
• human-machine interface (HMI) devices

The devices help enable real-time manufacturing control and monitoring. 

Level 3 (Manufacturing operations management) is represented by MES software. It helps coordinate and control manufacturing processes using a centralized system.

Level 4 (Business planning and logistics) is represented by enterprise resource planning (ERP) software. It helps ensure that production operations are perfectly aligned with business goals.

MES Implementation Guide

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Benefits of a robust MES system

The implementation of a manufacturing execution system enables process management and optimization and digitized manufacturing. The five core benefits of MES software integration are as follows.

Workflow Automation and Digitalization

Integrated MES solutions enable automation of business operations. Programmable logic controllers can be fully and autonomously managed by MES software. It can collect sensor data and issue commands to PLCs based on pre-developed algorithms. 

Developing custom user agents helps create custom rules to automate manufacturing processes, including production scheduling, quality control, or supply chain management. All the data can also be processed and outputs presented as custom reports.

Real-Time Visibility and Control

Data-sharing pipelines in a MES architecture enable real-time data collection and command issuing. Hence, responsible managers can monitor and control in-progress processes with minimal delay. It can range from 10ms to several seconds, depending on the implemented MES system architecture and process optimization. 

Increased Performance

Adopting smart manufacturing and shop floor management helps reduce the manual input required to run processes. Moreover, automated data processing and input help reduce the number of errors and lower the administrative burden on the labor force, promoting continuous improvement.

Performance Analytics

Business intelligence (BI) modules in a MES architecture help analyze, summarize, and visualize collected data. Also, BI solutions can automatically generate custom reports.

Advanced technologies like Artificial Intelligence (AI) help streamline data collection and analysis by turning large raw information into valuable insights, forecast changes, and run predictive maintenance.

Standardization and Flexibility

Data mapping and standardization are a crucial part of a robust MES architecture. It enables the opportunity to build cross-system pipelines and integrate third-party solutions. Hence, stakeholders can rapidly enhance or adjust the functionality of existing systems, making change management smooth.

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Key considerations for MES architecture design

The MES system architecture design and implementation require a business to clearly understand the following.

1. As-Is Architecture

Examine the existing design of the manufacturing environment. The as-is design helps outline the existing hardware and software solutions used for monitoring and managing production processes. Define technical boundaries to consider when designing a MES architecture.

2. Bottlenecks and Challenges

Identify the bottlenecks in existing processes and workflows that should be optimized. List all the challenges and boundaries that hold you back from implementing innovative solutions.

3. Business Objectives

Define and prioritize business goals to achieve. All the objectives should be described in detail. The MES architecture implementation outcomes should be defined considering the realistic budget and business capabilities. Also, it’s recommended to set clear “definition of done” characteristics for each business objective.

4. Integration Capabilities

Clearly understand the need for your MES to be integrated with third-party systems like ERP, CRM, or SCADA. Define the data formats the system uses that should be connected and integration compability requirements. In some cases, additional data transformation software is required to enable real-time data sharing across different departments.

5. Scalability

Analyze your business vision to understand if the functionality of your MES architecture will need to be expanded in the future. The scalability plan helps to ensure that new machines can be added, new features incorporated, and more third-party services integrated into the existing MES.

6. Regulatory Compliance

Map out all the industry-specific regulatory requirements and standards. It helps avoid change requests aimed at improving reporting capabilities and quality control procedures. Also, adherence to industry standards helps build a safe and compliant manufacturing environment.

7. Automation Capabilities

Assess workflows in your organization to enable automation of business operations. Streamline routine tasks, reduce manual intervention, and improve operational efficiency with data-sharing pipelines and automated activities.

8. Mobile Accessibility

Consider the need to have mobile access to your MES. Choose the best-fitting technologies for developing mobile applications and syncing all the data in real time. Also, choose additional features to incorporate into mobile applications, allowing managers, engineers, and operators to monitor and control processes remotely.

MES for operational digitalization

The implementation of manufacturing execution systems (MES) helps manufacturing businesses digitize operations, adhering to Industry 4.0 practices and automation.

The digital transformation promoted by MES development and implementation are:

• Production scheduling

  Run effective production planning by aligning with digital batch records and production routings. Ensure all the processes are synchronized, maintaining smooth workflows.
• Work order management

  Manage work orders and track production processes thoroughly, enabling transparency and traceability in a paperless environment.
• Real-time production monitoring

  Collect manufacturing data from different sources in live time. Stay alerted about unforeseen issues that need immediate response.
• Data analysis and reporting

  Gather machinery data and analyze it to get production insights. Make all the data convenient to consume by composing custom reports and charts on dashboards.
• Document management

  Organize, store, and control all manufacturing and quality-related documents within a centralized digital system.
• Electronic signatures

  Streamline processes and comprehensively track all activities on a shop floor with electronic signatures.
• Quality control

  Monitor and maintain product quality through integrated quality control checks. 
• Workflow automation

  Create digital agents that help automate bothersome and repetitive tasks.

MES mobile applications

Mobile applications integrated with manufacturing execution systems help users access crucial information and functionality on the go. Moreover, mobile apps help improve the efficiency of the shop floor teams.

The foremost applications of MES-integrated mobile applications are:

• Daily task management

  Run real-time tracking of production metrics. Get push notifications to stay alerted about production issues and performance drops.
• Labor management

  Implement attendance tracker for shop floor workers and enable KPI monitoring. 
• Quality assurance

  Ensure top production quality by allowing operators to quickly identify and address defects and enable access to inspection documentation.
• Energy monitoring

  Enable real-time energy monitoring with the help of mobile data collection. Track and analyze energy consumption to identify high-usage areas. 
• Maintenance management

  Use a maintenance calendar to schedule regular inspections and repairs, reducing the chance of unexpected breakdowns.

Statistics and future trends

As per statistics, the global manufacturing execution system market is expected to reach $42.2 billion by 2030, growing at a CAGR of 13.1%. 

The other key statistics that highlight the high-paced growth of manufacturing execution system adoption are:

• Over 80% of manufacturing businesses use MES
• MES usage helps reduce the manufacturing time by 45% or more
• Manufacturers report that MES helps decrease paperwork by 50%
• 60% of businesses state that MES usage is a crucial component of success
• MES implication helps reduce the machine downtime by 15%

The future trends that shape the manufacturing execution system market are as follows.

• Cloud-based infrastructure.

  The usage of remote servers that a third-party vendor manages. 
• IIoT integration.

  Smart devices that are interconnected by one network. The industrial Internet of Things, as a part of the MES system architecture, helps monitor and remote manufacturing workflows remotely.
• Edge computing.

  Dedicated computing nodes help pre-process data collected from PLCs. Edge nodes help decrease the amount of data transferred over a network.
• Artificial intelligence.

  AI algorithms help analyze large amounts of data and detect hidden patterns. Also, using AI helps enable computer vision and predictive machine maintenance.
• Modular design.

  The integration of third-party services helps enrich the functionality of manufacturing execution systems. Modular-based SAP MES architecture combines solutions built using diverse technologies.

CodeIT expertise

Our team has developed and implemented many successful solutions for monitoring and controlling manufacturing processes.

The machine uptime monitoring software is one of the completed projects. Let’s check the business problems and solutions delivered by the CodeIT team.

Problem

The client is a manufacturer that experienced a lack of work-in-progress control and monitoring. The CodeIT team was tasked with developing a new software solution that enables the opportunity to: 

• monitor work-in-progress
• analyze manufacturing data
• create charts and custom reports
• present real-time data on a dashboard
• manage user roles and permissions

Solution

The business analysis expert has examined business goals and prepared a detailed software development plan. It included user stories, definition of done, KPIs, etc.

The team of front-end and back-end developers have designed the MES architecture and implemented all the features, following the prepared plan. 

The software and dashboard can be accessed via the Internet from any location.

Summing up

The MES architecture combines hardware and software components to monitor and control production machines. It connects sensors and PLCs, offering the opportunity to manage production processes effectively and check work-in-progress data in real time.

The key components of an MES architecture are as follows.

Hardware Components	Software Components
Servers and databases	MES software
Sensors and actuators	MES modules
Barcode scanners and RFID readers	Database management system
Programmable logic controllers	Third-party integration tools
Human-machine interface (HMI) devices	User interface
Networking hardware
Desktops and mobile devices

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FAQ

The task has been finished (except it’s not): meet Definition of Done (DoD)

“Almost done, but…”—and then weeks spent on squeezing out bugs, polishing, reworking, and updating documentation. Déjà vu, anyone? Without a clear definition of what is considered done, the team (most probably each member!) and the customer live in different worlds.

The Definition of Done (DoD) is a list of criteria by which a task is considered complete. Not just “the code has been written,” but “the code is there, covered with tests, reviewed, deployed, and verified.” That is, ready for use, no surprises. The Definition of Done is a critical part of transparency that aligns everyone and helps to avoid misunderstandings.

Why everything falls apart without DoD:

• Tasks are stuck in the “almost done” status
• Sprints are not closed, and the velocity is barely known
• The team believes that the feature is ready, but the customer never saw it
• The team is arguing about who should have done what

An example of a simple DoD:

• Code is written
• Unit tests are done
• Code review has been passed
• Uploaded to staging
• Checked by QA
• No critical bugs
• Documentation has been updated

How to implement DoD:

1. Discuss DoD with the team (at a retro session or project start)
2. Consider the specifics—for internal tools and production services, DoD may differ
3. Record it in the project space (Confluence, Wiki, Notion) so that everyone can see and follow

The Definition of Done reminds me of the rules of a game. Without them, a project turns into a neverending football game: we run and kick, but no one knows how to score a goal.

Technologies in inventory management help get a competitive advantage by optimizing the use of resources, increasing customer satisfaction, conducting detailed analysis, and minimizing losses. Therefore, 88% of retailers plan to upgrade their inventory by integrating new-age digital solutions like live-time tracking, detailed analytics, and data-driven demand forecasting.

In the post below, you will discover how emerging technologies in inventory management help optimize warehousing and improve customer experience. Also, you will learn more about essential and advanced inventory management technologies to adopt.

Essential Technologies Used In Inventory Management

It’s impossible to underestimate the important role of technologies in inventory management and warehouse maintenance. They help facilitate the process of goods ordering, storing, and shipping. Some of them have been widely used by businesses for many years.

1. Warehouse Management Software

These days, most digital solutions have the form of warehouse management software (WMS). It is a kind of information technology in inventory management software with many features for seamless item ordering, storing, and shipping. It helps get rid of the need to use paper spreadsheets to keep track of every action.

Warehouse management software can be custom or ready-made. Also, a digital solution can be on-premises or cloud-based. It can have a lot of different features aimed to solve several problems of businesses.

The key features of a warehouse management system are the following:

1.1. Import and export of items

Indeed, an inventory management system should provide the opportunity to add products to a database manually. However, it should also be capable of importing and exporting many items to a database using a comma-separated values (CSV) file. 

1.2. Inventory information

It is an essential feature for WMSs to track all the items. Data can be entered and updated manually or automatically. Inventory information can imply a lot of details like stock keeping unit (SKU) codes, name, availability, number of items, size, price, image, arrival date, etc. 

1.3. Order fulfillment

A WMS needs to store information about clients and other crucial details to deliver goods fast. It helps properly organize reserving to speed-up cargo loading and shipping. Besides, the order fulfillment functionality helps manage returns.

1.4. Real-time tracking

It’s vital to get accurate information on items stored in a warehouse. Real-time inventory tracking can help monitor all the processes efficiently and update them effectively upon the need. Real-time data facilitates warehouse management because managers can monitor all the processes remotely from one place. 

1.5. Dashboard

The proper use of technologies in inventory management can help consume data conveniently. Moreover, a dashboard can be configured for different roles. Consequently, various users get access to insights arranged in different order to facilitate every user’s work.

1.6. Picking and packing options

A warehouse management system should provide detailed information on the types of items and how they need to be packed. For instance, fragile or expensive tech items must be packed well to be safely delivered. Moreover, a WMS needs to comprise information on how a customer will pick up a delivery. 

1.7. Billing and invoicing

A WMS should have the billing and invoicing functionality to send quotes, receive invoices, and manage all payments. Also, it should hold information about all the transactions securely.

1.8. Labor management

To run warehousing processes smoothly, a WMS should be capable of assigning tasks to workers. Also, it needs to help track the performance of employees with the help of key performance indicators (KPIs). 

1.9. Reporting

A WMS should have an inventory reporting feature to get valuable inventory management insights. A system needs to analyze large amounts of raw data gathered about a warehouse using custom algorithms to help businesses get detailed reports and data-driven forecasts.

Developers and businesses constantly come up with innovative ideas for inventory management systems, so they build a large assortment of useful features for efficient supply chain management (SCM). Therefore, you can develop a system that has a lot of useful and custom-designed features to facilitate warehouse management.

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2. Barcode System

Barcodes are one of the most popular types of technologies in inventory management because they help make item tracking less time-consuming. A barcode is an image that helps assign a unique value that holds certain information to any item. Since a barcode is unique and can be scanned in less than a second, the system helps facilitate inventory management significantly.

Businesses can create custom barcodes that contain the required information and print them to facilitate inventory management. There are two types of barcodes:

2.1 Linear

It is the most widespread type of barcode that has been used since 1951. It is a one-dimensional type of barcode that has the form of vertical lines arranged in a certain order. Moreover, it implies digits for manual barcode input. 

Different kinds of linear barcodes can hold various amounts of data. The most common information types that a linear barcode can keep are the following:

• SKU number
• Name
• Weight
• Manufacturing and expiry date
• Manufacturer’s name

2.2 Matrix

It is an upgraded type of barcode that was introduced in 1994. The most popular type of matrix barcode is the Quick Response (QR) code. It is a two-dimensional type of barcode that can hold more information than linear barcodes. Nevertheless, it has additional benefits that are the following: 

• 360-degree scanning
• Low background contrast requirements
• Automatic error correction
• Any scanning distance

QR codes are one of the most popular types of matrix barcodes. They are divided into two types:

• Static – A code cannot be changed once it is printed. Users get the same information when scanning a static QR code. 
• Dynamic – A QR code contains a link that leads a user to a certain resource. Information fetched from an external resource can be updated, which is convenient for real-time tracking and more efficient inventory management.

The most common type of data that a matrix barcode can hold are:

• URL
• SKU number
• Name
• Weight
• Text data

Barcodes can be read by scanners or mobile apps that use the devices’ cameras. Also, managers can input the digits from linear barcodes to WMSs if there is no option to use a scanner.

3. RFID Technology

Radio-frequency identification (RFID) is a technology that helps enhance inventory management by using radio waves. Every item gets a unique RFID tag that stores data and can be easily scanned.

This wireless technology used in inventory management helps increase productivity because many tags can be read automatically. Therefore, the use of radio-frequency identification tags is one of the top trends in inventory management because of real-time tracking and fast information updates. 

Inventory management technology requires a business to install antenna, reader, and RFID tags on assets. The system foresees the opportunity to write information to chips and read it wirelessly. The minimum distance to a chip may vary from a few to thirty feet. There are two types of RFID chips:

• Active – An expensive and less popular type of RFID tag that requires a source of energy to transmit radio waves.
• Passive – Radio waves transmitted by an antenna generate current in a tag, so it can receive, update, and send information. It is the most popular type of RFID tag.

The data that can be updated facilitates real-time tracking. It is worth noting that the serial number of RFID tags is the only information that cannot be updated. 

The RFID technology for inventory management helps find a product in a warehouse quickly because all the information about moving assets can be tracked and stored in a database. An RFID tag can store up to 2 kilobytes (KBs) of data. The most common data types that an RFID tag holds are the following:

• Serial RFID tag’s number
• SKU number
• Serial product’s number
• Name
• Location
• Weight

4. LiFi Technology

The light fidelity (LiFi) technology is an alternative to a WiFi network. The technology uses sources of light and sensors to transmit information. It offers the opportunity to connect more devices into one system.

The light source is generated by LED bulbs that update the frequency to transmit data in the form of light. The frequency changes are not visible to the human eye. It is widely used for building warehouse networks because this technology can connect many types of devices without any issues. Also, the inventory management technology is applied for 3D positioning for robots.

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What Is Inventory Management And How Tech Makes An Impact

Inventory management is a complicated process that requires keeping the correct number of items in a warehouse to avoid product shortages or oversupplies. Moreover, it requires managing all the processes effectively to deliver items fast, overcome competitors, and keep recurring clients.

Usually, businesses use warehouse management systems (dedicated inventory software) to keep track of their inventory. Digital WMSs are advanced enterprise resource planning (ERP) solutions. 

The role of inventory management and information technology in a supply chain is vital. Inventory management features help companies that operate in the logistics niche achieve the following:

• Optimized resources usage
• Enhanced customers satisfaction
• Automated inventory management
• Data-driven analytics
• Reduced product delivery time
• Lowered operational expenses
• Minimized fraud and theft

Many types of inventory management techniques are applied to track all items and maintain a consistent supply chain. The top three are the following:

• Push strategy – Items are delivered from a manufacturer to a warehouse. To not run out of stock, retailers need to forecast the approximate number of products to store.
• Pull strategy – A store or warehouse requests products from suppliers when clients order them. Usually, this technique is applied to custom or expensive items. 
• Just-in-Time (JIT) strategy – A warehouse stores the minimum number of items. All extra requests get processed by applying the pull strategy, which may cause delivery delays.

Nevertheless, there are also other inventory management strategies:

• Dropshipping – Product management and shipment are delegated to a third-party company that has a large warehouse.
• Bulk shipment – Products get purchased in bulk to avoid shortage and get a discount.
• Cross-docking – Items get sorted and shipped simultaneously after being delivered to a warehouse. The strategy eliminates the need to hold products.
• Consignment – A supplier delivers a certain number of products without upfront payments. A retailer pays for products only when they are sold to customers.
• Cycle counting – A small variety of items get counted on certain days. It helps monitor the number of different product groups left effectively.

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Types Of Inventory Management Systems

Inventory management can be performed in different ways, applying various approaches. Moreover, the type of workflow depends on the technologies used. The foremost types of inventory control systems that businesses use are as follows.

1. Spreadsheet-based

It is a baseline inventory management approach involving manual counting and spreadsheet-based inventory management systems. All the records are calculated using in-built tools for data analysis and visualization.

Implementing a spreadsheet-based IMS is quick and easy. However, it is labor-intensive and prone to human error. Also, digital spreadsheets have record limitations, making it challenging to process large amounts of data.

2. Cloud-based

The usage of cloud-based inventory management software helps streamline processes, enabling access to the right amount of storage and computing resources. It unlocks integration opportunities, streamlining workflows, and automated data-sharing. Also, businesses need to pay for the computing power they use only, eliminating expenses for infrastructure idling time.

A stable internet connection is required for running a cloud-based infrastructure since servers are owned and managed by a third-party provider.

3. Automated

Automated inventory management systems leverage cross-platform integrations, custom user agents, and IoT infrastructure to collect and analyze data automatically. Fully-automated systems help monitor inventory in real time and manage items across multiple locations effectively. 

It reduces the risks of manual errors, streamlines data management, and unlocks complete inventory visibility. These systems serve as powerful asset inventory management tools, providing better accuracy and efficiency. However, implementing a fully automated system requires a high initial investment and top-tier expertise for developing new solutions and custom integrations.

4. Mobile-based

Mobile-optimized graphical user interface helps employees to seamlessly access the functionality of an inventory management system. Workers can improve their productivity by using tablets or mobile devices that they can carry and utilize remotely. This approach is often an extension of periodic inventory management systems or basic stock management systems, allowing flexibility for on-the-go operations.

Manual vs. Tech-Advanced Inventory Management

Let’s explore the min/max inventory management approaches, highlighting the differences between manual and technologically advanced IMS.

	Manual	Tech-advanced
Description	Traditional methods of inventory tracking and physical inventory count. The approach involves manual item counting and setting min/max levels to trigger replenishment.	A technologically advanced inventory management. The usage of IoT, AI, cloud computing, barcode scanning, and other technologies to automate operations and enable instant visibility.
Advantages	Low cost Easy to implement Flexibility	Real-time tracking Warehouse automation and accuracy Reduced costs and errors Easy to scale Predictive analytics Improved inventory valuation and sales decisions Smarter purchasing
Disadvantages	Manual error Time-consuming Poor scalability No real-time data	High implementation cost Complexity of maintenance

Manual inventory management example

A company’s employees manually observe the in-stock inventory by conducting physical inventory count and cycle counting. All the data is recorded on paper and further moved to digital spreadsheets. Item replenishment activities are triggered when inventory reaches the minimum level.

Tech-advanced inventory management example

An enterprise-grade e-commerce company uses radio frequency identification (RFID) tags to track inventory in real time. Smart cameras identify and track activities. Actuators and automated guided vehicles enable automated item picking, packing, and transportation. All the data is sent to the cloud server and becomes available simultaneously to different departments.

Rising Trends In Inventory Management

Let’s discover how technology aided inventory management with new advancements. The new technology for inventory management of the future are as follows.

1. Internet of Things

The Internet of things (IoT) is a concept of developing systems capable of connecting different types of devices. Various technologies integrated into one system can facilitate inventory management optimization and warehouse monitoring by gathering different information types in one place.

The variety of IoT devices is wide. Most have the “smart” prefix to highlight the devices’ capabilities of transmitting data over a network and being managed remotely by people or other digital systems.

The most popular types of IoT technologies in inventory management are:

• Smartphones
• Smart sensors
• GPS trackers
• RFID tags
• Smart cameras
• Smart HVAC systems
• Smart controllers

The implication of IoT technologies like smart sensors and controllers helps automate a lot of processes and improve management. For instance, IoT devices can measure the temperature or moisture in a warehouse and adjust the HVAC system to keep the required condition automatically.

Application example: A company uses IoT-driven technologies like smart cameras, GPS trackers, and RFID tags to track inventory in real-time. Using the data received from sensor companies can automatically monitor the location and condition of goods. 

2. Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are the leading technologies that can process a large amount of data and perform some human-like actions. AI can recognize the speech or analyze videos captured by cameras. Machine learning can identify patterns in the supply chain to automate labor force and assets management. For instance, it can analyze workers’ behavior or incorrect placement of items.

However, in most cases, the technologies are used for analyzing data and getting valuable insights. AI helps get data-driven demand forecasts and predictive planning. The report prepared by McKinsey & Co states that AI-enabled supply-chain management can help cut logistics costs by 15%.

Application example: A chain of grocery stores uses an AI-driven system trained on a wide variety of data, including sales records, local events, weather forecasts, etc. The insights gained are used to optimize replenishment strategies based on future customer demand. The system helps order the right number of perishable products, reducing the wastage rate.

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3. Cloud Infrastructure

The cloud technology impacts just-in-time inventory management by enabling simultaneous access to data from any location—via the Internet. The cloud-based infrastructure is easy to set up and scale up. Businesses can seamlessly adjust the storage space and computing power of their servers. The single cloud infrastructure makes it easy for large enterprises to sync their inventory data across multiple facilities located in different locations.

Application example: A global manufacturing company uses a cloud-based inventory management system to synchronize all the data across multiple production sites. It enables C-level executives to access all the information in one place remotely at any time. It helps stay updated about all the processes, improve coordination between departments, and receive real-time alerts.

4. Automated Picking Tools

Enterprise-grade businesses that need to manage an enormous number of items in their warehouses integrate automated picking tools. Such systems use robotic manipulators, barcodes, or RFID tags to pick all the assets automatically. The technology helps reduce operating costs, enhance productivity, and minimize human error chance.

Application example: A logistics company uses robotic arms for automated item picking. The system automatically scans RFID tags on packages and picks and sorts items for shipment. Hence, the company has automated bothersome operations, tackling large numbers of items efficiently.

5. Automated Guided Vehicles

Automated guided vehicles (AGVs) are usually small devices that help move items in a warehouse from one location to another. It is an advanced inventory management technology that helps replace the need to have a lot of vehicles and drivers to move assets in a warehouse. AGVs work autonomously and can perform monotonous tasks 24/7. They navigate using floor stickers, LiFi technology, vision cameras, or wires.

Application example: An enterprise has implemented AVGs to transport racks in a warehouse automatically. The AGVs navigate using vision cameras and floor markers. The technology helps reduce the required manual input for transporting goods in a warehouse—the employees are focused on sorting items and collecting orders at defined locations.

6. Blockchain

Blockchain is a great technology that is used in many industries. It has become trendy in the financial sector because the distributed data management approach helps make secure and transparent transactions. 

The adoption of blockchain positively impacts information technology in inventory management. By adopting blockchain, businesses can enhance the security of their warehouse management systems and make safe transactions. It’s tough to hack blockchain-driven systems. 

Application example: A company uses blockchain technology to secure its inventory transactions. This helps prevent the purchase of counterfeit products and builds trust with clients. 

7. Predictive Analytics

The analysis of historical inventory data helps predict future demand changes, including seasonal spikes/drops. Predictive analytics run by custom algorithms help ensure smooth inventory management, avoiding stockouts and overstocking. 

Application example: An e-commerce company integrates business intelligence tools to analyze large datasets of sales to predict future demand changes. The insights help adjust item purchasing strategies to meet seasonal demand changes.

8. Augmented Reality

Augmented reality (AR) is utilized for employee guidance and support, helping improve the performance of the labor force. AR glasses overlay the physical environment with digital objects, helping employees understand what items to pick or where to put items in a warehouse.

Application example: A company has provided its warehouse workers with AR glasses. They guide workers to the correct locations and provide real-time instructions. The inventory management technology helps reduce human error, improve picking accuracy, and decrease the training time for new employees.

9. Third-Party Integration

Integrating software from external vendors helps enrich the functionality of the existing IMS. Using application programming interfaces (APIs) helps sync inventory data with other systems securely and automatically get responses from third parties.

Application example: An online marketplace uses an API to integrate shipping software from an external provider. The system enables automatic synchronization with multiple couriers, allowing users to track packages accurately and automatically share delivery updates with customers.

Challenges Of Inventory Management Systems

A business must tackle many challenges to successfully implement a custom inventory management system. This includes implementing cross-system integration for continuous data collection and developing smart algorithms to interpret it correctly.

Challenge	How We Solve
Isolated solutions	Build custom data-sharing pipelines, enabling smooth cross-system communication. Moreover, we implement data validation tools to ensure no pieces of information are missed or changed.
Diverse integration requirements	Develop a custom data mapping tool that automatically transforms data, matching the unique integration requirements.
Demand forecasting accuracy	Implement a machine learning model and train it using historical data. Clean and validate the data used for training to eliminate outliners, missing entries, or irrelevant data. Also, involve a data scientist to understand the real meaning of ML outcomes for better model fine-tuning.
Tech stack limitations	Identify the bottlenecks and refactor legacy systems to fix the issue and ensure future growth.
Human factor	Automate workflows and build cross-system integrations, eliminating the need to manually input data. Also, develop additional validation tools that highlight possible errors and offer improvement suggestions.
Multiple location management	Establish a main server with real-time connections to the company-wide network of IoT devices. Enable a centralized inventory monitoring and management approach.

1. Isolated Solutions

Workers must manually input records due to a lack of cross-platform data pipelines or the use of isolated solutions, which can lead to potential errors. For example, they may misspell item names, leading to duplicate inventory records or input incorrect item numbers, locations, etc. This can result in inaccurate records that disrupt operations.

2. Diverse Integration Requirements

Automation implementation requires businesses in the logistics industry to integrate multiple digital systems from various vendors. Moreover, digital solutions may utilize various data formats, making it challenging to develop cross-system data-sharing pipelines. In some cases, it’s required for companies to develop and implement additional data validation and transformation solutions to enable automation in an organization.

3. Demand Forecasting Accuracy

The accurate prediction of future demand requires analyzing historical datasets, seasonal trend change patterns, and unforeseen events. Consequently, organizations have to collect and prepare large amounts of data from various sources.

Implementing an ML-driven solution helps overcome the limitations of traditional mathematical algorithms by leveraging the technology’s capabilities to identify hidden patterns. The development and implementation of machine learning algorithms for demand forecasting require niche-specific expertise.

4. Tech Stack Limitations

Cross-system communication, advanced analysis solutions, and real-time data processing can be implemented when all systems can communicate seamlessly. However, in some cases, businesses may utilize legacy software built using outdated technologies. In such a case, the refactoring of the existing applications may be required.

Also, it might be challenging to identify data sources in each system. As a consequence, business analysis experts have to thoroughly examine the existing solutions to map out all the data sources. 

5. Human Factor

Workers play a crucial role in digitalizing inventory management operations. They must grasp a significant amount of information by exploring and studying manual documentation. To overcome this issue, it’s advisable to create a detailed worker re-skilling plan that includes scheduled learning sessions, educational materials, workshops, and other relevant resources.

One of the pitfalls that need to be considered when planning digital solution implementation is employees’ possible resistance to switching from tried-and-tested workflows. This resistance can also affect the speed of tasks such as stock picking time, further delaying operations.

6. Multiple Location Management

With multiple storage locations and complex warehouse layouts, an organization needs to utilize a centralized solution that synchronizes data in real time. The architecture of cross-warehouse management systems gets more complex when a business has inconsistent storage layouts and needs to tackle slow-moving inventory. Additionally, theft and spoilage can occur when items are stored inefficiently or when warehouse environments lack proper monitoring.

When To Upgrade Your Inventory Management System

Upgrading the existing IMS and implementing new technologies need organizations to invest a sustainable amount of time and money. The advantages of using accurate inventory control and automation outweigh the efforts in many cases, including these ones.

1. Inefficient Workflows

Upgrade your operations with custom digital solutions if your business is struggling with inefficient, manual workflows. Start by implementing an automated barcode inventory management system to enable complete visibility of your inventory. Automating repetitive tasks will help reduce the chance of error and enhance efficiency.

2. Fast Business Scaling

It’s challenging to scale your business fast when using a periodic inventory system. A limited number of employees can hardly tackle an overwhelming number of orders, new products, bulk orders, etc.

The use of centralized and cloud-based inventory software helps organize processes. Cloud-based inventory software can be easily scaled, matching a business’s ever-changing demands. Moreover, the integration with third-party solutions enables automated data exchange for better inventory information fetching and sharing.

3. Information Update Delays

Implementing custom solutions that gather data from IoT devices and send it to the main server simultaneously can solve issues such as inconsistent data across platforms and lags in data updates. Business intelligence solutions also help promptly analyze the data, delivering real-time inventory management insights in the form of dashboards and custom reports.

4. High Inventory Holding Costs

The lack of inventory visibility and poor planning can lead to increased inventory holding costs. Furthermore, it can cause inventory piling up and increased wastage. Businesses that utilize advanced analytics solutions develop accurate demand forecasting based on historical data, repairing events, seasonal changes, and other external factors. Using data-backed insights ensures better management of inventory valuation and helps calculate the cost of goods sold (COGS) more effectively.

5. Isolated Solutions Usage

Disconnected systems from different vendors used within an organization need employees to import/export records manually. This method requires labor-intensive operations and leads to data update lags. The development of custom integrations helps enable cross-platform communication, streamline workflows, and enable automation, particularly for e-commerce fulfillment processes.

Tips For Selecting Inventory Management Technologies

Selecting and implementing the right technologies helps streamline inventory management operations and fosters transparency throughout the organization. The following tips will help you build the systems your business really needs and avoid rework.

1. Identify Business Objectives

It’s crucial to clearly define the goals you want to achieve by implementing new technologies. Map out your business processes and identify bottlenecks for developing solutions that deliver real results, unlocking future growth opportunities.

2. Create Software Requirements Specification

Define the features you need to implement and describe their functionality. Plan for integrations, like ERP systems, e-commerce platforms, logistics companies, financial software, etc. Also, outline scalability and flexibility requirements to ensure the sustainable growth of your business.

3. Research Ready-To-Use Components

Streamline new inventory management solution development and implementation by choosing pre-built components, including libraries, SDKs, open-source modules, third-party services, etc. Moreover, pre-built components help proof of concept (PoC) fast for validating an idea.

4. Run Vendor Analysis

Ensure your day-to-day operations and future growth can’t be limited by the capabilities of third-party service providers. Make a thorough analysis of vendors if using pre-built components and systems. Using custom-built or open-source technologies is the best way to avoid vendor lock-in.

Statistics On Adoption Of Technologies In Inventory Management

Indeed, modern technologies help optimize inventory management and make it more efficient. Let’s take a deeper dive into the statistics to discover the benefits of information technologies in inventory management for small businesses and large enterprises.

• 10% of cost reduction – Businesses can cut expenses by eliminating stock-outs and overstocks with the help of smart data analysis and accurate forecasts
• 57% of responders use inventory management systems – Less than half of small companies don’t monitor their inventory or apply the manual management approach instead
• 88% of retailers plan to upgrade – Most supply chain managers don’t want to lose a competitive advantage brought by modern technologies and plan to enhance their warehouses
• 37% of businesses monitor performance – Roughly a third of businesses are concerned about the performance of their warehouses and monitor them constantly to spend resources wisely
• 73% of warehouses plan to adopt mobile devices – Most businesses want to use logistics software installed on mobile devices to facilitate inventory management

CodeIt Expertise

Our team has developed many innovative warehousing and inventory management solutions. Learn more about the two of them, GIObikes and ConnectSx, in more detail.

GIObikes

GIObikes is a fully functional eCommerce platform from scratch that thousands of users use daily.

Problem

The client struggled because of the insufficient functionality offered by eBay to sell products online. He contacted our team, requesting us to develop an innovative platform for switching from eBay to a personal website, keeping eBay’s auctioning functionality.

Solution

We’ve gathered a team of tech experts and selected a tech stack. Also, we’ve created an incremental solution development plan and implemented it. The core components of the web app are as follows.

• Online website — A site launched on a live server capable of managing more than 5,000 active daily users.
• Auction — The opportunity to add new products, share bids, manage auction fees, and buy products now.
• Warehouse management software — A comprehensive software for keeping track of all the items in a warehouse. Items’ location, automatic barcode creation, and out-of-stock alerts are the core features of the inventory management system.
• Affiliate system — The system offers the ability to run affiliate programs automatically. Meanwhile, the admin users have full access to all the information about affiliate programs and their participants.
• Shipping integration — The system offers the opportunity to pick different shipping methods. API-integrated shipping calculators foresee the opportunity to estimate the cost of shipping simultaneously.
• Secure payments — Integrated payment gateways offer the opportunity to make stress-free payments using credit cards, PayPal, or bank wire transfers.

ConnectSx

ConnectSx is a set of web and mobile applications for medical inventory management.

Problem

The client aimed to help clinics resolve poor medical inventory visibility by developing a forward-looking healthcare supply chain management solution. He hired and onboarded the CodeIT team to the existing app development project.

Our team was required to optimize it. Further, we were tasked with ideating a solution, selecting a tech stack, and developing new applications.

Solution

We conducted a technical audit and came up with the best option—to reboot the project. Our team has developed three standalone applications that enable seamless medical inventory management.

1. Web inventory management app
2. Mobile inventory management app
3. Mobile UDI scanning app

The developed applications enable the opportunity to:

• Manage and track medical inventory
• Import data from external systems
• Track medical product expiration dates
• Check inventory usage statistics
• Submit transfer requests
• Scan barcodes using a smartphone

Main Takeaways

The adoption rate of inventory management technology systems rises at a high pace. Technologies help automate many warehouse processes and make data-driven forecasts. Warehouse management and barcode systems are the essential technologies that help keep track of all the items meticulously.

However, there are a lot of advanced technologies that help increase performance and get rid of manual management. Enterprise-grade warehouses actively adopt AI and ML, automated guided vehicles, automated picking tools, and blockchain to deliver products fast and increase customer satisfaction.

FAQ

Think artificial intelligence (AI) running your shop floor still belongs to the future?

This isn’t the future—it’s happening now, and it’s delivering real results for manufacturers. 

By automating documentation and improving workflows, AI-powered tools help achieve results like these:

• 95% reduction in documentation search time
• 30% increase in demand forecasting accuracy
• 100% compliance with food safety standards
• 25% cut in maintenance and repair expenses
• 15% reduction in energy consumption
• 8% boost in productivity

Scroll down below to discover how AI is used in manufacturing. Learn more about benefits, implementation strategies, and challenges. 

Generative AI Use Cases In Manufacturing

Generative AI can be applied to solving different kinds of problems in a shop floor. It unlocks access to rich data analysis and information management capabilities. The technology eliminates the need to hire specialists in certain fields and increases the performance of employees by providing instant access to the instructions they need.

The top seven generative AI in manufacturing use cases are as follows:

1. Text-to-SQL

The text-to-SQL functionality enables users to submit requests in natural language. AI-driven manufacturing solutions understand users’ intent and translate their requests into SQL code. The code is automatically processed to query and analyze defined data.

A solution delivers data analysis results as requested by a user. Integrating text-to-SQL tools with business intelligence (BI) software enables the automatic generation of custom reports using charts.

Real-life applications in manufacturing:

• Production performance monitoring
• Overall machine health insights 
• Inventory management optimization
• Production quality control
• Material and energy usage control

Example: A production manager uses a text-to-SQL AI agent, asking the following question: “What was the output of the assembly line last month?” The system translates the request into SQL code and runs it. Consequently, the solution retrieves the relevant production data and generates a report summarizing output quantities, efficiency metrics, and any anomalies.

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2. Document Retrieval

Implementing generative AI in the manufacturing industry helps facilitate document/instruction management and search. A solution analyzes all the internal documentation employees need to use to perform their duties. 

They can easily ask an AI agent to find a specific document containing information and retrieve it in seconds. The software provides links to the corresponding documentation and summarized answers based on the only requested documents.

Real-life applications in manufacturing: 

• Maintenance manuals and instructions
• Safety protocols and compliance documents
• Standard operating procedures 
• Inventory and material handling instructions
• Regulatory and certification documents

Example: A technical team representative asks an AI agent to retrieve technical documents for on-site repairs or machinery adjustments of a defined shop floor machine. The system analyzes the request and pulls out the searched documents instantly from the corporate database. 

3. API-Enabled Agent

Integrate third-party software with your custom AI-driven solution so that they can automatically exchange data. API-enabled solutions help manufacturers incorporate industry 4.0 AI technologies into their shop floors. Businesses can automate queries to machines or IoT devices to retrieve operational data and process it. 

Automated workflows help improve performance as shop floor workers don’t need to manually collect data and set programs using isolated digital solutions.

Real-life applications in manufacturing: 

• Automated production & machine monitoring
• IoT-driven predictive maintenance
• Production planning and scheduling
• AI automation in manufacturing
• Inventory management automation
• Data exchange between ERP and MES

Example: An AI system queries factory machines data in real-time. It analyzes the collected metrics and composes live-time charts. Moreover, it automatically detects performance drops or idling machines. 

4. Question-Answering

It is one of the most popular generative AI use cases in the manufacturing industry. An AI chatbot trained on certain documentation can help shop floor workers by providing answers that are based on instructions, corporate rules, and internal documents.

Users can ask questions using natural language in free form. Moreover, the technology enables the opportunity to adjust the answers delivered to users, making them concise or detailed with solid step definitions.

Real-life applications in manufacturing: 

• Equipment troubleshooting assistance
• Production process guidance
• Safety protocol enforcement
• Corporate policy and hr queries review
• Training and onboarding support
• Maintenance history and logs

Example: An assistant asks a chatbot to provide safety regulations updates for the last three years. The chatbot analyzes the existing documentation and lists all the updates in safety regulations for the selected period of time.

5. Knowledge Base

Generative AI in the manufacturing industry sets the interaction with a database to a new level. It enables the opportunity to leverage extensive databases of industry standards, best practices, and operational guidelines. An AI system uses the knowledge base information to provide accurate and relevant information upon request. Moreover, it provides links to the original articles so that they can be examined in more detail. 

Real-life applications in manufacturing:

• Access to industry standards
• Best practices for equipment setup
• Operational guidelines and procedures
• Product design and engineering standards
• Real-time troubleshooting knowledge
• Compliance and safety protocols
• Training and skill development

Example: An engineer queries, “What are the best practices for quality control in electronics manufacturing?” question. The system retrieves and summarizes relevant guidelines, checklists, and case studies.

6. Real-Time Data Retrieval

Develop data-sharing pipelines to retrieve real-time data on production lines and equipment health. AI agents can analyze real-time data and provide up-to-date insights into shop floor performance, inventory levels, production issues, and more. Moreover, it can simultaneously detect issues and instantly alert responsible managers, describing the detected challenges.

Real-life applications in manufacturing:

• Equipment health monitoring
• Production line efficiency tracking
• Inventory level monitoring
• Predictive maintenance scheduling
• Real-time production issue detection
• Supply chain optimization

Example: An AI-driven system runs real-time monitoring of factory performance or machine wear-and-tear. Any negative changes in machine health that may result in future machinery breakdown.

7. Entity-Linking

It enhances data retrieval and analysis by linking specific entities such as components, materials, and processes to relevant databases and knowledge bases. Moreover, an AI system facilitates more accurate and contextual information retrieval, helping streamline data-driven research and development.

Real-life applications in manufacturing:

• Component tracking and maintenance
• Material traceability and quality control
• Process optimization through data linking
• Spare parts management
• Supplier performance evaluation
• Regulatory compliance and documentation linking

Example: An engineer asks the following question: “What are the properties of aluminum alloys used in aerospace?” The system identifies and links entities like “aluminum alloys” and “aerospace” to its database, retrieving detailed information about material properties, applications, and relevant industry standards.

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Generative AI Development Services

Benefits Of Generative AI In Manufacturing

The advantages of generative AI applications in manufacturing help businesses optimize resource usage and decrease the amount of manual input required to tackle tasks.

1. Improved Operational Efficiency

Optimize manufacturing operations by getting custom shop floor insights. The generative AI in the manufacturing industry helps enable automated data sharing and processing, eliminating the need for shop floor workers to use isolated software. Moreover, it streamlines document management, helping employees to instantly find appropriate instructions and internal guides.

Businesses use the technology’s ability to generate answers to instantly generate custom reports by submitting requests in natural language. Also, the usage of generative AI in manufacturing can provide real-time user guidance by following defined instructions or machinery maintenance manuals.

2. Automation of Repetitive Tasks

One of the core generative AI’s benefits is the ability to automate tedious tasks, allowing human workers to dedicate their time for more complex activities. The integration of smart factories and AI enables data collection for automated production with AI, including quality inspection, equipment monitoring, and basic data entry, and more. Furthermore, the automation of repetitive tasks helps reduce the chance of human error. 

3. Effective Data Analysis

The generative AI’s capability to understand a user’s intent and generate code enables the easy running of custom data analysis queries. It eliminates the need to hire a team of data analysis experts and SQL developers. 

Custom data analysis enables the transformation of datasets into insights. You can pull specific datasets from various sources and get concise reports in minutes. The thorough analysis of production data helps identify trends and hidden patterns to maintain high machine throughput and minimum downtime.

4. Enhanced Decision-Making

Make data-baked decisions by thoroughly analyzing insights delivered by generative AI. Use chatbots to ask questions and clarify your assumptions. Use your shop floor data to simulate different activities to run scenario planning to pick the best workflow optimization options. 

Also, the usage of generative AI in manufacturing for real-time data analysis and visualization helps stay informed about plant floor metrics and unforeseen issues. Instantly generate custom reports on equipment performance, energy consumption, quality metrics, etc.

5. Personalized User Guidance

AI agents can serve as personal assistants by delivering instant answers to any questions. It can briefly describe processes and provide step-by-step instructions on configuring programs or maintaining shop floor machines. 

Having access to all the articles in a corporate knowledge base, an AI-driven tool can provide answers that are based on the defined internal instructions and manuals only, avoiding the chance of generating irrelevant responses.

6. Automated Customer Service

Customer support via live chat is one of the foremost generative AI in manufacturing use cases. The usage of chatbots helps automate interactions with clients, guiding them and answering their questions instantly.

Being integrated with third-party systems, customer interactions and support solutions can process user requests and simultaneously provide information about their orders (e.g. actual status, number of items produced, estimated delivery).

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Generative AI Implementation Challenges

The implementation of generative AI in the manufacturing industry requires businesses to create custom plans tailored to their capabilities and business goals. Moreover, they need to solve many challenges, with the following being the foremost.

• Data access and quality—Centralized repositories for the data in defined formats need to be created. Moreover, all the documents need to be checked, ensuring an AI agent doesn’t index outdated or irrelevant information. A company should create a strong document creation and management policy to ensure new information is fully accessible for an AI-driven solution.
• Integration with third-party systems—A business needs to implement data-sharing pipelines, making a system easy to retrieve up-to-date shop floor metrics. The integration challenges software engineers need to overcome include diverse data exchange protocols, custom application programming interface (API) development, and implementation of industry-specific security standards.
• Technical debt—Skilled specialists with extensive experience in the new technology should be involved. The lack of skills for refactoring legacy solutions and developing data-sharing pipelines may disrupt innovation adoption. When technical debt is not addressed, it can reduce agility and make it harder to integrate new AI technologies, leading to performance bottlenecks and increasing maintenance costs.
• Use cases adjustments—Use case development requires manufacturing businesses to thoroughly plan all the activities and map them out so that software engineers can implement all the required functionality. Moreover, they need to review and refine the software solutions if a use case updates by adding new activities or changing the existing ones.

Implementation Strategies For Generative AI

The development and implementation of generative AI in manufacturing may comprise many stages, depending on the existing infrastructure and business goals.

The five key components of AI-driven solution implementation are:

1. Discovery

Review your business process to clearly understand existing challenges and points of growth. It’s advisable to involve a business analyst in the discovery phase to clearly map out all the activities and everything that impacts your business goals.

2. Proof of Concept

Compose a minimum viable product (MVP) with the most crucial features to test your idea. Rapidly create a prototype using pre-built components to use your resources efficiently. Collect user feedback to understand how your AI-driven solution can be improved.

3. Planning

Establish a comprehensive AI-driven solution development plan that comprises all the stages. The foremost artifacts that need to be prepared include:

• Backlog of tasks—a prioritized list of tasks that need to be completed by software engineers and other involved specialists. Each task should be clearly defined and described in detail.
• Estimates—definition of predicted time and resources needed to complete all the tasks. The estimates can be provided in time or user stories.
• Key performance indicators (KPI)—defined measurable values for assessing the performance of software engineers.
• Definition of done (DoD)—a shared understanding of a completed task, comprising detailed descriptions of requirements of each increment that should be met.
• Team set—a document comprising information about tech experts who need to be involved. It should clearly outline the skills and seniority of software engineers.
• RACI matrix—a document that describes all roles and their responsibilities, making the communication and reporting processes straightforward.
• Risk management plan—a list of potential risks that can be faced during the application of generative AI applications in manufacturing, as well as mitigation strategies.

4. Development

The process usually involves incremental software development (MVP + new features that are released gradually). It can comprise the following activities:

• Legacy software refactoring—rewriting the code of outdated software using modern technologies.
• Data collection and preparation—data collection and storage procedures review. Unification of data formats so that it can be easily processed by an AI agent.
• Use case development—definition of specific AI solution use cases and workflows. 
• LLM selection and adjustment—the review of existing large language models (LLMs), integration, and configuration as per the developed use cases.
• New software development—incremental creation and release of new features.
• Software testing—unit, integration, and user acceptance testing to ensure a new system works error-free, delivering the required functionality.  
• Infrastructure configuration—setting up cloud or on-premise servers to run the developed AI-driven manufacturing solutions and store data.
• API development & system integration—building and integrating APIs to allow the AI solution to interact with other systems and applications. 

5. Release and Refinement

The stage includes the deployment of the developed solution and the continuous release of new features to upgrade the functionality. It also includes performance monitoring and troubleshooting of unforeseen issues.

Employee training is also required to ensure that a shop floor staff can properly use the AI-driven software implemented. The training comprises workshops, video guides, and knowledge base creation so that your team members always have access to up-to-date instructions.

Future Of Generative AI In Manufacturing

Generative AI has the potential to revolutionize manufacturing processes, enabling the effective analysis of vast amounts of data and the provision of augmented responses tailored to users’ specific needs.

As per statistics, only 20% of businesses use generative AI in their processes. The others:

• 20% sometimes use gen AI 
• 55% rarely use gen AI 
• 5% almost never use gen AI

The top statistics that highlight the rapid growth of AI-driven solutions used by businesses are as follows.

• AI is the #1 technology decision-maker in manufacturing plans to implement in the future
• 91% of surveyed manufacturers say that AI is important to future business development
• 82% of businesses plan to increase their AI investments in the future

CodeIT AI Lab

CodeIT has a dedicated AI department with extensive experience in building AI-driven solutions. We build custom software leveraging AI to automate bothersome tasks and enable smart suggestions.

The top AI solutions developed by CodeIT for our clients in the manufacturing niche are:

1. AI compliance management tool—monitors regulatory requirements from different sources all around the globe. The AI assistant provides answers, generates summaries, triggers alerts, and more.
2. AI documentation assistant—AI chatbot facilitates the operations of technicians and field workers by providing tailored instructions based on internal documentation. It is capable of sharing image- and video-based instructions.
3. AI supply chain optimization software—analyzes large amounts of historical data and develops accurate demand forecasts. It also identifies risks and runs what-if analyses.
4. AI predictive maintenance tool—collects data from IoT sensors and forecasts when a piece of equipment is likely to fail. Moreover, it guides technicians by providing custom instructions.
5. AI vision and QA software—automatically inspects products on lines and triggers actions when defects are detected. Also, it collects product metrics gathered by IoT sensors for deeper analysis.
6. AI energy saver—collects data from smart meters, analyzes it, and adjusts the machinery settings to optimize energy usage.
7. AI workforce scheduling tool—enables a near-100% shift coverage by running smart analysis of many factors, including employees’ custom preferences, skills, time-offs, etc. It can adjust the schedule in real time when new changes are issued.
8. AI talent acquisition and retention software—runs automated CV pre-analysis, selecting the best-matching candidates with the most relevant skills for each job opening. Also, it can automatically schedule interviews with candidates.
9. AI waste reduction tool—a set of computer vision, machine learning, and workflow automation helps reduce the volume of waste, saving raw materials and disposal expenses.
10. AI changeover optimization tool—helps minimize the time needed to change tools and adjust machine programs, using computer vision, augmented reality, digital twins, and other technologies.

Final Words

The capabilities of generative AI help businesses in the manufacturing industry to streamline processes and improve the productivity of shop floor workers.

Generative AI helps manufacturing businesses improve performance by delegating bothersome and time-consuming tasks to AI-driven tools. The application of AI-driven tools helps businesses to:

• Manage documentation and provide tailored instructions
• Run custom data analysis queries
• Deliver data-backed insights
• Monitor operations and adjust settings in real time
• Analyze image and IoT sensor data
• Build predictions and analyze root cases

The capabilities of AI unlock extensive optimization and automation opportunities for businesses that translate into improved performance and saved expenses.

FAQ

Imagine you are assembling a puzzle, but half the pieces are hidden in another room. This is roughly what software development without transparency looks like: participants waste time guessing, and the result may not be what everyone expected. Transparency—it is when everyone can see where we are going, what is in the way, and why each detail is needed.

Why is this important for everyone?

• Clients stop guessing where the budget went and when the result will be. They see progress, understand the difficulties, and can adjust requirements in a timely manner.
• The team clearly knows the priorities, context, and expectations; there is less stress due to unexpected edits at the last minute.
• Managers control risks rather than put out fires and can proactively manage the project—not just react.

The benefits are obvious: fewer conflicts, more trust, faster product release, and guaranteed everyone’s peace of mind.

At CodeIT, transparency is not a buzzword but a daily practice. Here’s how we implement it:
The client is always in the loop. We show the clients our raw prototypes, design, and requirements and ask for their opinions before development begins. Even if they are not techies, their perspective helps avoid misunderstandings.

• Demos every sprint (usually every two weeks). Instead of waiting for the “perfect” release, we show intermediate versions of the product. This helps the client see progress, and we can make edits in time.
• Open task boards. Our collaboration tools are available to everyone, from developers to top managers. Everyone can take a look and understand who is working on what, what has already been done, and where help is needed.
• Everyone is engaged. From daily meetings and monthly reports to quarterly business review meetings—we do our best to ensure every stakeholder gets what they need and when they need it.
• The truth, even if it’s bitter. If deadlines are shifted or a critical bug has surfaced, we report it immediately. So instead of panic, an action plan appears, and trust only grows.

For us, transparency is about respect for the client’s time, the team’s work, and the business goals. Even complex projects become predictable and successful for each party when everyone sees the same thing.

According to statistics, 81.5% of businesses use microservice-based applications, and 17.5% of them plan to adopt microservices. Roughly 9 out of 10 companies state that they are satisfied with the microservice architecture adoption.

Microservices security is strongly affected by the distinctive decoupled approach to developing apps. The inability to follow the monolithic approach needs developers to tackle new security challenges in microservices. 

Below, you will learn more about the core security challenges, how to implement security in microservices, and ways to detect breaches.

What Is Microservice Architecture?

The term microservices define the decoupled architecture of an application. It comprises many loosely coupled services that use diverse technologies to deliver various functionality. Every service has a dedicated database and can be developed using diverse tech stacks.

Unlike the monolithic architecture, the usage of microservices doesn’t need developers to update the entire application to add more functionality or fix detected issues. Moreover, the failure of one service doesn’t make the entire application down, which is one of the core advantages of using the microservice architecture.

What are the benefits of microservices architecture? The microservice architecture usage foresees the opportunity to create scalable and reliable applications, providing the following benefits. 

• Self-sufficiency. Microservices are independent nodes that perform defined functionality.
• Technical variety. Developers can implement various solutions by using different technologies.
• Scalability. The functionality of an application can be quickly scaled up by developing and adding new services.
• High sustainability. The failure of one service does not affect the entire application, which helps enable top microservices security.
• Continuous delivery. Certain services of an application can be updated without disabling the entire app.
• Easy changes implementation. The functionality of defined services can be updated without releasing major app updates.
• Simplified onboarding. New software engineers can be quickly onboarded to a new project to start working on a new service.

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What are some common use cases for the microservices architecture? Considering the peculiarities of the microservice architecture, it is best suitable for the following:

• outdated applications
• large-scale applications with complex logic
• data-heavy applications
• apps that process real-time data
• highly-resilient applications

Many popular companies have switched from using the monolithic architecture to microservices to get the opportunity to scale up their apps effectively. The most popular microservices adopters are:

• Amazon
• Netflix
• Uber

Read also:

Microservices Use Cases and Real-World Examples

How does microservices architecture differ from monolithic architecture? In a nutshell, the monolithic architecture is a single unit that uses one database and a defined tech stack. Contrastly, the microservice app is a set of interconnected services that use diverse technologies.

Let’s elaborate on the core differences between the monolithic and microservice architectures below.

	Monolith	Microservices
Scalability	The entire application should be re-deployed to release new changes or updates	New services can be deployed without affecting the entire application
Database	An app uses a shared database	Every service uses a database
Development Team	A single team of software engineers should develop, test, and maintain an app	Disparate teams are allocated to developing, testing, and maintaining services
Technologies	Developers are required to use a defined tech stack	Every service can be developed by using different technologies

The monolithic architecture usage foresees the opportunity to quickly launch an app. However, the time needed to develop and release new features/changes increases exponentially, according to the complexity of an app. 

The microservices architecture needs software engineers to invest a large amount of time to release the initial version of a product. The product can be easily scaled up by adding new services.

Read also:

Monolithic Architecture vs Microservices

Microservices Security Challenges And Concerns

The usage of many loosely coupled services connected into one application is a different app development approach compared to the monolithic architecture, increasing the attack surface. 

The foremost microservices security challenges are mainly caused by the following:

• Decoupled architecture—every microservice uses a dedicated database and uses diverse technologies, which can lead to data consistency issues and expose access points vulnerable to unauthorized access. Moreover, the developers need to create a more complex solution to enable microservices authentication and authorization for every service, including applying security protocols and security patches regularly.
• Microservices communication—many decoupled services with dedicated functionality need to communicate and share sensitive information across distributed components, forming chains of dependencies.

Best Microservices Security Implementation Practices

Discover how to secure your microservice app by addressing the foremost microservices security challenges and implementing the best practices.

1. Authentication and Authorization

Authentication refers to the identity verification of a user by checking provided login credentials with the ones stored in a database.

Authorization refers to granting logged users permission to access defined resources. It also defines what actions users can perform, depending on their roles, in line with a security-first mindset.

A new authentication and authorization solution is needed since the microservices security architecture comprises many interconnected services. It should be different from solutions used in the monolithic architecture because every service should verify a user ID and fetch additional user info using environment variables for secure access.

Three microservices authentication and authorization implementation options

Due to the decoupled structures, the microservices security can’t follow the monolithic architecture approach. Check out the solution the monolithic architecture offers to perform core functionalities and microservices concerns.

	Monolithic Architecture	Microservices Concerns
Login	Usage of a one database	A dedicated login service is required to run authentication and authorization
Identity Token Verification	One backend app issues and verifies ID tokens	ID tokens are issued and verified by different services
Additional User Info Access	All the data is stored in the same database	Data and user info are stored in different databases

Three microservices authentication and authorization implementation options

Three options to implement secure authentication and authorization patterns in the secure microservices architecture are as follows.

• All-in-one authentication service — The authentication service verifies users’ identities and creates a session, issuing an identity token. Every service verifies tokens and gets additional user info separately in a security-aware culture.
• Shared session storage — The authentication service checks users’ identity and creates sessions that are stored in dedicated shared storage. Services verify user ID and get additional user info by connecting to the shared session storage.
• JSON web tokens (JWT) — The authentication service checks the user identity and issues a JWT token. The JWT token is provided to services, which verify it and get basic user info.

	Pros	Cons	Key Facts
All-in-One Auth Service	Great capsulation; Basic user info is always up-to-date.	High load on the authentication service; Resource services strictly depend on the authentication service.	The best fit for microservices but a performance tradeoff should be considered.
Shared Session Storage	Easy to implement; No strict dependencies between authentication and resource services.	Worse encapsulation because of using a shared session storage The authentication service may become a bottleneck/point of failure; Basic user info is not up-to-date.	Great performance with a tradeoff of some microservices principles.
JSON Web Tokens (JWT)	No performance bottlenecks or points of failure; Promotes the decentralization approach; Basic user info can be read from a JWT.	More data to transfer over a network; JWT digital signature should be validated; Hard to implement; Not secure out of the box; Worse support in web frameworks; Revoke JWT problem.	A comprehensive option, but it can hardly handle logout. Senior developers should implement it.

Let’s untangle the three microservices authentication and authorization options by discovering how they enable the microservices security functionality.

	All-in-One Auth service	Shared Session Storage	JSON Web Tokens (JWT)
Login	Distinctive login API	Distinctive login API, but sessions are created in a shared storage	The authentication service creates a JWT containing user info
Identity Token Verification	The authentication service is called every time it is needed	Services connect the shared session storage instead of the authentication service	Services verify received tokens and get user info from them
Additional User Info Access	The usage of a dedicated authentication service API	The microservice authentication service API is used to fetch additional user info	Additional info can be stored in a JWT or fetched using the authentication service API upon request

Ready-to-use customer identity and access management solutions

Regardless of a microservices authentication and authorization option you select for your microservice app, it should be implemented by software engineers. 

Ready-to-use customer identity and access management (CIAM) systems can help speed up the development and address most security concerns by opting for a well-tested solution. The top-three CIAM platforms are:

• AWS Cognito
• FusionAuth
• Auth0

Read-made CIAM solutions offer access to many features to enable top-tier security in microservices and user experience. The foremost features of the top-three CIAM platform are as follows.

CIAM Platform	Top Features
AWS Cognito	Self-registration, Migration options, Customizable UI, Multi-factor authentication, Compromised credential protection, Microservices security risks assessment, Machine-to-machine authentication, Identity store, Access to AWS resources.
FusionAuth	Single sign-on, Multifactor authentication, Biometric authentication, Social and gaming logins, Breached password detection, IoT authorization, Rate limiting.
Auth0	Social login, Single sign-on, Branding, Multifactor authentication, Real-time breaches detection, Serverless developer tools, Passwordless authentication.

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2. Secured Communication Between Microservices

Microservices need to communicate with each other to send and receive data constantly. Secured communication between services is one of the pillars for enabling security in microservices. The two best practices for securing microservices communication are as follows.

Use HTTPS

Hypertext transfer protocol secure (HTTPS) is a secure protocol that enables services to communicate with each other using end-to-end encryption. Besides, it’s advisable to configure every service to validate the certificates of services they need to connect automatically, using secure configuration management tools.

Use a service mesh

The term “service mesh” defines an extra layer in a network that adds more features and improves the microservices security. A mesh network implies sidecars enabled for services, which work as proxies. The usage of a mesh network in microservices foresees the opportunity for developers to:

• simplify communication between services
• detect communication errors and failures
• encrypt and validate data
• streamline the development, penetration testing, and deployment of applications

3. Secured Data Storage

Distinctive services can be developed by using different technologies. They need to ensure that all services store data securely and minimize the chance of the exposure of services because of exploited vulnerabilities, which can be detected early via vulnerability scanning.

Check out the top three recommendations to develop secured data storage below.

Enable data encryption

The encryption of data stored in a database helps keep information safe in case of a breach because it cannot be read.

The two data encryption approaches are:

• In-transit — data is encrypted when sent from one service to another. Recipient services decrypt and read received information. It helps minimize the chance of data leakage caused by breached data communication channels.
• At-rest — data is encrypted for further storage on a hard drive. The approach helps enable additional microservices security measures when storing data and keeps it undisclosed in case of loss.

Manage data life cycle

A data lifecycle defines the amount of time data should exist in a system. Consider what data should be automatically erased, depending on the records’ creation date. This aligns with time-based security practices.

Create backups

Due to the distributed nature of the microservices architecture, the backup creation involves the configuration of every service’s database. It’s advisable to do the following.

• Conduct an audit to define crucial data that should be backed up to optimize resource usage
• Configure automatic backup creation tools as part of CI/CD pipelines
• Check the ability to restore backups error-free
• Store data backups on dedicated servers

Read also:

Database Security Best Practices

4. Sensitive Data Management

Sensitive data include login credentials, secrets, tokens, and keys that help grant access to users and interpret encrypted information. The top three sensitive data management practices are as follows.

Don’t hardcode login credentials

Do not embed sensitive data and login credentials directly into your application’s source code. The hardcoding can lead to serious microservices architecture security vulnerability as hackers can access secrets to obtain unauthorized access to services, especially when environment variables are not used securely.

Follow the principle of least privilege

It is one of the principles of microservices security that lies in limiting user and service access as much as possible. Defined roles should have access to only services, resources, or applications they require to perform assigned tasks, enforced via policy-enabled traffic control.

Use specialized solutions for keeping secrets

Many ready-to-use solutions can help store and manage sensitive information, including login credentials, tokens, keys, etc. Also, they offer additional functionality to enhance the microservices security architecture, including fault-tolerant and resilient designs.

The top three ready-to-use specialized solutions for keeping secrets are:

• AWS Secrets Manager
• Google Cloud Key Management
• Microsoft Azure Key Vault

5. API Gateway + Firewall

An API gateway connects the frontend and backend. It accepts requests from users and routes them to microservices. The primary functionality of an API includes authentication, authorization, and request routing.

A firewall is an additional microservices security layer that protects networks and applications from common exploits. A firewall can filter traffic, detect suspicious activity, follow custom security rules, and integrate with CI/CD pipelines to ensure dynamic testing and deployment consistency.

An API gateway can work without a firewall. However, to enable the maximum security in microservices, it’s recommended to implement the combination of an API gateway and firewall, like:

• API gateway: AWS API Gateway + Firewall: AWS Web Application Firewall (WAF)
• API gateway: Google Cloud Load Balancer + Firewall: Google Cloud Armor

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Tips To Monitor And Detect Security Breaches

Continuous app monitoring helps ensure that no security issues occur and no users obtain unauthorized access, supporting incident response plans and post-incident analysis.

The three recommendations listed below can help detect security problems.

1. Logs and Key Metrics Monitoring

Logs monitoring is one of the basic approaches that help identify technical and security issues. It is recommended to implement a centralized log management system to monitor all the activity to detect security breaches or suspicious activity using monitoring tools and enabling security event monitoring.

Monitoring crucial metrics helps developers detect issues with security in microservices or uncommon activity. Some metrics that help define suspicious activity are:

• failed login attempts
• password reset cases
• passwordless login attempts
• user permission changes
• network traffic
• system downtime

2. Vulnerability Scanning

Possible security flaws can be detected by conducting vulnerability scans. Automated tools can analyze the codebase and detect pieces of code containing possible security vulnerabilities. Developers should further inspect the flagged pieces of code. These activities support forensic analysis as part of comprehensive incident handling. The most popular tools are:

• SonarQube
• Checkmarx SAST
• Veracode

3. Penetration Testing

In a nutshell, penetration testing is a simulation of a spear attack on an application by ethical hackers. It helps detect security exploits in advance. All the vulnerabilities detected by ethical hackers during the microservices security testing should be documented and reported to developers.

Read also:

Software Code Audit — Processes Breakdown

Codeit’s Microservice Security Expertise

The CodeIT team has broad expertise in developing microservice architecture applications. Check out how we’ve built a feature-rich platform applying the microservice approach.

Problem

The client has requested the CodeIT team to develop an online service for sports fans. The project’s main idea focused on eliminating the need to use many diverse platforms by combining all the useful features in one super-app.

Solution

The app developed by our software engineer implies a lot of services that securely process large datasets and deliver rich functionality in one place. The core features of the app are as follows.

• News aggregation. The system analyzes user activity and collects the most relevant news from different sources.
• Communication platform. Users can send direct messages, create group chats, and run audio and video calls. Also, they can share screens and run public channels.
• Blockchain-driven platform. Users can exchange cryptocurrency, create smart contracts, and run blockchain-driven auctions.
• Social functionality. The application delivers advanced social media functionality, including thread and post creation. Users can make comments, reactions, and polls.
• Live streaming. Users can run live streams, share screens, and send files. Also, users can embed external resources into their live streams.

To Recap

Microservices security aims for developers to create secured applications with the minimum number of vulnerabilities.

The top microservices security best practices are:

1. Authentication and authorization
2. Secured communication between microservices
3. Secured data storage
4. Sensitive data management
5. API gateway + firewall 

In order to monitor and detect security branches, it’s advisable to do the following:

• monitor logs and key metrics
• scan an app to detect vulnerabilities
• conduct penetration testing

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