Program

All times are EDT (local time in New York that is 6 hours behind CEST, Central European Summer Time, e.g. Paris). Click on the presentation or scroll down to see its abstract and author biography.

Presentation Abstracts and Author Biographies

Exploring the Real Impact of Decision Intelligence (DI) in Modern Organizations by Arash Aghlara (FlexRule)

Decision Intelligence (DI) and its Platform (DIP) are becoming a hot topic in the industry, sparking diverse viewpoints on its definition and organizational benefits.

Some consider DI and its Platform (DIP) as successors to various existing technologies, including:
– Decision Management Suites (DMS)
– Data-driven decision tools and dashboards (e.g., PowerBI)
– Optimization modeling (e.g., Constraint Programming, Mixed Integer Programming)
– Machine Learning and Data Science platforms
– Business Process Management (BPM) for process automation
– Generic app low/no-code app development platforms such as PowerApp, OutSystems

Given these comparisons, how do DI and DIP differ from current technologies? Is it merely a rebranding of existing toolsets and methods?

In this presentation, I will demonstrate how DI and DIP distinguish themselves from these technologies and methodologies. Through a practical example, we will explore the different levels of intelligence that organizations can achieve by combining multiple techniques, such as business rules, machine learning, optimization, and orchestration, in a single cohesive decision model to showcase the true potential of DI beyond the hype.
Keywords: Decision Intelligence, Decision Intelligence Platform, Decision Management Suite, Business Rules Management, Machine Learning, Optimization, Composite AI, Decision Model and Notation – Slides

Arash Aghlara is the CEO and the founder of FlexRule, a leading global provider of Open Decision Intelligence Platform that empowers leaders in organizations to improve the speed and quality of key business decisions in changing environments.
[email protected]

Mastering Decision Centric Orchestration: Balancing Human Insight and AI Automation for Justifiable, Context-Aware Business Choices by Denis Gagne (Trisotech)

In today’s dynamic business landscape, decision-centric orchestration is pivotal for organizational success. This presentation delves into the diverse spectrum of decisions within an organization, ranging from those that are purely human to those that are fully automated. We will explore how various types of artificial intelligence (AI) support decision automation, with a particular emphasis on the crucial role of context in making informed business choices. Attendees will gain a comprehensive understanding of how context-aware AI can enhance decision-making processes, ensuring that outcomes are not only efficient but also relevant to specific business needs.

Highlighting the necessity of explainable and justifiable decisions, we will discuss the imperative of maintaining human oversight in all business processes. The presentation will address the challenges and opportunities associated with integrating AI into decision-making frameworks, focusing on the balance between human intuition and AI-driven efficiency. Attendees will learn strategies for achieving a harmonious integration of these elements, ensuring that their organization’s decision-making is robust, transparent, and aligned with ethical standards. Ultimately, this session aims to equip business leaders with the knowledge and tools to leverage both human insight and AI automation for superior organizational performance.

Keywords: Decision, Decision Centric Orchestration, DMN, AI, GenAI, Explainable decisions, Justifiable decisions – Slides

Denis Gagné is CEO and CTO of Trisotech, a leading Standard Based Low-Code Intelligent Automation enterprise software vendor. For more than two decades, Denis has been a driving force behind most international BPM standards in use today. Denis is a member of the steering committee of the BPM+ Health Community of Practice, where he also leads the Ambassador program. For the Object Management group (OMG), Denis is Chair of the BPMN Interchange Working Group (BPMN MIWG) and an active contributing member to the Business Process Model and Notation (BPMN), the Case Management Model and Notation (CMMN), and the Decision Model and Notation (DMN) work groups. [email protected]

Advancing Decision Modeling: Unveiling DMN 1.6, SDMN 1.0, and SCE 1.0 by Falko Menge (Camunda)

This presentation introduces significant enhancements in decision modeling standards. Decision Model and Notation (DMN) 1.6 improves error handling, introduces a new strict execution mode, and integrates ONNX for AI and ML applications. The Friendly Enough Expression Language (FEEL) 1.6 includes a mapping to JSON, a descendant operator similar to JSONPath, and the introduction of B-FEEL, an alternative dialect aimed to be even more intuitive for business professionals while resolving prior design limitations.

Shared Data Model and Notation (SDMN) 1.0 seeks to harmonize data modeling features across OMG standards such as BPMN and DMN. It enables the declaration of Data Items and Item Definitions in a single model, which can then be shared across any processes and decisions that use them. SDMN also takes the initiative to deliver the long-awaited visual notation for DMN Item Definitions.

Specification Common Elements (SCE) 1.0 generalizes the structural patterns introduced by BPMN 2.0, which have since been embraced by subsequent standards such as DMN, underscoring a commitment to interoperability and streamlined processes in decision modeling. This session promises to provide attendees with a comprehensive understanding of the latest advancements and their practical applications in the field.

Keywords: DMN, FEEL, SDMN, SCE, BPMN, Standards – Slides

In addition to his “day job” as Senior Principal Solution Architect, Falko has more than 14 years of experience representing Camunda in standardization organizations such as the Object Management Group (OMG). He is a co-author of the BPMN 2.0 specification, among others, and currently serves as co-chair of the Revision Task Forces for DMN, SDMN, and SCE.

[email protected]

Extracting decision models from images using deep learning by Jan Vanthienen, Caroline Heijmans, Aurelie Leribaux, and Alexandre Goossens (KU Leuven)
In the initial conceptualization phase, DMN models are often hand-drawn with first sketches and still need to be manually redrawn in a modeling tool. Also, models can be available as images within documents, either as screenshots, pictures or tables, without access to the original source format.

Re-using these images then entails the manual process of remodeling or redrawing them, a task that is both time-consuming and complex.

In this presentation, deep learning techniques are employed to extract DMN models from both digitally drawn or hand-drawn DMN images (graphs and tables). Subsequently, the work’s outcome has been integrated into a DMN Computer Vision Tool application that reconstructs the DMN source files.

Keywords: DMN, Deep Learning, Computer Vision – Slides

Prof. Jan Vanthienen received his PhD degree in Applied Economics from KU Leuven, Belgium. He is a full professor of Information Systems at the Department of Decision Sciences and Information Management, KU Leuven and (co-)authored more than 200 full papers in international journals and conference proceedings. His research interests include information and knowledge management, business rules, decisions and processes, and business analysis and analytics. He received an IBM Faculty Award on smart decisions, and the Belgian Francqui Chair at FUNDP. Currently he is department chair at the Department of Decision Sciences and Information Management of KU Leuven. [email protected]

Alexandre Goossens is a fourth year PhD student in Applied Economics from KU Leuven, Belgium within the research team of Prof. Jan Vanthienen. His research spans multiple domains with a general theme of decision modeling, execution and discovery. More specifically, his research deals with extracting DMN models from textual descriptions using NLP and deep learning as well as mining DMN models from object-centric process event logs. Lastly, his research also deals with automated DMN chatbots which perfectly fit in the current trend of explainable AI. [email protected]

Caroline Heijmans completed her Master of Business and Information Systems Engineering at KU Leuven, Belgium. Her Master’s thesis focused on developing a method to extract decision models from images. She will be starting her career as a Data Engineer / Scientist at Aivix (Intellus Group) in September 2024.

Aurélie Leribaux completed her Master of Business and Information Systems Engineering at KU Leuven, where she developed a strong foundation in data analytics and business process optimization. She is now embarking on a joint PhD program at KU Leuven and the University of Melbourne. Her research will focus on several aspects of process mining, including the development of innovative techniques for analyzing and improving business processes through data-driven insights.

Tending the Knowledge Ecosystem: Lessons from Life by Dr. Alan Fish (FICO)

“Ecosystem” is a term now commonly used in information systems, for example to describe service platforms. Of course, it is a term taken from the life sciences, particularly biology and ecology, and used as a metaphor. But what does it originally mean, and what can biological ecosystems tell us about the conditions required by artificial ecosystems for augmented decision-making? In this presentation I will look closely at some concepts from the life sciences, particularly cognition, play, culture and ecosystem, and ask what they might teach us about how to build a knowledge ecosystem that delivers business success while protecting human values and experiences.

Keywords: Ecosystem, Decision model, Methodology, Simulation, Business Intervention Model, KPI – Slides

Dr. Alan Fish is a thought-leader in Decision Modelling and Decision Management. He invented the Decision Requirements Diagram (DRD) which exposes the structure of a domain of decision-making, and developed Decision Requirements Analysis (DRA): a methodology for building and using such decision models. Alan is the author of “Knowledge Automation: How To Implement Decision Management in Business Processes” (Wiley), which has been translated into Chinese. He is editor and co-author of the OMG specification Decision Model and Notation (DMN), and co-chairs the OMG DMN task force. He continues to develop notations, methodologies and ontologies to provide the conceptual environment for business users of FICO Platform. [email protected]

Outside work Alan is a musician: singer-songwriter, guitarist and saxophonist. He is a keen member of several musical ensembles and has released an album of his own songs: Yes Why Not.

Approximating a Global Objective by Solving Repeated Sub-problems for an Oven Scheduling Problem by Helmut Simonis (Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork)

We present results for an oven scheduling problem studied during the European ASSISTANT project. This is a multi-stage scheduling problem arising in the production of rotor assemblies for compressors, provided by one of the industrial partners in the consortium. The main resource type is a set of identical ovens, which are used to heat-treat components in different ways. The process for one product may require multiple consecutive steps using these ovens, with specific temperature and process requirements at each step. Multiple tasks of different orders can be processed together in the same oven, if the temperature and process parameters for the tasks are identical. Processing multiple tasks together is more energy efficient, but typically forces some tasks to wait until all scheduled items are available, possibly impacting product quality and creating delays for the orders. The main difference to the oven scheduling problem studied in the literature is that we are not just trying to find an optimal solution to the short-term, detailed scheduling problem, but rather are interested in how selecting different parameters and constraints for the short-term scheduling problem affects the overall long-term, global objective of minimizing energy use, while maintaining the quality of products. Turning ovens off and then on again is considered bad for energy and maintenance reasons, we therefore try to minimize the number of shutdown events over the full planning horizon, while dealing with demand fluctuations over time. Information about jobs to be scheduled is only available within a limited time horizon, we therefore cannot solve the overall problem as one global optimization problem. Results indicate that we obtain a good overall schedule with a simple detailed scheduling model.

Keywords: scheduling, global objective, oven scheduling, constraint programming – Slides

Helmut Simonis is a senior research fellow at the Insight Centre for Data Analytics in Cork, Ireland, working on the combination of Machine Learning and Optimization. He has contributed to the area of Constraint Programming since 1986, has been involved in multiple start-up companies, and is a past president of the Association for Constraint Programming. Current research interests are automatically learning constraint models from example solutions, and improving decision making under uncertain forecasts. [email protected]

The Third Wave of AI: From rules, to statistics, to cognition by Peter Voss (Aigo.ai)

The pursuit of Artificial General Intelligence (AGI) represents a return to the original vision of AI from 1955 — building machines that can think, learn, and reason the way humans do.

ChatGPT has given us a taste of AGI, however, while its amazing language capabilities are in many ways close, it is lightyears away from AGI in several crucial aspects. For one, it is missing the accuracy, scrutability, and real-time incremental learning of rule-based systems.

What else is missing? DARPA talks about ‘The Third Wave of AI’, also referred to as Cognitive AI. This talk explores these ‘waves’ as a path to AGI.
Keywords: Artificial General Intelligence, Cognitive AI, AGI, The Waves of AI – Slides

Peter Voss is the Founder/ CEO/ Chief Scientist at Aigo.ai and Founder, CEO at AGI Innovations Inc.. Additionally, Peter Voss has had 1 past job as the Founder, CEO, Chief Scientist at SmartAction. Peter’s life’s mission is to bring human-level AI to the world to optimize human flourishing. This, the Holy grail of AI, will significantly lower the costs for many products and services; providing PhD-level AI researchers to help us solve problems like disease, energy, and pollution; and help individuals via expert personal assistants that will enhance productivity, problem-solving ability, and their overall well-being. He dedicated to this mission for more than 20 years and is leading a new major initiative to leverage our deep experience and standard-setting commercial Aigo technology to rapidly close the gap to wide-ranging human-level capabilities.https://www.linkedin.com/in/vosspeter/ [email protected]

Machine Learning + Symbolic Reasoning: unleashing the full potential of Artificial Intelligence by Mario Fusco (Red Hat)

What AI can do nowadays is simply mind-blowing. I must admit that I cannot stop being surprised and sometimes literally jumping from my seat thinking: “I didn’t imagine that AI could ALSO do this!”. What is a bit misleading here is that what we tend to identify with Artificial Intelligence is actually Machine Learning which is only a subset of all AI technologies available: ML is a fraction of the whole AI-story, while Symbolic Artificial Intelligence enables experts to encode their knowledge of a specific domain through a set of human-readable and transparent rules.

In fact there are many situations where being surprised is the last thing that you may want. You don’t want to jump from your seat when your bank refuses your mortgage without any human understandable reason, but only because AI said no. And even the bank may want to grant their mortgages only to applicants who are considered viable under their strict and well-defined business rules.

Given these premises why not mixing 2 very different and complementary AI branches like Machine Learning and Symbolic Reasoning? During this talk we will demonstrate with practical examples why this could be a winning architectural choice in many common situations and how Quarkus through its langchain4j and drools extensions makes the development of applications integrating those technologies straightforward.

Keywords: Symbolic reasoning, Artificial intelligence, Large language model, Hybrid reasoning – Slides

Mario is a Principal Software Engineer at Red Hat working as Drools project lead. Among his interests are functional programming and Domain Specific Languages. He is also a Java Champion, the JUG Milano coordinator, a frequent speaker, and the co-author of “Modern Java in Action” published by Manning.

[email protected]

Decision Modeling for Scheduling and Resource Allocation Problems by Dr. Jacob Feldman (OpenRules)
Scheduling and Resource Allocation are traditionally considered as very complex decision management problems. These problems are usually out of reach for the most rule engines but have been successfully addressed with constraint solvers. Still, the necessity to learn specialized constraint programming languages or APIs for Java, C++, or Python prevents business users (subject matter experts, not programmers) from modeling and solving these problems. For years we tried to simplify access of business users to the powerful constraint solvers with a limited success. One of the most promising approach is to use DMN-like decision modeling constructs to represent complex constraint satisfaction and optimization problems and rely on the standard search capabilities of constraint solvers to solve them.

In this presentation I will describe how the advanced Rule Solver (http://RuleSolver.com) allows business users to use DMN-like decision tables to represent and solve various scheduling and resource allocation problems without becoming gurus in constraint programming. I will demonstrate these new Rule Solver’s capabilities by using classical examples from the great product “ILOG Scheduler” created by Claude LePape and Jean-Francois Puget almost 30 years ago. Rule Solver gives a new life to very intuitive ILOG Scheduler’s constructs by migrating them from C++/Java to user-friendly decision tables.
Keywords: Scheduling, Resource Allocation, Rule Solver, Rule Engine, Constraint Solver, Declarative Decision Modeling, Smart Decision Engines – Slides

Dr. Jacob Feldman is the CTO of OpenRules, Inc., a US corporation that created and maintains the highly popular Business Rules and Decision Management System commonly known as “OpenRules”. He has extensive experience in development of decision-making engines using business rules, optimization, and machine learning technologies for real-world mission-critical applications. Jacob is the DecisionCAMP’s Chair,  the manager of DMCommunity.org, and an active contributor to BR&DM forums. He is also the Specification Lead for the optimization standard JSR-331. Dr. Feldman is an author of two books “DMN in Action with OpenRules“ and “Goal-Oriented Approach to Decision Modeling“. He has 5 patents and many publications in the decision intelligence domain. [email protected]

Contracts Create Workflows for Knowledge Workers by Tom Debevoise (Advanced Component Research) and Denis Gagné (Trisotech)

Legal contracts often lead to intricate workflows that traditional modeling techniques, such as BPMN, struggle to represent accurately. This presentation explores the complexity of real estate sales agreements and how retrieval-augmented generation (RAG) can effectively model their dynamic nature. Real estate contracts, which encompass numerous critical elements like purchase price, property condition, financing terms, and regulatory compliance, are not static but evolve through negotiations and contingencies.

The dynamic and multifaceted nature of these contracts necessitates a sophisticated approach to workflow management for knowledge workers. Current methods fall short in capturing the detailed nuances, resulting in inefficiencies and errors as professionals manually extract essential information. By leveraging RAG, it is possible to maintain an adaptive list of activities and events, akin to a “checklist” with a closing calendar, guiding the efforts of real estate professionals more effectively.

This presentation by Tom and Denis will delve into the challenges of modeling complex contracts and demonstrate how RAG can address these issues, ultimately enhancing the workflow and efficiency of knowledge workers involved in real estate transactions.

Keywords: Legal Contracts, Workflows, Knowledge Workers, Real Estate Sales Agreements, Retrieval-Augmented Generation (RAG), BPMN. Dynamic Modeling, Activity List, Closing Calendar, Negotiations, Contingencies, Entity Extraction, Intelligent Data Processing, Workflow Management, Real Estate Transactions, Complex Workflows, Regulatory Compliance – Slides

Tom Debevoise has extensive experience in Process and decision modeling using BPMN, DMN and FEEL. Tom Debevoise focuses on next-generation IT solutions for business operations as a technology leader and cloud solutions architect. Tom works on the next generation of intelligent, practical, cloud-based services. Tom is developing these with an “intelligent digital assistant”, using Natural Language Processing, APIs to massively integrated services, and a core set of responsive processes, decision making, and analytics. Tom has held various positions at companies such as Oracle, Bosch, and Signavio. [email protected]

Denis Gagné is CEO and CTO of Trisotech, a leading Standard Based Low-Code Intelligent Automation enterprise software vendor. For more than two decades, Denis has been a driving force behind most international BPM standards in use today. Denis is a member of the steering committee of the BPM+ Health Community of Practice, where he also leads the Ambassador program. For the Object Management group (OMG), Denis is Chair of the BPMN Interchange Working Group (BPMN MIWG) and an active contributing member to the Business Process Model and Notation (BPMN), the Case Management Model and Notation (CMMN), and the Decision Model and Notation (DMN) work groups. [email protected]

How to Build a Rating Engine that’s Easy to Manage by Carole-Ann Berlioz (Sparkling Logic)

Quoting and pricing is becoming more complex and many insurers are finding their rating engines too inflexible. Modifications require custom code that is opaque to the business analysts responsible for managing the underlying business rules. As a result, rating engines are often error prone, costly, and cumbersome to maintain.

In this session, you will learn how a major title insurance company effectively transformed their rating engine to empower non-technical business teams to easily manage it. Gain practical insights on how to minimize IT resources, make calculations easy to understand, and increase agility.

Keywords: Rating Engine, Business Rules, Business Analysts. Agility – Slides

Carole-Ann Berlioz is Co-Founder and Chief Product Officer at Sparkling Logic, a leading Decision Management Platform vendor known for its innovation. Over a few decades, she has led product management and strategy for generations of award-winning business rules, predictive analytics, and optimization products. In 2010, she teamed up with Chief Architect and CTO Carlos Serrano-Morales to create Sparkling Logic, a “Cool Vendor” that has gained momentum around the world, uniquely serving Business Analysts with an intuitive yet comprehensive, fully integrated decision manager, SMARTS™. In addition to her visionary role, she also takes pride in building client projects for financial services and insurance companies. Her hands-on expertise fuels her creativity in this industry, recognized with several patents in Decision Management and Adaptive Analytics. [email protected]

IP – Intellectual Property or Integer Programming…Your Enterprise Needs Both! by Rob Parker (AngleFinance)

In this presentation I will describe two practical problems in the Asset Finance domain and some innovative approaches to providing optimal solutions using integer programming models. Sample problems will include determination of optimal pricing rate cards and optimal assignment of loans to securitisation pools. Users often have difficulty describing their multi objective optimisation criteria when presented with unfamiliar solution approaches. The use of tools to rapidly prototype, refine and jointly converge on a model will also be discussed.
Keywords:Asset Finance, Integer Programming, Multi objective optimisation, Securitisation, ZIMPL, Rate Cards – Slides

Rob Parker is an enterprise architect with more than thirty years of experience within the information technology industry. Rob has worked in industries from Government utilities, telecommunications through to banking and finance. Rob has a passion for process and automation, and currently heads up the Engineering and Architecture functions for AngleFinance. [email protected]

How Decision Intelligence from Space improves the Livelihood of Small-hold Farmers in India by Dr. Roger Moser (University of St. Gallen, Switzerland)

SatSure, a company in the space-tech industry, leverages satellite data and advanced analytics to apply Decision Intelligence (DI) for various sectors. By integrating satellite imagery with AI and big data analytics, SatSure enables organizations to make informed decisions with unprecedented accuracy and timeliness. This innovative approach to DI from space has significant implications and offers valuable lessons for multipe industries.SatSure uses high-resolution satellite data to monitor and analyze changes on the Earth’s surface. In agriculture, for instance, it helps farmers and agribusinesses with crop monitoring, yield prediction, and resource management.

By analyzing patterns and anomalies in vegetation health, soil moisture, and weather conditions, SatSure provides actionable insights that improve crop productivity and sustainability. This real-time monitoring and predictive analysis enable timely interventions, optimizing resource use and minimizing losses. In finance, SatSure aids in assessing the viability of agricultural loans and insurance claims by providing accurate, data-driven assessments of crop health and potential yields. This reduces the risk for financial institutions and ensures fair compensation for farmers. Additionally, in disaster management, SatSure’s technology assists in rapid damage assessment and resource allocation, enhancing response times and effectiveness during natural calamities.

Lessons for Others:• Integration of Multiple Data Sources: Combining satellite imagery with other data sources (e.g., weather, soil, market trends) can enhance decision-making across industries.• Real-Time Analytics: Implementing real-time monitoring and predictive analytics can significantly improve operational efficiency and risk management.• Cross-Sector Applications: The principles of space-based DI can be adapted to various sectors, from urban planning to environmental conservation, highlighting the versatility and broad applicability of this technology. By harnessing the power of satellite data and DI, SatSure demonstrates real-world cases for innovative, data-driven decision-making, offering a model of efficiency and precision for other industries to emulate.
Keywords: Decision Intelligence, New Space Sector, Agriculture – Slides

Dr. Roger Moser is a Faculty, Board Member & Investor (SatSure, Switzerland/India/UK/US), Executive Coach and Decision Intelligence Thought Leader. His is focusing on Decision Intelligence & Strategic Management, Decision Model Innovation & Decision Avatars, and Strategic Management. Link [email protected]

Adithya Vasudevan is a Co-Founder at CI Contextual Intelligence AG, a Swiss startup (where Prof. Roger is the Founder). CI AG is developing a Decision-Intelligence OS and Platform based on the philosophy that all knowledge is conjectural, that knowledge must be created as well as validated, usually over multiple cycles, before it can impact the real world. Decisions play the role of gatekeepers or interfaces between knowledge-wielding agents and reality, serving both as validation mechanisms for new knowledge, and as action-generators or factories. We call this philosophy ‘The Knowledge Chakra’, and we consider Decision-Intelligence or DI a practice that initially helps bootstrap such knowledge cycles, and once running, helps capture value from it. A knowledge cycle or chakra that is spinning is said to hold ‘Intelligence’, and that ‘intelligence’ is distributed amongst agents embedded in the chakra. Adithya’s role at CI AG is to work with Prof. Roger to further develop our conceptualization of DI as a knowledge creation and validation practice, and to turn DI concepts into software artefacts.

Innovations making Decision modelling easy by Niall Deehan and Maciej Barelkowski (Camunda)

At Camunda, we’ve been very big fans of DMN since its very beginning. We’ve been trying to make modelling and executing DMN an easy and painless experience. In this talk, we want to discuss some of the problems that our users had and the ways we tried to solve them in specifically 3 categories:

  • Giving users better context when they’re modelling
  • Helping users understand, learn, and even love FEEL (Friendly Enough Expression Language)
  • Ensuring that executed decisions can be understood by everyone.

You’ll hear about the journey to making the features and even live demos of each.

Keywords: DMN, FEEL, Decision Modeling – Slides

Niall Deehan is a Senior Developer Advocate with Camunda. Specializing in helping software developers and architects understand how they can orchestrate distrusted systems using process orchestration. He does this through code examples, presentations on various architectures and video recording. 

Maciej Barelkowski is a Senior Software Engineer at Camunda. In his role, Maciej contributes to Camunda’s bpmn.io project, which provides free BPMN and DMN modeling tools. His focus on accessibility aids the project’s mission of making process and decision modeling available for everyone, everywhere. Leveraging his expertise on the open standards, he also participates in shaping DMN as a member of the DMN Revision Task Force.

Optimizing Performance and Scalability in Low-Code/No-Code DMN Platforms by Octavian Patrascoiu (Goldman Sachs, UK)

Low-code / no-code platforms (LCNCP) are on the rise, a trend that represents a step towards the decade-long goal to automate coding. The quality of an LCNCP can be measured by external attributes such as functionality, reliability, usability, flexibility, efficiency, and maintainability. When businesses decide on using an LCNCP, they place significant importance on efficiency, which can be evaluated based on factors like performance and scalability.

This presentation describes the performance and scalability aspects in the space of business decisions specified using the Decision Model And Notation (DMN) language, an industry standard for the specification of business decisions, and the execution engine jDMN. Furthermore, it outlines a range of efficient optimization techniques within the DMN domain.

Keywords: DMN, Modelling, Performance, Scalability – Slides

Dr. Octavian Patrascoiu is a Vice President at Goldman Sachs, UK. He is currently working in the no-core / low-code solutions space. This includes the translation of well-known modelling languages (e.g., BPMN, DMN, CMMN and UML) to existing platforms(e.g., AWS lambda functions, JPA persistence) and translating decision data points to DMN models using ML techniques. I am author of four books on programming languages, programming techniques and programming language processors and author, co-author or co-editor of more than fifty research papers on programming languages, natural language processing, machine learning and model driven software development. [email protected]

DMN Solver/Helper for Sudoku by Bruce Silver (Bruce Silver Associates)

There are many solvers for the puzzle game Sudoku, including those based on constraints, multiple linear equations, and brute force guessing/backtracking, but just a few based on the way humans solve the puzzle, which is looking for patterns in the possibility sets (“posets”) of the unsolved cells. I am not a programmer, but I will demonstrate a solver/helper app for Sudoku I created with DMN, using a BPMN decision flow process also based on FEEL. Finding the patterns in FEEL is actually more fun than finding them visually, and if you are a FEEL geek you will learn things by studying the model.

The demo is a screen flow app that does not take user input to solve the puzzle – which would require javascript to do it right – but periodically stops and presents hints for patterns recognized along the way, then applies those hints and continues.
Keywords: DMN, FEEL, Sudoku – Slides

Bruce Silver provides training and consulting on Business Modeling and Automation using the BPMN and DMN standards. The training features the Method and Style approach, designed to make business process and decision logic understood from the printed diagrams alone. In combination, methodandstyle.com and BPMessentials are the leading providers of BPMN training and certification, with nearly 5000 students trained and over 1500 certified. We also offer the most comprehensive training on decision modeling with DMN. Training is delivered live and online to students around the world. [email protected]

Revolutionizing Credit Risk Management in Banking: The Power of DMN by Stefaan Lambrecht (The TRIPOD for OPERATIONAL EXCELLENCE)

Unlocking Real-Time Insights and Regulatory Compliance using Decision Modeling & Execution

The European Banking Authority (EBA) Dear CEO letter, typically issued to provide guidance and expectations for banks on key regulatory issues, emphasizes the need for stringent credit risk management, continuous monitoring, and compliance with evolving regulations.

The primary challenge for banks in monitoring customers and credit risks is the complexity and volume of data that must be continuously analyzed and acted upon. This complexity arises from several factors: the variety of triggers, the volume and complexity of metrics, continuous monitoring, quickly adaptable regulatory compliance, a comprehensive 360-degree customer view.

By leveraging DMN modeling & execution banks can effectively meet the EBA’s expectations outlined in the Dear CEO letter. DMN engines provide a robust solution for automated decision-making, continuous monitoring, regulatory compliance, and transparency, ensuring that banks can manage credit risks proactively and efficiently while maintaining the required standards set by the EBA and other regulatory bodies. This alignment not only helps in fulfilling regulatory obligations but also strengthens the overall financial health and stability of the bank.

During his presentation Stefaan Lambrecht will demonstrate an end-to-end solution to these challenges inspired by a real-life case, and making use of an integrated use of DMN, CMMN and BPMN.

Keywords: Business Process Management Plus, BPM+, Credit Risk Management, Regulatory Compliance, European Banking Authority, Continuous Monitoring, Credit Portfolio Management – Slides

As a very experienced business architect and a business process guru, Stefaan brings companies to the next level of customer experience and operational excellence. With an educational background as interpreter and political scientist, Stefaan has always been a visionary outsider in the twilight zone between business and ICT. From the early 90s onwards, Stefaan understood the power of business processes in achieving customer-oriented operational excellence and in aligning business with IT. Specialties: Strategic Performance Management, Enterprise Architecture, Business Process Analysis & Redesign, Business Rule/Decision Management. [email protected]