This year our presenters represent 12 countries: and so far we have 260 registered attendees from 46 countries.
Presenters, Presentations, Recordings: (click on the title to see the abstract, photos, biographies, see also Slides and Recording)
- Welcome to DecisionCAMP by Jacob Feldman
- Derek Miers: What The Real World Needs From Decision Management, Reasoning and AI
- Dr. Alan Fish: Incomplete Decision Models or “The Joy of X” (Slides Recording)
- Guilhem Molines and Kayla Martens: Ruleflow or Decision Model? Why not both? (Slides)
- Denis Gagne: Thinking inside the box – Introducing new boxed expressions to DMN (Slides Recording)
- Larry Goldberg: Discovering Decision Models in Legacy Code (Slides Recording)
- Silvie Spreeuwenberg: Artificial Intelligence needs explanation (Slides Recording)
- Bruce Silver, Matteo Mortari: Making Executable DMN Modeling More Business-Friendly (Slides Recording)
- Brian Stucky: A Decision Framework for Machine Learning (Slides Recording)
- Prof. Jan Vanthienen, Vedavyas Etikala: Automated decision making from A to Z (Slides Recording)
- Dr. Jacob Feldman: Developing Decision Optimization Microservices for real-world decision-making applications (Slides Recording)
- Chris Berg: Good beginnings (Slides Recording)
- Simon Vandevelde, Bram Aerts and Joost Vennekens : cDMN: Combining DMN with Constraint Reasoning (Slides Recording)
- Frederick Simkin: Sweating the “Squishy” Stuff: Knowledge Analysis best practices for Effective Automated Decisioning (Slides)
- Arash Aghlara : How End to End Decision Automation Increases Business Agility (Slides Recording)
- Wilfried Kurth: Modeling Insurance Products with DMN (Slides Recording)
- Daniel Schmitz-Hübsch: Decision Requirements Level: The real key feature of DMN (Slides Recording)
- Dario Campagna, Carlos Kavka, Sara Nicastro and Alessandro Turco: Visualization of DMN models using Design Structure Matrices (Slides Recording)
- Gediminas Vedrickas: Creating executable decision service designs using DMN and Excel Workbook (Slides)
- Dennis Aarts : The Triple Crown in Practice (Slides Recording)
Interactive Discussions: moderated by Sandy Kemsley
- “Ask a Practitioner”: June 29, 20:30-21:15 CET (Recording)
- Bob Moore, JETset Business Consulting
- Mark Woods, Allstate
- Brian Stucky, Quicken Loans
- James Taylor, Decision Management Solutions
- Carole-Ann Berlioz, Sparkling Logic
- Jan Purchase, Lux Magi
- “Ask a Vendor”: June 30, 19:15-20:00 CET (Recording)
- Guilhem Molines, IBM
- Fernando Donati Jorge, FICO
- Carlos Serrano-Morales, Sparkling Logic
- Denis Gagne, Trisotech
- Larry Goldberg, Sapiens DECISION
- Jacob Feldman, OpenRules
- Matteo Mortari, Red Hat
- Alessandro Turco, Cardanit
- “Ask a Practitioner”: June 29, 20:30-21:15 CET (Recording)
Presentation Abstracts and Brief Biographies
Keynote: What The Real World Needs From Decision Management, Reasoning and AI by Derek Miers, Sr Director at Gartner (United Kingdom)
As the real world opens up to the possibilities of machine learning and the use of analytics to inform operational decision making, the emphasis changes from development of fundamentally new approaches and methods, to driving widespread adoption. This session will span the spectrum of data science, machine learning, analytics and decision management as we explore what Gartner calls “Enterprise AI” – better automation, access and acceleration efforts are the keys to success. Another theme we will touch on is the need for technologists and product developers to focus on solving the real business problems and pain points of business rather than assuming a horizontal capability will somehow deliver long-term success.
Derek Miers is a Sr Director Analyst at Gartner and has been an industry analyst and consultant for more than 25 years, focused on the areas of digital transformation, business architecture, process management and technology innovation. He has been advising major organizations across industry and government. Prior to Gartner, Mr. Miers worked in research and consulting companies including Forrester Research where he published over 60 reports on business architecture, digital transformation and business process management. He covered business and enterprise architecture, digital transformation and business process management. He has been advising major organizations across industry and government. His deep competence is around process modeling/automation and enterprise architecture, especially as they relate to setting up, scaling and running major digital transformation initiatives. He focuses on how the use of models drive adaptability and agility; how they inform, connect and translate technology strategy into digital transformation initiatives; and then on into operational execution and analytics. He has developed a range of co-creation techniques for driving engagement in change programs and scaling transformation efforts. His primary focus is on establishing governance around change programs, innovation and transformation initiatives, linking that back to organizational and corporate/business strategy. That also entails establishing Centers of Expertise (CoEs) and other vehicles for scaling the engagement of the business and the adoption of technology. He also covers business process architecture and the fusion of design thinking through into operational excellence. That process architecture also includes the industrialization/harmonization of processes across federated enterprises, as well as the methods and techniques associated with knowledge workers and their processes.
Incomplete Decision Models or “The Joy of X” by Dr. Alan Fish, FICO
Decision models are typically used to define the decision logic for specific decision services, but it is sometimes necessary to see such services as instances of a class or set of similar decision services. For example, a lender might participate in a credit marketplace by configuring its products, policy rules and offer parameters in its own instance of a generic decision service managed by a third party. DMN may be used to analyse the decision-making required for making offers, and to demarcate the areas of logic which are configurable from those which are common across the marketplace. The resulting DRD provides a framework which is precise but simple to understand for the officers of the participating banks. In such cases DMN is used to define a template for the structure of a domain of decision-making, rather than the specific decision logic for any particular instance of that domain. The approach is proposed as a general way to define blueprints for decision-making systems. Keywords: DMN, Decision Model, Template, Credit. Dr. Alan Fish is an authority in Decision Modelling and Decision Management, especially in the support and/or automation of organisational decision-making. With over 30 years experience in this field, he has been responsible for many projects at the forefront of current technology. 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. He is the author of “Knowledge Automation: How To Implement Decision Management in Business Processes” (Wiley), and a co-author of the OMG specification Decision Model and Notation (DMN).
Thinking inside the box – Introducing new boxed expressions to DMN by Denis Gagne, Trisotech
The Decision Model and Decision (DMN) standards aims at being a business-friendly language for specifying and automating business decisions. In DMN, Decision Requirement Diagrams (DRD) are visual depictions of decisions’ requirements and Boxed Expressions (BE) are visual depictions of the decisions’ logic. The current version of Boxed Expressions provides visual constructs for simple Friendly Enough Expression Language (FEEL) expressions but does not provide Boxed Expression constructs for more complex FEEL expression statements that business users struggle with. In this presentation we will explore the problems faced by business users in specifying various more advanced FEEL statements and introduce new Boxed Expressions that address these shortcomings of the current DMN spec. Keywords: DMN, FEEL, Boxed Expressions, DMN Modeling
For over a decade Denis Gagné has been a driving force in the majority of international BPM standards in use today. He is a member of the Workflow Management Coalition (WfMC) Steering Committee, chair of the Business Process Simulation Working Group (BPSWG), and co-Editor of the XPDL 2.2 process definition standard. For the Object Management group (OMG), Denis is the Chair of the BPMN Interchange Working Group (BPMN MIWG), and a member of the Business Process Model and Notation (BPMN), Case Management Model and Notation (CMMN) team and Decision Management (DMN) team.
Sweating the “Squishy” Stuff: Knowledge Analysis best practices for Effective Automated Decisioning by Frederick Simkin, Smartfixllc.com
The purpose of this presentation is to focus on one of the ignored phases of developing and maintaining a decision automation solution, Knowledge Analysis (KA). Much time has been focused on implementing the structure of solutions as if the nature of the domain knowledge is a given and the correct knowledge representation schema is known and is in hand. This is understandable clients want to see developers with fingers on keyboards coding the solution. The “squishy” elements, locating organizing and modeling domain knowledge, are often denigrated or passed over with the assurance “we know our rules” (which assumes that “rules” are the correct knowledge representation) or “our analysts have it all done”. . However, experience shows that this often not the case. In this presentation we will first examine the knowledge analysis process and define three sub processes. This section will also discuss best practices to determine the client’s level of experienced in knowledge analysis and practices designed to foster client ownership in the process. Second, we will look in detail at each of the three sub processes Knowledge Acquisition, Knowledge Validation, and Knowledge Modeling and illustrate best practices with examples from three domains Insurance, consumer products manufacturing and oilfield services. Finally, we will conclude with an examination of how these practices contribute not just building applications which are complete, consistent and correct but also an application which is current because the process is critical to maintain the solution throughout its lifecycle.
Fred Simkin is President and Sr. Knowledge Engineer at SmartFix LLC. Fred has over 37 years of experience, across a wide range of industries from financial services to oil and gas, heavy construction, manufacturing and agriculture. He has developed solutions for Fortune 100 companies including Verizon, General Electric, Nabisco, Kodak, GMAC, MetLife, New York Life, MasterCard, AT&T and Merrill, using the full spectrum of knowledge representation schemas, including rules, case based reasoning, fuzzy logic, NLP and ANN. Fred studied computer science at Ripon College and in Houston, Texas. He has worked in the North and South America. He currently lives in New Jersey with his wife Mary Ellen and Gloria the KE Kitty. .
Automated decision making from A to Z by Jan Vanthienen and Vedavyas Etikala, KU Leuven
Organizations are shifting towards the automation of business decisions and processes for improving efficiency and effectiveness in business applications. Usually, modelling business decisions is a time-consuming manual process; it needs expertise in understanding, representing and validating the decision knowledge which comes from large numbers of unstructured policy texts and legal documents or semi-structured decision log data. State-of-the-art knowledge and text mining technologies provide us with an opportunity to address this problem. This presentation is about automatically discovering the knowledge and dependencies of business decision models within knowledge-intensive processes, from the unstructured business texts or semi-structured event logs, into the Decision Model and Notation (DMN) standard, ready for explainable execution. Keywords: Decision Modeling, Decision dependencies, Decision Mining, Decision Model and Notation DMN
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.
Vedavyas Etikala is a Ph.D. student in the Faculty of Economics and Business, KU Leuven, where he started his research at the Research Centre for Information Systems Engineering (LIRIS) in the Department of Decision Sciences and Information Management. His research interests focus on the application of Knowledge-based Artificial Intelligence (KBAI) technologies for decision making in information systems, with specific emphasis on knowledge representation and reasoning, and decision modeling. He received his Master in Technology degree in Computer Science and Engineering from IIT Madras, India. He currently works with Prof. Jan Vanthienen, on a project PRODIGY (Process-Decision Integration for KnowledGe-Intensive Process Management)
Modeling Insurance Products with DMN by Wilfried Kurth, AXA CH Switzerland
The modeling of insurance products, in particular the General Insurance conditions (GCI), and their use for computable contracts is a necessary prerequisite for the automation of operational insurance processes, such as analysis of insurance needs or claims processing. It seems obvious to represent insurance conditions as rules in decision tables. However, rules can be used to model insurance conditions for one use case only. If different use cases are to be used, the representation of insurance conditions as rules in decision tables is insufficient.
DMN allows the modeling of relations for the provision of embedded data. Insurance conditions can be represented with such data relations. As with input data, arbitrary decisions with decision logic can then be applied to these data relations. Both the data relations and the decision logic can be made available in a reusable form using Business Knowledge Models (BKM). This enables product experts to model products and business experts to develop the business logic needed in business processes and make it available in a reusable form for calling up in decisions.
The results are automated insurance capabilities using DMN, which to a certain extent replace specially programmed functionality for product modeling, application or claims processing in the business applications. This presentation will use the example of a home contents insurance product to show,
– how DMN relations can be used to model the GIC of insurance products,
– how functional decision logic can be applied to it for various purposes,
– how relations and decision logic can be made (reusable) available by means of BKMs
Key Take-away: Use of DMN for modeling computable insurance products
Audience: business, new
Industry Sector: Insurance
Wilfried Kurth works for IT AXA Switzerland in the field of Enterprise Architecture. He has been involved in information architecture for many years. In 2013, he introduced the “The Decision Model” method to business analysts and ensured that decisions modeled in the business could be automatically converted into decisions executable for a rule engine. In 2017 he introduced the DMN specification and a standard modeling tool for DMN to the business analysts. For 4 years he has been training business analysts in decision modeling and supporting project teams and product teams in the creation of decision models. .
Making Executable DMN Modeling More Business-Friendly by Bruce Silver, MethodAndStyle.com and Matteo Mortari, Red Hat
While DMN is sometimes used simply to create “decision requirements” handed off to developers, the standard was designed for non-programmers to create executable decision models themselves. Although decision model design is nominally business-friendly, business users still struggle to create DMN models that are correct and complete. This can be partially explained by the lack of disciplined approach and application of engineering principles that come naturally to technical users, presenting an opportunity for vendors’ tools to assist them.
Decision tables are DMN’s most business-friendly feature, but business users have problems ensuring they are complete and consistent and difficulty applying the correct hit policy. Decision Table Analysis built into the DMN tool should be able to detect flaws like gaps in the rules, unintended overlaps, subsumption, and incorrect hit policy. We have developed such an algorithm and are incorporating it into multiple DMN tools.
Beyond completeness and consistency, the decision table logic must be correct, the output always matching the expected value. That is what test cases are for, but business users do not know how to use them or create test cases that fully test the logic. This again presents an opportunity for tool-based assistance.
Automated test case generation is not an easy problem. A decision table with N inputs, each of which has m possible values, requires m**N test cases for complete coverage. From the world of automated software testing, a method called Modified Condition/Decision Coverage (MC/DC) has been shown to provide excellent coverage with far fewer test cases. We have developed an algorithm for applying MC/DC to test case generation for DMN decision tables. . Keywords: DMN, Decision Tables, Hit Policy, Test Cases
.Founder and co-chair of the annual bpmNEXT conference, Bruce Silver is a well-known consultant, industry analyst, and educator specializing in BPM. He is founder and Principal of BPMessentials, the world’s leading provider of BPMN training and certification, and methodandstyle.com. Author of BPMN Method and Style and DMN Method and Style, Dr Silver served on the OMG technical committees that developed the BPMN 2.0 and DMN 1.1 standards. Previously, he served on the board of directors of Captiva Software until its acquisition by EMC in 2005, and was Vice President and head of workflow and document management research at the industry analyst firm BIS Strategic Decisions (which became Giga Information Group, now Forrester Research). In the 1980s, he was engineering manager at Wang Labs in charge of one of the very first commercial document imaging and workflow systems. He holds Physics degrees from Princeton and MIT, and four patents in imaging and workflow.
Matteo Mortari is a Software Engineer at Red Hat, where he contributes in Drools development and support for the DMN standard. Matteo graduated from Engineering with focus on enterprise systems with a thesis involving rule engines which sparked his interests and influenced his professional career since. He believes there is a whole new range of unexplored applications for Expert Systems (AI) within the Corporate business; additionally, he believes defining the Business Rules on the BRMS system not only enables knowledge inference from raw data but, most importantly, helps to shorten the distance between experts and analysts, between developers and end-users, business stakeholders.
Artificial Intelligence needs explanation by Silvie Spreeuwenberg, Librt
A Dutch court has ordered the immediate halt of an automated surveillance system for detecting welfare fraud because it violates human rights, in a judgment likely to resonate well beyond the Netherlands. The court’s main argument is that it is not transparent how data is processed and analysed. Explainable algorithmic decision-making is feasable, has a better return on investment and will be required by society. In this session you will learn why black box AI can never be successful and how it can be different. System’s like #SyRI (Acronym for the Dutch translation of System Risk Indication) are intended to detect fraud with public money using public data from multiple sources. This is an important quest and fraud must be actively researched. However, like the financial industry, who are obliged to implement frauddetection tasks under a highly regulated regime, they should be able to generate a good explanation every case. Keywords: artificial intelligence, explainable artificial intelligence, decision support systems, Return on investment, transparency, algorithms, data analytics, decision bias
Silvie Spreeuwenberg is an experienced entrepreneur and consultant. She combines the ability to be a holistic thinker while, at the same time, she has detailed knowledge about artificial intelligence, compliance and software development. Therefore, she is a good strategy advisor for founders, scaleups and start-ups. Her inspiration is rooted in a strong need to create sustainable partnerships based on trust. She has an urge to share knowledge by being extremely transparent. These are the themes in her work.
Ruleflow or Decision Model? Why not both?by Guilhem Molines, IBM and Kayla Martens, Principal
Decision Modeling emerged recently as an industry modern approach, well suited to model decisions from the top, starting from the general and drilling into the details. But what happens if you want to model a decision that should take part in an already existing decision service, which was developed with a ruleflow approach? Do you start from scratch and model everything? How do you reuse an existing rule base and complement it with a Decision Model? This talk shows lessons learned from a use case at a major life insurance company. Keywords: decision model, ruleflow, hybrid, evolution.
With a background in fundamental Computer Science and Artificial Intelligence, Guilhem Molines has been involved with decision technology for more than two decades, both on the field as a consultant and as an architect of the ILOG, then IBM product team behind ODM (Operational Decision Manager). With a special focus on the modeling and authoring experience, Guilhem is always in close contact with users and practitioners and willing to find innovative ways to make the authoring of decisions an easier task for the industry..
Kayla Martens is a Software Engineer for Principal Financial Group, headquartered in Des Moines, Iowa, USA. She began her career as a decisions and rules focused Business Analyst and has transitioned into an engineer role still focused on decisions and rules with a focus on Decision Modeling.
Decision Requirements Level: The real key feature of DMN by Daniel Schmitz-Hübsch, Materna
DMN differs between the Decision Requirements Level (DRL) and the Decision Logic Level (DLL). While the DLL and the creation of the decision logic are currently often discussed. However, the DRL and the associated Decision Requirements Diagram (DRD) are of particular importance, especially at the beginning of the decision modeling process, since it lays the foundation for the modeling of the logic. This presentation shows which elements of a DRD are important for business analysts and where difficulties may occur when dealing with some elements. In addition, it shows how business analysts in actual projects proceed when creating the DRL and especially modeling the DRD. The presentation lays focus on how analysts break down a question into manageable individual decisions. Furthermore, the application of the modeling of data structures will be discussed. At the end of the presentation, suggestions will be given on how DRL can be changed and improved in the future, based on project experience. Keywords: DMN, DRL, DRD.
Daniel Schmitz-Hübsch is a Software Developer at Materna GmbH.Daniel holds a Master degree in Business Informatics with focus on mobile oriented analysis of business processes. For five years, he has been involved in the modelling and technical implementation of business process- and decision management systems. As a software developer for an independent IT company, he is responsible for the development of high-availability decision applications using rule engines like IBM Operational Decision Management.
Good Beginnings by Chris Berg, InRule
Just over a year ago, a customer expressed several questions about their new decision management project. The more we dug into their problems, the more we realized they were suffering from basic yet enduring challenges. Essentially, it’s the same set of problems we all face when staring at a blank piece of paper: at some point, you start – even though you may not know what you are doing. As projects progress other problems emerge threatening go-live or follow the team long after the celebration of success has faded. This presentation highlights best practices for how to start decision management project, even before touching a product. In this session we will playback the journey of a project and explore a maturity model that will measure progress, risks and planning effectiveness. Keywords: team building, design thinking, planning strategy, data science, playbacks.
Chris Berg Director, Product Strategy and Design Chris has been leading people, products, design and technology for over 15 years in the enterprise software space. He has led significant design explorations (BPM, PaaS, API Management and DevOps) while at IBM and covers more than 10 years of product leadership in the Decision Management space. In much of this time, he focused on transforming business behavior and empowering business users. In his role at InRule Technology, Chris is responsible for product strategy, design and alignment with the market.
“Developing Decision Optimization Microservices for real-world decision-making applications” by Dr. Jacob Feldman, OpenRules
This presentation will describe a test-driven approach to development of decision optimization microservices and their incorporation into the modern decision-making applications. The proposed approach covers the following development stages:
- Starting a user-facing GUI (client) that utilizes a decision optimization service;
- Creating a service-stub using the actual client’s interface, deploying it on-cloud and testing an integrated solution;
- Splitting the problem in two parts: Business problem and Optimization Problem
- Incremental modeling and implementation of the decision optimization microservice;
- Testing and continuing development of the end-to-end solution.
The approach will be demonstrated using real-world decision optimization problems using off-the-shelf constraint and linear solvers and AWS cloud framework. Keywords: Decision Optimization, Microservices, Decision-making applications, JavaSolver, Constraint Solver, Linear Solver
Dr. Jacob Feldman is the CTO of OpenRules, Inc., a US corporation that created and maintains the highly popular Open Source Business Rules and Decision Management System commonly known as “OpenRules”. He has extensive experience in development of decision support software using business rules, optimization, and machine learning technologies for real-world mission-critical applications. Jacob is the DecisionCAMP’s Chair, an admin of DMCommunity.org, and an active contributor to BR&DM forums. He is also the Specification Lead for the JCP standard JSR-331. Dr. Feldman is an author of two books “DMN in Action with OpenRules“ and “Goal-Oriented Approach to Decision Modeling”
A Decision Framework for Machine Learning by Brian Stucky, Quicken Loans
We know that digital decisions, along with machine learning, are key components to realizing artificial intelligence. DMN gives us the capability for “predictive decision automation” by virtue of facilitating the use of predictive models within decisions. However, we can gain even more by using decisions as part of an enterprise machine learning framework.
We explore three such areas based on a foundation of decision management:
- Construction of a permissible use and compliance process that ensures data and predictive models created from that data are properly vetted across numerous constraints.
- Development of a human and machine hybrid model of boundary rules to assure proper use of data and predictive models. This leverages an “enforcement level” of decisions (based on notions presented by Ron Ross at Decision Camp 2019) to provide a range of decisions that may include human intervention to provide boundaries for data and model use. These boundary decisions are a combination of static and dynamic rules meant to confirm compliance.
- Establishing a foundation for explainable AI using decisions. Interpretable machine learning algorithms are quite useful for data scientists. However, financial services will ultimately necessitate providing explanations to a wide variety of audiences. Replicating the predictive models in decision models is an important step to provide this necessary foundation.
This framework will be discussed using numerous examples relevant to financial services and mortgage lending with respect to permissible use, compliance, predictive models and explanation. Keywords: Decision Management, Machine Learning, Financial Services, Mortgage Lending, Compliance, Explanations.
A recognized thought leader in decision management, Brian Stucky brings three decades of experience designing and implementing business rule and process management systems. Domain experience includes the secondary mortgage market, credit card processing, mutual fund portfolio analysis, insurance underwriting, and Federal civilian agencies.Brian works closely with decision management vendors and frequently speaks at professional events. He was a contributing author to three decision management books, currently serves on the editorial board of the Business Process Management Institute and has well over 60 publications. Brian is now in his fourth year as co-chairman of the Mortgage Industry Standards and Maintenance Organization Business Rule Exchange Workgroup. His efforts there have resulted in finalizing the Decision Model and Notation (DMN) standard as the official mortgage industry standard.
Discovering Decision Models in Legacy Code by Larry Goldberg, Sapiens Decision
A very significant cost of legacy modernization is the discovery and recovery of business logic embedded in legacy systems. Furthermore, today’s methods do not ultimately solve the underlying architectural problems of automating the process of converting legacy business logic to maintainable, understandable, and executable decision models.
At Sapiens Decision, using machine learning, we have implemented a solution, ALE (Automated Logic Extraction), that automates the discovery of logic in legacy code and its conversion to normalized decision model structures. With the Sapiens Decision ALE solution, users get highly structured models with clear business context, are able to A/B test the converted in the Sapiens Decision suite, and generate executable decision services available for the to-be architecture. Sapiens Decision ALE is currently in widespread pre-release Beta, and addresses COBOL legacy code.
We will present the theory of the discovery process, and share some examples of actual results we have achieved using the ALE engine. We will also share lessons learned and the a road map to a future of extracting decision logic from a wider range of legacy languages such as Java, and ultimately from unstructured documents. Keywords: Discovering Decision Models in Legacy Code, Rule Mining from Cobol, Decision Mining, Legacy code
Larry Goldberg is an evangelist for Sapiens DECISION, and as a member of the senior management team is responsible for all products in the Sapiens Decision company. He was Co-founder and Managing Partner of Knowledge Partners International LLC, acquired by Sapiens Decision, and has over forty years of experience in building technology based companies on four continents. Commercial applications in which he played a primary architectural role include such diverse domains as banking, healthcare, supply chain, property & casualty insurance, and enterprise modeling tools. He has been the business lead and/or business sponsor on many major projects in both the public and private sector, and is a trusted adviser to senior executives from major corporations. Larry is a leading international authority on business requirements, and is the co-author of the best-selling book “The Decision Model: A Business Logic Framework Linking Business and Technology” (Auerbach, New York 2009).
Visualization of DMN models using Design Structure Matrices by Dario Campagna, Carlos Kavka, Sara Nicastro and Alessandro Turco, ESTECO SpA We present a novel way to visualize a Decision Requirement Graph (DRG) using a Design Structure Matrix (DSM). By translating a DRG into a DSM we get a compact and concise visual representation of the whole graph. This visualization can help in managing models with many Decision Requirement Diagrams (DRDs). A good practice when dealing with a complex DRG is to use many DRDs. Each DRD visualizes a partial view of the DRG. In this way you avoid having a single large and intricate DRD. Such a DRD would be difficult to read and to work with. This practice poses a question: ”How can we visualize the DRG as a whole?”. Possible answers are: with a list of all the DRG elements, with a tree-like representation of the DRG. Both answers are valid. None of them is satisfactory when it comes to the representation of requirements. Our answer to the above question is to use a DSM to visualize the DRG. A DSM is a square matrix showing relationships among elements of a system. Diagonals squares represent the elements of the system. Off-diagonal marks represent relationships. DSMs can model organization architectures, product architectures and processes. They have been applied in automotive, aerospace, electronics and many other fields. In this session we present the translation from a DRG to a DSM. The DSM has DRG elements as row and columns. Off-diagonal marks model requirements. We show the translation using an example from the DMN 1.2 specification. We examine the advantages and disadvantages of the DSM-based visualization of DRGs. We discuss possible future research directions. Keywords: DMN, DSM, DRG Visualization.
Dario Campagna received his Master Degree in Computer Science in 2008 from the University of Udine, Italy. In 2012 he received his PhD in Mathematics and Computer Science from the University of Perugia, Italy. The same year he started working at ESTECO SpA, an independent technology provider that delivers software solutions to the engineering industry. Dario spent four years as developer for the Research and Development group. He took part to different research projects and worked in the field of business process management. In 2016 Dario took the role of Agile Coach in ESTECO while continuing to contribute to Research and Development activities as Senior Researcher. Since 2017 he is involved in the COMPOSELECTOR H2020 project, where he contributes to the modeling of processes and decisions underlying the industrial application cases.
Carlos Kavka has a PhD in Computer Science from the University of Paris Sud (France). He is currently the Head of Research and Development of ESTECO SpA (Italy), a company specialized in multi-objective and multi-disciplinary design optimization. In particular, he is in charge of the coordination of scientific, technical and technological aspects in most of the European research projects in which ESTECO SpA participates. Carlos Kavka has been an Instructor, Lecturer and co-Director of international workshops at the International Center for Theoretical Physics ICTP (Italy) since 1993 till 2012, Professor at the Universidad Nacional de San Luis (Argentina) from 1994 till 2005. He has also participated in research activities at the LHC-CMS CERN experiment from 2005 to 2007.
Alessandro Turco has a PhD in Applied Math from the International School of Advanced Studies (SISSA) of Trieste and a Master in Management from the School of Management of Milan Polytechnic (MIP). He has been working for ESTECO SpA for ten years, starting as a researcher for the Numerical Method Group. He is now the project manager of Cardanit, the BPM solution recently launched by ESTECO.
Sara Nicastro has a Bachelor Degree in Physics and a Master Degree in Computer Engineering from University of Trieste, Italy. After a brief experience as developer in the Corporate Software Application group of ESTECO in 2019, she joined the Research and Development team where she wrote her master thesis entitled ‘Conversion and visualization of business processes in DSM format’, regarding the conversion between BPMN and DSM. She is now working as a developer of Cardanit, the BPM solution recently launched by ESTECO.
cDMN: Combining DMN with Constraint Reasoning by Simon Vandevelde, Bram Aerts and Joost Vennekens, KU Leuven
The Decision Model and Notation (DMN) standard is a notation for modeling decision processes, designed to be easy to read and interpret. While DMN succeeds in achieving these goals, we find that it is not expressive enough for modeling complex problems.
To address this problem, we propose a rich extension to DMN, which adds a number of additional concepts that facilitate the modelling of complex problems. The key feature of this extension is constraint modelling, hence its name: Constraint Decision Modelling and Notation (cDMN). It offers a number of tools to express or use knowledge in an intuitive way: constraint tables, data tables, quantification, built-in optimization and new data concepts. Because it is an extension to DMN, simplicity and readability are key; even though expressivity is greatly improved, cDMN’s notation closely resembles that of DMN.
After a general overview of cDMN and its key features, we perform a more in-depth analysis of our proposed notation, by discussing a concrete cDMN implementation. We use a real-life example for this, which we submitted to the DMcommunity website: a doctor planning for a hospital. Using cDMN, we constructed an implementation that is readable like DMN, but expressive enough to represent all the rules the challenge poses. We show how the autoconfig interface can be used for easy interaction with the knowledge. We then compare our implementation to the other available solutions submitted to the website.
We find that cDMN is able to create models that are both more compact and more readable than models created in standard DMN. Moreover, it is able to model more complex problems. Keywords: Decision Modeling, Knowledge Representation, Constraint Reasoning, Hybrid Systems.
Simon Vandevelde is a PhD researcher at KU Leuven campus De Nayer in Sint-Katelijne-Waver, Belgium. His research is focused around user-friendly knowledge representation languages. He is interested in learning more about combining DMN together with powerful knowledge driven reasoning tools. As his first project, he worked together with researcher Bram Aerts to develop the cDMN framework.
Joost Vennekens is an associate professor at KU Leuven Campus De Nayer in Sint-Katelijne-Waver, Belgium. His research is concerned with AI technology (both Knowledge Representation and Machine Learning) and its industrial applications. He belongs to the research group EAVISE, which focuses on AI, computer vision and embedded systems, and to the research group DTAI, which studies declarative languages and AI. He is a member of the board of the Benelux Association for Artificial Intelligence and of the board of the Leuven.AI institute.
Bram Aerts is a doctoral researcher at the KU Leuven University. His research situates in the field of Knowledge Representation and Artificial intelligence, more specifically applying state-of-the-art techniques in industrial applications.
Creating executable decision service designs using DMN and Excel Workbook by Gediminas Vedrickas, FICO
Prototyping is a common technique to automation and this equally applies to creating decision services. Traditionally, preparing for decision service implementation project include requirements analysis, creating decision service design. Implemented decision services are tested to validate they match to the design before deployment. For many businesses regular testing in many cases is not enough therefore subject matter experts (SMEs) further minimize technology and translation risks by challenging production decision service with executable version of the decision logic created using alternative technical means to production decision service ranging from Excel to SAS, Python, etc. Decision Model and Notation (DMN) is a new industry standard to decision modeling and formal expression of the decision logic. Microsoft Excel is a powerful tool for structured design and prototyping, also widely used by SMEs. So why not to use DMN to prototype decision services by creating executable decision service designs that not only expedite production service implementation, but as well as provides means for second level decision logic validation, round trip rule repository reporting and standards-based exchange of the decision logic across DMN tools? In the presentation it will be discussed what does it take to use DMN and Excel workbook for formally capturing a decision logic while preserving decision concepts, it’s potential for enhancing the overall decision implementation lifecycle. Keywords: DMN Decision Modeling, Decision Logic, FEEL, Decision Service, Executable Design, Prototyping, Decision Implementation.
Gediminas Vedrickas is the Director of Technology Consulting EMEA (TCE) Decision Solutions at FICO. He is leading development of Decision Implementation best-practice for FICO Decision Solutions Professional Services. The practice focuses on creating Decision Solutions based on products and services from FICO Decision Management Suite including use cases for business rules, predictive analytics and optimization. .
The Triple Crown in Practice by Dennis Aarts, The Business Analysts
During last year’s conference, the term “triple crown” (the comprehensive term for case management, business process management and decision management) was mentioned regularly, yet no practical examples were discussed. During my presentation “Decision Management as a Service” concerning a project of the Government of Flanders, I briefly touched upon the integration of a business process engine with a business rules engine, yet I didn’t dive into the details of this integration.
Coincidentally, we have recently been given the opportunity to realize a project that applies the “triple crown” as a whole. This project concerns an application that allows a reseller to follow-up the licenses of the software they sell; giving a notification if a license has to be renewed, supporting the creation of an offer, etc. Within this project case management is used for the global architecture, whereas business process management is used for the underlying processes. Decision management is used across the two whenever a business rule has to be executed, such as the computation of a rate.
For the realization of this project, we use Camunda. A product that enjoys a relatively large adoption in the Belgian market, since it is open source and tries to integrate the three standards related to the “triple crown” (CMMN, BPMN and DMN) as closely as possible. Although I will give a demonstration of the resulting application, I will not elaborate on the use of Camunda specifically. What I will discuss, is the general modeling approach that has been applied to realize the project. This includes giving you a detailed view on the diagrams that have been designed using the three different standards, their integration with each other, as well as how these standards differ in their execution. Seeing the “triple crown” in action, allows you to identify its potential for future projects. Keywords: Triple Crown,Decision Management,DMN, Business Process Management, BPMN, Case Management, CMMN, Business rules engine, Workflow engine.
After a study in the Netherlands, Dennis Aarts came to Belgium, where decision management was far less mainstream. In Belgium, Dennis realized multiple analysis in terms of decision management, yet always followed by a hard coded implementation. Right now, he supports different customers with the purchase of a business rules engine and guides their analysts in the definition of business rules. The Business Analysts, part of the Cronos Groep, is a company focusing on functional and business analysis. Due to an expanded interest in the domain of Enterprise Architecture, the subsidiary Bespoke Services was founded in 2017. For the same reason a second subsidiary, called B. Adapted, focusing on change management, was founded in 2018. Currently The Business Analysts and its subsidiaries have about 90 employees working in a broad range of market segments for both governmental and privately held organizations.
How End to End Decision Automation Increases Business Agility by Arash Aghlara, FlexRule
Organizations, from large corporations, government departments to SMEs, and from any industry are facing the pressure of managing mounting changes arising from market dynamics and volatility, policies and regulations, and massive amounts of data and information. Learning and adapting to these changes with the right decisions at the right time while complying with business rules in business operations and determining full business impact of change in the context of outcome is critical. Therefore, operational decisions must become the front-line focus of the day-to-day operations as they drive business behaviour in an organization. And, increasing business agility is the need of the hour and must be a key capability of an organization. Organizations’ ability to sense and deliver required changes need the end-to-end view and automation of decisions, more specifically, end-to-end operational decision automation using decision-centric approach. End-to-end decision automation is a holistic approach that
- Focuses on the end-to-end requirements of the whole value chain to ensure business agility is improved
- Is based on the OODA loop and drives an entire decision cycle by sensing and responding to changes effectively and efficiently
- Makes operational decisions the first-class citizen of organizations. Enables collaboration between decisions, internal and external systems, and information creating a synergic combination of process, data, people, and rules to deliver business value.
- Not only senses and identifies opportunities but, takes actions and helps overcome the last mile challenge of the automation, AI, and analytics.
Arash Aghlara is the CEO and the founder of FlexRule – a leading global provider of End-to-End Decision Automation which empowers organisations to make optimised, customer-centric, and situation-aware decisions. Created Decision-Centric Approach, a methodology that brings People, Data, Rules, and Processes together to automate decisions – recognised by Gartner. I am an expert in architecture, design, and implementation of operational decisions, business rules and process automation. I share insights on Decision automation and decision-centric approach for both business and technical people. Access my posts on https://www.flexrule.com/blog or https://medium.com/decision-automation .