Program

[Preliminary]

Day 1: Monday September 17, 2018 

Authors Title (click to see Abstracts)
 Dr. Alan Fish (FICO) Specifying collaborative decision-making systems using BPMN, CMMN & DMN
Denis Gagne (Trisotech) Decision Automation using Models, Services and Dashboards
Prof. Jan Vanthienen, Thibaut Bender and Faruk Hasić
(KU Leuven)
The support of Decision Modeling features and concepts in tooling
Dr. Jacob Feldman (OpenRules) Goal-Oriented Business Decision Modeling
Dr. Jolyon Cox (RapidGen) and Dr. Jan Purchase (LuxMagi) High-Performance Decision Model Execution by Compilation of DMN into Machine Code
Bas Janssen and Stijn van Dooremalen (IAM4) Smart contracts from legal text: interpretation, analysis and modelling
Dan Selman and Dr. Jerome Simeon (Clause) Accord Project for Smart Legal Contracts
Octavian Patrascoiu (GoldmanSachs) jDMN: An execution engine for DMN in Java
Yoshihito Nakayama, Masahiro Mori, Dr. Yoshiaki Narusue and Dr. Hiroyuki Morikawa (University of Tokyo) Decision-making in sales activity: applying process mining approach

Day 2: Tuesday September 18, 2018 

Authors Title (click to see Abstracts)
Carole-Ann Berlioz and Carlos Serrano-Morales (Sparkling Logic) Spreadsheets in Decision Management
Daniel Schmitz-Hübsch and Ulrich Striffler (Materna) Managing a Decision Zoo (Requirements Engineering for Complex Decisions
Yoshihito Nakayama (Intra-Mart) Automatic judgment of decision authority using OpenRules
Dr. Jan Purchase and David Petchey
(LuxMagi)
Explaining the Unexplainable: Using DMN to Justify the Outcomes of Black-box Analytics
Martin de Villiers (MedScheme) Fusion Project – replacing a legacy system elephant, one bite at a time
Walter Berkowicz and Mark Woods (AllState) Implementing Decision Modeling in a Large Organization
Matteo Mortari (RedHat) Introduction and Updates on DMN TCK
Q&A Panel Real-world Business Decision Modeling (moderated by Carole-Ann Berlioz )
Conference Dinner

Day 3: Wednesday September 19, 2018 

Authors Title (click to see Abstracts)
Susara van Den Heever (IBM) Make Faster, Smarter Decisions by combining Machine Learning and Decision Optimization
Roy Robinson and Alan Giles (Boxever) Case Study – Building an enterprise ready decision management platform for customer engagement and next best action
Wilfried Kurth (AXA CH) Decision Modeling at AXA CH
Helmut Simonis   (Insight Centre) Outpatient Waitlist Analysis for Irish Hospitals
Mark Proctor (RedHat) Modernizing Production Rule Systems
Tim Stephenson (OmnyLink) Modeling progress on Climate Change and Social Value Acts
Brian Stucky (Alligiance) Driving FinTech and RegTech with Industry and Technology Standards

Presentation Abstracts


Specifying collaborative decision-making systems using BPMN, CMMN & DMNBy Alan Fish

  • In Knowledge Automation (2012) I proposed a decision-modelling methodology (DRAW) for defining automated decision making. This approach, although successful, focuses on the functionality of decision services. It models the surrounding business processes in only enough detail to provide context for the required decision making, and models user interactions quite crudely. This predisposes analysts to specify systems with relatively simplistic human interactions, neglecting the rich possibilities of collaboration between people and computers in decision making.
    Using the OMG “Triple Crown” (with other standards) it is possible to model the totality of functional requirements for a decision-making system, including complex interactions between automated business processes and human case workers. BPMN models the processes, CMMN models the user participation, and DMN models the automated decision making. However, it is not always clear how these standards should be applied together.
    I suggest a set of principles for partitioning functional requirements between these three modelling domains so that there is no omission or duplication of functionality between them, and so that all interactions – between the domains and with external components – are explicitly modelled. These principles use only existing features in the “Triple Crown” standards, supported by other UML-based models, particularly use case models, object models and state models, and are therefore already supported by existing toolsets.
    While modelling can be approached from many directions, an “Outside-In” approach would adopt the sequence: case modelling, business process modelling, decision modelling. This may be most appropriate when specifying systems with complex human interactions. The presentation uses examples from real-world modelling problems.
    Key take-away: A clear, simple method of specifying the complete functionality of organisational decision management systems. [Back]


Decision Automation using Models, Services and Dashboards by Denis Gagne

  • The Decision Model and Notation (DMN) offers the perfect solution to specifying Business Decisions. Symbolic and sub-symbolic artificial Intelligence (AI) approaches can effectively be used jointly within DMN, delivering explainable decision automation that is desired if not mandatory in any business context. The resulting DMN Decision Models are aso the perfect architecture for the creation of decision support dashboards.
    In this session we will demonstrate how line of business people can define business decisions that are explainable, auditable, and traceable. These business decisions can be assembled and consumed as services via a modern platform API architecture and visualized via graphical dashboards. The resulting dashboards help visualize information and knowledge that that are critical for the operations of any type of business.
    Key take-away: DMN is a key enabler of many forms of Decision Support
    Keywords: DMN, FEEL, XML, PMML, REST, Trisotech Digital Enterprise Suite [Back]


The support of Decision Modeling features and concepts in tooling by Prof. Jan Vanthienen, Thibaut Bender and Faruk Hasić

  • This presentation examines to which extent some of the important Decision Model and Notation (DMN) features and concepts are supported by tooling. It is not a tool comparison, and no product names are revealed, but the analysis tries to give an indication of which elements of decision requirements diagrams, decision logic specifications and the (S)FEEL expression language are commonly present in current decision modeling (and execution) tools.
    This analysis complements the DMN Technology Compatibility Kit (TCK), as attention is also paid to tools which can only be examined manually or which do not obey the exact specification of the standard. The approach does however give an indication of which modeling features and concepts are considered important by tool providers.
    Key take-away:
    – Which DMN elements (decision requirements diagrams, decision logic specifications, the expression language) are commonly present in current decision modeling tools
    – Which modeling features are considered important by tool vendors.
    Keywords: Decision Modeling, DMN [Back]


Goal-Oriented Business Decision Modeling by Jacob Feldman

  • Goal-oriented business decision modeling is driven by the need to simplify communication between business analysts and operational business decision models while extending the capabilities of traditional business rules and decision management systems. Decision modeling tools that create decision models in accordance with the current DMN standard usually address only one question and expect the decision model to determine a single answer given different input data. They also require a user to define knowledge and information requirements by drawing DRD’s arrows between different logical and data components of the decision model.
    The proposed Goal-Oriented approach extends DMN capabilities by allowing a business analyst to only select not one but various business objectives (goals) in terms of DMN decision variables and without necessity to specify any relationships which can be automatically inferred from business knowledge modules. Then the supporting system will automatically generate an execution path within the decision model that leads to a selected goal.
    We will demonstrate a goal-oriented decision modeling approach using an interactive GUI that  allows a business analyst to do the following:
    1) Select a decision model from an easily configurable list of decision models
    2) Select a business goal from an automatically generated list of output decision variables
    3) Select a test case and run 
    an underlying rule engine that automatically builds an execution path for the selected goal and executes it against the selected test case
    4) Display the automatically found decision in the user-friendly format with explanations why certain sub-decisions where made while reaching the goal.
    We will utilize well-known decision models to demonstrate the goal-oriented approach.
    Keywords: Decision Model, DMN, Goal-oriented business decision modeling [Back]


High-Performance Decision Model Execution by Compilation of DMN into Machine Code  by Jolyon Cox and Jan Purchase

  • The burgeoning scale of automated decision-making in developing economies, such as that required by financial fraud and customer personalization in China, will create a demand for high performance decision execution several orders of magnitude higher than today’s workhorses. Multiplying this by the new scale of data imposed by the Internet of Things and the accountability required by increasingly rigorous compliance regulations will demand unprecedented volumes of complex decision-making; volumes requiring not just scalable hardware, but software purpose-built for the execution of compiled decisions.
    This presentation looks at implementation strategies for supporting very-high-performance, DMN-based decision-making using modest hardware, and outlines the results. It examines the use of a single-pass compiler for decision tables based on a bitwise maintenance of rule masks to minimize execution time and enable real-world decisions to be made in microseconds. It also articulates the challenges of creating a DMN XML parser in XSLT.
    Attendees of this session will discover which of DMN’s features posed the biggest challenges, both in terms of satisfying the TCK tests and during performance optimization. Further, we discuss some proposed revisions to DMN’s type system to improve performance with no practical impact to its flexibility. During a demonstration, several demanding decision models will be compiled, benchmarked, verified and executed. In addition, the presentation will highlight some of the technical challenges imposed by practical application of the DMN standard such as null propagation and hit policy enforcement.
    Key Take-Aways:
    Acquire an understanding of state of the art DMN compilation and execution environments that lower the hardware bar for high volume systems and enable new levels of throughput. Gain insights into DMN modifications that could improve performance still further.
    Keywords: DMN compilation, DMN execution, decision services, optimization [Back]


Smart contracts from legal text: interpretation, analysis and modelling by Bas Janssen and Stijn van Dooremalen

  • Many believe blockchain will be a disruptive technology that will facilitate digital economy in a trusted way. We hope that promise will be kept, because it will be a major boost to the business rules / decision management community.
    For such an economy to actually work, many of the legal interactions between parties have to be digitized. And this requires Interpretation , Analysis and Modelling of legal texts and translating those models into smart contracts or other implementations that can be accessed via blockchain solutions. And the business rules community has the goodies in place to make this work.
    Our goal is not only to demonstrate the way this process works, but also what is needed to make the resulting smart contracts trustworthy for the community that uses such smart contracts. In our view this requires transparency through documentation of the interpretation, analysis and modelling process and repeatable code generation. We propose to publish all the information and capabilities on the blockchain so the blockchain community can actually check if the smart contracts they use are in line with that documentation. [Back]


Accord Project for Smart Legal Contracts by Dan Selman and Jerome Simeon

  • Accord Project is the leading community of legal and technical professionals, creating the standards and software for the formation and execution of smart legal contracts. In this presentation, we will present the goals of the Accord Project and the key technologies being developed to support legal contracting on top of blockchain distributed ledger technologies.
    The standards defined in Accord Project are designed to be open and portable across blockchain platforms. We will give an overview of the Accord Project technologies, which include a templating system, a domain specific language and compiler for legal contracts, and an execution engine for those contracts.
    A key purpose, and also a challenge, of the Accord Project is to provide a forum where both lawyers and technology experts can collaborate in the area of smart contract management. We will describe how the structure and governance for Accord Project makes this possible. Finally, we will explain the similarities, and differences, between the Accord Project and previous technologies in the area of expert systems, business rules, and decision management systems.
    The presentation will be of interest to both a technical and a business audience. Most of the presentation will be easily accessible to an audience with little legal or blockchain background. If time permits, the presenters will be happy to give a short demonstration of a legal contract written with the Accord Project and deployed on Hyperledger Fabric.
    More information on the Accord Project can be found at https://www.accordproject.org/
    Keywords: accord project, law, blockchain, rules, declarative business logic, smart contracts, legal-tech, hyperledger, ethereum, DMN. [Back]


jDMN: An execution engine for DMN in Java by Octavian Patrascoiu

  • The Decision Modeling and Notation (DMN) is a modeling language for decisions. DMN is an industry standard maintained by OMG. Successful Domain Specific Languages (DSLs) must be simple, easy to understand and use, with a higher level of abstraction and supported by a mature language workbench (e.g. editors and executions engines).
    This paper presents jDMN an open-source execution engine for DMN implemented in Java. The attendees will have the opportunity to understand the internals of jDMN and the benefits of adopting DMN based solutions and executing them in jDMN.
    jDMN provides support for DMN models validation, transformation, evaluation or translation to Java followed by an execution on JVM. The provided framework is flexible and configurable. For example, the users can define their own DMN transformers, validators and translators.
    A jDMN dialect is a collection of certain DMN features, for example the built-in library and the mapping of the FEEL types to native types. The main purpose of a jDMN dialect is to be able to support DMN features variation. The jDMN dialects are organized as a taxonomy. jDMN supports several dialects, for example Signavio and Java 8 dialects. The framework is extensible, for example users can define their own dialect and execute DMN models accordingly.
    Several code generation optimizations supported by jDMN such as: tree and DAG models execution, linked decisions and lazy evaluation for sparse decision tables are also presented.
    Keywords: DMN, FEEL, decision validation, transformation, evaluation, translation. [Back]


Decision-making in sales activity: applying process mining approach by Yoshihito Nakayama, Masahiro Mori, Dr. Yoshiaki Narusue and Dr. Hiroyuki Morikawa

  • In decision making on sales activities, the dependence on individual skills becomes a problem, as human judgment from past experiences play a primary role. It is important to visualize the regularity between decision-making processes and their outcomes to support sales staff and enhance sales activities. To solve this problem, we are developing a business decision support system using a machine learning model. For that, it is necessary to learn the process of sales activities with high probability of obtaining orders; therefore, the technology of process discovery that extracts regularity from the decision-making process is essential.
    However, it is difficult to apply the process discovery method of conventional process mining in the decision-making process of sales activities, because the rules are not known in advance and the input information is unstructured data, such as business diaries.
    In this study, we provide an activity estimation system based on unstructured data, and a process discovery method for stochastic expression of regularity in an atypical process.
    Keywords: Sales activity, Decision making support, Process mining, Decision mining, Process discovery [Back]


Spreadsheets in Decision Management by Carole-Ann Berlioz and Carlos Serrano-Morales

  • Spreadsheets have been used from the beginning of times. Many businesses continue to maintain their decision logic in this beloved format. From that point on, humans may have to referred to them by hand, or they might be consumed by automated systems.
    In this presentation, we will focus on the integration of spreadsheets with Decision Management Systems. In some cases, they translate to business rules. In others, spreadsheets remain the format of choice for maintenance, while systems must learn how to interpret them at runtime.
    We will demonstrate both scenarios through live examples. [Back]


Managing a Decision Zoo (Requirements Engineering for Complex Decisions) by Daniel Schmitz-Hübsch and Ulrich Striffler

  • In recent years, the use of DMN in decision management has increased, but the scope still tends to be limited to simple requirements and decision logics. Our talk aims to demonstrate how to handle complex decisions and how to model them in a structured way.
    We aim to provide you with an overview in modelling large human knowledge into consistent, structured modeling decisions, based on our own experiences. We will present how to gather the business rules activities built on analyzed business processes formalized by BPMN. Further, we would like to explain our approach to build DRDs that arise from these activities. As this sounds quite uncomplicated, we identified that the real challenge with building DRDs is to consider the whole cardinality of business requirements.
    We will demonstrate our approach on building complex decisions partitioned into multiple DRDs. If one wants to know how to successfully manage them, we suggest that particular tools and structured procedures are necessary to fulfill this job. Especially complex decisions require comprehensive tools and sophisticated processes.
    Instead of showing the description of the decision logic level with decision tables, we will rather use boxed expressions, contexts, complex data structures and FEEL. In particular, we will demonstrate the Logic structure of our decisions and BKMs as well as some of our best practices. Finally, we explain how business users can test their models in a timely, tool-supported manner. Therefore, we show how a web application, based on RedHat Drools, allows users to define and execute test cases.
    Key technologies: BPMN, DMN, FEEL, RedHat Drools [Back]


Automatic judgment of decision authority using OpenRules by Yoshihito Nakayama

  • In China and Southeast Asia including Japan, there are many companies with complex authorization of approval, so complicated control such as complex conditional branches, consultation, delegation of authority to approve and concurrent administration is required when introducing the workflow system.
    However, since the setting patterns are different for each company, it is difficult to provide all of them as standard functions of the workflow, so far it has often been customized individually.
    Therefore, in order to solve these problems, automatic judgment of decision authority was realized by combining workflow system with rule engine.
    It is possible to control the conditional branching with noncoding and to dynamically arrange authority for decision.
    Since automatic processing of workflow can be realized without customizing, it is possible to drastically reduce the cost of introducing the workflow.
    In this paper, we explain concrete cooperation method of workflow and rule engine by taking “intra-mart” and “OpenRules” as an example.
    Keywords: workflow, complex authorization, intra-mart, rules. [Back]


Explaining the Unexplainable: Using DMN to Justify the Outcomes of Black-box Analytics by Jan Purchase and David Petchey

  • Ethics, regulation and software safety are just a few of the pressures pushing organizations to make automated decisions more transparent, especially when people’s lives are directly and significantly impacted by the outcome. This transparency is essential to gain some insight into, and comfort from, why a particular decision was made. In some cases it may soon be a legal requirement. At the same time, automated decisions are increasingly relying on sophisticated analytics and machine learning models to increase their predictive power. Although a few machine learning models are noted for their transparency, most of the more powerful ones are, by their very nature, inscrutable: their internal state gives no human readable clue regarding the reason for their outcome. This opacity may limit their application. Can we bring the noted transparency of DMN to bear on this problem?
    Academia and industry have proposed fledgling ideas to make black box machine learning models explainable and in this presentation we explore how decision modelling in DMN can be used to both improve the transparency of an analytic model and explain outcomes. We will focus, using practical examples, on techniques that attempt to provide a post-hoc explanation of an analytic, independently of the details of that analytic.
    At last year’s Decision CAMP we saw how DMN can be used to contextualize analytics, being explicit about its inputs and how it is used. Whilst this is very useful, it doesn’t address how the analytical decision can justify its outcome in a specific case. This presentation will explore this and look at how DMN can be used to explain outcomes using the latest ideas currently available for analytic explain.
    Attendees of this session will see the importance of analytic transparency and gain an overview of some of the explain techniques. The use of DMN as a vehicle for representing the explanation will be demonstrated using a real, publicly available dataset.
    Key Take-Aways:
    Attendees of this session will see the importance of analytic transparency and gain an overview of some of the explain techniques. The use of DMN as a vehicle for representing the explanation will be demonstrated using a real, publicly available dataset.
    Keywords: Decision Modeling, Decision Transparency, Predictive Analytics, Machine Learning, Analytic Explain, Compliance
    [Back]


Fusion Project – replacing a legacy system elephant, one bite at a time by Martin de Villiers

  • Medscheme is a Medical Aid Administrator in South Africa, administering several Medical Schemes. At the heart of the business is processing medical claims of members and healthcare providers. Nexus is the bespoke system, over 20 years old, on which the Medical Aid Administration business is conducted. Documenting Nexus while changing and growing fast was historically lacking. Maintaining and changing it has become difficult as a lot of the rules are hidden in data and procedural code. This affects business agility as well as being time and labour intensive, a team of 30 people maintains the claims module alone.
    This presentation will elaborate on how the Fusion Project set out to solve the problem, using Decision Analysis and DMN. How we adopted the methodology, made it part of our lives and how it is implemented to replace parts of the claims process. Showcasing the technical implementation, using FICO’s Blaze Advisor and the modern architecture used to host it and integrate it. Elaborating how we dealt with performance, testing the accuracy as well as coexistence with the legacy system. A phase of this is live and we share the challenges, successes and progress of this phase as well as the ongoing phases.
    I would like to share the following lessons and highlights from our journey so far, as key take away points:
    • Business and IT’s appetite and readiness for the change is key to our success
    • Understand the methodology and make a principle decision to stay within its bounds
    • Heed against solving the technical problems while doing the Decision Analysis
    • Agree and document the common language that business and technical will talk – DHW, object model and business terms
    • Make fit for purpose decisions when it comes to solving problems – put the solutions where they belong
    • Plan a phased approach that can proof success and show value sooner (small bites of the elephant)
    • Build a capability as part of the project to ensure that momentum, continuity and business value is realized
    • Modern architecture for a really scalable, modern solution
    Key technologies: FICO Blaze Advisor, Java 1.8, SpringBoot, Hazelcast, Docker, Kubernetes [Back]


Implementing Decision Modeling in a Large Organization by Walter Berkowicz and Mark Woods

  • Starting over five years ago, a small group of early adopters working principally autonomously within a large organization began implementing Decision Modeling. Over that time and with little tool support, the group has obtained many successes through leveraging the principles of Decision Modeling. Even so, there has been and still remain significant doubts and a great deal of skepticism regarding the Decision Modeling methodology’s capabilities at larger scale, both from business and technology. This presentation will describe the journey that Decision Modeling has taken in a large organization: from the first set of rule families to current state. Presentation will be a case study that cover lessons learned in overcoming organizational hesitation and resistance to adopting Decision Modeling in a large company.
    Key take-away: Learn how one organization has traveled on the journey to adopting Decision Modeling. In addition, learn the techniques that were applied in broadening the implementation of Decision Modeling.
    Keywords: Decision Modeling, Implementation, Large Organization
    [Back]


Introduction and Updates on DMN TCK by Matteo Mortari

  • The Decision Model and Notation (DMN) standard allows organization to describe, model and execute business decisions; DMN also enables interchange of defined models across organizations via the standardized xml interchange format. The DMN standard provides three level of
    Conformance, which aims to define helpful scopes of support for Vendors providing implementations of the standard; this in turn is beneficial for organizations adopting the standard, for a responsible awareness of the level of support provided by the chosen tools and
    implementations. As the DMN standard receive more traction from Decision Management practitioners and increased adoption from organizations, a verifiable method for testing the level of
    Conformance claimed by the implementation would generally benefit all actors involved.
    The DMN Technology Compatibility Kit (DMN TCK) is a community-led proposal for a verifiable and executable method to demonstrate the Conformance level of support provided by a Vendor-supplied DMN implementation. In addition, this method provides more finer-grained details on the actual support for specific DMN constructs for each implementation.
    The DMN TCK working group is composed by vendors and practitioners of DMN, with the goal to assist and ensure Conformance to the specification, by defining test cases and expected results, by providing tools to run these tests and validate results; the outcome also represent an additional and pragmatical way to recognize and publicize vendor success.
    Joining the TCK is free, it also holds weekly conference calls and new members are always welcome.
    Key take-away:
    Introduction for those new to DMN TCK and why is important. How DMN TCK benefits Organizations, Practitioners and Vendors. Updates on current DMN TCK status. How to get involved.
    Keywords: DMN, DMN TCK, Community, Conformance. [Back]


Make Faster, Smarter Decisions by combining Machine Learning and Decision Optimization by Susara van Den Heever

  • What if you could reduce your planning process from one week to one hour, or from one hour to one second? What if you could, at the click of a button, improve your bottom line by double digits? In this session, you will learn to do just that by leveraging machine learning (ML) and Decision Optimization (DO) technologies together. You will learn the differences and complementary strengths of ML and DO, learn about best practices, and see examples of combining these technologies to achieve financial gains and efficiencies. The session includes demos covering use cases such as marketing campaign planning and predictive maintenance.
    Keywords: Decision Optimization, Machine Learning, Prescriptive Analytics, Planning [Back]


High-Performance Decision Model Execution by Compilation of DMN into Machine Code by Dr. Jolyon Cox and Dr. Jan Purchase

  • The burgeoning scale of automated decision-making in developing economies, such as that required by financial fraud and customer personalization in China, will create a demand for high performance decision execution several orders of magnitude higher than today’s workhorses. Multiplying this by the new scale of data imposed by the Internet of Things and the accountability required by increasingly rigorous compliance regulations will demand unprecedented volumes of complex decision-making; volumes requiring not just scalable hardware, but software purpose-built for the execution of compiled decisions.
    This presentation looks at implementation strategies for supporting very-high-performance, DMN-based decision-making using modest hardware, and outlines the results. It examines the use of a single-pass compiler for decision tables based on a bitwise maintenance of rule masks to minimize execution time and enable real-world decisions to be made in microseconds. It also articulates the challenges of creating a DMN XML parser in XSLT.
    Attendees of this session will discover which of DMN’s features posed the biggest challenges, both in terms of satisfying the TCK tests and during performance optimization. Further, we discuss some proposed revisions to DMN’s type system to improve performance with no practical impact to its flexibility. During a demonstration, several demanding decision models will be compiled, benchmarked, verified and executed. In addition, the presentation will highlight some of the technical challenges imposed by practical application of the DMN standard such as null propagation and hit policy enforcement.
    Key Take-aways:
    Acquire an understanding of state of the art DMN compilation and execution environments that lower the hardware bar for high volume systems and enable new levels of throughput. Gain insights into DMN modifications that could improve performance still further.
    Keywords: DMN compilation, DMN execution, decision services, optimization. [Back]


Decision Modeling at AXA CH by Wilfried Kurth

  • In 2014 we introduced the “The Decision Model” method by B. v. Halle and L. Goldberg to our business analysts and launched the OpenRules rule engine in IT. At the same time, we could win a first project to use both the method and the new rule engine for the implementation of underwriting decisions in the individual life insurance. In 2017 we switched from “The Decision Model” to DMN and introduced the tool Innovator for Decision Modeling.
    After a total of more than four years of experience in decision modeling, we should have developed hundreds of Decisions by now. In fact, to date there have been about 4,000 rules in 54 decisions. What were the reasons why we haven’t already turned all business logic into decisions? Many requirements must be met for the use of decisions to be accepted in both business and IT.
    The business must be ready to take responsibility for the business logic again and to manage it. Business analysts must be affine with the decision method and have appropriate tools to support them in their work.
    IT must be ready to leave the business logic back to the business. However, there are already many business applications that contain their own rule components and in which the business logic is partly maintained by IT.
    The transition from the modeled decision in Business to the executable decision in IT must take place without additional effort if possible. For this purpose, we have implemented a model-driven approach from the outset. And finally, the tools used in business and IT must not require too high investments, at least at the beginning.
    This presentation describes,
    • how we introduced a method for the presentation of rules in Business for the first time with “The Decision Model” and started with a cost-effective solution in initial projects,
    • switched to the OMG standard DMN in 2017 and used Innovator for decision modeling in Business,
    • which hurdles are to be expected when using Decisions.
    Key take-away: Challenges in application of decision models in Business and IT
    Audience: business, new
    Industry Sector: Insurance
    Key Technology: DMN, Innovator, OpenRules [Back]


Outpatient Waitlist Analysis for Irish Hospitals by Helmut Simonis

  • Waiting times for patients in Irish hospitals have grown in recent years to unprecedented levels, at the beginning of 2018 there were just over 500,000 people waiting to see a specialist for an outpatient appointment, of a total population of 4.77 million in Ireland. We investigate the use of data analytics and optimization to understand the current situation, predict future short and medium term demand, and study the impact of changes in processes and resource levels. While overall resource levels are clearly inadequate, we also study the impact of did-not-attend no-shows, the possibility of overbooking, and of load distribution across clinics and hospitals. Some of the analysis is based on publicly available data, with results made available in at https://patientwaitlistanalysis.wordpress.com/
    Keywords: medical, public health, resource management [Back]


Modernizing Production Rule Systems by Mark Proctor

  • Product Rule Systems are well known for being at the heart of Decision Management, however the paradigm, with all its pitfalls, has not changed that much over the decades. They typically offer a monolithic design with a single working memory and rulebase, with limited rule orchestration and very quickly hit the limitations with rule chaining complexity. Approaches like DMN take a different strategy and simplify things by reducing the capabilities down to those needed for the specific types of problems DMN aims to solve. However, there is still a need for more powerful systems too, but they must modernize if they are to retain industry relevance.
    This talk will discuss the ongoing innovations that are helping to modernize Product Rule Systems, while keep within the context of the Drools rule engine. Those innovations involve new techniques for rule modularization, rule orchestration, recursion control and argumentation as well as breaking away from the traditional monolithic design of a single working memory and rule base.
    Keywords: Production Rule Systems, Rule Engine, Rete, Rules [Back]


Case Study – Building an enterprise ready decision management platform for customer engagement and next best action by Roy Robinson and Alan Giles

  • Boxever have been providing 1:1 customer personalisation capabilities to some of the worlds top airlines and financial services businesses for over 6 years. Engage our customer engagement engine was built on Drools and had a coding interface which allowed coding of rules in a user friendly domain specific language created by Boxever to provide real time personalisation
    As our customers matured however they demanded the ability to arbitrate between marketing messages, service messages and customers service messages. They also were seeking ways to apply AI and machine learning across all customer touch points. This case study will discuss the challenges we faced both from a technical and user experience perspective in creating a decisioning capability based on the DMN standard for first best action and next best action and how these were overcome.
    The key takeaways will be:
    • Building the right DMN modeller for enterprise decisioning – augmenting the DMN standard to improve flexibility
    • Choosing the right scalable architecture to support multi-tenant enterprise deployment
    • Decisioning and collections – To contain or not to contain
    Testing Decision Models – Component, Model and Variant
    Analytics – Choosing the right KPI’s
    • Challenges of building optimisation workflows (A/B/N) into model deployment
    Keywords: Decision Management, DMN, Collections, Optimisation, Decision Model Testing, Decisioning Analytics [Back]


Modeling progress on Climate Change and Social Value Acts by Tim Stephenson

  • The Climate Change Act and Social Value Act in the UK place responsibilities on many private sector and all public sector organisations to consider not only the value to customers and stakeholders of their activities but also the effect on greenhouse gases and the society they operate within.
    The healthcare sector has a particularly significant place in this work, forming as it does a large part of the public sector and being present in every part of UK society. This programme that we’ve been involved with for some years now aims to recruit that considerable geographic & economic muscle to positively drive social and environmental action.
    The programme collates the best and most up to date climate and social value modelling available and applies it to the problem of calculating a complete Sustainability report for each and every hospital trust and clinical commissioning group (CCGS – who commission primary healthcare providers) in England. In numerical terms this deals with almost 500 data inputs for each of around 200 hospital trusts, a further 200 CCGs as well as Ambulance Trusts, Mental Health providers and so on.
    This case study will show how DMN has been used to document the previously black box system allowing far more people to understand, critique and improve the reporting process. Furthermore, by making these decision models executable we have dramatically reduced the complexity of maintaining and updating the system as new and updated data becomes available.
    Keywords: Climate change, Non-financial reporting, Social value, Decision modelling, Statistical modelling [Back]


Driving FinTech and RegTech with Industry and Technology Standards by Brian Stucky

  • The financial services industry has long been a driver for technology innovation. Volatile regulations, increased demands for compliance, and a requirement for transparency necessitate the ability to quickly create and easily manage decisions across systems. The evolution of disruptive “FinTech” systems has attempted to address some of these needs. However, those requirements coupled with a constant demand for faster online processes has now created the need for Regulatory Technology or “RegTech”. A key component of new systems will certainly be the ability to create services or disseminate decisions in a consistent, unambiguous and transparent fashion.
    The Mortgage Industry Standards and Maintenance Organization (MISMO) strives to allow participants in the mortgage industry (mortgage lenders, investors, servicers, industry vendors, borrowers) to exchange information and do so more securely, efficiently and economically. Although primarily focusing on an XML-based data standard thus far, they now seek to enable seamless exchange of all information between mortgage industry partners. To that end, MISMO is in the final stages of officially endorsing DMN as the recommended decision modeling standard for the mortgage industry.
    This presentation will focus on the integration of business standards in the mortgage industry (MISMO) with technology standards (DMN, BPMN) to enable a powerful approach to handling FinTech and RegTech. These approaches will be demonstrated with their application to new mortgage requirements and regulations around the Uniform Residential Loan Application (URLA) and eMortgage. In addition, a new set of industry players will be empowered to succeed by having their compliance burden eased.
    Keywords: FinTech, RegTech, Standards integration, Financial services,DMN [Back]
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