ABSTRACT. 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.

ABSTRACT. 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.

ABSTRACT. 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.

ABSTRACT. 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.

ABSTRACT. 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.

ABSTRACT. 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.

ABSTRACT. 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.