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BA and Beyond 19 - Jan Vanthienen - Decision and process modeling

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Business Process Management (BPM) and its life cycle activities — design, modelling, execution, monitoring and optimization of business processes — have become a crucial part of business management. Most processes and business process models incorporate decisions of some kind that describe the premises and possible outcomes of a specific situation.

Decisions, however, are often hidden or hard-coded in process flows or activities. Traditional process modeling techniques turn out to be rather inadequate for modelling decisions. DMN (Decision Modeling & Notation) has become a standard for representing and implementing business decisions in real business situations and processes.

This presentation takes you from the secrets behind decision modeling to productive decision management in real business situations and processes.

What you will learn:

- The concepts, objectives and application areas of decision tables for business analysis and business process management.
- Modeling decisions and how they relate to business processes.
- But mainly: lessons from a long experience on how to build, analyze, verify and optimize decision table models according to simple guidelines.

Published in: Business
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BA and Beyond 19 - Jan Vanthienen - Decision and process modeling

  1. 1. * Jan Vanthienen KU Leuven Faculty of Economics and Business Business Information Systems Group Research and teaching: • Business Analysis • Information & Knowledge Management • Business intelligence & Analytics • Decision models & tables • As a business analyst, you identify business needs and determine solutions to business problems. To this end, you model and improve processes and manage organizational change from a holistic view of the situation. • One of the most important things for business is not just data and processes, but improving and automating decision making. We often spend a lot of effort on modeling business processes, but forget about important elements in the process: the decisions. • Decision management has a new standard, DMN. This may look trivial, but DMN fully fits the business analysis mission to describe decisions and decision logic from a business point of view, with a straightforward road to implementation. • Modeling decisions and processes raises an interesting challenge: how to combine both approaches and which comes first? That is what this session is about.
  2. 2. Prof. dr. Jan Vanthienen jan.vanthienen@kuleuven.be KU Leuven (Belgium) Leuven Institute for Research in Information Systems *
  3. 3. * LIRIS (Leuven Institute for Research in Information Systems) Decision & Process Modeling Jan Vanthienen KU Leuven Faculty of Economics and Business Business Information Systems Group Research and teaching: • Business Analysis • Information & Knowledge Management • Business intelligence & Analytics • Decision models & tables IBM Faculty Award Belgian Francqui Chair 2009 at FUNDP - Bpost bank Research Chair Actionable Analytics - Colruyt-Symeta Research Chair Smart Data and Decisions in Marketing - IBM Fund Intelligent Business Decision Making - Microsoft Research Chair on Intelligent Environments - PricewaterhouseCoopersChair on E-Business Email: jan.vanthienen@kuleuven.be (c) Jan Vanthienen, 2019 3
  4. 4. * *Modeling Small and Large Data *Large Volumes of data * Many objects (e.g.: Customers, Products, …). * Many features (e.g.: Age, Income, …). *Velocity: data streams * Phone calls, Bank transactions, Web site visits, … *Variety: different types of “data” * numbers, text, speech, video, audio, scanned documents, photos, social media updates, sensor data, … *Veracity: * messy, untrustworthy data Decision & Process Modeling(c) Jan Vanthienen, 2019 4
  5. 5. * Decision & Process Modeling(c) Jan Vanthienen, 2019 5
  6. 6. Why would we only care about the (big) data or the processes? Decisions are important for business. Where is the decision? How do we model the decision? • Correct • Complete • Consistent • Comprehensible • Compliant • Modular • Traceable Who makes the decision? Can we improve the decision? Can we automate the decision? * ? Decide acceptance? Decision & Process Modeling(c) Jan Vanthienen, 2019 6
  7. 7. * The evolution of computing Calculation Data processing Communication Decision making •Big Data •Analytics •Knowledge-based AI Decision & Process Modeling(c) Jan Vanthienen, 2019 7
  8. 8. * *Made Frequently *Non-trivial *Made Rapidly *Made Consistently, Repeatable *High volume *Measurable business impact *Deterministic *Frequent change (but compliant) *Comprehensibility, Explainability *Automated or manual Decision & Process Modeling(c) Jan Vanthienen, 2019 8
  9. 9. * Decision & Process Modeling(c) Jan Vanthienen, 2019 9 Mileage accrual Customer Type Traveller data
  10. 10. * (Source: Decision Management Solutions)  Separating (decision) rules from the process simplifies the process  Improved agility, reuse, focus, visibility  Simplify nested decision paths: Model the decision separately Decision depends on: o data o decision logic o intermediate decisions Decision & Process Modeling(c) Jan Vanthienen, 2019 10
  11. 11. * Layered decisions DMN Decision tables
  12. 12. * •Law •Procedure Text • Specification • Pseudocode Schema •Code •Execution System Domain expert IT * Hard to define * Not standard * Not executable * Hard to maintain  Manual work  Expensive to maintain  Low traceability Decision & Process Modeling(c) Jan Vanthienen, 2019 12
  13. 13. * *Decision model & notation, knowledge representation *Flexible execution •Law •ProcedureText Decision logicModel Executable knowledgeResult  Comprehensible  Xplainable  Made Consistently  Executable  Automated  Flexible  Generic Decision & Process Modeling(c) Jan Vanthienen, 2019 13
  14. 14. 1. Decisions requirements 2. Decision logic * Applicant Risk Rating U Applicant Age Medical History Applicant Risk Rating 1 > 60 good Medium 2 bad High 3 [25..60] - Medium 4 < 25 good Low 5 bad Medium Decision & Process Modeling(c) Jan Vanthienen, 2019 14
  15. 15. Decision(s) (logic) need to be modeled * A standard for processes (BPMN) is not enough * What is the decision? * Eligibility, rating, retention, offer, selection, hire, credit, … * What is required to make this decision? * Information * Knowledge sources (regulations, analytics, expertise) * Other decisions * Decision logic Decision Modeling & Notation standard (DMN) * Decision & Process Modeling(c) Jan Vanthienen, 2019 15
  16. 16. * Informaton requirement Knowledge requirement Authority requirement Decision & Process Modeling(c) Jan Vanthienen, 2019 16
  17. 17. * *If CustomerType = US and OrderType = Web then Discount = 8% *If CustomerType = US and OrderType = Phone then Discount = 5% *If CustomerType = non US then Discount = 0% * Inputs in one rule are always ANDed. No ORs, no parentheses * Every row is a rule * The order of the inputs is the same for every rule * “-” means: does not matter in this rule (irrelevant) Inputs Outputs Information items "rules" (rows) values Decision & Process Modeling(c) Jan Vanthienen, 2019 17
  18. 18. End of year premiums (simplified real case, French company) 1. Anciennity premium If the employee has an anciennity of at least 3 years and (s)he was absent for less than 4 weeks during the year, (s)he will get an anciennity premium. 2. Presence premium If the employee has an anciennity of at least 1 year and (s)he was absent for less than 2 weeks during the year, (s)he will get a presence premium. 3. Productivity premium If the employee has an anciennity of at least 3 years, (s)he is entitled to a productivity premium, but the premium is only awarded if: - (s)he was absent for less than 2 weeks during the year - or (s)he was absent for less than 4 weeks during the year and his/her appreciation score was excellent. Premiums can be combined. *DIY: Build your own table Decision & Process Modeling(c) Jan Vanthienen, 2019 18
  19. 19. * *E.g. If CustomerType = US and OrderType = Phone then Discount = ? Various solutions: * Take the first rule that applies (from top to bottom) and stop (8%) FIRST * Make sure the rules do not overlap UNIQUE * Allow rules to overlap, but only if the output is the same ANY * Take the output with the highest priority, listed in decreasing order (5%) PRIORITY * Collect (and add) the output of every rule that applies (13%) COLLECT -> We need a standard Decision & Process Modeling(c) Jan Vanthienen, 2019 19
  20. 20. * *Decision tables (DT) * Decision rules in a tabular format *Decision table methodology (DTM) * How to use a constrained form of decision tables in order to model decisions * Goal-oriented decision modeling network * Good decision table design * Single hit tables (complete, consistent and correct), relations between tables, table notation, contraction, optimization, normalization. *Decision Modeling & Notation (DMN) standard * Standard syntax and notation for exchange * Recognize other forms of tables * Combine tables with other concepts in decision modeling * Standard expression language FEEL Decision & Process Modeling(c) Jan Vanthienen, 2019 20
  21. 21. Good decision table models are a proven technique to represent decision rules Consistency, completeness and correctness by design * Decision & Process Modeling(c) Jan Vanthienen, 2019 21
  22. 22. * DMN identifies different table types, indicated by the first letter: Single hit (returns one rule): *If only one rule can match every case: (U)nique hit indicator *If more then one rule can match a certain case: Choose only one The Hit Indicator tells which one: - A(ny), P(riority of outcome) - F(irst) Multiple hit: *If all applicable rules are required: * results in an output collection (list, or operation on list) * List order is No order (arbitrary), Rule order, Output order * An operation may be applied, e.g. C+ Good Ugly Bad Decision & Process Modeling(c) Jan Vanthienen, 2019 22
  23. 23. * *Some of this is methodology *But if you can choose 2 out of 3, what do you choose? Compact Consistent Correct Decision & Process Modeling(c) Jan Vanthienen, 2019 23
  24. 24. • Decisions, decisions, decisions • Decisions in process modeling • Process variability • ’Simple’ operational decisions • Eligibility, credit, churn, social security, finance, insurance, healthcare, security, definitions, … • Regulations, legislation • Enumerating and evaluating alternatives, constraints, options • Combinatorial optimizations • Scheduling and resource allocation *Application areas & Tools Decision & Process Modeling(c) Jan Vanthienen, 2019 24
  25. 25. *It is not a rebirth of rule-based systems *It belongs at the business side, not the IT side. It is part of BA *Emphasis on structuring, verification, model quality, … *It is a model (& notation) standard *No lock-in, but compatibility (TCK) *Straightforward execution is common. More sophisticated execution (inferences) is possible but the standard ≠ the tools (-> research area) *It is not the ultimate full-blown knowledge representation *Complexity has its price *Already so much better than not modeling decision (logic) * Decision & Process Modeling(c) Jan Vanthienen, 2019 25
  26. 26. * Issues DMN solves How to connect decisions and processes
  27. 27. * *What is the decision? * eligibility, price, insurance, theft rating, customer offer, retention, supplier selection, hire, credit, … *Who, what, how? *Who owns the decision? *Who makes the decision every day? *Who is impacted? *What triggers the decision? *When are we making this decision? *What is required to make this decision? * Information requirements * Knowledge sources (regulations, analytics, expertise) * Other decisions * Decision logic Decision & Process Modeling(c) Jan Vanthienen, 2019 27
  28. 28. *Separating decisions and processes * Using a standard modeling notation. *Decision table types * Recognize, and unambiguously exchange. *Decision modeling methodology * Keep the insights of the past and avoid confusion. *Separating decision structure and decision logic * Allows to model decision relations, even if not all logic is expressed in tables. *Standard notation for exchange and implementation * Strict notation and simple expression language. * Decision & Process Modeling(c) Jan Vanthienen, 2019 28
  29. 29. * *Separating decision structure and decision logic *Allows to model decision relations, even if not all logic is expressed in tables. Decision & Process Modeling(c) Jan Vanthienen, 2019 29
  30. 30. * *Decision modeling methodology *Keep the insights of the past and avoid confusion. *Good decision table models are a proven technique to represent decision rules Consistency, completeness and correctness by design column 4 subsumes column 1• Exclusivity • Completeness Decision & Process Modeling(c) Jan Vanthienen, 2019 30
  31. 31. * *Standard notation for exchange and implementation *Strict notation and simple expression language ((S-)FEEL). *FEEL (“Friendly Enough Expression Language) implements the required mechanisms * Built-in types, functions and operators * Every decision in a DRD can be defined using a formal expression that specifies how the decision’s output is determined from its inputs * Complete decision models can be defined * Formal expressions may also be encapsulated as functions *S-FEEL (“Simple FEEL”) is a basic subset of FEEL designed to cover the essential requirements of Decision Table-based DMN models *DMN tools Decision & Process Modeling(c) Jan Vanthienen, 2019 31
  32. 32. * *Separating decisions and processes *Using a standard modeling notation. refuse high riskapplicant accept low risk applicant GoodMedicalRecord Age<20 21<=Age<50 Age>=50 BadMedicalRecord accept medium risk applicant Decision & Process Modeling(c) Jan Vanthienen, 2019 32
  33. 33. * Process-decision consistency Which comes first?
  34. 34. Decision models are not lower level details of one process * Decisions models can span over multiple activities, and even multiple processes * Separation of concerns * Sometimes the process is about one large decision Model of the decision(s) * Decision & Process Modeling(c) Jan Vanthienen, 2019 34
  35. 35. * Decision & Process Modeling(c) Jan Vanthienen, 2019 35
  36. 36. * Decision & Process Modeling(c) Jan Vanthienen, 2019 36
  37. 37. Decision & Process Modeling *Decision outcomes and gateways *Order of decisions *Available inputs (c) Jan Vanthienen, 2019 37 *Repeat all decisions in process? Hasić, F., De Smedt, J., Vanthienen, J.: Redesigning Processes for Decision-Awareness: Strategies for Integrated Modelling. QUATIC 2018: 247-250.
  38. 38. * *How to integrate processes and decisions? *How to redesign a process to split off decisions? *Where to start? *From an existing process? *From a decision? Decision & Process Modeling(c) Jan Vanthienen, 2019 38
  39. 39. * *This approach views decisions as local concerns (details) with respect to decision points in the process model (decision activity before an XOR-split). But: * decisions do not only happen at XOR-splits (decision logic ≠ routing logic). Decisions can impact other things. *The holistic business decision is lost and fragmented. *Redesigning the process takes out relevant input data and all possible decision outcomes for one decision at the time. Decision & Process Modeling(c) Jan Vanthienen, 2019 39
  40. 40. * *This approach tries to identify decision constructs from the existing process model and externalizes the decision constructs into a separate decision model. *It uses mappings and transformation rules from process constructs to decision constructs. *This impacts and simplifies the process model. *But: The extracted decision model is strongly dependent on how the process was modelled (decisions can be in gateways, activities or data). Decision & Process Modeling(c) Jan Vanthienen, 2019 40
  41. 41. * *The decision-centric approach starts with the decision (model). *The process model is constructed afterwards in such a way that it is automatically consistent with the decision model. *The process model needs to provide the relevant data for the decision model and needs to take care of data storage, and data and decision outcome propagation within the process. Decision & Process Modeling(c) Jan Vanthienen, 2019 41
  42. 42. *Sometimes the entire process is about a decision *Model the decision first, and then think about how to execute it *The same decision can be processed in many ways *The process of making a decision depends on the desired criteria (throughput, efficiency, data collection, customer comfort, …) * Decision & Process Modeling(c) Jan Vanthienen, 2019 42
  43. 43. * Start each individual decision activity as soon as all its preconditions are fulfilled Avoid superfluous decision activities (unnecessary work) Group customer contacts naturalization Decision & Process Modeling(c) Jan Vanthienen, 2019 43
  44. 44. * Decision & Process Modeling(c) Jan Vanthienen, 2019 44
  45. 45. * *DMN models the logic of a decision, but allows to answer different decision questions. *The basic decision/process: * John Doe is 50 and has 30 years of service, process John’s case * The customer puts in an claim/order/loan request. * Do we accept the order? What is the price? How to process the order, etc. *There is more than this basic decision • What-If, Optimization and Incomplete data • Why do I get this result? • What can I already decide with these incomplete data? • Goal seeking: What do I have to change to obtain that result? • Optimization: How do I get the maximum? • Overview: Who finally gets this result? Where is my case? • Decision Analysis: Are there some strange assignments in giving this result? • Decision Maintenance: What if the policy changes? • Traceability: How easy is it to trace back to the original text (rules)? What is really the question? Different inferences of the same decision logic Decision & Process Modeling(c) Jan Vanthienen, 2019 45
  46. 46. * * Hasić, F., De Smedt, J., Vanthienen, J.: Augmenting processes with decision intelligence: Principles for integrated modelling, Decision Support Systems 107 (2018) 1 – 12. * Hasić, F., De Smedt, J., Vanthienen, J.: Redesigning Processes for Decision-Awareness: Strategies for Integrated Modelling. QUATIC 2018: 247-250. * De Smedt, J., Hasić, F., vanden Broucke, S.K., Vanthienen, J.: Towards a holistic discovery of decisions in process- aware information systems, International Conference on Business Process Management, Springer (2017). * Batoulis, K., Haarmann, S., Weske, M.: Various notions of soundness for decision-aware business processes. In: International Conference on Conceptual Modeling, Springer (2017) 403–418. * Janssens, L., Bazhenova, E., De Smedt, J., Vanthienen, J., Denecker, M.: Consistent Integration of Decision (DMN) and Process (BPMN) Models, CAiSE forum (2016). * De Smedt, J., vanden Broucke, S., Obregon, J., Kim, A., Jung, J., Vanthienen, J.: Decision Mining in a Broader Context: An Overview of the Current Landscape and Future Directions, BPM Workshop DeMiMoP (2016). * Vanthienen, J., Caron, F.: Modeling business decisions and processes – which comes first?, Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing (2014), pp. 451 - 456. Research topics: Decision & Process Modeling, Mining and Analytics http://feb.kuleuven.be/drc/LIRIS/misc/JVResearch Decision & Process Modeling(c) Jan Vanthienen, 2019 46

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