Business Process Management

Transcription

1 Business Process Management Paolo Bottoni Lecture 11: Process Mining Adapted from the slides for the book : Dumas, La Rosa, Mendling & Reijers: Fundamentals of Business Process Management, Springer

2 Where are we? Business Process Management 2

3 Types of Process Monitoring Runtime Monitoring (Business Activity Monitoring) Viewing the load of the process Identifying problematic cases Identifying late cases (risk of missing deadlines), etc. Post-mortem Monitoring (aka Business Process Analytics) Performance KPIs: cycle times, resource utilization, error rates, Identification of bottlenecks See for example: BizAgi BAM: Analytics:

4 Process Monitoring: Dashboards Process Cycle Time of Order Processing Process Frequency of Order Processing Process Cycle Time of Order Processing split up to different Plants IDS (2003)

5 Process Mining Tools ARIS Process Performance Manager Percetive Reflect Fujitsu Interstage (BPM Analytics) ProM

6 Process Mining

7 Starting point: Event Logs <B A E F E> <A A D F C D E> <A B D F E> <D D A D F C D E> <F A D E>

8 Starting point: Event Logs Case ID Task Name Originator Timestamp Case ID Task Name Originator Timestamp 1 File Fine Anne :00:00 3 Reminder John :00:00 2 File Fine Anne :00:00 2 Process Payment system :05:00 1 Send Bill system :05:00 2 Close case system :06:00 2 Send Bill system :07:00 4 Reminder John :10:00 3 File Fine Anne :00:00 4 Reminder Mary :10:00 3 Send Bill system :00:00 4 Process Payment system :01:00 4 File Fine Anne :00:00 4 Close Case system :30:00 4 Send Bill system :10:00 3 Reminder John :00:00 1 Process Payment system :05:00 3 Reminder John :00:00 1 Close Case system :06:00 3 Process Payment system :00:00 2 Reminder Mary :00:00 3 Close Case system :01:00

9 Process Mining

10 Process Models

11 Process discovery 1 start begin proces 2 collectief of particulier particulier klaar voor controle collectief is collectief 4 dubbele aanvraag? dubbele 5 navraag VA voldoende (telefoon) onvoldoende 3 controleren opvagen gegevens compleetheid/juistheid niet compleet/onjuist 6 opvragen ontbrekende gegevens P1 ontbrekende D1 Geen reactie gegevens wachten compleet/juist 7 ontvangst 8 verlopen deadline gegevens particulier en invoeren incompleet 9 Bepalen vervolg1 particulier en afwijzen α algorithm collectief klaar voor registreren 10 registreren afgewezen klaar voor invoeren 11 afwijzen 12 Bepalen offerte standaard of NIET Standaard offerte Niet Standaard offerte 13 inv., 1e controle, 15 inv, 1e controle, printen STANDAARD printen NIET STD. offerte uitgeprint NS uitgeprint afgekeurde offerte Afgekeurd NS 14 eindcontrolere, 16 eindcontrolere, tekenen Standaard tekenen niet std. Goedgekeurde offerte 17 bepalen vervolg P of C retour gewenst retour gewenst particulier zonder retour 19 wachten op collectief retour reeds ontvangen accoord verklaring naar registreren P2 accoord verklaring 20 ontvangst verklaring D2 geen retour ontvangen wachten2 21 registreren offerte afgelegd klaar voor einde 18 registreren offerte gesloten 22 Opbergen en einde

12 Conformance Checking Prepare shipment Ship goods Register order (Re)send bill Archive order Receive payment Requirement for material has arisen Contact customer Purchase Requisition Requisition released for scheduling agreement schedule/sa release Purchase requisition released for purchase order Decide To Buy Computer Purchasing [bad reviews] Choose Model Save Money Order Machine [desktop] Goods received Inbound delivery entered Purchase order created Read Test Reviews [enough] Order Screen [laptop] Choose Operating System [reviews ok] Receive Machine Receive Screen [laptop] [windows] [linux] Goods Receipt Work Hard [desktop] Order Windows Download Linux Goods receipt posted Receive Windows Check Bank Account [not enough] Set Up And Connect Open Lid Plug In And Power On Warehouse/ Stores Material is released Invoice received Install Operating System TO item confirmed without differences Transfer order item is confirmed Invoice Verification Payment must be effected

13 ProM Demo

14 Motivation Up until now: Designed or pre-defined models Assumption that they are appropriate Process Mining Logs Consideration of information from the execution of proceses This is covered in log data Sequence of log entries, which capture events in a company that relate to processes Business Process Management 15

15 Log entries Examples of log entries Check Invoice for Invoice No completed on at 9:19:57 Function StoreCustomerData( Müller, c1987, Bad Bentheim ) completed on at 9:22:24 Send Invoice for Invoice No completed on at 9:23:18 Function ContactCustomer(c1987, PromoMailing) completed on at 9:24:10 Function StoreCustomerData( Miller, c1988, Osnabrück ) completed on at 9:26:08 Check Invoice for Invoice No completed on at 9:26:38 Function ContactCustomer(c1988, PromoMailing) completed on at Send 9:27:32 Business Process Management 16

16 Logs bear valuable information Logs bear valuable information to answer questions like When and how many process instances have been executed? Are there recurring patterns in the execution of activities? Can process models be derived from the data? Which paths of execution are used how often in the process models? Are there paths which are never taken? Business Process Management 17

17 Process Discovery Process Discovery is a technique for deriving a process model from log data Input: execution logs as ordered lists of activities with time stamp and case id Output: process model which could have generated the execution logs The case id is often not directly covered in the data, and needs to be generated in pre-processing Business Process Management 18

18 Process Conformance Process Conformance is a technique to analyze the relationship between log data and process models Input: Logs and process model Output: information on the relationship, e.g. fitness Business Process Management 19

19 Overview Business Process Management 20

20 Execution Logs Assumption Execution log defines complete order of events, which can all be related to process activities All events in the execution log relate to process instances of the considered process Hint Often log entries refer to different process models This warrants filtering activities Abstraction Techniques often work on abstraction of logs Focus on case id and activities Business Process Management 21

21 Execution Log Format Log format (caseid, activity) Example Check Invoice for Invoice No completed on at 9:19:57 Function StoreCustomerData( Müller, c1987, Bad Bentheim ) completed on at 9:22:24 Send Invoice for Invoice No completed on at 9:23:18 Resulting Log (4567, Check Invoice), (c1987, StoreCustomerData), (4567, Send Invoice), etc. Business Process Management 22

22 Execution Log Further abstraction A s and B s (case id, task id) Additional information Event type, time, resource, data Not considered here Assumption Activity execution captured by one event No intermediate activities case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D Business Process Management 23

23 Order relations Log based order relations for pairs of activities a, b T in a workflow log W: Direct successor a > w b i.e. in an execution sequence b directly follows a Causality a w b i.e. a > w b and not b > w a Concurrency a w b i.e. a > w b and b > w a Exclusiveness a w b i.e. not a > w b and not b > w a Activity pairs which never succeed each other

24 Execution log analysis W = {ABCD, ACBD, EF} Direct successor Causality Concurrency case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D

25 Execution log analysis W = {ABCD, ACBD, EF} Direct successor Causality Concurrency 1) A>B A>C 2) 3) B>C A B B>D C>B C>D E>F A C B C C B case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D B D C D E F

26 α-algorithm The idea is to utilize order relations for deriving a workflow net that is compliant with these relations Precisely, each order relation results in a petri net fragment, which imposes the respective relationship

27 α-algorithm Idea (a) a b

28 α-algorithm Idea (b) a b, a c and b # c

29 α-algorithm Idea (c) b d, c d and b # c

30 α-algorithm Idea (d) a b, a c and b c

31 α-algorithm Idea (e) b d, c d and b c

32 The Alpha-Algorithm (simplified) 1. Identify the set of all tasks in the log as T L. 2. Identify the set of all tasks that have been observed as the first task in some case as T I. 3. Identify the set of all tasks that have been observed as the last task in some case as T O. 4. Identify the set of all connections to be potentially represented in the process model as a set X L. Add the following elements to X L : a. Pattern (a): all pairs for which hold a b. b. Pattern (b): all triples for which hold a (b#c). c. Pattern (c): all triples for which hold (b#c) d. Note that triples for which Pattern (d) a (b c) or Pattern (e) (b c) d hold are not included in X L.

33 The Alpha-Algorithm (cont.) 5. Construct the set Y L as a subset of X L by: a. Eliminating a b and a c if there exists some a (b#c). b. Eliminating b c and b d if there exists some (b#c) d. 6. Connect start and end events in the following way: a. If there are multiple tasks in the set T I of first tasks, then draw a start event leading to an XOR-split, which connects to every task in T I. Otherwise, directly connect the start event with the only first task. b. For each task in the set T O of last tasks, add an end event and draw an arc from the task to the end event.

34 The Alpha-Algorithm (cont.) 7. Construct the flow arcs in the following way: a. Pattern (a): For each a b in Y L, draw an arc a to b. b. Pattern (b): For each a (b#c) in Y L, draw an arc from a to an XOR-split, and from there to b and c. c. Pattern (c): For each (b#c) d in Y L, draw an arc from b and c to an XOR-join, and from there to d. d. Pattern (d) and (e): If a task in the so constructed process model has multiple incoming or multiple outgoing arcs, bundle these arcs with an AND-split or AND-join, respectively. 8. Return the newly constructed process model.

35 α-algorithm Example case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D

36 α-algorithm Example case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D α-algorithm (W):

37 Log Completeness Level of completeness required for a log Assume for the execution sequence EF, there is a log missing Then, the correct process model cannot be derived Basic assumption: each execution sequence must be part of the log Consequence: the complete behaviour is visible Problem: amount of required instances grows dramatically Example: 10 activities are executed in parallel Amount of potential execution sequences: 10! =

38 Log Completeness Result For the α-algorithm it is sufficient to have completeness in terms of the successor relationship (> w ) Reason All other relations are derived from direct successorship Interpretation Hint Each time two activities may succeed each other, this must be visible in at least one execution sequence In case of highly concurrent process models, this reduces the amount of required execution sequences dramatically

39 Advanced Features: Decision mining

40 Advanced Features: Social network mining