Examining Traces

In order to get familiar with the process, we will first have a look at how structured it is. To start, (1) draw a graph of the trace coverage, and (2) indicate which of the statements is true. Afterwards, (3) visualize the top frequent process variants using trace_explorer().

The trace coverage can be obtained using the trace_coverage() metric with level = "trace". The result shows the relationship between number of traces and cumulative coverage of the event log.

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Business Process Analytics in R

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# Create trace coverage graph
otc %>%
  ___ %>%
  ___

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Cet exercice fait partie du cours

Business Process Analytics in R

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The amount of event data has grown enormously during the last decades. A considerable amount of this data is recorded within the context of various business process. In this chapter, you will discover a methodology for analyzing process data, consisting of three stages: extraction, processing and analysis. You will have our first encounter with the specific elements of process data which are required for analysis, and have a first deep dive into the world of activities and traces, which will allow you to reveal of first glimpse of the process.

Exercise 1: Introduction to process analytics Exercise 2: Identify process elements Exercise 3: Process Analysis Workflow Exercise 4: Activities as cornerstones of processes Exercise 5: Retrieve basic process information Exercise 6: Explore activities Exercise 7: The happy path Exercise 8: Create process maps Exercise 9: Components of process data Exercise 10: Event data identifiers Exercise 11: Constructing event data objects Exercise 12: Inspecting event data objects

A process can be looked at from different angles: the control-flow, the performance and the organizational background. In this chapter, you will make a deep dive into each of these perspectives. The control-flow refers to the different ways in which the process can be executed, and thus, how it is structured. Considering performance, we are both interested in discovering how long things take, as well as when they take place. Finally, the organizational perspective looks at the actors in the process.

Exercise 1: Organizational analysis Exercise 2: Specialization of resources Exercise 3: Detecting roles and specializations Exercise 4: Involvement of resources Exercise 5: Structuredness Exercise 6: Process variants Exercise 7: Rework Exercise 8: Precedence matrix Exercise 9: Performance analysis Exercise 10: Eating habits Exercise 11: Eating habits - Dotted chart Exercise 12: Eat, sleep, repeat Exercise 13: Linking perspectives Exercise 14: The right person on the job I Exercise 15: The right person on the job II Exercise 16: The right person on the job faster

Event data rarely comes in a form which is ready to analyze. Therefore, you often require a set of tools to get the data in the right shape, before we can answer our research question. At the end of this chapter, you will be familiar with three common preprocessing tasks: filtering data, aggregating events and enriching data.

Exercise 1: Filtering cases Exercise 2: The path well traveled Exercise 3: The downside of rejections Exercise 4: The downside of refusals Exercise 5: Filtering events Exercise 6: The vicious disapproval circle Exercise 7: Working with high-paid R&D candidates Exercise 8: Aggreggating events Exercise 9: Disapprovals are disapprovals Exercise 10: Zooming out Exercise 11: Admiring the view Exercise 12: Enriching events Exercise 13: Everything has a cost Exercise 14: Cost versus urgency

In this final chapter we will use everything we have learned so far to do and end-to-end analysis of an order-to-cash process. Firstly, we will transform data from various sources to an event log. Secondly, we will have a helicopter view of the process, exploring the dimensions of the data and the different activities, stages and flows in the process. Finally, we will combine preprocessing and analysis tools to formulate an answer to several research questions.

Exercise 1: Preparing the event data Exercise 2: Transforming Sales Data (1/3)Exercise 3: Transforming Sales Data (2/3)Exercise 4: Transforming Sales Data (3/3)Exercise 5: Putting the process together Exercise 6: Creating the event log Exercise 7: Getting to know the process Exercise 8: Examining Traces

Exercice en cours

Exercise 9: Zooming Out Exercise 10: Roles and Rules Exercise 11: Resource Roles Exercise 12: The 4-Eye Principle Exercise 13: Control-flow deviations Exercise 14: Fast Production, Fast Delivery Exercise 15: Throughput time analysis Exercise 16: Course Recap