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  3. Business Process Analytics in R
  • 1

    Introduction to process analysis

    Gratuito

    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.

  • 2

    Analysis techniques

    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.

  • 3

    Event data processing

    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.

  • 4

    Case study

    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.

Initializing

Exercicio

Zooming Out

We just saw that there are many unique traces. The reason for this is that there are several stages in the process where various activities occur in parallel. This will create a unique sequence for each different ordering. When three activities can occur in any order, there are six different possible orderings. When we have another set of three parallel activities later in the process, the combined number of different orderings will increase to six times six, etc.

Let's work around this by collapsing those activities into a single activity instance. We have already collapse the sub processes Create Quotation, Production and Accounting. The sub process Delivery still needs to be collapsed.

Instruções 1/3

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  • Collapse the prespecified sets of activities. Give them the name "Delivery". Store the result in otc_high_level.