Master Thesis - Identification of Advanced Activity Concepts in SAP

 

Master Thesis - Identification of Advanced Activity Concepts in SAP

SAP ERP is a popular enterprise resource planning system, used by worldwide organizations, which supports important processes such as the procure-to-pay and the order-to-cash. Process mining techniques have been used to analyze and optimize these processes.

Data in SAP is contained in many different tables. Also, the definition of a case notion for a particular context (for example, the sales order or purchase order) lead to problems in the recordings (convergence/divergence). Some scientific papers introduce the definition of a set of tables to extract for a particular process and of strategies to resolve the convergence/divergence issues.

Another important topic is the definition of an activity concept. For example, a row of the EKKO table corresponds to a purchase order; hence, we could create an event with activity “Create Purchase Order” for every row of the table EKKO. However, the situations inside other processes of SAP are not straightforward, for example:

• For sales orders, there is an [a] unique table (VBAK) which contains different rows for different types of documents (inquiries, quotations, sales orders …).
• For invoices, the same row contains information about the creation of the invoice, the posting of the invoice and the period of payment of the same.
• Goods receipts can have different outcomes (quality issues).

Defining the activity concepts for such situations is done manually in most of the available event log extractors. However, exploiting natural language and other attribute analysis technique, it would be possible to define advanced activity concepts (considering the previous examples on purchase orders).

The goal of this thesis is to provide a set of techniques that, given a table in SAP, provide the possible (advanced) activity concepts related to the same. The output of the techniques should be a list of SQL statements which are able to extract events using the discovered activity concepts.

The implementation language, the way of connection of the database, and the intermediate storage structures are left up to the decision of the student.

Prerequisites:

The student should be familiar with the Python programming language and have some knowledge of process mining. In particular, hands-on experience with process mining (practical courses, pro-seminar, BPI course, IDS course, APM course) and data extraction is preferred. Experiences in using/dealing with ERP systems (specifically, SAP) will be highly valued.