Process Mining - 02/12/2023 07:27 EST

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Description of the Process and of the Event Dataset

The dataset consists of an event log that refers to executions of instances of the purchase-order process as

carried on by a SAP system at a German company. To ensure the anonymity of this dataset, the names of the

employees, products and companies have been pseudonymized, that is, replaced with fictitious names. This has

been done consistently: e.g., if the real name X of a company is replaced with Y, the replacement has been done

for all instances of name X.

The purchase order process enables to create, update, and send purchase orders, which can contain:

1. Stock materials, such as spare parts or raw materials

2. Non-stock materials, such as laptops or printers

3. Services, such as consulting or cleaning services

4. Expenses related to materials or services ordered

According to domain knowledge, the process is executed as follows.

The process typically starts with the creation of a purchase requisition, which is implemented in an electronic

form and is used to obtain the formal authorization to carry on the purchase. For small orders, this can be often

skipped. It might also be that the purchase requisite is first created and, then, if deemed unnecessary,

subsequently removed.

After the purchase requisition item is created, if necessary, the actual purchase order can be created. Then, the

purchase order is printed for internal reference and sent. At this moment, the purchase order can possibly be

repeatedly blocked (no changes to the purchase order are allowed) and reactivated, if the management needs

to do further investigation. Eventually, the order is received by the good’s provider, who sends an order

confirmation. This leaves trace in the SAP system through the event “Receive Order Confirmation”. After the

order confirmation is received, the process waits until the goods are received; the reception of the goods is

recorded through event “Record Goods Receipt”. However, after the order confirmation is received and before

the actual reception of the good, the good’s provider can inform about the change of price; when this happens,

an event “Change Price” is observed in the event log. Also, the ordering company can decide to change the order

(activity “Send Purchase Order Update”).

Once the goods are received, two things can happen:

1. If the goods contain problems of different sorts, they need to be replaced. This requires the process to

execute the activity “Cancel Good Receipt”, which will eventually be followed again by “Record Good

Receipt”.

2. If the goods are satisfactory, after some time, the good’s provider sends the invoice to the ordering

company (event “Record Invoice Receipt”)

The process ends by clearing the invoice, namely the company paying it.

Questions

Using process-mining techniques, answer the following questions.

[login to view URL] and Validation of a Process Model

Prelude: The company wants to gain further insight into how the process is being executed. Suppose to be a

process analyst: you want to provide a Petri net that represents the actual process executions.

What to do:

1. Use the process description above to draw a Petri net and validate it by checking its

conformance using alignments against the event log.

2. If the alignment shows that the model is not satisfactory, iteratively improve the model until

the quality of satisfactory. The model is considered as satisfactory if the fitness value is at least

0.8, and the value of precision is at least 0.9.

[login to view URL] Discovery

Prelude: You do not want to trust the normative model at all. To do so, you need to discover models using the

different miners and find the model that balances precision and fitness at the best.

What to do:

3. Provide at least 3 different Petri-net models that you discover with different algorithms and

configuration, along with the values of fitness and precision. Identify the best model. The best

model needs to at least score of 0.8 in fitness, and it needs to have at least a score of 0.8 for

precision.

4. After identifying the best model, provide a discussion where the best discovered model

deviates from your normative model.

Q3. Simulation Parameters and Simulation

Prelude: The company is unsatisfied about the process and aims to deal and improve bottlenecks and over

utilization of resources. Build a simulation model and run it, and then vary different settings with the aim of

improving the process. Ensure to properly take care of the stochasticity of the simulation.

What to do:

5. Retrieve the simulation parameters using ProM tool and PM4Py library: case arrival rate,

branching probabilities, tasks durations, resource roles and calendars and create the

simulation model via the BIMP simulator.

6. Analyze the resource utilization and the time aspects (e.g., looking at result’s graphs in BPMN

or loading the simulation logs in DISCO or ProM).

7. Focus on improving the process via simulation: what advice would you give?

tiedonlouhinta Tilastotiede

Projektin tunnus: #37500807

Tietoa projektista

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tangramua

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