University of Twente Student Theses


Simulating the digital production process of a hybrid big data consultancy agency in Kotlin

Heijs, É.G.S.G. (2022) Simulating the digital production process of a hybrid big data consultancy agency in Kotlin.

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Abstract:The company Information Resources Incorporated (IRI), a big-data consultancy agency in the fast-moving consumer goods sector, faces serious efficiency problems in their operational department regarding the throughput time of data, workload deviation between employees and insight into the complex and abstract operational processes. The operational department of IRI is responsible for converting raw transaction data (containing a product code, units sold and a transaction amount which is obtained from retailers) to categorised databases that can be used to visualise and gain insight into this data. This research investigates the added value of a set of process modifications that could increase the efficiency of the operational department, which are defined based on an in-depth analysis of the key activities of the operational process. These key activities include: assigning a product category to the product code, assigning attributes to this product code, placing the classified product code in the right database and submitting the modified database to the IRI mainframe. This research implements a Discrete Event Simulation (DES) application in the Kotlin (a modern Java Virtual Machine) programming language. With the application of the Kotlin language, we aim to provide a future-proof and practical solution for management to gain quantitative insight into the production process. We use the DES application to evaluate the set of process modifications to both propose valuable improvements to the operational processes and offer a foundation for future research into several areas of interest substantiated by model results. The model makes use of a variety of historical internal process data. Results of the model show a number of modifications that could improve the efficiency of the operational processes: - A workload assignment rule that assigns workload to employees by ordering the jobs based on the occurrence of product categories in historical data could decrease workload deviation between employees by 50% to 75%. - Setting up a separate team that handles the assignment of new product codes to a product category could increase total jobs submitted by about 2% without affecting the performance of the rest of the system. This would mean a “free” increase of throughput. - Increasing the “raw” data delivery moments from once a week to 2 times a week, smoothing out the arriving workload could decrease the throughput time by about 10% while decreasing the workload of employees by 10%. Follow-up research is needed to increase the accuracy of certain aspects of the DES model and better investigate more complex aspects of the process such as new retailer integrations. With the implementation of the operational process of IRI in a DES model using the Kotlin programming language, we have been able to highlight promising modifications to improve the efficiency of the production processes. The research complements existing research in the analysis of stochastic production processes using DES by applying the technique to an abstract and digital manual production process in an unconventional way. Also, by using the Kotlin language and showing its functionality outside of the domain of Android development where it is predominantly used at the moment, we show a new side to DES research. Besides this new use of the Kotlin language, we also show how an existing open-source simulation library can be implemented effectively to a complex production process. This research can furthermore be used as a framework for the development of effective and practical DES applications for production system analysis using open-source DES libraries for general-purpose programming languages.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
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