Current industrial companies are highly pressured by growing competitiveness and globalization, while striving for increased production effectiveness. Meanwhile, flustered markets and amplified customer demands are causing manufacturers to shift strategy. Hence, international companies are challenged to pursue changes, in order to continue being competitive on global markets. Consequently, a new industrial revolution has taken place, introduced as Industry 4.0. This new concept incorporates organizational improvement and digitalization of current information and data flows. Accomplished by data from embedded systems through connected machines, devices and humans into a combined interface. Thus, companies are given possibilities to improve current production systems, simultaneously saving operational costs and minimizing insufficient production development. Smart Factories, being the foundation of Industry 4.0 results in making more accurate and precise operational decisions from abilities to test industrial changes in a virtual world before real-life implementation. However, in order to assure these functions as intended, enormous amount of data must be collected, analysed and evaluated. The indicated data will aid companies to make more self-aware and automated decisions, resulting in increased effectiveness in production. Thus, the concept will clearly change how operational decisions are made today. Nowadays, Discrete Event Simulation is a commonly applied tool founded on specific data requirements as operational changes can be tested in virtual settings. Accordingly, it is believed that simulation can aid companies that are striving for implementing Industry 4.0. As a result, data requirements between Discrete Event Simulation and Industry 4.0 needs to be established, while detecting the current data gap in operational context. Hence, the purpose of this thesis is to analyse the data requirements of Discrete Event Simulation and Industry 4.0 for improving operational decisions of production systems. In order to justify the purpose, the following research questions has been stated: RQ1: What are the data challenges in existing production systems? RQ2: What data is required for implementing Industry 4.0 in production systems? RQ3: How can data requirements from Discrete Event Simulation benefit operational decisions when implementing Industry 4.0? The research questions were answered by conducting a case study, in collaboration with Scania CV AB. The case study performed observations, interviews and other relevant data collection to accomplish the purpose. In parallel, a literature review focusing on data requirements for operational decisions was compared to the empirical findings. The analysis identified the current data gap in existing production systems, in correlation to Industry 4.0, affecting the accuracy of operational decisions. In addition, it was shown that simulation can undoubtedly give positive outcome for adaptation of Industry 4.0, and a clear insight on data requirements.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-42724 |
| Date | January 2019 |
| Creators | Mirzaie Shra, Afroz |
| Publisher | Mälardalens högskola, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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