The competition in the manufacturing industry has never been higher. Owing to the technological changes and advancements in the market, readily available data is no longer a thing of the past. Numerous studies have discussed the impact of industry 4.0, digital transformation as well as better production planning methods in the manufacturing industry. The Mass-Manufacturing industry, in specific, has gained efficiency levels in production that were previously unimaginable. Industry 4.0 has been discussed as the ‘next big thing’ in the manufacturing context. In fact, it is seen as a necessity for manufacturing companies to stay competitive. However, efficient production planning methodologies are a preliminary requirement in order to successfully adopt the new manufacturing paradigms. The Engineering-to-order (ETO) industry is still widely unexplored by the academia ETO industries, barely have any production planning methodologies to rely on owing to their complex production processes and high reliance on manual-labour. Regression techniques have repeatedly been used in the production planning context. Considering its statistical prowess, it is no surprise that even the newer machine-learning techniques are based on regression. Considering its success in the mass-manufacturing industry for production planning, is it possible that its usage in the ETO industry might lead to the same results? This thesis involves a case study that was performed at an electrical transformer manufacturing plant in Sweden. After understanding the several operations that are performed in the production process, regression techniques are employed to estimate man-hours. The results from the study reconfirm the statistical prowess of regression and show the possibility of using regression in order to estimate man-hours in the ETO industry. In addition, several factors that can affect successful adoption of this tool in the production planning context are discussed. It is hoped that this study will lay the foundation for better production planning methodologies for the ETO industries in the future which might subsequently result in more data-driven decision making rather than instincts.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-422186 |
| Date | January 2020 |
| Creators | Anand Alagamanna, Aravindh, Juneja, Simarjit Singh |
| Publisher | Uppsala universitet, Industriell teknik, Uppsala universitet, Industriell 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 |
| Relation | SAMINT-MILI ; 20010 |
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