Towards Better Demand Forecasting Using Artificial Intelligence
| dc.contributor.author | Cederberg, Line | |
| dc.contributor.author | Elliot, Josefin | |
| dc.contributor.author | Engdahl, Alva | |
| dc.contributor.author | Hedenborg, Fredrik | |
| dc.contributor.author | Sikström, Jakob | |
| dc.contributor.author | Wranå, Linnea | |
| dc.contributor.author | Åberg, Johanna | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation | sv |
| dc.contributor.examiner | Bohlin, Erik | |
| dc.contributor.supervisor | Shurrab, Hafez | |
| dc.contributor.supervisor | Jonsson, Patrik | |
| dc.date.accessioned | 2020-08-14T10:52:06Z | |
| dc.date.available | 2020-08-14T10:52:06Z | |
| dc.date.issued | 2020 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | Balancing customer demand and supply capacity is crucial for business survival, especially in competitive environments that are subject to short product life cycles and require minimal waste. Most high performing companies rely on demand forecasting to better balance demand and supply, but still embrace significant errors. In this respect, many emerging technologies are finally becoming mature, opening up a new world of potential improvements that forecasting can benefit from. One of these technologies is artificial intelligence (AI), whereby tasks that previously required human cognition now can be solved better and more efficiently. Therefore, the study aims to increase the understanding regarding the potential improvements AI can bring to supply chain performance in the context of demand forecasting in manufacturing companies. Related literature was first reviewed to identify baseline knowledge and develop a theoretical framework. Afterwards, interviews with manufacturing companies in the region of Gothenburg were performed in order to gather qualitative data. The results from seven interviews show that AI within demand forecasting is not used to a wide extent in the region of Gothenburg. Businesses that have implemented AI have seen several improvements as a result, while companies still using traditional forecasting methods are facing challenges to realise potential improvements. Multiple prerequisites needed for implementing AI have been identified. Some prerequisites were identified in both literature and interviews, while others only appeared in one or the other. The conclusion is that AI is expected to improve; better downstream planning, quick adaptation to erratic demand, increased service levels, decreased working capital, and reduced manual work by being implemented in demand forecasting. The general prerequisites are; clean data, sufficient technological infrastructure, available resources, understanding of the improvements AI can bring, and sufficient competence of AI. | sv |
| dc.identifier.coursecode | TEKX04 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/301468 | |
| dc.language.iso | eng | sv |
| dc.relation.ispartofseries | TEKX04-20-18 | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Artificial Intelligence | sv |
| dc.subject | Demand Forecasting | sv |
| dc.subject | Improvements | sv |
| dc.subject | Prerequisites | sv |
| dc.subject | Machine Learning | sv |
| dc.subject | Operations planning | sv |
| dc.title | Towards Better Demand Forecasting Using Artificial Intelligence | sv |
| dc.type.degree | Examensarbete på kandidatnivå | sv |
| dc.type.uppsok | M2 |