Predicting Deviation in Supplier Lead Time and Truck Arrival Time Using Machine Learning - A Data Mining Project at Volvo Group
| dc.contributor.author | Huang, Meng | |
| dc.contributor.author | Bagheri, Masood | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Technology Management and Economics | en |
| dc.date.accessioned | 2019-07-05T11:57:42Z | |
| dc.date.available | 2019-07-05T11:57:42Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The deviation in delivery performance from a company’s suppliers directly affects the company’s performance, causing availability loss for the customer orders and large costs for the rush transportation. If the deviation can be predicted in advance and used as deviation alerts, actions can be taken in advance either to prevent the deviation or decrease the impact of the deviation. To predict the deviation in the supplier delivery performance from a buying company’s point of view, this thesis work specifically focuses on the first two phases of a supply chain, namely supplier lead time from material suppliers and truck arrival time from logistics service providers (LSP). In order to examine the possible implementation of machine learning, a data mining project has been conducted at Volvo Group Service Market Logistics. The factors associated with deviation of supplier lead time and truck arrival time are identified, while the corresponding features are prepared under the constraint of the case company’s data availability. For predicting deviation in the two phases, two machine learning models are constructed accordingly based on the characteristics of output and input features. The opportunities and obstacles along the data mining process in the case company are identified. The results show currently in the case company, both generated machine learning models do not have enough predictive power in lead time deviation. This could be caused by the absence of some key features that have strong associations with deviation. However, the performance of the prediction model for truck arrival time is regarded to be improved to a deployable level when the desired features are constructed into the model by the case company. Future recommendations regarding constructing the desired features and improving the model performance are proposed. In comparison, predicting deviation in material suppliers’ lead time could be practical when the buying company get more information sharing from material suppliers. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/257041 | |
| dc.language.iso | eng | |
| dc.relation.ispartofseries | Master thesis. E - Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden : E2019:033 | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Transport | |
| dc.subject | Övrig industriell teknik och ekonomi | |
| dc.subject | Transport | |
| dc.subject | Other industrial engineering and economics | |
| dc.title | Predicting Deviation in Supplier Lead Time and Truck Arrival Time Using Machine Learning - A Data Mining Project at Volvo Group | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Supply chain management (MPSCM), MSc |
Ladda ner
Original bundle
1 - 1 av 1
Hämtar...
- Namn:
- 257041.pdf
- Storlek:
- 1.47 MB
- Format:
- Adobe Portable Document Format
- Beskrivning:
- Fulltext