There is little room for error when the societally important production must increase in a fast-growing company in the midst of the corona pandemic. In order for production to be able to increase, process flow and an enormous amount of data must be analyzed and described in detail in order to provide a good foundation to be able to create new efficient processes where risks are minimized so that the production can continue without problems. By applying well-proven models and theories from the financial world’s risk management, new opportunities are created to visualize otherwise forgotten and overlooked information. Data that can provide the production planning department with important information on whether to set up production to minimize waste and the risk of production stoppage. The purpose of this work is to investigate how uni- and multivariate time series analysis can be implemented in the best possible way, to be able to visualize and forecast lead times for the logistics department at Cytiva Umeå. The goals are to create both a new way of using otherwise disregarded data and a methodology to implement similar models in supply chain-related activities. There is a huge amount of limitations and opportunities so the work has thus been carried out to create a version as simple as possible so that the benefit for the company in future implementations is the greatest. In the report, we have succeeded in producing, sorting, and modeling the relevant data. Several different models are created based on theories and methods applied in a new area to, in an efficient and simple way describe the current situation. The best implementation was one where we included additional information about the system so that the model learns how to distinguish different properties which have their derivation from what the process looks like. Based on this model, the company can then forecast the lead time and thus be able to use all the information that is available. This is to then be able to act on information and thereby create an additional value for the company.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-184429 |
| Date | January 2021 |
| Creators | Trigell, Martin |
| Publisher | Umeå universitet, Institutionen för matematik och matematisk statistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| 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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