Difference between revisions of "Machine Learning and LeadTime Prediction"
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|Summary=Machine Learning and LeadTime Prediction | |Summary=Machine Learning and LeadTime Prediction | ||
|TimeFrame=Fall 2018 | |TimeFrame=Fall 2018 | ||
| − | |Supervisor= | + | |Supervisor=Slawomir Nowaczyk |
| + | |Level=Master | ||
| + | |Status=Open | ||
}} | }} | ||
| + | This is a project together with Volvo Group Truck Operations - Service Market Logistics | ||
| + | Apply here: https://xjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25079&siteid=5171&PageType=JobDetails&jobid=663603#jobDetails=663603_5171 | ||
| + | |||
| + | Background | ||
| + | |||
| + | Variations in lead time are today impacting the customer perception of service market capabilities, driving a lot of cost and tied up capital. | ||
| + | If we could predict lead time in a better way we could give better service to our customer as well as reducing the logistics cost heavily. | ||
| + | Since we know that lead time variations is not easy to predict with traditional time series data we believe that using other technologies, such as machine learning, could be part of the solution. | ||
| + | |||
| + | Thesis questions and expected outcome | ||
| + | |||
| + | We would like the students study current lead time setup for a number of flows and the variations and the potential of forecasting the lead time variation. Data collection, processing and quality review would be relevant during this step of the process as well. | ||
| + | Look into technologies to forecast lead time variations such as ML, Neural networks, and others. | ||
| + | Develop model with features, variables, and vectors. Proof of concept how the lead time forecast would benefit the supply chain. | ||
| + | |||
| + | Student profile and application | ||
| + | |||
| + | Master students in Logistics or in Mathematics, or similar fields | ||
| + | Application deadline: Nov 5th 2018 | ||
Latest revision as of 11:32, 25 October 2018
| Title | Machine Learning and LeadTime Prediction |
|---|---|
| Summary | Machine Learning and LeadTime Prediction |
| Keywords | |
| TimeFrame | Fall 2018 |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Slawomir Nowaczyk |
| Level | Master |
| Status | Open |
This is a project together with Volvo Group Truck Operations - Service Market Logistics
Apply here: https://xjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25079&siteid=5171&PageType=JobDetails&jobid=663603#jobDetails=663603_5171
Background
Variations in lead time are today impacting the customer perception of service market capabilities, driving a lot of cost and tied up capital. If we could predict lead time in a better way we could give better service to our customer as well as reducing the logistics cost heavily. Since we know that lead time variations is not easy to predict with traditional time series data we believe that using other technologies, such as machine learning, could be part of the solution.
Thesis questions and expected outcome
We would like the students study current lead time setup for a number of flows and the variations and the potential of forecasting the lead time variation. Data collection, processing and quality review would be relevant during this step of the process as well. Look into technologies to forecast lead time variations such as ML, Neural networks, and others. Develop model with features, variables, and vectors. Proof of concept how the lead time forecast would benefit the supply chain.
Student profile and application
Master students in Logistics or in Mathematics, or similar fields Application deadline: Nov 5th 2018
