Forecasting transport demand in a warehousing context – Mainfreight Warehouse
Overview
| Company Name / Department |
Mainfreight Warehouse |
| Contact Person |
Marc van Aalst |
| Location | ‘s-Heerenberg |
|
Optional remote work |
parttime |
| Travel expenses (own account or reimbursed by the company) | Covered by Mainfreight |
| Housing arranged by company | No |
| Housing expenses (how much per month, own account or subsidized by the company) | Not applicable |
| Internship compensation | €500, gross per month |
| Study program | Industrial Engineering |
| ESCF community |
CDT |
| Start date |
September 2024 |
Company Description
Mainfreight is global logistic service provider active in Air&Ocean, Transport and warehousing.
Project Description
Project description:
For the vast majority of Mainfreight’s warehousing customers, also the transport and customer delivery is organized by Mainfreight, a big advantage for our customers is, that the lead times can be very tight. That gives Mainfreight Transport the challenge to plan those shipments as optimally as possible without making concessions to the requested service and at the same time still achieving optimal loading for our trucks. Once (or even before) the warehousing process starts, the transport department already needs to receive information to start their planning activities, that means we must work with assumptions since the order hasn’t been packed yet.
In the situation where time is not so critical the warehouse can process the order (pick and pack) and once that is done the packaging details/result can be shared with the transport planners, at that point in time all relevant planning details (number of pallets, type of pallets, pallet height and weight) are known and final. This hardly happens and processing orders a day ahead of pickup is also not as this requires a big staging area.
We do have lots of data available from historical orders eg: what products where on the order, classification of products, dimensions of product, requested quantities, shipped quantities and how the order was packed: number of pallets, type of pallets, pallet height and weight.
The historical data could help us to predict the packing result of “fresh orders”
Goals of the project:
Predict the packaging outcome (number of pallets per pallet type) with a 95% reliability: predicted versus realized. Measurement is on individual order level.
Deliverables:
We are looking for a conceptual design that can support the above goal, including the prerequisites , does and don’ts, potential pitfalls and required process changes or adjustments.
The design can be – if doable – supported by a high level simulation model.
Essential student knowledge:
- Familiarity with basic warehousing and transport processes is a must have.
- Analytical skills (process and data)
- Interest and affinity with process optimization
- MS Excel” moderate level
- Basic understanding data flows from an IT perspective (eg: EDI, SQL, API) is big benefit.
More information: escf@tue.nl
