Data management within a digital transformation – Shimano Europe
Overview
| Company Name / Department |
Shimano Europe |
| Contact Person |
Gillis van de Zande / Cally Kurvers |
| Location | Eindhoven, High Tech Campus |
|
Optional remote work |
Yes, 50% remotely possible |
| Travel expenses (own account or reimbursed by the company) | Own account (option to receive NS business card if required) |
| Housing arranged by company | No |
| Housing expenses (how much per month, own account or subsidized by the company) | |
| Internship compensation | €500,- per month |
| Study program | OML – IS (or maybe OPAC) |
| ESCF community |
Full member |
| Start date |
February, 2024 (earlier or later possible in consultation with Shimano) |
Company Description
On high level Shimano develops, manufactures and markets products that give people the opportunity to enjoy nature around them through cycling, fishing and rowing.
Project Description
Project description:
In Shimano we have an extensive Information System landscape, with different platforms for Sales, Planning & Operations. Within this landscape a major project is coming up in which we will change our ERP system. This ERP system is directly linked to many of our crucial processes within our Supply Chain department. In order to have a successful migration, we need to have a plan on how to manage our Supply Chain data from to as-is situation toward the to-be situation. We want this plan to be based on quantitative analyses. Which data-science technique(s) to use is up for discussion based on the final scoping, but we are open for all techniques that help in creating the best input for the migration (e.g. Process Mining, Business Simulation Testing, etc.).
Within the project you have the opportunity to work together with experts on Information Systems within Shimano, as well as with Operational Experts within the Supply Chain department.
Goals of the project:
- Identify the key (master) data changers & settings (within supply chain) for a successful ERP migration.
- Propose on a data improvement plan, based on quantitative analysis / machine learning; e.g. which fields/settings, when, how, etc.
Deliverables:
- Scope
- Create overview of as-is data landscape within Supply Chain
- Create overview of the to-be data landscape within Supply Chain
- Identify the key (master) data changers & settings (within supply chain) for a successful ERP migration.
- Propose on a data improvement plan, based on quantitative analysis; e.g. which fields/settings, when, how, etc.
- Recommendations
Essential student knowledge:
- Being able to deal with lots of data & Python
- Outgoing – Not afraid to have interviews with different stakeholders across the company
- Organized – Good planning skills
- Curious
More information: escf@tue.nl
