In the production of highly customized goods with extensive production chains, traditional predictive models face the “black box” barrier. While technical accuracy is often the focus of analytics developments, the ultimate success of an initiative strongly depends on user acceptability and integration into daily decision-making.
This presentation details the development and implementation of a predictive model designed for a complex manufacturing environment. We shift the focus from pure algorithmic performance to a methodology rooted in collaborative design and transparency. By involving end users throughout the development lifecycle, we ensured the tool was not only technically robust but also intuitive and trustworthy for those managing the actual work.
We will explore the specific methodological framework used to bridge the gap between data science and operational reality. Along the way, we will investigate the concept of acceptability, its implication for the success of a project and common biases. This case study demonstrates how non-technical aspects of a technical project can truly be the driving force of adoption.
Related Domains:
- Domain I: Business Problem (Question) Framing
- Domain II: Analytics Problem Framing
- Domain III: Data
- Domain IV: Methodology (Approach) Framing
- Domain V: Analytics/Model Development
Relevant to:
- Essential (Early Career)
- Professional (Mid-Career)
- Executive (Senior Level)
