Optimisation de la taille de la s\'erie: illustration par un cas industriel de sous-traitance m\'ecanique
Barbara Lyonnet (SYMME), Maurice Pillet (SYMME), Magali Pralus (SYMME)

TL;DR
This paper develops a decision support model for mechanical subcontracting companies to optimize production size, balancing opportunity mix, economic constraints, and risk of unsold inventory, to reduce manufacturing costs.
Contribution
It introduces a novel decision model that considers sales opportunities, economic factors, and inventory risks for production size optimization in subcontracting firms.
Findings
The model highlights the importance of high ownership rates.
It assesses the limits of producing beyond demand.
Provides decision elements for balancing costs and sales opportunities.
Abstract
Reducing costs of manufactured products is one of the key issues of companies. Bar turning companies (mechanical subcontracting companies) are faced with the following dilemma: use a pull strategy or use a push strategy. Instinctively these companies produce more than demand required by customers. This strategy allows them to respond to requests forecasts and reduce their cost of changeover time. These companies make a bet on sales opportunities and think to realize an additional profit. We have tried to find in this study to provide elements to know the limits of this strategy. Our proposal focuses on developing a model to support the decision taking into account the mix of opportunities, economic constraints and mean constraints. This model features the particular importance of high rates of ownership and the risk of not selling. R\'eduire les co\^uts de revient des produits…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSupply Chain and Inventory Management · Operations Management Techniques · Scheduling and Optimization Algorithms
