Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach

Raúl Mazo; Gloria Lucia Giraldo; Leon Jaramillo; Camille Salinesi; Cosmin Dumitrescu

Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach

Raúl Mazo ¹ Gloria Lucia Giraldo ² Leon Jaramillo ² Camille Salinesi ¹ Cosmin Dumitrescu ¹

1 CRI - Centre de Recherche en Informatique de Paris 1

2 Universidad Nacional de Colombia (COLOMBIA)

Abstract : Among the many product line analysis operations, the computation of material needs for the production of reusable components is one of the most challenging issues. This paper aims at an automatic forecasting of reusable components procurement starting from a product line model. The proposed approach exploits Bayesian networks produced from product line models. The approach is applied on a case study developed at a motor company. Results show effectiveness of the proposed approach while scalability has not yet been reached.

Keywords : product lines procurement management analysis of product line models Bayesian network

Document type :

Conference papers

Domain :

Computer Science [cs] / Artificial Intelligence [cs.AI]

Complete list of metadatas

https://hal-paris1.archives-ouvertes.fr/hal-00913810
Contributor : Raul Mazo <>
Submitted on : Wednesday, December 4, 2013 - 2:10:12 PM
Last modification on : Wednesday, September 18, 2019 - 9:42:09 AM

Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach

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Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach

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