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

Raúl Mazo (1) , Gloria-Lucia Giraldo-Gómez (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)

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

Domains: Computer Science [cs]; Software Engineering [cs.SE]

Raúl Mazo, Gloria-Lucia Giraldo-Gómez, Leon Jaramillo, Camille Salinesi, Cosmin Dumitrescu. Material Needs Forecast for Product Lines, a Bayesian-based Analysis Approach. 25th International Conference on Software and Systems Engineering and their Applications (ICSSEA), Nov 2013, France. ⟨hal-01071290⟩