Improving sawmill agility through log classification

Jean Wery (1, 2, 3) , Jonathan Gaudreault (2, 3) , André Thomas (4) , Philippe Marier (2, 3)

1 ENSTIB - Ecole Nationale Supérieure des Technologies et Industries du Bois
2 Forac Research Consortium
3 ULaval - Université Laval [Québec]
4 CRAN - Centre de Recherche en Automatique de Nancy

Sawmill production is characterized by divergent processes and coproduction. In this context, it is difficult for a production manager to establish a production plan which meets customer demand. This is one of the reasons why the North American lumber industry produces with a mainly make-to-stock strategy. The aim of this research is to evaluate the impact that a better classification of the raw material (logs) would have on sawmill agility. Using a mathematical model to create production plans, we evaluate the performance of the mill to meet the demand in light of the knowledge it has of the raw material. The process of softwood lumber production is one that shows interesting characteristics. Felled trees that have been limbed and cut to length arrive at a sawmill which then transforms this raw material (logs) into various products (lumber) of different standard 1 sizes and lengths. The sawing of a single log generates an assortment of different products (divergent process) simultaneously (coproduction). In North America, there is generally little classification of logs prior to sawing, whether at the log yard (location where the logs are stored before entering sawmill) or along the forest roads.

Mots clés: Agility in supply chain; optimization; timber industry; classification; sawmill

Domaines: Recherche d'information [cs.IR]; Automatique / Robotique

Jean Wery, Jonathan Gaudreault, André Thomas, Philippe Marier. Improving sawmill agility through log classification. 4th International Conference on Information Systems, Logistics and Supply Chain, ILS 2012, Aug 2012, Québec, Canada. ⟨hal-01281174⟩