Internal supply chain management in the Chilean sawmill industry

Abstract

PurposeThe existing business paradigm in the sawmill industry is that improvements are achieved by enhancements at each individual sawmilling plant. The authors investigate a collaborative approach, and propose a model for optimizing production and inventory planning decisions within a system of plants.Design/methodology/approachThe methodology models the sawmill production process in terms of two transformation stages and two inventory stages. The authors then extend the model to account for supply chain decisions consisting of timber transfers among plants. They apply the model at 11 Chilean sawmills to evaluate whether they can benefit from operating in a more integrated manner.FindingsThe authors find an opportunity to increase profits by more than 15 percent through a higher utilization of the capacity and a better assignment of production orders.Research limitations/implicationsThe study neglects the possibility of exchanging raw material among plants. Also, the findings are derived from a computational simulation, so are estimates, not actual observations.Practical implicationsThe study challenged the business paradigm of the company the authors performed the study for. Now, it is implementing the suggested improvements.Originality/valueAn investigation into how a number of sawmilling companies working together can profit from their distinctive competitive advantages, and which proved successful in challenging one company's business paradigms.

Supply Chain Network Optimization of the Canadian Forest Products Industry: A Critical Review

“…For example, a study on Quebec mills, showed that by effectively managing all nodes in a supply chain, the overall cost can decrease [37]. Another study in the Chilean sawmill industry found that internal supply chain management would increase the profitability of the sawmills by approximately 15% [38]. Ronnqvist [39] conducted an extensive review of literature to define the set of decisions that need to be made in a wood products supply chain, and concluded that operations research (OR) and especially optimization can be used as decision support tools in forestry.…”

“…There has been a lot of emphasis on supply chain management in the forest industry as a result of consolidation of upstream and downstream companies [37,38]. The studies focus on each of the operational areas in the forest industry separately, examining the effect of different management scenarios on the performance of individual companies as well as the entire sector in different regions and countries.…”

“…However, in recent years modeling the entire supply chain that combines tactical and operational level decisions has received more attention [39,41,42,50,51]. Most of the studies in the forest products industry supply chain networks optimization have used linear programming (LP) or mixed-integer programming (MIP) models [9,38,40,[52][53][54][55][56]. These models have been used to minimize the net present value of the total cost [57], for combined facility location and shipping route problem for pulp mills [9,52,58], and for modeling a network of biomass energy production facilities [53].…”

A comparison of optimization models for lumber production planning

“…Although the problem has been formulated in literature in a similar way, in some cases it has been combined with bucking decisions Adams 1991, Maness andNorton 2002) and timber transfer decisions among plants in a supply chain context (Singer and Donoso 2007). In Todoroki and Rönnqvist (2002), a sawing optimization model is implemented in a sawing simulator hence the optimal cutting pattern is determined on a log-by-log basis; an approach that does not find the optimal log-pattern mix that best satisfies the customer demand.…”

“…In Todoroki and Rönnqvist (2002), a sawing optimization model is implemented in a sawing simulator hence the optimal cutting pattern is determined on a log-by-log basis; an approach that does not find the optimal log-pattern mix that best satisfies the customer demand. Multi-period models (Maness and Norton 2002, Singer and Donoso 2007, Maturana et al 2010 have also been used with the possibility of handling inventory, of either logs or lumber, to add more flexibility. Maturana et al (2010) compare a sawing optimization model with a heuristic that emulates the decision behavior of the sawmill scheduler.…”