Simulation and evaluation of coordination mechanisms for a decentralized lumber production system with coproduction
Résumé
Sawmilling activities in softwood mills (i.e., wood-sawing, drying, and finishing) cannot be efficiently planned at the operational level in a centralized manner because of the complexity of the production process. Sawmills plan their activities in a decentralized manner (although they try to coordinate them). Thus, specific mathematical models have been developed over the years to support planning for each activity. In the literature, these planning models are usually evaluated and tested independently, or connected using heuristics and evaluated for a fixed demand–planning horizon, assuming a known demand for the entire planning period. In this study, we simulate the use of planning models for decentralized sawmill production, but in a context where new orders arrive randomly and replanning is carried out periodically using a rolling horizon. We also simulated and evaluated different coordination mechanisms at the operational level, highlighting that previously published coordination mechanisms for decentralized planning of sawmilling operations may lead to a low order-fill rate when used in such a dynamic environment. We then propose a more advanced push–pull coordination mechanism based on the concept of decoupling point, revealing that this new mechanism may be more appropriate regarding the market characteristics considered in the study, while leading to a sales increase and reduced inventory. Actual numbers vary depending on specific market conditions.
Lumber production for softwood mills involves three main production stages: sawing, drying, and finishing. Many products are generated from a single raw material, so the process is known as divergent (from one log several products are obtained) with uncontrolled coproduction, which means that several products are produced at the same time (see Öner and Bilgic 2008) and each change made at a production stage affects the following phases. Sawing, drying, and finishing operations are typically planned using different models in a decentralized environment. Even though these three activities share the same goal, they may lack coherence because each unit is optimized independently (Gaudreault et al. 2010). This could explain why this industry often suffers from a low order-fill rate, high inventory, and significant lead time. Planning these operations using a centralized approach (based on a single coordinator responsible for establishing a centralized plan that must be followed by the planners of each subsystem) limits the specific operational details that can be taken into account (Gaudreault et al. 2010). Furthermore, Gaudreault et al. (2009) mentioned that centralized approaches cannot be used because of the complexity of the process involved, and highlighted the fact that there are not enough powerful computers able to process this data system. Thus, in order to keep the system decentralized (based on the fact that each unit is responsible for its own planning) while ensuring customer demand satisfaction, the lumber production process may be synchronized using efficient coordination mechanisms. These coordination mechanisms can be tools, agreements, and information that are used to ensure the coordination of the entire production process (Arshinder et al. 2011).
This research focuses on the coordination of these production stages in order to increase the volume of sales and decrease the average inventory. A simulation approach inspired from Dumetz et al. (2016) is proposed to simulate the entire softwood sawmilling production process at the operational level and evaluate the use of different coordination mechanisms, based on different order acceptance policies. The planning processes are further simulated by integrating mathematical optimization models for each processing activity.
In particular, two coordination mechanisms reported by Gaudreault et al. (2010) as being effective for the sawmilling industry are analyzed. In their original article, the authors evaluated these mechanisms using static data sets, namely a fixed planning horizon as well as a fixed and known demand (that is, a set of orders that is known in advance, before planning) for the whole planning horizon. Two mechanisms were already tested and showed good results in a particular context; therefore, we wanted to evaluate such coordination mechanisms with a more realistic dynamic order arrival process, which calls for periodic production replanning (rolling horizon) as well as the implementation of order acceptance policies (i.e., Available-to-Promise, Capable-to-Promise, and Stock). The environment is then considered to be dynamic because new orders arrive from one week to another and they must be taken into account. Results show that in this dynamic context, using the aforementioned mechanisms leads to poor performance, which may be explained by the high level of coproduction that affects coordination. A hybrid push–pull coordination mechanism, taking into account the decoupling point concept, is therefore proposed and evaluated. Simulation reveals that such a mechanism may lead to a higher order acceptance rate as well as a lower inventory. From an industrial point of view, this study provides information regarding how better coordination can be achieved in a decentralized production system with coproduction.
The remainder of this article is organized as follows. “Preliminary Concepts” introduces preliminary concepts about the North American lumber industry and coordination in supply chains and the coordination mechanisms evaluated. “Assessing Coordination Mechanisms in a Dynamic Context” describes the simulation framework needed to carry out the experiments and presents the experiments and the results. Finally, “Conclusions” concludes the article.