The forest industry plays a key role in the economy for the province of Quebec, Canada, contributing $5.8 billion to its gross domestic product in 2023. All costs presented in this study are in Canadian dollars, except where noted. Forest operations are carried out by public and private companies involved in timber harvesting, wood processing, and silviculture, including tree nurseries. Forest cooperatives, organizations owned by their employees, are an important contributor to the Quebec wood supply system. They are involved in a wide range of operations and are active in all major forest regions. Like most companies, their operations are sometimes constrained by insufficient transportation capacity due to a shortage of drivers. One potential solution would consist of aggregating transportation capacities across cooperatives. However, few studies have documented the benefits of mutualizing trucking resources, and the lack of demonstrated benefits may hinder the implementation of collaboration in transport operations. This study aims to investigate collaborative timber transportation planning. A case study involving three forestry cooperatives examines the impact of pooling trucks for wood transportation from the forest to sawmills. A mathematical model designed to minimize transportation costs is proposed to measure the benefits of such a strategy. Results demonstrate a 1.2% reduction in transportation costs from eliminating subcontracting requirements when trucks are pooled among cooperatives. Although case specific and influenced by the local availability of contract trucks, these findings highlight the potential for efficiency gains and cost savings through freight capacity pooling, while promoting sustainable practices and enhancing competitiveness for forestry companies.

The Virtual Wood Supply Arena is an on-line training environment for managing roundwood purchase, production and transport in cut-to-length supply systems. The purpose of its development was accelerated training for coordination of these functions under realistic operating conditions. It offers 8- and 12-week scenarios for supplying five mills. Weekly planning is done for 10 harvesting teams and 10 trucks in a Swedish case geography while tracking mill delivery fulfillment under weekly trafficability restrictions. The purpose of this paper is to introduce the training environment and report the progression of student performance after 2 years of use in university-level training. Student teams reached full delivery fulfillment within three training runs. After familiarization during an introductory run, a complete 12-week scenario took four effective hours to complete. Delivery fulfillment increased from 82 to 95 and 100% between the first, second and third training runs. The progression of team performance included a 36% reduction of relocation distances for harvesting teams and 11% reduction of transport distances for hauling from forest to mill. By the third training run these performance levels were attained with less than 2 weeks of inventory for both the purchase bank and roadside stocks.

Continuous high-frequency wood drying, when integrated with a traditional wood finishing line, allows correcting moisture content one piece of lumber at a time in order to improve its value. However, the integration of this precision drying process complicates sawmills logistics. The high stochasticity of lumber properties and less than ideal lumber routing decisions may cause bottlenecks and reduces productivity. To counteract this problem and fully exploit the technology, we propose to use reinforcement learning (RL) for learning continuous drying operation policies. An RL agent interacts with a simulated model of the finishing line to optimize its policies. Our results, based on multiple simulations, show that the learned policies outperform the heuristic currently used in industry and are robust to sudden disturbances which frequently occur in real contexts.

Planning and scheduling wood lumber drying operations is a very difficult problem. The literature proposes different methods aiming to minimize order lateness. They all make use of pre-established kiln loading patterns that are known to offer good physical stability in the kiln and allow full kiln space utilization. Instead, we propose a mixed integer programming (MIP) model, which can be used to generate loading patterns “on the fly.” This MIP model can be integrated into existing kiln drying operation planning/scheduling systems in order to improve their solutions. We show how this integration can be done by adapting a state of the art drying operations planning and scheduling methodology from the literature. We compare the solutions obtained by this system using the predefined loading patterns versus the solutions it generates if it is connected to our loading patterns generator MIP model. The study shows it is much better to dynamically create loading patterns than to use predefined ones, as most North American sawmills do.

Abstract Various optimization tools have been used in industry to facilitate production planning at different levels of aggregation. Choosing the interoperability mechanisms of these systems, such as the planning frequencies, the information passed between them and the interpretation that other systems must make of them, has always been a challenge. This work focusses on production planning at the tactical and operational levels in North American sawmills, a commodity industry characterized by volatile prices and a divergent production process with coproduction. In this context, tactical planning produces aggregated plans, and information from these plans can be used as targets and/or constraints at the operational level (e.g., quantities to be produced/kept in stock per product and per period, sales targets, etc.). A simulation of this production system was therefore developed, encompassing the planning process and the market dynamic, to compare and evaluate the impact of different coordination approaches on business economic performance. Results showed that the type of information which should be shared from the tactical level to the operational level varies according to several factors, including the company’s order acceptance policy, price seasonality, and the presence or absence of overcapacity on the market. Highlights A simulation approach is used to evaluate coordination between tactical and operational planning The context of North American sawmills is the one investigated The production and the planning process as well as the market behavior is considered Results show that the information shared between the two levels impact the income The order acceptance policy chosen also has an influence on the revenue generated

This paper presents an online educational game focusing on hierarchical procurement planning in a simulated forest supply chain with multiple companies. The purpose is to provide an understanding of the importance of individual decisions and their medium- to long-term impacts on the entire supply chain. The transportation game comprises three phases, each simulating hierarchical decision making when three competing companies (i.e., the game players) are making simultaneous decisions on the available resources. Each game phase also requires concurrent collaboration and competition. The phases represent different planning levels from long-term to short-term planning, considering the collaboration concept within the supply chain. The simulated supply chain objective is to minimize resource purchasing and transportation costs. The purchasing cost will be fixed after the first phase. The chance of decreasing transportation costs, however, is available until the end of the game. We develop three optimization models for each game phase. Once the game is finished, it compares the players’ results with optimal solutions prepared upfront. Finally, we present some comments about the game experience in various classrooms.

The arrival of digital technology in production systems represents a major challenge for manufacturers. The "4.0 Industrial Revolution" is pushing companies to review these same systems in order to develop decision-making tools that contribute to better capture any relevant opportunities while increasing profitability. In this context, this article shows a tactical planning model, specially developed for the lumber industry, integrating the electric energy cost in the decision process in order to minimize electric energy consumption. The model calculates the energy consumption based on equipment nominal power, the time at which the equipment is used, and a certain load factor. It also includes the energy used to heat or cool workspaces. Using real data from a North American sawmill collected from August 2017 to July 2018, the model showed that with a load factor calculated for each month and a good approximation of the heating energy consumed, the total energy consumption calculated is close to the one billed by the electricity supplier. Hence, the tactical planning tool could now be exploited by any sawmill aiming to integrate energy cost as a decision variable in its production planning.

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.

The North American lumber industry produces mostly commodity products (i.e. products with standard dimensions and properties). However, some customers also want products showing very specific characteristics. Because sawing involves co-production (many different types of lumbers are obtained from a single tree), sawmills do not know how the introduction of a new “speciality” product will affect quantities for the other products they also produce. We propose a simulation-optimisation based framework to tackle the kinds of problems such as these, where classical formulations cannot be used. A log breakdown simulator is used in combination with a tactical planning model in order to realise Sales and Operations Planning. The plan gives the information to the decision maker about which orders for speciality products should be accepted, what to produce and when, as well as the equipment settings to use and the raw material to buy/consume at each period. Through an industry-inspired case study, we show how the framework can lead to substantial benefits.

In complex industrial contexts, planning is a challenging problem. The companies can use two different approaches : planning can be carried out "manually" by a human planner or they can rely on a mathematical model to provide an optimal solution. In this paper, we introduce an Excel add-in allowing the decision-maker to generate their own interactive dashboard. The user can modify the value of some variables and all the other variables are updated in real-time. Moreover, we provide an algorithm to overcome to major drawback of the original approach. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Mathematical models are frequently used in various industries to help decision makers to plan their activities. Many researchers develop planning models for a specific business unit. As an example, the forestproducts industry has access to specialised mathematical models for each production centre of thesawmill (sawing, drying, finishing units) which calls for decentralised planning. In the literature, these models are most of the time evaluated/tested separately, or connected using heuristics and testedin a static context ( one shot demand datasets are provided).In this paper, we want to simulate theuse of these tools by companies for a long period of time (new orders are arriving dynamically, planning need to be updated periodically, etc.).Different coordination mechanisms are compared. We show what the performance of the company would be in terms of accepted orders and average inventory using various coordination mechanisms and order acceptance policies such as available-to-promise (ATP) and capable-to-promise (CTP). Our main finding is that previously published coordination mechanisms for decentralised planning leads to bad CTP implementations which open very interesting research avenues.

This paper describes the Wood Supply Game (WSG), a prize-winning e-learning tool that is freely available for players all over the world. The game effectively helps students and managers realize the challenges in managing demand and supply in wood supply chains, and gain insight into the types of measures required to make these divergent chains effective. The WSG is an adaptation of the Beer game, a popular didactic tool used to empirically demonstrate demand amplification in a simple and generic context. The supply chain modeled by the Beer game does not involve co-products, and thus is very different from the wood supply chain, which is divergent by nature. The WSG presented in this paper models a supply network with one point of divergence and demand for two products. This preserves the simplicity of the game but enables it to offer a base for supply network simulation in a large number of industrial sectors with divergent processes. We describe an online version of the WSG, discuss our experiences playing it with students and managers, and provide hints to the instructor.

Mixed-Initiative-Systems (MIS) are hybrid decision-making systems in which human and machine collaborate in order to produce a solution. This paper described an MIS adapted to business optimization problems. These problems can usually be solved in less than an hour as they show a linear structure. However, this delay is unacceptable for iterative and interactive decision-making contexts where users need to provide their input. Therefore, we propose a system providing the decision-makers with a convex hull of optimal solutions that minimize/maximize the variables of interest. The users can interactively modify the value of a variable and the system is able to recompute a new optimal solution in a few milliseconds. Four real-time reoptimization methods are described and evaluated. We also propose an improvement to this basic scheme in order to allow a user to explore near-optimal solutions as well. Examples showing real case of how we have exploited this framework within interactive decision support software are given. (C) 2016 Society for Computational Design and Engineering. Publishing Servies by Elsevier.

The impacts of using different order acceptance policies in manufacturing sectors are usually well known and documented in the literature. However, for industries facing divergent processes with co-production (i.e. several products produced at the same time from a common raw material), the evaluation, comparison and selection of policies are not trivial tasks. This paper proposes a framework to enable this evaluation. Using a simulation model that integrates a custom-built ERP, we compare and evaluate different order acceptance policies in various market conditions. Experiments are carried out using a case from the forest products industry. Results illustrate how and when different market conditions related to divergent/co-production industries may call for available-to-promise (ATP), capable-to-promise (CTP), and other known strategies. Especially, we show that advanced order acceptance policies like CTP may generate a better income for certain types of market and, conversely to typical manufacturing industries, ATP performs better than other strategies for a specific demand patterns.

The impacts of using different order promising policies in traditional manufacturing industries are usually well known and documented in the literature. However, for industries facing divergent processes with co-production (i.e. several products simultaneously produced from a common raw material) as in the sawmilling industry, the evaluation, comparison, and selection of policies is not a trivial task. In this paper we compare different sawmilling industry order promising policies for various market conditions and demonstrate how and when these characteristics may call for Available-To-Promise (ATP), Capable-To-Promise (CTP), or other policies. It has been demonstrated that the best policy often differs from what would have been optimal in a classical manufacturing context (e.g. assembly).

Raw material heterogeneity, complex transformation processes, and divergent product flows make sawmilling operations difficult to manage. Most north-American lumber sawmills apply a make-to- stock production strategy, some accepting/refusing orders according to available-to-promise (ATP) quantities, while a few uses more advanced approaches. This article introduces a simulation framework allowing comparing and evaluating different production planning strategies as well as order management strategies. A basic ERP system is also integrated into the framework (inventory management, lumber production planning algorithms, ATP and CTP calculation, etc). The user can configure the production planning and order management process, and evaluate how they will perform in various market contexts using the discrete event simulation model.

Hardwood flooring mills transform rough wood into several boards of smaller dimensions. For each piece of raw material, the system tries to select the cutting pattern that will generate the greatest value, taking into account the characteristics of the raw material. However, it is often necessary to choose less profitable cutting patterns in order to respect market constraints. This reduces production value, but it is the price to pay in order to satisfy the market. We propose an approach to improve production value. We first use simulation on a training set of virtual boards in order to generate a database associating cutting patterns to expected production value. Then, we use an optimization model to generate a production schedule maximizing the expected production value while satisfying production constraints. The approach is evaluated using industrial data. This allows recovering approximately 30 % of the value lost when using the original system.

In the lumber industry, we can observe that commodity products prices fluctuate according to seasonal patterns. Nevertheless, it is believed by the industry that it is impossible to take advantage of this information for many reasons. Firstly, the fact that many different products are produced at the same time from the same material input (coproduction) makes it difficult to produce exactly and only what is needed. Secondly, equipment is already being used at 100% capacity all year long, so there is no room to increase production when product selling price increases. Finally, the belief is that keeping finished products in stock till the moment for the right price arrives would increase inventory holding cost too much. For these reasons, the typical sawmill produces using a “push” strategy and sells its production, without much consideration of yearly price fluctuation. We have developed a mathematical model that allows planning the sales and operations of a network of sawmills at the tactical level. Using that model, we were able to show it is in fact possible to modulate production and inventory levels to increase sales revenue. We generated a single plan which, if it had been used for each of the last twelve years, would have increased the gross margin generated by an average of 1,47% of sales revenue.

In North-America, the lumber industry almost solely produces commodity (i.e. with standard dimensions and properties). Yet some customers also want with very specific characteristics. The current situation in softwood lumber manufacturing does not enable the sawmills to know how the introduction of a new product will affect quantities for the other they are also producing (i.e. they do not know how the introduction of a new product will affect the global mix of products of the company). This is due to some characteristics of the lumber manufacturing process (divergent processes, co-production, automation). We propose a decision-making framework for tactical planning in order to take into account the demand for specific never produced by the company. This framework connects a log breakdown simulator and a tactical planning model. Through an industry-inspired case study we developed, we show the potential and the relevance of the approach.

An understanding of supply chain management is a prerequisite for efficient supply operations. This paper presents the structure of training currently used in Sweden to prepare master's-level foresters for managing wood supply operations. Based on a basic framework of professional tasks, eight key learning outcomes are targeted; one focuses on raw material requirements, three on securing supply, three on enabling delivery, and one on control and coordination. Sixteen exercises are used to meet the eight learning outcomes. An overview of the exercises is presented as well as the pedagogical approach used. Current training is focused on developing student understanding of the industrial context as well as competences and skills required to solve typical professional tasks. The paper concludes with a discussion of further development opportunities including a coupling of tasks and learning outcomes with applicable operations research methodology.Keywords: educationforest engineeringoperations managementoperations research AcknowledgementsThe members of the forest sector advisory group from Holmen Skog, SCA, SDC/VMF, Skogforsk, Skogsåkarna, StoraEnso, Sveaskog and Södra are gratefully acknowledged for their initiation and support of the wood supply training at SLU.