1. Introduction
As public awareness of environmental issues grows and environmental legislation and policies become more stringent, improving the sustainability of supply chains and achieving “green profit” (i.e., profit generated by an environmentally sustainable business) is emerging as a pressing issue in manufacturing industries [
1,
2,
3]. For original equipment manufacturers (OEMs), remanufacturing can be an effective strategy to address this problem. Remanufacturing is a process of returning used products (i.e., end-of-life products) to like-new condition by rebuilding or replacing their component parts [
4]. As parts from end-of-life products are reused, remanufactured products can offer the same level of performance and quality at only a small fraction of the original cost while consuming less material and energy [
5]. With remanufactured products, firms can reduce the adverse environmental impact of manufacturing (e.g., greenhouse gas emissions, waste, and resource depletion), and also can comply with environmental legislation and policies, such as the Waste Electrical and Electronic Equipment (WEEE) Directive, End of Life Vehicles (ELV) Directive, and climate change-related laws and policies. Remanufacturing also enables firms to expand their product line. By embracing more affordable greener offerings, firms can target a wider range of the market as compared to firms producing brand-new products only [
6,
7]. Accordingly, remanufacturing has gained increasing interest in recent years as a means to achieve both economic profitability and environmental sustainability.
However, when facing a choice of whether or not to remanufacture their products, OEMs choose not to remanufacture in many cases [
8,
9]. They are concerned that offering remanufactured products will cannibalize their new product sales [
6,
7,
8,
9]. The technological obsolescence of end-of-life products is another barrier to remanufacturing. Given rapid advances in technology, it is less likely that the original design from past years will meet customer needs in the current market. For technology products with short life cycles, such as cell phones and computers, simple restoration of end-of-life products that maintain the original design specifications is especially hard to justify. In order to address these two challenges in OEM remanufacturing, decision-making tools are required that can help OEMs overcome the risks of cannibalization and obsolescence and assist them in achieving greater profit and market share from the line of new and remanufactured products. The environmental value from remanufacturing should be justifiable as well.
In order to support OEMs who produce both new and remanufactured products, this paper proposes a design tool for optimizing a line of new and remanufactured products in the form of a mixed-integer programming model.
Figure 1 illustrates the research problem of interest. As shown in
Figure 1, the proposed model differs from other studies [
10,
11,
12] which optimized the closed-loop of new and remanufactured products (i.e., new products in the first life and their remanufactured versions in the second life). It assumes that both new and remanufactured products are launched into a competitive market at the same time. Aiming at two marketing objectives, i.e., maximizing the total profit and maximizing the total market share, the model simultaneously optimizes new and remanufactured products, in terms of their (1) design specifications, (2) selling prices, and (3) production quantities and the detailed production plan. In other words, the model conducts design optimization, pricing, and production planning of the new and the remanufactured products in an integrated manner (
Figure 2). In order to overcome the technological obsolescence of end-of-life products, the model considers a design upgrade for the remanufactured product and optimizes it with the design of the new product. Environmental considerations are also taken as a key constraint in the optimization, indicating that the total environmental impact caused by manufacturing should remain under a certain limit.
Although there has been a great deal of research conducted on remanufacturing, research on the simultaneous optimization of new and remanufactured products remains in its early stage. Most existing studies have only focused on optimal pricing (i.e., how to optimize the selling prices and production quantities), and little attention has been given to product design optimization. Including those by Ferguson and Toktay (2006) [
8], Vorasayan and Ryan (2006) [
13], Atasu et al. (2008) [
14], Ferrer and Swaminathan (2010) [
15], Ovchinnikov (2011) [
16], Zhou et al. (2017) [
17] and Kwak and Kim (2017) [
2], most studies thus far have assumed that product design is predefined and fixed for both new and remanufactured products; that is, the remanufactured product has the same design as the end-of-life product and no upgrade is considered.
The research of Aydin et al. (2015) [
18] is an exception, in that it addressed both pricing and design optimization for a line of new and remanufactured products, including the possibility of an upgrade in remanufacturing. Their approach, however, has a limitation with respect to the cost model. They assumed the per-unit production cost of remanufacturing as a constant and overlooked the effects of their decisions on part reuse and procurement in remanufacturing. As Steeneck and Sarin (2013) [
19] and Kwak and Kim (2017) [
2] pointed out, the per-unit remanufacturing cost is interdependent with product design, pricing, and production planning decisions, and it should thus be considered as a function of decision variables. Another study worth mentioning is the work done by Kwak and Kim (2013) [
20], where the authors proposed an integrated model for pricing, design optimization, and production planning of a remanufactured product. However, their model is applicable only to a single remanufactured product (not a product line) and it also does not consider the environmental impacts of its decisions.
The proposed model in this paper presents an advanced approach toward optimal line design, extending the approach by Kwan and Kim (2013) [
20]. The model addresses the simultaneous optimization of design specifications, selling price, and production plans for the new and remanufactured products. With this simultaneous optimization, the model can more effectively and proactively differentiate the new and the remanufactured products to minimize the effect of cannibalization and maximize the profit and/or market share. The integration of design optimization, pricing, and production planning for the line of new and remanufactured products is the major contribution of the proposed model. Upgrade-related decision-making for the remanufactured product and environmental-impact consideration differentiate the model from others.
The rest of the paper is organized into sections.
Section 2 describes the proposed mathematical model.
Section 3 illustrates the application of the model using the example of a desktop to validate the applicability and effectiveness of the model.
Section 4 concludes the paper with future research directions.
4. Conclusions
In order to support OEMs who produce both new and remanufactured products, this paper presented a mixed-integer programming model for designing an optimal line of new and remanufactured products. Considering the two marketing objectives of maximizing the total profit and maximizing the total market share, the proposed model simultaneously optimizes design specifications, selling prices, and production quantities, and the detailed production plan for the new and remanufactured products. The model contributes to the literature in three ways: (1) the model conducts design optimization, pricing, and production planning of the new and the remanufactured products in an integrated manner; (2) the model considers an upgrade possibility in remanufacturing; and (3) the model stipulates that the total environmental impact of manufacturing remains below a certain limit and helps improve the environmental sustainability of the decision. The proposed model can help firms overcome the two major challenges in OEM remanufacturing: Cannibalization of new product sales, and the technological obsolescence of used products.
To validate the applicability and effectiveness of the model, this paper presented a case study using the example of desktop computers. Three analyses were discussed in the case study: First, with the objective of profit maximization, the case of offering the line of new and remanufactured products (Scenario NRW) was compared with the case of producing brand-new products only (Scenario NO); second, the other objective of market-share maximization was considered to see which scenario has a greater potential for market share; finally, the two marketing objectives were considered at the same time using a bi-objective optimization in order to analyze whether the optimal product line can increase both profit and market share, given an environmental limit.
The case study demonstrated that remanufacturing can be an effective strategy for a firm to achieve both economic profitability and environmental sustainability. Despite the cannibalization of the new product sales, embracing remanufacturing achieved a higher profit and a greater market share with reduced environmental impact. Scenario NRW dominated Scenario NO in all three analyses and showed that there exist multiple opportunities for “green profit” and “green market share.” Upgrading in remanufacturing was revealed as one factor that maximizes the potential of remanufacturing. The results of the case study do not mean that such profit and market-share opportunities always are available, or that incorporating remanufacturing is always a good choice. It depends on the business case. However, if any opportunities exist, the proposed model can help reveal them and can assist in the OEM’s decision-making.
The current model considers only a single period and ignores the effects of current decisions on the next period. In the future, the model can be improved for multi-period planning by including future effects. Uncertainty consideration is another possibility for future research. Many parameters in the optimal line design are stochastic and uncontrollable in reality. A stochastic model can be developed in the future to deal with such uncertainties. Demand modeling was beyond the scope of this paper, but it is a critical element for the success of the proposed model. More research is needed in the future to clarify how the market responds to the pricing decisions. The current model also did not consider the case in which the competitors change their product line design in response to the OEM’s optimal decisions. Game theory can be utilized in the future to better reflect competition among firms.