Real option approach to evaluate cores for remanufacturing in service markets

Abstract

Acquisition of cores is important for the success of remanufacturing business, but evaluating the value of cores is difficult due to many uncertain issues in the remanufacturing system. In this paper, we examine the value of cores using the real option valuation (ROV) approach, which well captures the production flexibility and represents different operations options in remanufacturing. First, in a system where the demand of remanufactured products is low, we focus on the uncertainty of price. We compare the cases where disposing cores occurs only at the expiration time and before that. The model also identifies under which circumstances disposing core early brings in more benefit. Furthermore, we study a remanufacturing system where demand and return are both uncertain. In this case, a spread option is used for evaluating the marginal value of obtaining additional cores. This approach also brings the advantage of linking the correlation between demand and return, which in fact is often exhibited in remanufacturing. The numerical experiments indicate that this correlation has an impact on collecting policies. This study illustrates the possibility of using ROV for core pricing modelling, and the study results also provide some managerial insights in managing returned cores.

Decision makings in key remanufacturing activities to optimise remanufacturing outcomes: A review

“…Some of the most commonly used modelling approaches are game theory (Bulmus et al, 2013), optimal control (Zhou and Yu, 2011), Markov chain (Vercraene et al, 2014) and mixed integer programming (Nenes and Nikolaidis, 2012). Our findings also uncover other modelling approaches such as non-linear programming (Seidi and Kimiagari, 2010), real option valuation (Wei and Tang, 2014), Bayesian estimation of distributed lag model (Clottey, 2012, Clottey, 2016 and multi-period stochastic dynamic programming (Xie et al, 2015). Table 6 illustrates that most papers about core acquisition (14 of 20) use optimal control as the modelling approach since optimal control is a mature mathematical discipline in science and engineering.…”

“…Return rate of cores Deterministic Wei and Tang, 2014, Kang and Hong, 2011, Xie et al, 2015Stochastic Xiang Li et al, 2013, Shi et al, 2011a, Shi et al, 2011b, Zhou and Yu, 2011, Xu, 2012, Guide et al, 2003, Clottey, 2012, Cai, 2014, Clottey, 2016Random Zhou and Yu, 2011, Clottey, 2016…”

Measuring the financial effects of mitigating commodity price volatility in supply chains

“…ROV has been introduced in the literature as an approach that overcomes the limits of traditional methodologies for evaluating investment opportunities under uncertain environments. Traditional methods, such as those based on DCF – net present value, internal rate of return and discounted pay back period – implicitly assume investment benefits and, therefore, the “expected scenario” of cash flows are known and presume management’s passive commitment to a certain operating strategy (Zhao et al , 2015; Wei and Tang, 2015; Boute et al , 2004). During project management and operations, especially in highly uncertain and dynamic environments, managers may make different choices about operating actions when new information from the market is available.…”

Association rules mining between service demands and remanufacturing services

“…Therefore, based on information technology and cloud manufacturing technology (Xu et al , 2016), a web model for remanufacturing and services, remanufacturing services (RMS) (Wang et al , 2016), has been established. In the RMS, the information flow among the demanders, manufacturers, and integrators is particularly complicated due to the uncertainty of recycling, demand, and price (Wei and Tang, 2015). The data flow between these domains constitutes a multi-dimensional complex data network, as shown in Figure 1.…”