When Variability Trumps Volatility: Optimal Control and Value of Reverse Logistics in Supply Chains with Multiple Flows of Product
When Variability Trumps Volatility: Optimal Control and Value of Reverse Logistics in Supply Chains with Multiple Flows of Product
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DOI：
10.2139/ssrn.3071398
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Alexandar Angelus;Ö. Özer
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文献类型：
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作者：
Alexandar Angelus;Ö. Özer
问题定义：我们研究如何最佳地控制一个多级供应链，其中每个位置可以启动多个产品流，包括订单的反向流。我们还量化了逆向物流产生的价值，并确定了该价值的驱动因素。学术/实践相关性：逆向物流已经在实践和理论上得到了认可，因为它可以帮助企业更好地匹配供需，从而降低供应链的成本。然而，在实践和研究文献中，关于逆向物流中究竟什么是如此重要，逆向物流究竟如何创造价值，以及这种价值的驱动因素是什么，仍然缺乏明确性。方法学：我们首先建立一个多阶段库存模型，以联合优化与物流供应链中产品的定期、反向和快速流动相关的订购决策，其中产品的物理转化在最上游位置完成。对于多个产品流，问题的可行域需要多维边界，这导致了维数灾难。接下来，我们将我们的分析扩展到产品转换供应链，其中产品转换允许在每个位置发生。在这样的系统中，有必要跟踪每个库存单元的位置和完成阶段;因此，状态和决策变量的数量随着位置数量的平方而增加。结果如下：为了解决物流供应链中的逆向物流问题，我们开发了一种不同的解决方法，使我们能够减少可行域的维数，并确定最优策略的结构。我们将此策略称为嵌套阶梯基本库存策略，因为不同产品流的决策顺序嵌套在彼此之间。我们表明，这一政策使模型的分析和数值易处理。我们的研究结果为企业提供了可操作的政策，以共同管理其供应链中的三种不同的产品流，并使我们能够深入了解逆向物流价值的主要驱动因素。我们的一个主要发现是，当涉及到逆向物流产生的价值时，需求可变性（即，跨时期的需求不确定性）比需求波动性（即，在每一个时期内的需求不确定性）。为了分析产品转型供应链，我们首先确定一项提供总成本下限的政策。然后，我们建立了一个特殊的分解的目标成本函数，使我们能够提出一种新的启发式策略。我们发现，我们的启发式政策相对于下界政策的性能差距平均小于5%，在一系列的参数和供应链长度。管理方面的影响：研究人员可以建立在我们的方法来研究更复杂的逆向物流设置，以及解决其他库存问题的多维边界的可行区域。我们的见解可以帮助参与逆向物流的公司更好地管理其产品订单，更好地了解这种能力所创造的价值以及何时（不）投资逆向物流。
Problem definition: We study how to optimally control a multistage supply chain in which each location can initiate multiple flows of product, including the reverse flow of orders. We also quantify the resulting value generated by reverse logistics and identify the drivers of that value. Academic/practical relevance: Reverse logistics has been gaining recognition in practice and theory for helping companies better match supply with demand, and thus reduce costs in their supply chains. Nevertheless, there remains a lack of clarity in practice and the research literature regarding precisely what in reverse logistics is so important, exactly how reverse logistics creates value, and what the drivers of that value are. Methodology: We first formulate a multistage inventory model to jointly optimize ordering decisions pertaining to regular, reverse, and expedited flows of product in a logistics supply chain, where the physical transformation of the product is completed at the most upstream location. With multiple product flows, the feasible region for the problem acquires multidimensional boundaries that lead to the curse of dimensionality. Next, we extend our analysis to product-transforming supply chains, in which product transformation is allowed to occur at each location. In such a system, it becomes necessary to keep track of both the location and stage of completion of each unit of inventory; thus, the number of state and decision variables increases with the square of the number of locations. Results: To solve the reverse logistics problem in logistics supply chains, we develop a different solution method that allows us to reduce the dimensionality of the feasible region and identify the structure of the optimal policy. We refer to this policy as a nested echelon base stock policy, as decisions for different product flows are sequentially nested within each other. We show that this policy renders the model analytically and numerically tractable. Our results provide actionable policies for firms to jointly manage the three different product flows in their supply chains and allow us to arrive at insights regarding the main drivers of the value of reverse logistics. One of our key findings is that, when it comes to the value generated by reverse logistics, demand variability (i.e., demand uncertainty across periods) matters more than demand volatility (i.e., demand uncertainty within each period). To analyze product-transforming supply chains, we first identify a policy that provides a lower bound on the total cost. Then, we establish a special decomposition of the objective cost function that allows us to propose a novel heuristic policy. We find that the performance gap of our heuristic policy relative to the lower-bounding policy averages less than 5% across a range of parameters and supply chain lengths. Managerial implications: Researchers can build on our methodology to study more complex reverse logistics settings, as well as tackle other inventory problems with multidimensional boundaries of the feasible region. Our insights can help companies involved in reverse logistics to better manage their orders for products, and better understand the value created by this capability and when (not) to invest in reverse logistics.