When Variability Trumps Volatility: Optimal Control and Value of Reverse Logistics in Supply Chains with Multiple Flows of Product

2020 ◽  
Author(s):  
Alexandar Angelus ◽  
Özalp Özer
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Reverse Logistics ◽  
Demand Uncertainty ◽  
Problem Definition ◽  
Feasible Region ◽  
Base Stock Policy ◽  
Multiple Flows ◽  
Stock Policy ◽  
Heuristic Policy

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.

Coordination of Multiechelon Supply Chains Using the Guaranteed Service Framework

2021 ◽  
Author(s):  
Tor Schoenmeyr ◽  
Stephen C. Graves
Keyword(s):  
Supply Chain ◽  
Wholesale Price ◽  
Problem Definition ◽  
Quantity Discounts ◽  
Minimum Order ◽  
Base Stock Policy ◽  
Decentralized Decision Making ◽  
Guaranteed Service ◽  
Stock Policy ◽  
Different Parts

Problem definition: We use the guaranteed service (GS) framework to investigate how to coordinate a multiechelon supply chain when two self-interested parties control different parts of the supply chain. For purposes of supply chain planning, we assume that each stage in a supply chain operates with a local base-stock policy and can provide guaranteed service to its customers, as long as the customer demand falls within certain bounds. Academic/practical relevance: The GS framework for supply chain inventory optimization has been deployed successfully in multiple industrial contexts with centralized control. In this paper, we show how to apply this framework to achieve coordination in a decentralized setting in which two parties control different parts of the supply chain. Methodology: The primary methodology is the analysis of a multiechelon supply chain under the assumptions of the GS model. Results: We find that the GS framework is naturally well suited for this decentralized decision making, and we propose a specific contract structure that facilitates such relationships. This contract is incentive compatible and has several other desirable properties. Under assumptions of complete and incomplete information, a reasonable negotiation process should lead the parties to contract terms that coordinate the supply chain. The contract is simpler than contracts proposed for coordination in the stochastic service (SS) framework. We also highlight the role of markup on the holding costs and some of the difficulties that this might cause in coordinating a decentralized supply chain. Managerial implications: The value from the paper is to show that a simple contract coordinates the chain when both parties plan with a GS model and framework; hence, we provide more evidence for the utility of this model. Furthermore, the simple coordinating contract matches reasonably well with practice; we observe that the most common contract terms include a per-unit wholesale price (possibly with a minimum order quantity and/or quantity discounts), along with a service time from order placement until delivery or until ready to ship. We also observe that firms need to pay a higher price if they want better service. What may differ from practice is the contract provision of a demand bound; our contract specifies that the supplier will provide GS as long as the buyer’s order are within the agreed on demand bound. This provision is essential so that each party can apply the GS framework for planning their supply chain. Of course, contracts have many other provisions for handling exceptions. Nevertheless, our research provides some validation for the GS model and the contracting practices we observe in practice.

scholarly journals Optimal Subsidies for Development Supply Chains

2020 ◽  
Vol 22 (6) ◽  
pp. 1131-1147 ◽  
Author(s):  
Jiayi Joey Yu ◽  
Christopher S. Tang ◽  
ManMohan S. Sodhi ◽  
James Knuckles
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Low Income ◽  
Demand Uncertainty ◽  
Stackelberg Game ◽  
Problem Definition ◽  
Chain Model ◽  
Retail Competition ◽  
Product Substitution ◽  
Substitutable Products

Problem definition: When donors subsidize products for sale to low-income families, they need to address who to subsidize in the supply chain and to what extent and whether such supply chain structures as retail competition, substitutable products, and demand uncertainty matter. Academic/practical relevance: By introducing and analyzing development supply chains in which transactions are commercial but subsidies are needed for affordability, we explore different supply chain structures, with product substitution and retail competition motivated by a field study in Haiti of subsidized solar lantern supply chains. Methodology: We incorporate product substitution, retail competition, and demand uncertainty in a three-echelon supply chain model with manufacturers, retailers, and consumers. This model has transactions among the donor, manufacturers, retailers, and consumers as a four-stage Stackelberg game, and we solve different variations of this game by using backward induction. Results: The donor can subsidize the manufacturer, retailer, or customer as long as the total subsidy per unit across these echelons is maintained at the optimal level. Having more product choice and having more retail channel choice can increase the number of beneficiaries adopting the products; this increase becomes more pronounced as demand becomes more uncertain. Managerial implications: Donors must coordinate across different programs along the entire supply chain. They should look for evidence in their collective experience of more beneficiaries when subsidizing competing retailers selling diverse substitutable products.

A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization

2021 ◽  
Author(s):  
Afshin Oroojlooyjadid ◽  
MohammadReza Nazari ◽  
Lawrence V. Snyder ◽  
Martin Takáč
Keyword(s):  
Supply Chain ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Problem Definition ◽  
Base Stock ◽  
Inventory Optimization ◽  
Base Stock Policy ◽  
Order Quantities ◽  
Stock Policy ◽  
Beer Game

Problem definition: The beer game is widely used in supply chain management classes to demonstrate the bullwhip effect and the importance of supply chain coordination. The game is a decentralized, multiagent, cooperative problem that can be modeled as a serial supply chain network in which agents choose order quantities while cooperatively attempting to minimize the network’s total cost, although each agent only observes local information. Academic/practical relevance: Under some conditions, a base-stock replenishment policy is optimal. However, in a decentralized supply chain in which some agents act irrationally, there is no known optimal policy for an agent wishing to act optimally. Methodology: We propose a deep reinforcement learning (RL) algorithm to play the beer game. Our algorithm makes no assumptions about costs or other settings. As with any deep RL algorithm, training is computationally intensive, but once trained, the algorithm executes in real time. We propose a transfer-learning approach so that training performed for one agent can be adapted quickly for other agents and settings. Results: When playing with teammates who follow a base-stock policy, our algorithm obtains near-optimal order quantities. More important, it performs significantly better than a base-stock policy when other agents use a more realistic model of human ordering behavior. We observe similar results using a real-world data set. Sensitivity analysis shows that a trained model is robust to changes in the cost coefficients. Finally, applying transfer learning reduces the training time by one order of magnitude. Managerial implications: This paper shows how artificial intelligence can be applied to inventory optimization. Our approach can be extended to other supply chain optimization problems, especially those in which supply chain partners act in irrational or unpredictable ways. Our RL agent has been integrated into a new online beer game, which has been played more than 17,000 times by more than 4,000 people.

scholarly journals Adaptive Supply Chain: Demand–Supply Synchronization Using Deep Reinforcement Learning

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 240
Author(s):  
Zhandos Kegenbekov ◽  
Ilya Jackson
Keyword(s):  
Supply Chain ◽  
Adaptive Control ◽  
Reinforcement Learning ◽  
Supply Chains ◽  
Optimization Algorithm ◽  
Base Stock ◽  
Base Stock Policy ◽  
Learning Agent ◽  
Policy Optimization ◽  
Stock Policy

Adaptive and highly synchronized supply chains can avoid a cascading rise-and-fall inventory dynamic and mitigate ripple effects caused by operational failures. This paper aims to demonstrate how a deep reinforcement learning agent based on the proximal policy optimization algorithm can synchronize inbound and outbound flows and support business continuity operating in the stochastic and nonstationary environment if end-to-end visibility is provided. The deep reinforcement learning agent is built upon the Proximal Policy Optimization algorithm, which does not require hardcoded action space and exhaustive hyperparameter tuning. These features, complimented with a straightforward supply chain environment, give rise to a general and task unspecific approach to adaptive control in multi-echelon supply chains. The proposed approach is compared with the base-stock policy, a well-known method in classic operations research and inventory control theory. The base-stock policy is prevalent in continuous-review inventory systems. The paper concludes with the statement that the proposed solution can perform adaptive control in complex supply chains. The paper also postulates fully fledged supply chain digital twins as a necessary infrastructural condition for scalable real-world applications.

scholarly journals Enhancing the Supply Chain in Organisations

2014 ◽  
pp. 156-171
Author(s):  
M. Reza Hosseini ◽  
Nicholas Chileshe ◽  
Raufdeen Rameezdeen ◽  
Steffen Lehmann
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Reverse Logistics ◽  
Adoption And Implementation ◽  
Wide Range ◽  
Adoption And Diffusion ◽  
And Diffusion ◽  
Lack Of Knowledge

Reverse Logistics (RL) is an innovation able to bring about immense benefits for organisations in a wide range of industries through enhancing the performance of supply chain procedures. Yet, evidence demonstrates that RL has remained unexploited mainly due to the lack of knowledge about its benefits, enablers, and major aspects of its adoption and implementation. In this context, promoting the adoption and diffusion of RL into the supply chain of organisations has been recommended frequently. This chapter provides a response to such need by (1) explaining the phenomenon and dispelling the confusions surrounding the RL concept, (2) clarifying the major drivers and barriers of RL and highlighting the role it can play in enhancing the performance of conventional supply chains; in addition, (3) the chapter intends to demystify the major aspects associated with implementing RL in organisations. The chapter also aims at familiarising potential readers with the major references available in the field.

Strategic Inventories Under Supply Chain Competition

2021 ◽  
Author(s):  
Xi Li ◽  
Yanzhi Li ◽  
Ying-Ju Chen
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Distribution Channel ◽  
Problem Definition ◽  
Supply Chain Competition ◽  
Managerial Implications ◽  
Holding Cost ◽  
Inventory Holding ◽  
Period Model

Problem definition: We consider the effects of strategic inventory (SI) in the presence of chain-to-chain competition in a two-period model. Academic/practical relevance: Established findings suggest that SI may alleviate double marginalization and improve the efficiency of a decentralized distribution channel. However, no studies consider the role of SI under chain-to-chain competition. Methodology: We build a two-period model consisting of two competing supply chains, each with an upstream manufacturer and an exclusive retailer. The retailers compete on either price or quantity. We characterize the firms’ strategies under the concept of perfect Bayesian equilibrium. We consider cases where contracts are either observable or unobservable across supply chains. Results: (1) SI still exists under chain-to-chain competition. Retailers may carry more inventory when the competition becomes fiercer, which further intensifies the supply chain competition. (2) Different from the existing findings, SI may backfire and hurt all firms. Interestingly, firms may benefit from a higher inventory holding cost. (3) Under supply chain competition, the prisoner’s dilemma can arise if competition intensity is intermediate; in other words, manufacturers are better off without strategic inventory, and yet they cannot help allowing strategic inventory, which is the unique equilibrium. Managerial implications: Despite its appeal among firms of a single supply chain, the role of SI is altered or even reversed by chain-to-chain competition. Conventional wisdom on SI should be applied with caution.

Manufacturing SMEs, network governance and global supply chains

2019 ◽  
Vol 27 (1) ◽  
pp. 130-147
Author(s):  
Tony Cragg ◽  
Tom McNamara ◽  
Irena Descubes ◽  
Frank Guerin
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Demand Uncertainty ◽  
Network Governance ◽  
Global Supply Chains ◽  
Informal Networks ◽  
Content Type ◽  
Case Research ◽  
Effective Form ◽  
Supply Chain Governance

Purpose The purpose of this paper is to investigate how small manufacturing firms develop and manage relationships with global suppliers and distributors. In so doing the authors aim to contribute to knowledge about SMEs and supply chain management (SCM). Design/methodology/approach The authors conducted 12 in-depth case studies of SME final assemblers of machinery in the French farm equipment sector. Findings The most effective form of global supply chain governance used by successful SMEs is informal networks involving managers in similar complementary firms, which serve to concatenate links with foreign suppliers and distributors. Research limitations/implications The principal limitation of this research is that it is specific to one sector and therefore questions of transferability are raised. Practical implications The important implication for managers in manufacturing SMEs is that links with other complementary local firms in the same sector need to be developed, leveraged and valued. Originality/value The originality of this case research is that the authors draw on inter-organisational boundaries, power asymmetries and network governance to develop a conceptual framework for the study of SMEs and global supply chains. By focusing on the perceptions of boundary-spanning managers, the authors show how, in circumstances of demand uncertainty, soft network governance is an effective strategic choice.

Supply Chain Thinking in Healthcare: Lessons and Outlooks

2020 ◽  
Author(s):  
Lidia Betcheva ◽  
Feryal Erhun ◽  
Houyuan Jiang
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Healthcare Delivery ◽  
Healthcare Services ◽  
Lessons Learned ◽  
Problem Definition ◽  
Healthcare Organizations ◽  
Chain Management ◽  
Managerial Implications

Problem definition: The lessons learned over decades of supply chain management provide an opportunity for stakeholders in complex systems, such as healthcare, to understand, evaluate, and improve their complicated and often inefficient ecosystems. Academic/practical relevance: The complexity in managing healthcare supply chains offers opportunities for important and impactful research avenues in key supply chain management areas such as coordination and integration (e.g., new care models), mass customization (e.g., the rise in precision medicine), and incentives (e.g., emerging reimbursement schemes), which might, in turn, provide insights relevant to traditional supply chains. We also put forward new perspectives for practice and possible research directions for the supply chain management community. Methodology: We provide a primer on supply chain thinking in healthcare, with a focus on healthcare delivery, by following a framework that is customer focused, systems based, and strategically orientated and that simultaneously considers clinical, operational, and financial dimensions. Our goal is to offer an understanding of how concepts and strategies in supply chain management can be applied and tailored to healthcare by considering the sector’s unique challenges and opportunities. Results: After identifying key healthcare stakeholders and their interactions, we discuss the main challenges facing healthcare services from a supply chain perspective and provide examples of how various supply chain strategies are being and can be used in healthcare. Managerial implications: By using supply chain thinking, healthcare organizations can decrease costs and improve the quality of care by uncovering, quantifying, and addressing inefficiencies.

An analysis of keywords used in the literature on green supply chain management

2015 ◽  
Vol 38 (2) ◽  
pp. 166-194 ◽  
Author(s):  
Amulya Gurtu ◽  
Cory Searcy ◽  
M.Y. Jaber
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Reverse Logistics ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chains ◽  
Green Supply Chains

Purpose The purpose of this paper is to analyze the keywords used in peer-reviewed literature on green supply chain management. Design/methodology/approach To determine the keywords that were used in this area, an analysis of 629 papers was conducted. The papers were identified through searches of 13 keywords on green supply chains. Trends in keyword usage were analyzed in detail focusing on examining variables such as the most frequently used journals/keywords, their frequencies, citation frequency and research contribution from different disciplines/countries. Findings A number of different terms have been used for research focused on the environmental impacts of supply chains, including green supply chains, sustainable supply chains, reverse logistics and closed-loop supply chains, among others. The analysis revealed that the intensity of research in this area has more than tripled in the past six years and that the most used keyword was “reverse logistics”. The use of the terms “green supply chains” and “sustainable supply chains” is increasing, and the use of “reverse logistics” is decreasing. Research limitations/implications The analysis is limited to 629 papers from the Scopus database during the period of 2007 and 2012. Originality/value The paper presents the first systematic analysis of keywords used in the literature on green supply chains. Given the broad array of terms used to refer to research in this area, this is a needed contribution. This work will help researchers in choosing keywords with high frequency and targeting journals for publishing their future work. The paper may also provide a basis for further work on developing consolidated definitions of terms focused on green supply chain management.

Exploring information sharing in the extended supply chain: an interdependence perspective

2015 ◽  
Vol 20 (4) ◽  
pp. 455-470 ◽  
Author(s):  
Joakim Kembro ◽  
Kostas Selviaridis
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Information Sharing ◽  
Demand Uncertainty ◽  
Analytical Framework ◽  
Content Type ◽  
Related Information ◽  
Market Shares ◽  
Shared Information ◽  
Meta Information

Purpose – This paper aims to empirically explore demand-related information sharing in the extended supply chain. Design/methodology/approach – Through a single, embedded case design, a range of methods are used to collect data from companies representing three different supply chain tiers, including focal company, first-tier suppliers and first-tier customers. The collected data are analysed through the theoretical lens of interdependence. Findings – The findings indicate that the supply chain actors adapt information sharing to the pooled, serial or reciprocal type of interdependence. Information sharing is thus increased with key dyadic partners representing, for example, unique offerings and high market shares as percentage of total expenditure/sales. The study also unearths several barriers to information sharing beyond dyadic ties, including problems related to dis-aggregated, misinterpreted and/or incomplete information. Research limitations/implications – The study empirically contributes to the existing literature by exploring information sharing in the extended supply chain and by suggesting different approaches to information sharing depending on the type and intensity of interdependence between supply chain partners. Further, the paper contributes to the existing literature on barriers of information sharing in supply chains by identifying barriers specific to multi-tier information sharing. “Meta-information” (i.e. information about the shared information) is needed to overcome some of the barriers of sharing information in cases of weak, pooled interdependencies in the supply chain. Practical implications – Similar to previous empirical research, this exploratory study indicates that companies, in general, refrain from sharing information beyond dyadic ties. Supply chain managers would instead mostly focus on stronger, reciprocal interdependencies and emphasise dyadic information sharing. To further guide managers, a demand profiling framework considering market share and demand uncertainty is presented. It may be interesting to engage in multi-tier information sharing in particular cases where strong interdependence exists between three or more partners. Originality/value – This study contributes to existing research on information sharing in supply chains by empirically studying information sharing in an extended supply chain, applying interdependence theory as its analytical framework and unearthing several barriers that are specific to multi-tier information sharing.

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