An Order-Up-to Inventory Model with Sustainability Consideration

Along with growing interest in environmental concerns these days, significant academic efforts have been exerted to incorporate sustainability issues into the existing inventory models except for fixed-review interval (i.e., order-up-to models). In this study, we develop an order-up-to model considering environment-related costs and investigate the value of this new policy over the naïve one. Results of an extensive simulation study reveal that sustainability consideration reduces the total costs and that its value is higher when the mean demand is higher, when demand is more variable, when the costs of transshipment or inventory holding are lower, or when an ordering setup cost or an additional indirect cost of having inventory are higher. These findings fill the research gap in existing literature and contribute to managerial implications for periodic inventory control in practice.

Dynamic sourcing strategies for supply disruptions under consumer stockpiling

This paper considers a make-to-order system where production gets disrupted due to a random supply failure. To avoid potential stock-out risk and responding price increase during disruption, customers might decide to stockpile extra units for future consumption. We investigate the contingent sourcing strategy for the manufacturer to cope with the disruption. To this end, we first discuss the optimal post-disruption stockpiling decision for customers. In view of expected disruption duration, price rise, and inventory holding cost, three types of stockpiling behavior are analytically provided for the customers: non-stockpiling, gradual stockpiling, and instantaneous stockpiling. Next, a model is formulated to optimize the joint decision of contingent sourcing time and quantity, with the objective of maximizing profit expectation. Finally, by conducting numerical analysis, we generate further insights into the role of relative factors and provide specific managerial suggestions on how to adapt dynamic contingent sourcing strategies to alleviate different disruptions, under different market environments and customer behaviors.

Operational challenges during a pandemic: an investigation in the electronics industry

PurposeThe recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.Design/methodology/approachThis study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.FindingsThe results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.Practical implicationsThe findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.Originality/valueThis study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.

Designing a disruption-aware supply chain network considering precautionary and contingency strategies: A real-life case study

Due to the high risk in the business environment, supply chains must adopt a tailored mechanism to deal with disruptions. This research proposes a multi-objective formulation to design a robust and resilient forward supply chain under multiple disruptions and uncertainty. The mentioned objective functions include minimizing the total cost, environmental impacts, and the network non-resiliency associated with the supply chain simultaneously countered using an augmented ε-constraint method. A Mulvey robust optimization approach is also utilized to deal with uncertainty. Ultimately, the developed model is validated based on three datasets associated with a case study of the steel industry. The results indicate that preventive and mitigation resilience strategies have significantly promoted the supply chain's capabilities to deal with disruptions. Controlling network resiliency via non-resiliency measures has also created a risk-aware and robust structure in the incidence of disturbances. Numerical results reveal that multiple sourcing, lateral transshipment, and fortification of facilities will lead to the greatest cost-efficiency in the case study. Observations also indicate that the fortified supply chain will be highly economically viable in the long run due to the reduction of costs resulting from lost sales, unnecessary inventory holding, and the company's credit risk.

A Multi-Stage Approach for Empty Container Repositioning Under Coordination Among Liner Carriers

This paper studies the empty container repositioning (ECR) problem considering the exchange of slots and empty containers among liner shipping companies. It is common for an individual shipping company to seek an optimal solution for ECR and cargo routing to maximize its own benefits. To achieve cooperation among shipping companies, a multi-stage solution strategy is proposed. With the inverse optimization technique, the guide leasing prices of slots and empty containers among shipping companies are derived considering the schedule of vessels and cargo routing. Based on the guide leasing price, a cooperative model is formulated to minimize the total cost, which includes the transportation cost for laden containers, the inventory holding cost, the container leasing cost, and the repositioning cost. All the involved shipping companies are expected to follow the best solution of ECR and cargo routing to achieve a cooperative and stable optimum. A real-world shipping network operated by three liner shipping companies is used as a case study with promising numerical results.

Measuring Circular Supply Chain Risk: A Bayesian Network Methodology

The world is facing economic, as well as social, crisis due totheCOVID-19 pandemic. Implementing sustainable practices is one of the possible ways to address these issues. Adopting circular oriented techniques throughout the supply chain not only guarantees economic profitability, but also provides an edge to the organization in the market of fierce global competition. The concept of implementing circularity in the supply chain is novel and dynamic in nature, and it involves certain risk. In this study, a Bayesian Network methodology is adopted to analyze how the risk propagation takes place in a circular supply chain network of an automobile organization. The circular supply chain network consists of a group of manufacturers, retailers and recyclers, located in the Delhi–NCR region. Economic, environmental, social, technological, waste management, agile vulnerability, and risk of cannibalization are the major risk categories that were identified through an extensive literature review. Further, the impact of risk on the performance of the circular supply chain is analyzed by considering performance parameters such as lost sales, impact on supply chain revenue, and inventory holding cost. Risk exposure index is incorporated into the study to analyze the vulnerability of each node. The findings of the study reveal that the reverse side of the circular supply chain can be a source of risk propagation during the implementation of the circularity concept. This work is carried out under a single industry domain. In the future, risk propagation analysis can be examined in the supply chain of other sectors. The findings of the study can assist the supply chain managers and the risk experts to focus on the areas that are more vulnerable to risk.

Make-To-Order Production Planning With Seasonal Supply In Canned Pineapple Industry

This research studies a make-to-order production planning in a canned pineapple industry. Pineapple is a seasonal perishable fruit. Thus, the cost of fresh pineapple which is the main raw material in canned pineapple is inexpensive during its season. The color of the pineapple also determines the price of the canned pineapple. However, the availability of different colors (called “choice” and “standard”) is dependent. Specifically, if the ratio of the choice color is more, the ratio of the other color is less. There are several costs involve such as fresh pineapple cost, can cost, sugar cost, water cost, labor cost, energy cost, and inventory cost. The problem is formulated as a mathematical model to maximize the total profit over four-months planning horizon. Two supply uncertainty cases are tested which are low and high ratios of the choice color. The results show that the profit depends on available color ratios of the pineapple. The production planning is best if it matches with the availability of the color ratios. In certain months, some fresh pineapple purchased exceed the need of the production because of the dependency of the two colors. The inventory holding cost also influences the production decision—whether to produce the canned pineapple in earlier months or it is better to produce only the canned pineapple when it is needed to serve the customer orders.

Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations

Reverse Logistics is the flow and management of products, packaging, components, and information from the point of consumption (i.e., the market) to the point of origin (i.e., manufacturers and suppliers). It is a collection of practices similar to those of supply chain management, but in the opposite direction, from downstream to upstream. Reverse logistics is a valuable solution to the hazards jeopardizing the environment, and it involves activities such as reuse, repair, remanufacture, refurbish, reclaim and recycle.Reverse logistics became an established line of research, covering several areas, including inventory control; though, several research gaps still exist, such as: ignoring switching costs between production and remanufacturing processes and learning effects, the assumption that production and remanufacturing processes are of perfect quality, remanufactured products are assumed to be as-good-as new, the assumption that returned products are treated as whole products while ignoring disassembly, collection rate of used items is independent of price and quality, and the assumption that pure remanufacturing and production policies are optimal. These research gaps are addressed in mathematical models to bring reverse logistics optimization closer to reality. Deterministic and stochastic components are considered here with numerical examples and results discussed. The key conclusions are as follows:The inclusion of the first time interval where no remanufacturing/repair exists, results in preventing the overestimation of inventory holding costs in the repairable stock. Assuming production and remanufacturing processes to be perfect, or ignoring learning effects in these processes, might not capture the benefits that product recovery programs are supposed to bring. Although works in the literature assumed pure remanufacturing is mathematically attainable but not feasible, this study shows that the pure remanufacturing case is not valid mathematically, which proves it to be infeasible. It is favourable to compensate customers to settle for remanufactured products instead of new ones. Considering disassembly of returns in the modelling of reverse logistics is proven beneficial. Finally, mixed production and remanufacturing policies are optimal rather than pure ones; and the inclusion of price and quality to determine return and collection rates is crucial.

Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations

Reverse Logistics is the flow and management of products, packaging, components, and information from the point of consumption (i.e., the market) to the point of origin (i.e., manufacturers and suppliers). It is a collection of practices similar to those of supply chain management, but in the opposite direction, from downstream to upstream. Reverse logistics is a valuable solution to the hazards jeopardizing the environment, and it involves activities such as reuse, repair, remanufacture, refurbish, reclaim and recycle.Reverse logistics became an established line of research, covering several areas, including inventory control; though, several research gaps still exist, such as: ignoring switching costs between production and remanufacturing processes and learning effects, the assumption that production and remanufacturing processes are of perfect quality, remanufactured products are assumed to be as-good-as new, the assumption that returned products are treated as whole products while ignoring disassembly, collection rate of used items is independent of price and quality, and the assumption that pure remanufacturing and production policies are optimal. These research gaps are addressed in mathematical models to bring reverse logistics optimization closer to reality. Deterministic and stochastic components are considered here with numerical examples and results discussed. The key conclusions are as follows:The inclusion of the first time interval where no remanufacturing/repair exists, results in preventing the overestimation of inventory holding costs in the repairable stock. Assuming production and remanufacturing processes to be perfect, or ignoring learning effects in these processes, might not capture the benefits that product recovery programs are supposed to bring. Although works in the literature assumed pure remanufacturing is mathematically attainable but not feasible, this study shows that the pure remanufacturing case is not valid mathematically, which proves it to be infeasible. It is favourable to compensate customers to settle for remanufactured products instead of new ones. Considering disassembly of returns in the modelling of reverse logistics is proven beneficial. Finally, mixed production and remanufacturing policies are optimal rather than pure ones; and the inclusion of price and quality to determine return and collection rates is crucial.