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Reducing carbon emissions plays a significant role in developing sustainable inventory systems. In a seller-buyer relationship, an allowable delay in payment is considered for the buyer to manage the stock and simulate the demand. Deteriorating items that usually have specific maximum lifetimes have become a challenge for most firms. Contrary to the importance of these issues, very little research has studied the impact of carbon emissions on deteriorating inventory systems. This paper provides a price-dependent demand for perishable items when carbon cap-and-trade regulation fills the mentioned gap. This model provides a carbon reduction investment scheme and illustrates this investment’s effect on the inventory system. This paper determines the optimal replenishment cycle and selling price, in which: (a) perishable items have specific maximum lifetimes, (b) a specific period of delay in payment is allowed for the buyer to accumulate revenue, (c) carbon is emitted due to ordering and storage operations and carbon cap and trade is regulated along with allowable carbon reduction investment. After developing the model, optimal values are obtained from necessary and sufficient conditions of optimality. Numerical experiments are proposed to validate the model. By developing an algorithm, the optimal values of replenishment cycle, selling price, and carbon reduction technology investment are obtained, and the impact of carbon emissions and efforts to control emissions are outlined. Finally, some managerial applications are mentioned, and future research directions are exposed.

Utilizing carbon emission regulations helps firms to develop a sustainable supply chain. In a two-echelon supplier-retailer-customer relationship, offering a trade credit period can manage the inventory level. However, there will be a risk of settling the accounts during the trade credit period. Therefore, a portion of purchasing cost is charged as prepayment to mitigate some risk of payment delays. Moreover, obtaining the optimal price for perishable items with expiration dates has become a challenge because of their perishing process. Despite the importance of these challenges, very few papers have concentrated on sustainability considerations for perishable items. Also, the contribution of these issues to the credit risk buyers has never been studied. To address the mentioned gap in the literature, this elaboration investigates a supplier-retailer-customer economic order quantity model for perishable items when two levels of partial delay in payment are permissible and a carbon cap and tax policy is regulated to mitigate the impact of emissions on the two-echelon supply chain. The developed model is applicable in food industries to outline the challenges associated with deterioration, payments, and carbon emissions. This paper determines the optimal replenishment and selling price taking into account the following: (a) perishable items have their specific expiration dates, (b) a prepayment is charged from the buyer when offering a permissible delay in payment, (c) a carbon cap and tax policy is applied to approach the sustainability. Necessary and sufficient conditions of optimality are provided as the solution approach. A case study is presented, and numerical examples and sensitivity analyses are illustrated for validation. Finally, managerial applications are exposed, and results are concluded using future research insights.

Due to the outbreak of COVID-19 around the globe in the last few years, the need for pharmaceutical supply chains is felt more than before. However, increasing uncertainties along with unpredictable demand for products led to disruptions in supply chains when receiving requests from retailers. These disruptions not only affected the economic aspect of supply chains but also caused shortages in hospitals and medical centers. Therefore, it has become significant for companies to select their suppliers to avoid disruptions in the case of the severity of infections. To address this issue in practice, this paper has been conducted based on a case study to address the role of lean, agile, resilience, and green (LARG) criteria in selecting the supplier in a pharmaceutical supply chain and compare the results obtained before and after the prevalence of COVID-19. The main purpose of this study is to determine and evaluate different indicators within the LARG concept to avoid disruptions when selecting suppliers. Besides, the significance of these criteria before and after the pandemic condition is addressed. Due to addressing multiple aspects of the problem, a hybrid fuzzy multi-attribute decision-making (MADM) approach is adopted for this elaboration when the four LARG criteria are integrated with eighteen supplier selection sub-criteria. To calculate the impact of each criterion (or sub-criteria), a fuzzy best–worst method (BWM) along with an additive ratio assessment (ARAS) is employed to propose a supplier ranking for a distributor of a pharmaceutical supply chain. The developed model is novel as LARG criteria in the context of supplier selection have not been studied to address the disruptions in the pharmaceutical supply chain. This is significant because it gives insight to both retailers and suppliers to emphasize the correct criteria, especially in the pandemic or related disrupting conditions. The results demonstrated that quality, collaboration, safety stock, and environmental criteria weigh the highest before the pandemic, while just-in-time delivery, lead time, safety stock, and environmental criteria weigh the highest after the pandemic. This study demonstrates that developing a supplier selection approach that meets the demand in a short time and recommends suppliers to hold surplus inventory helps the healthcare systems better respond to the market needs.

The fourth industrial revolution has significantly changed the traditional way of managing supply chains. The applications of Industry 4.0 (I4.0) technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) in different processes of supply chains have assisted companies to improve their performance. Procurement can be considered a critical process in supply chain management since it can provide novel opportunities for supply chains to improve their efficiency and effectiveness. However, I4.0 applications can be costly and may not be reasonably affordable. Therefore, the benefits of implementing these technologies should be clarified for procurement managers before investing in the digitalization of the procurement process. Despite the importance of this issue, few papers have attempted to address the effects of I4.0 technologies and smart systems in procurement. To fill this gap, a Systematic Literature Review (SLR) on the applications of I4.0 technologies in procurement has been used in this study. By reviewing 70 papers through appropriate keywords, a conceptual framework is developed to classify different value propositions provided by the different applications of I4.0 technologies in procurement processes. Results reveal nine value propositions that can provide a better understanding for the procurement department to analyze the benefits of implementing the related I4.0 technologies in different activities. Finally, findings and future study opportunities are concluded.

Wastewater treatment has been a challenge for beverage companies in recent years which has led them to establish specific units to purify the expired returning items before agricultural uses or disposal. This issue has never been studied from the perspective of inventory and production management. This paper proposes an inventory formulation in the case of Behnoush beverage company in Iran to determine the replenishment cycle and the optimal selling price of items when: (a) items deteriorate continuously and have their specific expiration dates, (b) a proportion of items that are expired are moved to the company and after the process of wastewater treatment, and (c) a trade credit period is offered to the buyer to make encouragement for ordering in larger quantities. Optimality conditions for the total cost function are elaborated. Thereafter, numerical experiments are adopted for validation. Eventually, managerial implications are outlined and findings of the study are summarized.

PurposeMaintenance dredging can often hinder port operations resulting in waiting times for seagoing vessels. The purpose of this paper is to investigate the dynamics between maintenance dredging activities and seagoing vessels, specifically focusing on how waiting times can be reduced. Then, the role of selecting different maintenance dredging strategies in reducing these waiting times is outlined.MethodsThe study analyzes historical automatic identification system (AIS) data to identify the interaction between maintenance dredging and seagoing vessels and quantify the hindrance periods for the Mississippihaven case study in the Port of Rotterdam, the Netherlands. The trajectories of the vessels are analyzed in a simple case to show how the vessels interact and how the waiting times are quantified. The interactions are checked with the Port of Rotterdam for different port calls to ensure that maintenance dredging was the reason for these delays.ResultsBy analyzing the AIS data analysis of vessels in a given time window, the dredgers for maintenance work can be identified and their activities within or near the terminal can be determined. In addition, the waiting time of the seagoing vessel caused by the maintenance dredging is quantified at the terminal entrance.ConclusionThe study discusses how the maintenance dredging operations could be improved by adjusting the loading and sailing phases of maintenance dredging and provides some theoretical and managerial insights. Alternative port maintenance strategies to minimize the waiting time caused by the hindrance are also discussed.

Due to the outbreak of COVID-19 around the globe in the last few years, the need for pharmaceutical supply chains is felt more than before. However, increasing uncertainties along with unpredictable demand for products led to disruptions in supply chains when receiving requests from retailers. These disruptions not only affected the economic aspect of supply chains but also caused shortages in hospitals and medical centers. Therefore, it has become significant for companies to select their suppliers to avoid disruptions in the case of the severity of infections. To address this issue in practice, this paper has been conducted based on a case study to address the role of lean, agile, resilience, and green (LARG) criteria in selecting the supplier in a pharmaceutical supply chain and compare the results obtained before and after the prevalence of COVID-19. The main purpose of this study is to determine and evaluate different indicators within the LARG concept to avoid disruptions when selecting suppliers. Besides, the significance of these criteria before and after the pandemic condition is addressed. Due to addressing multiple aspects of the problem, a hybrid fuzzy multi-attribute decision-making (MADM) approach is adopted for this elaboration when the four LARG criteria are integrated with eighteen supplier selection sub-criteria. To calculate the impact of each criterion (or sub-criteria), a fuzzy best–worst method (BWM) along with an additive ratio assessment (ARAS) is employed to propose a supplier ranking for a distributor of a pharmaceutical supply chain. The developed model is novel as LARG criteria in the context of supplier selection have not been studied to address the disruptions in the pharmaceutical supply chain. This is significant because it gives insight to both retailers and suppliers to emphasize the correct criteria, especially in the pandemic or related disrupting conditions. The results demonstrated that quality, collaboration, safety stock, and environmental criteria weigh the highest before the pandemic, while just-in-time delivery, lead time, safety stock, and environmental criteria weigh the highest after the pandemic. This study demonstrates that developing a supplier selection approach that meets the demand in a short time and recommends suppliers to hold surplus inventory helps the healthcare systems better respond to the market needs.

Objective: To compare ratings of self-esteem and depressive mood in a sample of stroke survivors in an acute inpatient rehabilitation setting to those of a matched control group. Design: Stroke survivors (n = 80) were matched on age and education to a group of neurologically intact community-dwelling control participants. Between-group analysis compared mean ratings of self-esteem and depressive measures. Within-group correlational analyses explored the relationship between self-esteem and mood. Between-group comparison of the correlations between self-esteem and mood explored differences in the strength of association between these constructs. Regression analyses explored the relationship of self-esteem measures after controlling for depressive mood. Main measures: Visual Analogue Self-Esteem Scale, Rosenberg Self-Esteem Scale, Geriatric Depression Scale. Results: Stroke survivors rated significantly lower mean levels of self-esteem on the Visual Analogue Self-Esteem Scale (37 versus 41) and the Rosenberg Self-Esteem Scale (21 versus 24) than the control group. Stroke survivors also rated higher mean levels of depressive mood on the Geriatric Depression Scale (9 versus 6). Significantly higher correlations between self-esteem and mood ratings were noted in the stroke group than in the control group. Lower self-esteem ratings do not appear to be a byproduct of depressive mood. Conclusions: Self-esteem is negatively impacted by stroke and is strongly, but independently, associated with depressive mood. Clinicians may better facilitate the emotional adjustment of the survivor by considering this facet of psychological impact and intervening to address self-esteem.