A carbon neutral supply chain management by considering emission-risk minimization and green purchasing through optimal decision-making.

This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO2e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO2e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society.

The benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions.

Supply chain disruptions and demand disruptions make it challenging for hospital pharmacy managers to determine how much inventory to have on-hand. Having insufficient inventory leads to drug shortages, while having excess inventory leads to drug waste. To mitigate drug shortages and waste, hospital pharmacy managers can implement inventory policies that account for supply chain disruptions and adapt these inventory policies over time to respond to demand disruptions. Demand disruptions were prevalent during the Covid-19 pandemic. However, it remains unclear how a drug's shortage-waste weighting (i.e., concern for shortages versus concern for waste) as well as the duration of and time between supply chain disruptions influence the benefits (or detriments) of adapting to demand disruptions. We develop an adaptive inventory system (i.e., inventory policies change over time) and conduct an extensive numerical analysis using real-world demand data from the University of Michigan's Central Pharmacy to address this research question. For a fixed mean duration of and mean time between supply chain disruptions, we find a drug's shortage-waste weighting dictates the magnitude of the benefits (or detriments) of adaptive inventory policies. We create a ranking procedure that provides a way of discerning which drugs are of most concern and illustrates which policies to update given that a limited number of inventory policies can be updated. When applying our framework to over 300 drugs, we find a decision-maker needs to update a very small proportion of drugs (e.g., < 5 % ) at any point in time to get the greatest benefits of adaptive inventory policies.

Health system lessons from the global fund-supported procurement and supply chain investments in Zimbabwe: a mixed methods study.

BACKGROUND: The Global Fund partnered with the Zimbabwean government to provide end-to-end support to strengthen the procurement and supply chain within the health system. This was accomplished through a series of strategic investments that included infrastructure and fleet improvement, training of personnel, modern equipment acquisition and warehouse optimisation. This assessment sought to determine the effects of the project on the health system. METHODS: This study employed a mixed methods design combining quantitative and qualitative research methods. The quantitative part entailed a descriptive analysis of procurement and supply chain data from the Zimbabwe healthcare system covering 2018 - 2021. The qualitative part comprised key informant interviews using a structured interview guide. Informants included health system stakeholders privy to the Global Fund-supported initiatives in Zimbabwe. The data collected through the interviews were transcribed in full and subjected to thematic content analysis. RESULTS: Approximately 90% of public health facilities were covered by the procurement and distribution system. Timeliness of order fulfillment (within 90 days) at the facility level improved from an average of 42% to over 90% within the 4-year implementation period. Stockout rates for HIV drugs and test kits declined by 14% and 49% respectively. Population coverage for HIV treatment for both adults and children remained consistently high despite the increasing prevalence of people living with HIV. The value of expired commodities was reduced by 93% over the 4-year period. Majority of the system stakeholders interviewed agreed that support from Global Fund was instrumental in improving the country's procurement and supply chain capacity. Key areas include improved infrastructure and equipment, data and information systems, health workforce and financing. Many of the participants also cited the Global Fund-supported warehouse optimization as critical to improving inventory management practices. CONCLUSION: It is imperative for governments and donors keen to strengthen health systems to pay close attention to the procurement and distribution of medicines and health commodities. There is need to collaborate through joint planning and implementation to optimize the available resources. Organizational autonomy and sharing of best practices in management while strengthening accountability systems are fundamentally important in the efforts to build institutional capacity.

Optimizing Robotic Mobile Fulfillment Systems for Order Picking Based on Deep Reinforcement Learning.

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators-such as shelf access frequency and the total processing time of the RMFS-are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.

Benchmarking circularity in supply chains: A systematic literature review.

The challenges posed by unsustainable practices in today's economy underscore the urgent need for a transition toward a circular economy (CE) and a holistic supply chain (SC) perspective. Benchmarking plays a pivotal role in managing circular SCs, offering a metric to gauge progress. However, the lack of consensus on the optimal benchmarking approach hampers effective implementation of circular business practices. To address this gap, we conducted a systematic review of the literature, identifying 29 pertinent publications. The analysis revealed 30 unique attributes and sub-attributes for benchmarking circularity, which were clustered into five main attributes. The main attributes are goals, subjects, key performance indicators (KPIs), data sources, and evaluation methods, while the sub-attributes are organised as features of the main attributes and depicted as a feature model. Drawing from selected publications, we illustrated each feature with examples. Our model offers a comprehensive benchmarking reference for circularity and will be a valuable tool for managers in the transition toward circularity. Supply chains seeking to benchmark their transition to circularity can apply the reference model to ensure that their benchmarking strategy is consistent with state-of-the-art knowledge. By providing a generic circularity benchmarking approach that is valid for diverse economic sectors, our findings contribute to theoretical efforts to address the lack of generic frameworks for CE.

Green supply chain management practices and triple bottom line performance: Insights from an emerging economy with a mediating and moderating model.

The current study explores the dynamics of business practices in Pakistan's Small and Medium Enterprises (SMEs). This research focusing on how "green supply chain management" (GSCM), Industry 4.0 technologies (IND), total quality management (TQM) and sustainable performance (SP) are interconnected. The study collected data from 382 SMEs owners and managers and data analysis was conducted utilizing by SPSS and SmartPLS. The results indicate that employ GSCM pattern has constructive impact on SMEs overall performance in terms of their social and financial aspects. Interestingly IND act as intermediaries among GSCM and sustainable performance underscoring their potential to translate sustainability efforts into outcomes. Moreover, our survey reveals that TQM have a crucial function in enriching the relationship between Industry 4.0 technologies and sustainable performance by executing as a moderator. It further highlighting the value of adopting a quality focused approach to maximize the causal factor of advancements on sustainability output. The findings of this survey research offering insights for practitioners, SMEs, and policy makers alike by highlighting the significance of integrating practices such as GSCM, IND and effective quality management to enhance SME efficiency. These outcomes further contribute to an understanding of the mechanics at play, within SMEs while offering guidance for organizations maneuvering the changing landscape of sustainable business practices and IND.

Drivers and Barriers to Implementing the Internet of Things in the Health Care Supply Chain: Mixed Methods Multicase Study.

BACKGROUND: Over the past 2 years, the COVID-19 pandemic has placed enormous pressure on the health care industry. There has been an increase in demand and, at the same time, a shortage of supplies. This has shown that supply chain management in the health care industry cannot be taken for granted. Furthermore, the health care industry is also facing other major challenges, such as the current labor market shortage. In the literature, the Internet of Things (IoT) is highlighted as an effective tool to build a more resilient and efficient supply chain that can manage these challenges. Although using IoT in supply chain management has been extensively examined in other types of supply chains, its use in the health care supply chain has largely been overlooked. Given that the health care supply chain, compared to others, is more complex and is under growing pressure, a more in-depth understanding of the opportunities brought by IoT is necessary. OBJECTIVE: This study aims to address this research gap by identifying and ranking the drivers of and barriers to implementing IoT in the health care supply chain. METHODS: We conducted a 2-stage study. In the first, exploratory stage, a total of 12 semistructured interviews were conducted to identify drivers and barriers. In the second, confirmatory stage, a total of 26 health care supply chain professionals were asked in a survey to rank the drivers and barriers. RESULTS: The results show that there are multiple financial, operational, strategy-related, and supply chain-related drivers for implementing IoT. Similarly, there are various financial, strategy-related, supply chain-related, technology-related, and user-related barriers. The findings also show that supply chain-related drivers (eg, increased transparency, traceability, and collaboration with suppliers) are the strongest drivers, while financial barriers (eg, high implementation costs and difficulties in building a business case) are the biggest barriers to overcome. CONCLUSIONS: The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides guidance for practitioners and health care provider leaders intending to implement IoT in the health care supply chain.

Cash and voucher assistance along humanitarian supply chains: a literature review and directions for future research.

This study reviews research on cash and voucher assistance (CVA) by applying a humanitarian supply chain management perspective. A systematic literature review was conducted to identify, analyse, and synthesise past academic research. The content, context, and process framework was used to structure the content analysis. The findings reveal that the outcomes of CVA programmes are dependent on critical context-specific variables that influence feasibility and operability. Humanitarian actors must consider factors that are external (the nature of disaster, politics, economy, and infrastructure) and internal (local market availability and accessibility, supplier/donor interest, supplier/vendor selection and contracting, and beneficiary preference) to the supply chain. The delivery process is influenced by them, impacting on programme responsiveness and cost-efficiency. The results provide insights that humanitarian practitioners can utilise to reconsider their supply chain strategies when deciding on the selection and implementation of CVA programmes. Potential literature gaps are identified, and recommendations for further research are provided.

Challenges in last mile distribution of family planning commodities: Effects on product availability and accessibility in Nigeria.

BACKGROUND: Family planning (FP) reduces maternal and child mortality risk. Despite policies and plans to improve FP in Nigeria, access remains poor leading to high unmet need. Contraceptive use is still as low as 4.9% in some regions. Thus, this study assessed challenges in FP commodities distribution and its effect on accessibility. METHODS: Descriptive survey was used to explore last mile distribution of FP commodities in 287 facilities across various levels of FP service provision. Also, 2528 end users of FP services were assessed to ascertain their attitudes towards FP services. Data were analysed using IBM Statistical Package for the Social Sciences version 25. RESULTS: Only 16% of the facilities had all the basic infrastructure requirements assessed with majority of the facilities having inadequate human resource capacity on logistics and supply chain management of health commodities. The study also identified positive attitudes towards FP (80%) and low incidence of stigmatising attitudes (5.4%). CONCLUSIONS: The study identified challenges in distribution of FP commodities including frequent stock out of commodities and socio-cultural barriers. Increased positive attitude and limited stigmatising attitudes provides policy directions that are relevant for decision makers to align FP policies and strategies to improve last mile distribution of FP commodities.

Critical success factor analysis of blockchain technology in agri-food supply chain management: A circular economy perspective.

In the sustainability system, which gives importance to the availability of resources, recycling, renewal, and reproduction strategies can be handled under the name of the circular economy. While there are many stages to be followed from food production to consumption and even recycling, one of the technologies that will contribute to the circular economy that can take an active role in these stages is blockchain technology. In this study, which is about the circular economy in sustainable supply chain management, the contribution of blockchain technology to agri-food supply chain management is discussed. The aim of the study is to research, analyze and prioritize the critical success factors of the use of blockchain technology for the agri-food sector on the way to the circular economy. As a first step, with the help of PESTEL analysis approach, 12 critical success factors for agri-food supply chain management under the political, economic, social, technological, environmental, and legal dimensions of blockchain technology within the scope of circular economy are determined. Analytic Network Process (ANP) and MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) methods are used in an integrated way to consider the degree of influence between the factors and to determine the ideal optimal factor. In the results, it is determined that the political and technological sub-criteria of blockchain technology is determined, and it is observed that the criteria handled in line with the sustainability system are compatible with the perspective of reducing the waste of resources of the circular economy. If blockchain technology is used in the agri-food sector, it has been determined that it will contribute to the circular economy with the success factors in this study. "Ability to prevent food waste"; "Increased food security"; "Product lifecycle tracking" factors take priority in their ranking.

The effects of digital technology application and supply chain management on corporate circular economy: A dynamic capability view.

Developing circular economy capability has emerged as an effective response to environmental pressures on firms. The proliferation of digital technology has created uncertainty in developing corporate circular economy capability. Although research has begun to focus on the impact of digital technology application on corporate circular economy capability, empirical evidence remains absent. Simultaneously, few studies have concerned corporate circular economy capability obtained from supply chain management. The answer to the correlation between digital technology application, supply chain management, and circular economy capability is unavailable in current research. Based on a dynamic capability view, we investigate how digital technology application affects corporate circular economy capability through supply chain management regarding supply chain risk management, collaboration, and integration. This underlying mechanism was verified with 486 Chinese-listed industrial firms and the mediating model. The findings demonstrate that digital technology application and supply chain management significantly affect corporate circular economy capability. The mediating channel whereby the digital technology application provides circular economy capability can facilitate the positive impact of supply chain risk management and collaboration while undermining the adverse effects of supply chain integration. These mediating channels differentiate in heterogeneous growth firms and are more pronounced in low-growth groups. It presents an opportunity to use digital technology to reinforce the positive impact of supply chain risk management and collaboration and mitigate the negative effect of supply chain integration on circular economy capability.

Bridging the gap: Building environmental, social and governance capabilities in small and medium logistics companies.

Nowadays, the popularity of environmental, social, and governance (ESG) performance measurement has dramatically increased, particularly to listed companies, for supporting various investment decisions. Companies with high ESG scores imply that their ongoing business development is recognised to be economically, socially, and environmentally sustainable. From the current ESG measurement practice, the measurement frameworks are built on rating schemes, such as KLD and ASSET4, so as to derive the ESG scores for listed companies. However, such existing measurement frameworks are difficult to be implemented in small and medium enterprises (SMEs) with unstructured and non-standardised business data, especially in logistics and supply chain management (LSCM) practice. In addition, it is inevitable for listed companies to work with SMEs, for example logistics service providers, but they need a systematic framework to source the responsible SMEs to maintain the ESG performance. To address the above industrial pain-points, this study proposes an ESG development prioritisation and performance measurement framework (ESG-DPPMF) by means of the Bayesian best-worst method enabling the group decision-making capability to prioritise the ESG development areas and formulate the performance measurement scheme. Through consolidating the opinions from logistics practitioners, it is found that fair labour practice, reverse logistics and human right in supply chains are the most essential areas to further enhance ESG capabilities in the logistics industry. In addition, the viability of the ESG performance measurement has been validated, and thus the sustainable and human-centric logistics practice can be developed to achieve business sustainability.

Clustering sustainable suppliers in the plastics industry: A fuzzy equivalence relation approach.

Nowadays, pure economic supply chain management is not commonly contemplated among companies (especially buyers), as recently novel dimensions of supply chains, e.g., environmental, sustainability, and risk, play significant roles. In addition, since companies prefer buying their needs from a group of suppliers, the problem of supplier selection is not solely choosing or qualifying a supplier from among others. Buyers, hence, commonly assemble a portfolio of suppliers by looking at the multi-dimensional pre-determined selection criteria. Since sustainable supplier selection criteria are often assessed by linguistic terms, an appropriate clustering approach is required. This paper presents an innovative way to implement fuzzy equivalence relation to clustering sustainable suppliers through developing a comprehensive taxonomy of sustainable supplier selection criteria, including supply chain risk. Fifteen experts participated in this study to evaluate 20 suppliers and cluster them in the plastics industry. Findings reveal that the best partitioning occurs when the suppliers are divided into two clusters, with 4 (20%) and 16 (80%) suppliers, respectively. The four suppliers in cluster one are performing better in terms of the capability of supplier/delivery, service, risk, and sustainability criteria such as environment protection/management, and green innovation. These factors are critical in clustering and selecting sustainable suppliers. The originality of this study lies in developing an all-inclusive set of criteria for clustering sustainable suppliers and adding risk factors to the conventional supplier selection criteria. In addition to partitioning the suppliers and determining the best-performing ones, this study also highlights the most influential factors by analysing the suppliers in the best cluster.

Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry.

This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.

Building viable stockpiles of personnel protective equipment.

Many stockpiled personnel protective equipment (PPE) were of no use during COVID-19 because they have expired. The need for rethinking past approaches of building PPE stockpiles without planning for their timely rotation has become clear. We develop a game-theoretic pandemic preparedness model for single and multiple PPE products for a budget-constrained governmental organization (GO) supplied by a manufacturer. The GO maximizes preparedness, measured by the service rate of PPE, whereas the manufacturer maximizes profit. The manufacturer supplies the PPE stockpile in the first year. Thereafter, the manufacturer buys back a quantity of older PPE from the GO annually and sells the GO the same quantity of new PPE. The manufacturer sells older PPE in the market place. We find that this approach induces the manufacturer to rotate inventory in the stockpile. Joint determination of the stockpile size and its rotation results in no waste from expired PPE and is better than separately determining the stockpile size and then determining how to rotate it. Using insights from the single PPE model, we examine the optimal budget allocation among multiple PPE products. We also consider the effect of spot market prices of PPE during a pandemic on the optimal stockpile sizes. We find that spot market prices of PPE can have a significant effect on the optimal stockpile sizes. We examine the performance of the proposed approach in a manufacturers-distributor-GO supply chain and with an option for the GO to invest in the manufacturer's volume flexibility and show its effectiveness.

A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment.

Minimizing a company's operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of uncertainty or risk involved. In this study, we focus on a processing production plant as a model for a chain of operations and supply chain actions. We consider the stochastically varying production and transportation costs from the site to the plant, as well as from the plant to the customer base. Through stochastic optimization, we demonstrate that the plant producer can benefit from improved financial outcomes by setting higher sale prices while simultaneously lowering optimized production costs. This can be accomplished by selectively choosing producers whose production cost probability density function follows a Pareto distribution. Notably, a lower Pareto exponent yields better supply chain cost optimization predictions. Alternatively, a Gaussian stochastic fluctuation may be proposed as a more suitable choice when trading off optimization and simplicity. Although this may result in slightly less optimal performance, it offers advantages in terms of ease of implementation and computational efficiency.

An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM.

Environmental deterioration, the COVID-19 pandemic and the Russian-Ukrainian conflict had brought chronic and dramatic impacts on agricultural supply chain around the world, resulting in high inflation rates and unavoidable costs. In order to reduce the adverse impacts and achieve sustainability in agricultural supply chain, it's necessary to scientifically explore composite indicators interlinked with agricultural sustainable supply chain management (ASSCM). The current study developed an integrated rough-fuzzy WINGS-ISM method to reveal the hierarchal and causal structure of indicators. It is found that environmental legislation, regulation, licensing, and government subsidies are the main drivers of ASSCM. Specifically, the government can guide the sustainable development of ASSCM by regulating the business environment. The financial support needs to be enlarged to optimize the structure in science and technology of ASSCM. Moreover, corporates and organizations are highly motivated by the increasing awareness of social responsibility and sustainability consciousness to improve the economic performance and achieve the ASSCM goals. A comparative analysis is proposed to illustrate the practicality and reliability of the results obtained from the proposed method, which can be utilized as a reference in ASSCM.

Identifying and ranking key technological capabilities in supply chain sustainability using ISM approach: case of food industry in Iran.

The food industry is one of the strategic industries in developing countries, such as Iran and plays a critical role in the economy, food security, and public health. The growing populations can only have food security when the food industry's supply chain is sustainable. Therefore, due to the sustainable food supply chain's great importance, having technological capabilities compared to others is considered a competitive advantage for the companies involved in the food industry, as it can distinguish them as pioneer actors. Although many technologies have been investigated and used in the sustainable supply chain recently, no study has focused on identifying and ranking key technological capabilities related to the food industry in sustainable supply chain management. Also, we have not found any study using the ISM-MICMAC method to identify, rank, and interdependence between key technology capabilities in supply chain sustainability. Accordingly, the present study sought to identify and rank key technological capabilities in the supply chain sustainability of food industry companies. In this study, after reviewing the relevant literature, eleven technological capabilities in supply chain sustainability were identified. Then using experts' opinions and Interpretive Structural Modelling (ISM), interdependence among the technological capabilities ranked. Finally, dependent and independent drivers were presented using the MICMAC analysis. The ISM analysis results indicated that communication and information technology infrastructure was the most significant driver for other technological capabilities in companies' supply chain sustainability. Moreover, logistic optimization is imperative for improving supply chain sustainability performance. Therefore, if logistic optimization is appropriately implemented, it can improve supply chain sustainability. The present study results can increase supply chain productivity and effectiveness in Iranian food industry companies.

Promotion strategies for environmentally friendly packaging: a stochastic differential game perspective.

With the evolution of the e-commerce and express delivery industry, the consumption of packaging materials is increasing rapidly. Many members of society encourage using environmentally friendly packaging. However, due to the attitude-behavior gap, i.e., expressing concerns about environmental issues does not necessarily lead to green consumption, promoting the use of green packaging remains a challenge. This paper considers a stochastic differential game between green packaging manufacturers and e-commerce platforms. The optimal promotion strategies are derived for scenarios involving cooperation as well as non-cooperation. In addition, a welfare allocation mechanism for attaining stable cooperation is also discussed under the bargaining model. Numerical simulations and a sensitivity analysis were conducted to demonstrate the results. This paper finds that the cooperation between manufacturers and platforms can expand the actual market demand and promote the consumption of green packaging. The proposed model provides an effective tool for manufacturers and platforms to devise optimal strategies for promoting the use of green packaging.

An industry survey of implementation strategies for clinical supply chain management of cell and gene therapies.

BACKGROUND AIMS: The novelty of cell and gene therapies (CGTs) has introduced unique supply chain challenges and considerations not seen by chemically synthesized (small-molecule) drugs. These complexities increase during the clinical phases, where drug safety and efficacy milestones are still underdeveloped. For example, for autologous therapies such as chimeric antigen receptor T-cell therapies, in which the treatment is developed from the patient's own cells, supply chain management plays an integral role in chemistry, manufacturing and control processes. Supply chain management requires proactive planning because of the strict cold chain requirements and time sensitivity of CGTs. This research examines strategies and responses to challenges experienced by industry stakeholders (e.g., sponsors and manufacturers) during the implementation phases of clinical supply chain management. This research further evaluates the adequacy of the current regulatory framework for distribution and supply chain management of CGTs in the US. METHODS: A survey methodology was used to query subject matter experts from the biopharmaceutical industry who were familiar with the clinical supply management of CGTs in the US. The survey instrument was developed using an implementation framework and disseminated electronically to mid- and senior-level subject matter experts who had experience with clinical trials, supply chain management and CGTs. RESULTS: A total of 128 respondents accessed the survey, and 105 respondents answered at least one question. Seventy-five respondents completed the survey. Results showed that a lack of harmonization in regulations across the supply chain, limited resources, challenges with vendor management, high costs and complexities in the supply chain due to product specificity and customization proved to be impediments for the industry. In addition, the coronavirus disease 2019 pandemic had a significant impact on supply chain implementation. The results revealed that less than half of the respondents had business continuity plans in place. These challenges increased for smaller and mid-size organizations. Thirty percent of small and mid-size organizations were less prepared to scale up than larger companies. CONCLUSIONS: Suggestions from industry stakeholders were to adopt and enforce Good Distribution Practices in the US (81%), pre-plan distribution strategies with internal and external stakeholders along the supply chain and develop agile systems and robust processes end to end. Hurdles in scaling up and scaling out from the clinical to commercial phases for time- and temperature-sensitive CGT products make it difficult to predict the supply chain's long-term feasibility. Although there are initiatives to improve these impediments, such as improving industry partnerships and creating global CGT transportation standards, there are still regulatory knowledge gaps present for CGTs. Therefore, it is essential to establish a baseline and foundation for CGT supply chains extending beyond the loading dock.