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The purpose of this paper is to attempt to realize a distribution network optimization in supply chain using grey systems theory for uncertain information.

There is much uncertain information in the distribution network optimization of supply chain, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in supply chain, however grey information of the supply chain has not been covered. In the distribution problem of supply chain, grey demands are taken into account. Then, a mathematics model with grey demands has been constructed, and it can be transformed into a grey chance‐constrained programming model, grey simulation and a proposed hybrid particle swarm optimization are combined to resolve it. An example is also computed in the last part of the paper.

The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about distribution of supply chain, but grey chance‐constrained programming, grey simulation and particle swarm optimization can be combined to resolve the grey model.

The method exposed in the paper can be used to deal with distribution problems with grey information in the supply chain, and network optimization results with a grey uncertain factor could be helpful for supply chain efficiency and practicability.

The paper succeeds in realising both a constructed model of the distribution of supply chain with grey demands and a solution algorithm of the grey mathematics model by using one of the newest developed theories: grey systems theory.

This study evaluated the influence of the coronavirus pandemic on the healthcare and non-cold pharmaceutical care distribution supply chain.

The model involves four objective functions to minimize the total costs, environmental impacts, lead time and the probability of a healthcare provider being infected by a sick person was developed. An improved version of the augmented e-constraint method was applied to solve the proposed model for a case study of a distribution company to show the effectiveness of the proposed model. A sensitivity analysis was conducted to identify the sensitive parameters. Finally, two robust models were developed to overcome the innate uncertainty of sensitive parameters.

The result demonstrated a significant reduction in total costs, environmental impacts, lead time and probability of a healthcare worker being infected from a sick person by 40%, 30%, 75% and 54%, respectively, under the coronavirus pandemic compared to the normal condition. It should be noted that decreasing lead time and disease infection rate could reduce mortality and promote the model's effectiveness.

Implementing this model could assist the healthcare and pharmaceutical distributors to make more informed decisions to minimize the cost, lead time, environmental impacts and enhance their supply chain resiliency.

This study introduced an objective function to consider the coronavirus infection rates among the healthcare workers impacted by the pharmaceutical/healthcare products supply chain. This study considered both economic and environmental consequences caused by the coronavirus pandemic condition, which occurred on a significantly larger scale than past pandemic and epidemic crises.

The purpose of this paper is to identify the critical distribution practices of agile supply chains and provide a comprehensive framework that can be used to improve the responsiveness of supply chains. The research is carried out in the context of different manufacturing industries and provides empirical evidence that agile supply chain distribution enhances organisational performance.

The paper employed survey research, using a sample of 128 manufacturing companies.

The paper explores the critical distribution practices of supply chains that make supply chains agile. Collaborative distribution, order commitment, distribution flexibility and inventory management are the key SCM distribution practices associated with agile supply chains, and have significant impact on organisational performance.

Data were collected from a single node/respondent of a supply chain. Further research could be carried out using mutiple node data of each supply chain to make the research more meaningful and generalisable.

The findings may be used to gauge the competitive capabilities of SCM distribution and to guide organisations to measure and improve supply chain responsiveness and organisational performance.

The paper provides evidence regarding the impact of the critical distribution practices of agile supply chains on performnace.

The food industry and its distribution solutions often lie at the center of sustainability-related arguments. However, little is known about the dynamic role of business capabilities for sustainable transformations in the context of local food distribution. Accordingly, this study aims to investigate how dynamic capabilities drive sustainable supply chain management (SSCM) business practices in short food supply chains (SFSCs) through the professionalization and expansion of online distribution channels.

The present study analyzes sustainability-related practices at six online distribution channels selling local food products in Germany and Austria. By applying a cross-case study and theory-elaboration approach, the study analyzes empirical data derived from these businesses and provides insights into how dynamic capabilities can facilitate SSCM practices within SFSCs. Hereby, potential pathways for a sustainable transformation in this industry context are deduced through abductive reasoning.

The empirical findings provide evidence that supply chain orientation, coordination, innovation practices and strategies are highly relevant for SFSCs seeking to reach upscaling effects in regional markets. However, because SFSCs may not be able to reach mass markets without weakening their own sustainability performance, the present study recommends addressing sustainability inefficiencies in the region and developing further expansion potentials through replication in other regions. In this approach, related and necessary SSCM dynamic capabilities were identified and validated based on the empirical findings.

Although SFSCs include sustainability aspects at their core – particularly regarding resource usage, environmental friendliness and social-standard assurance – missing distribution-related capabilities limit growth such that these businesses often remain in a niche. To address this issue, the study builds on dynamic capabilities theory by identifying and describing core SSCM practices and capabilities; moreover, this study is among the first to elaborate empirically on the use of dynamic capabilities theory in this specific industry context.

This case study aims to examine the role of demand management in balancing distribution efficiency and responsiveness to customer needs in the downstream of a retail supply chain.

A major machine part supplier in Australia is used as a case study to investigate the challenges faced by the industry in distributing goods to customers. The use of demand management techniques to help improve distribution efficiency without significantly impacting on responsiveness is also explored.

The findings of the case study reveal that appropriate demand management measures, such as customer segmentation and price discrimination, can help improve overall distribution efficiency of the supply chain while providing the required responsiveness to meet genuine customer needs. Other management attempts, such as vendor‐managed inventory and rationalisation of retail network, can facilitate demand aggregation and improve vehicle utilisation in distribution with minor impact on customer service. These changes require a full understanding of customer requirements and supply capabilities of the company as well as corresponding adjustments in business strategy, leadership style, and organisational culture.

This study lends insight into the use of demand management techniques to improve efficiency in downstream wholesale and retail distribution, thereby enhancing sustainability and profitability of business. To serve mainly as a case study and an illustration of the approach, the scope of the study is limited to six stores in the distribution network of the case company.

Retailers can explore the use of demand management techniques to increase distribution efficiency and hence competitiveness of the company. The approach can also assist managers in adopting best practices among stores and facilitate more effective allocation of distribution resources to serve different market segments.

Using demand management techniques to increase distribution efficiency can reduce delivery frequency and total travel distance. This will help lessen energy usage, carbon emission, traffic congestion, and other negative impacts on the environment.

Research in retail distribution efficiency to date focuses mainly on delivery optimisation through routing and scheduling. Attempts to link demand with supply and use demand management techniques to improve distribution efficiency are relatively limited. This paper fills the gap in the literature by investigating the value of demand management in distribution and explores empirically the significance of the approach to achieve higher wholesale and retail distribution efficiency.

This study aimed to develop the integration of the multiperiod production-distribution model in a closed-loop supply chain involving carbon emission and traceability. The developed model was for agricultural food (agri-food) products, considering the reverse flow of food waste from the disposal center (composting center) to producers.

The results indicate that integrating the production and distribution model considering food waste recycling provides low carbon emissions in lower total costs. The sensitivity analysis also found that there are trade-offs between production and distribution rate and food waste levels on carbon emission and traceability.

This study focuses on the mathematical modeling of a multiperiod production-distribution formulation for a closed-loop supply chain.

The model of the agri-food closed-loop supply chain in this study that considers food recycling and carbon emissions would help stakeholders involved in the agri-food supply chain to reduce food waste and carbon emissions.

This special “Anbar Abstracts” issue of the International Journal of Physical Distribution & Logistics Management is split into eight sections covering abstracts under the following headings: Distribution and supply chain management; Logistics; Air/road/rail transport; Retail/wholesale; Freight and delivery services; International; Purchasing; Accounting.

The purpose of this paper is to identify various risks in the power distribution supply chain and further to prioritize the risk variables and propose a model to the power distribution industry for managing the interruptions in its supply chain. To accomplish this objective, a case of a major power distribution company has been considered.

Failure mode and effects analysis (FMEA) analysis has been done to identify the potential failure modes, their severity, and occurrence and detection scores. Then an interpretive structural model (ISM) has been developed to identify and understand the interrelationships among these enablers followed by MICMAC analysis, to classify the risk variables in four quadrants based on their driving and dependency powers.

The results of this study exhibit that technical failure in the information and technology system, the use of improper equipment, poor maintenance and housekeeping in the internal operations are the major risk drivers. Exposure to live wires and commercial loss in power supply has strong dependence power.

This study is limited to a single power distribution company and not the whole power distribution sector.

This study suggests the managers of the power distribution company develop an initial understanding of the drivers and the dependent powers on the supply chain risks.

Through prioritization, identification of drivers and the dependent risks, the losses in the power distribution supply chain can be minimized.

Various failures in the power distribution have been studied in the past, but they have not investigated the supply chain risks in the power distribution of a power distribution company.

The pharmaceutical industry is one of the most essential areas of health in any country. It is defined as a system of processes, operations and organizations involved in discovering, developing and producing drugs. The supply chain in the pharmaceutical field is one of the most important strategic issues in the pharmaceutical and health-care industries. The purpose of this study is to reduce the total cost of the supply chain network and reduce the amount of distribution scheduling.

In this study, the authors designed a drug supply chain network with uncertainty-related corruption. The optimal number and location of potential facilities, the optimal allocation of flow between facilities, the optimal routing of vehicles and the optimal amount of inventory in production and distribution center warehouses were determined to achieve these two objective functions.

In evaluating the small sample size problem, it was found that the comprehensive benchmarking method was more efficient than the other methods in obtaining the mean index of the first objective function. The utility function method has also proved its efficiency in obtaining the mean of the second objective function indices, the spacing index and the computational time. Because of the inefficiency of GAMS software in resolving size issues, the modified NSGA II and MOPSO algorithms with modified priority-based encryption have been used. First, using the Taguchi method, the initial parameters of the metaheuristic algorithms are adjusted, and then, 15 sample problems are designed in larger sizes. To avoid generating random data, five problems were equally designed, and the averages of objective functions and metrics of met heuristic algorithms (number of efficient solutions, maximum expansion index, spacing index and computational time) were analyzed as the basis of evaluation and comparison. Therefore, using all the indicators and results of the NSGA II algorithm is recommended.

In this research, a biobjective modeling approach is proposed to minimize the total costs of the supply chain network (construction costs, storage costs and product transportation costs between centers) and advertising costs and to minimize distribution and transportation scheduling across each level of the supply chain network.

The purpose of the research is to shed light on the evolution of distribution structures and its consequent implications for supply chain management (SCM) in the context of the emerging markets of Central and Eastern Europe (CEE).

A structured literature review followed by two case studies, which combine qualitative and quantitative analysis. Mainly in‐depth interviews were used, with company sales data analysis in terms of variation and forecast accuracy.

It was found that CEE distribution structures are overlapping, and along complex traditional structures there exists a possibility for a more direct approach. This modern key‐account approach improves supply chain performance, mainly due to echelon elimination and information sharing. The case studies also illustrate that supply chain demand distortion originating practices create uncertainty in demand, even in the case of modern key accounts. The findings therefore suggest that general SCM approaches of the “West” are evident and appropriate also in the “East”.

Owing to the limited number of case studies, this research is considered exploratory. The presented two case studies are essentially illustrative examples of the distribution operations of two international companies in CEE markets.

For practitioners, the two case studies provide important insight on the nature of alternative distribution structures in CEE, and what the level of forecast accuracy and the demand fluctuation may be expected. It is proposed that the emerging opportunities for supply chain partnership development should be carefully reviewed.

The paper draws upon real‐life data from emerging CEE markets with an approach that is not commonly used in distribution and SCM studies on CEE.