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The purpose of the paper is to investigate the factors that affect the decision‐making process of Hong Kong‐based manufacturers when they select a third‐party logistics (3PL) service provider and how 3PL service providers manage to retain customer loyalty in times of financial turbulence.

The paper presents a survey‐based study targeting Hong Kong‐based manufacturers currently using 3PL companies. It investigates the relationship between the reasons for using 3PL services and the requirements for selecting a provider, and examines the relationship between customer satisfaction and loyalty. In addition, the relationships among various dimensions – in small to medium‐sized enterprises (SMEs), large enterprises and companies – of contracts of various lengths are investigated.

In general, the reasons for using 3PL services and the requirements for selecting 3PL service providers are positive‐related. The dimension of “reputation” of satisfaction influences “primary customer loyalty” positively.

Constructive suggestions are provided to help 3PL service providers allocate their limited resources to business areas that help them to meet the demands of their target customers, increase the number of customers, and improve customer loyalty.

The paper is an attempt to help 3PL service providers find ways to survive in a climate of financial crisis.

The purpose of this paper is to propose a real‐time food receiving operations management system (RFRS), focusing on demonstrating the use of a case‐based reasoning (CBR) and radio frequency identification (RFID) technology in managing the complex food receiving activities in distribution centers, to deal with the global concerns in food safety management.

The proposed system includes a CBR engine for generating customized operating procedures by retrieving and analyzing relevant knowledge stored in the database and case library. In addition, RFID technology is adopted to gather real‐time inventory information for visualizing inventories, work stations, and equipment status.

The significance and contribution of RFRS in the context of managing unstructured operations in distribution centers for the food receiving process are demonstrated by adopting the system in one of the Hong Kong‐based logistics companies. The achieved improvement of order fulfillment helps achieve a systematic management in operations process flow. Moreover, the time saving in resource assignment helps improve the operations efficiency of the receiving zone. Hence, the decrease in the faults of quality checking helps improve the service and quality level. All of the above demonstrate the feasibility of the RFID‐based decision support system.

A superior method for the management of quality control and service of the receiving process is essential for the success of food distribution centers. However, the complexity of these management processes increases with the rise of customization of service in the supply chain. Hence, such increase in the operation complexity leads to the increase in operation time and, consequently, a decrease in the service quality. The real‐time food receiving operations management system proposed in this paper integrated RFID and case‐based reasoning technology in dealing with food warehouse receiving operation assignment. This is a brand new application combining the two technologies in the field of food reception in warehouses. With the support of the system, the efficiency and the customer satisfaction are improved.

With the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this paper is to propose a system that helps facilitate and improve the quality of decision making, reduces the level of substandard goods, and facilitates data capturing and manipulation, to help a warehouses improve quality assurance in the inventory‐receiving process with the support of technology.

This system consists of three modules, which integrate the radio frequency identification (RFID) technology, case‐based reasoning (CBR), and fuzzy reasoning (FR) technique to help monitor food quality assurance activities. In the first module, the data collection module, raw warehouse and work station information are collected. In the second module, the data sorting module, the collected data are stored in a database. In this module, data are decoded, and the coding stored in the RFID tags are transformed into meaningful information. The last module is the decision‐making module, through which the operation guidelines and optimal storage conditions are determined.

To validate the feasibility of the proposed system, a case study was conducted in food manufacturing companies. A pilot run of the system revealed that the performance of the receiving operation assignment and food quality assurance activities improved significantly.

In summary, the major contribution of this paper is to develop an effective infrastructure for managing food‐receiving process and facilitating decision making in quality assurance. Integrating CBR and FR techniques to improve the quality of decision making on food inventories is an emerging idea. The system development roadmap demonstrates the way to future research opportunities for managing food inventories in the receiving operations and implementing artificial intelligent techniques in the logistics industry.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

Robust and efficient warehouse management is pivotal to the success of contemporary logistics and business productivity. Given the recent technological revolutions and the rising pressure for sustainable warehouse activities, the recent literature continues to grow on Industry 4.0 technologies and sustainable warehousing. Following this, the review aims to explore current state-of-the-art, knowledge development, the focus of research, methodological development and knowledge gaps for more impactful future research on this important yet underexplored topic.

A widely accepted systematic literature review (SLR) methodology was employed on a sample of 46 articles retrieved from multiple search queries in the three large databases: Scopus, Web of Science and Google Scholar.

The authors’ analysis unveils several interesting findings: the positive linkage between various Industry 4.0 technologies and the three main pillars (economic, social, environmental) of sustainable warehousing; the paucity of large scale empirical research, specifically in the context of COVID-19; imbalance amid studies on three main aspects of warehouse sustainability; partial application of Industry 4.0 technologies; and disproportion in scholarly focus between numerous warehouse activities and Industry 4.0 technologies.

The authors integrate research findings from a pool of studies, thereby improving the understanding of practitioners on how different Industry 4.0 technologies relate to sustainable warehousing.

To the best of the authors’ knowledge, this is the first SLR at the intersection of Industry 4.0 technologies and sustainable warehousing. Our review expands the existing knowledge base, devises a conceptual framework and suggests avenues for fruitful future research on this emerging area.

The contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making.

The research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings.

The provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis.

Warehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management.

The paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support.

Information and communication technology (ICT) plays a critical role in supporting logistics services outsourcing arrangements. Despite the literature highlighting the benefits of ICT usage by logistics service providers (LSPs), a gap between what is expected of ICT and satisfaction with LSP performance has persisted. Seeking to reduce this gap, the purpose of this paper is to propose an ICT implementation process designed for LSPs.

A theoretical model was built, which took into account the ICT implementation models identified in the literature and the LSP requirements from ICT usage. The model was adjusted according to technology innovation and change management practices. A multi‐case descriptive study was conducted to evaluate the adequacy of this model in practice.

The proposed ICT implementation model designed for LSPs combines both theoretical and empirical research. It comprises 39 activities, of which 32 were available in the literature and seven are new – mainly for aspects related to external integration. Through multiple‐case analysis, the model proved itself appropriate for supporting a step‐by‐step ICT implementation in LSPs.

The paper considers LSP requirements related to integrating and coordinating supply chains in the proposed model. In addition to an engineering perspective, it addresses behavioral issues for technology acceptance. It also exposes the problem posed by the definition of implementation process boundaries. Moreover, it features case studies in the Brazilian LSP industry, which has received little attention in the mainstream literature.

This paper aims to systematically review the literature on quality management in agri-food supply chains (SCs) and propose an integrated conceptual framework.

A systematic literature review that analyses 93 papers in peer-reviewed academic journals published from 1996 to November 2021 is conducted. A conceptual model is advanced.

Based on a hierarchy of capabilities perspective, the authors develop an integrated conceptual framework in which SC quality (SCQ) management practices promote three levels of SC dynamic capabilities, which in turn lead to agri-food SCQ performance.

The authors propose a hierarchy of capabilities perspective of quality management in agri-food SCs and develop a conceptual framework. Furthermore, a number of propositions based on dynamic capabilities and the review findings are provided. Four future research directions are presented.

A flexible production rate has been discussed and analyzed in the literature for different business situations. This paper aims to consider this for a make‐to‐order (MTO) environment.

Generally the production rate and cycle time are significant parameters among others, where other parameters might include demand, and production time cost. An improvement in the cycle time or the cycle time reduction is a prime objective in the context of an overall productivity improvement particularly in the MTO environment. In order to gain some insights, an interaction of the production rate and cycle time is described.

The focus of the present paper is on the supply chain cost using these parameters among others. A framework for the conceptual understanding and analysis is provided along with the practical implementation issues.

A relevant measure for the degree of flexibility (DOF) in the context of supply chain is also discussed in this paper.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.