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Whether or not a company is able to compete in the local and global arena depends significantly on how effectively the company can build up the partnership synergy with their business counterparts. With the emergence of a dispersed manufacturing network, the important issue is the criteria for the selection of the right partners for the right tasks. In this respect, it is essential to develop the necessary techniques to measure the performance of potential business partners prior to making the decision of partner selection. This paper suggests a performance measurement technique incorporated with on‐line analytical processing (OLAP) technology to support enterprise decisions related to selecting appropriate business partners. It also includes examples to illustrate how to apply this technique in real industrial situations.

Logistics practitioners must continually improve inventory management processes as they daily respond to the twin drivers of customer satisfaction and cost efficiency. The purpose of this paper is to investigate the scenario of sourcing goods through lateral transshipments in a periodic-review policy setting, against a backdrop of cost optimization objectives.

The authors develop decision rules that make cost-optimized selection between backordering and combined reactive and proactive lateral transshipment options possible. This necessarily takes account of the trade-off between purchasing, holding and backorder cost components. In addition, the authors use simulation studies to illustrate the superior performance of the proposed decision options.

According to results of the simulation studies, the proposed two-step decision rule generates the lower inventory cost than the alternative decisions rules. The outperformance of proposed two-step decision rule is valid in different scenario.

This study develops the decision rules to assist wholesaler logistics practitioners to make optimized decisions with regard to whether they should proactively lateral transshipments and if selected, the optimum size of the extra lateral transshipment.

This study has made a significant contribution to the existing knowledge base as it develops decision rules for a combined proactive and reactive approach using lateral transhipments to meet both urgent demand and a part of the demand expected during the supplier lead time in a cost-efficient way.

The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.

This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.

The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.

The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.

Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

This research aims to identify, examine and evaluate barriers to the adoption of digital servitization in manufacturing firms in the case of the Sri Lankan manufacturing sector and analyze the inter-relationships among digital servitization barriers.

Based on the comprehensive literature review, 13 barriers were identified. The grey decision-making trial and evaluation laboratory (grey-DEMATEL) approach was used to uncover and analyze the relationships among barriers in terms of their overall influence and dependencies.

A prominent barrier to the success of adopting digital servitization in the Sri Lankan manufacturing sector is the lack of digital strategy in developing activities related to the design of digital service packages, organizational structures and processes. Supply chain integration is the most influential factor, which plays an important role in developing a competitive advantage by encouraging innovation process capabilities in servitized companies.

Industry practitioners can develop guidelines for adopting digital servitization practices based on the importance and interdependencies of different barriers and thereby prioritize projects within a program of digital servitization adoption in their organizations.

Research studies on barriers to digital servitization are limited to exploratory nature and have adopted mainly the qualitative approach, such as in-depth interviews. No empirical study has investigated the inter-relationships among digital servitization adoption barriers in the manufacturing sector. This study provides a holistic view of different barriers to the adoption of digital servitization in the manufacturing sector as a basis for developing comprehensive digital servitization strategies to manage and leverage complexity in digital transformation.

In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.

A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.

Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.

A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

There is a well-documented trend among businesses for applying continuously improving, technologically-supported processes. This trend, in part, responds to evolving and challenging business environments and competitive pressures. It also increasingly mandates the need for businesses to invest in improving their digital capabilities and is driven by the expectation that such investments will better equip them for uncertain times. The COVID-19 pandemic presented disruptions to the supply chain, logistics, operations, market demand and labour supply, with industry reports providing evidence that businesses with digital capabilities were better able to respond to such disruptions promptly and appropriately. The study aims to investigate the effects of Industry 4.0 (I4.0) technologies on business operations and supply chain resilience.

The authors surveyed 117 Australian manufacturing firms using an online survey and analysed the data by using the partial least square structural equation modelling method.

The authors found I4.0 capabilities directly and positively impact supply chain resilience and that incremental innovation acts as a complementary mediator for the I4.0 technologies’ relationship with supply chain resilience. I4.0 technology capability needs to first transfer to incremental innovation for operations resilience. The authors also found that incremental innovation and operations resilience are serial mediators in the relationship between I4.0 technologies and supply chain resilience.

This research linked the three research areas of I4.0 implementations, innovation capabilities and resilience. To the best of the authors’ knowledge, there has not been a previous study that investigated all three constructs together. Also, this study considered operations resilience and supply chain resilience as two distinct constructs and found I4.0 technologies had differential effects on them. The findings, thus, provide a novel contribution to the resilience, organizational capability and innovation literature. The investigations make clear to business practitioners how investments in technology and innovation capabilities translate into the resilience that is required in periods of disruption to business certainty.

The purpose of this paper is to investigate supply chain relationships in an urban local fresh food system from a retailer perspective to examine the types of relationships and the factors underpinning the development of such relationships.

Using the multiple case study method, interview data from twelve urban local fresh food retailers in Sydney were analysed using the thematic analysis.

This study finds that balanced power relationships in the supply chain allow reasonable power to sit with growers in product price determination irrespective of the dependency of small-scale growers on relatively large local retailers. Trust-based relationships are developed over multiple transactions, where shared values across the supply chain and consistently low opportunistic behaviour in reward sharing are demonstrated to be the crucial factors underpinning close relationships. This study also found evidence of horizontal supply chain linkages among retailers in a competitive environment.

Findings of this study have implications for policymakers in designing urban fresh food systems and for practitioners in large urban retailers including supermarkets that attempt to integrate local food into their product portfolio.

This study extends the local food system literature dominated by rural studies to include new knowledge about the dynamics of collaborations in contemporary urban local fresh food supply chains. It provides the first empirical evidence of lateral inventory transshipment between retailers in a competitive environment confirming previous simulation studies.

The purpose of this paper is to propose a three-tier assessment model (TAM), aiming to identify and evaluate the competitiveness level of companies. The existing problem is that companies find it difficult to choose a proper model which can be deployed to benchmark with competitors in terms of their competiveness level in the marketplace. Most of the available models are not appropriate or easy to use. The proposed assessment model is able to provide an insight for better planning and preparation so as to gain a better chance of success comparing with their competitors. Most importantly, the proposal model adopts a pragmatic approach and can be implemented without going through tedious mathematical calculations and analysis.

TAM embraces three different approaches deployed in various stages of the application process. The first stage is to identify the relevant criteria using hierarchical holographic modeling and the second stage is to assess the associated weightings of these criteria used to rate the potential competitiveness of related companies. The technique used in stage two is known as fuzzy analytic hierarchy process (FAHP) which is a combination of two well-established methods including fuzzy logic and analytical hierarchical programming. In stage three, a technique known as technique for order preference by similarity to the ideal solution (TOPSIS) is adopted to benchmark the level of competitiveness covering several companies in the same industry.

In this paper, a case study is conducted in order to validate the feasibility and practicality of the proposed model. Results indicate that TAM can be easily applied in various industrial settings by practitioners in the field for supporting operations management practices.

Significant amount of work is needed to ensure that the proposed model can be practically deployed in real industrial settings.

This proposed model is able to capitalize on the benefits of the HMM, FAHP and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification.

This paper suggests a hybrid model to assess competitiveness level embracing three different techniques with the unique feature which is able to provide an insight for better planning and preparation in order to excel competitors. Companies may be able to follow the procedures and steps suggested in the paper to implement the model which is proven to be pragmatic and can be applied in real situations.

Despite much research on supply chain (SC) integration and the growing emphasis on recent information technology advancements as an enabler of improved performance, there has been limited research focussed specifically on information integration in supply chains (SCs). The purpose of this paper is to systematically review the literature on information integration in the fresh food supply chain (FFSC) from a holistic perspective.

Literature review is done by systematically collecting and analysing the recent literature to identify various participant entities of the FFSC information network and their specific information needs.

The information needs of FFSC entities are diverse but the needs are common across multiple entities.

This study only reviewed the FFSC-related literature; an extended study of the food industry may reveal a more comprehensive view.

These findings are useful for practitioners in understanding the participant entities in the information network and their information needs and for policymakers in formulating FFSC development initiatives.

The authors are not aware of another study that investigates the FFSC in a holistic approach, one that identifies the actors, their interactions and information needs.

The purpose of this paper is to examine the potential impacts of various variables on product return activities after online shopping. Previous studies on customer behaviour have been predominantly concerned with return on used products and other product-quality-related constructs in the model. This study aims to specially examine the logistics service-related and customer intention–related variables for general products under the e-commerce circumstance.

Structured questionnaire data for this study were collected in the two southeast cities of China (162 useable responses). Structural equation modelling was used to examine the latent variables.

The results confirmed that product return intention has the greatest impact on online shopping returns with a direct effect of 0.63, followed by the flexibility in return (logistics service) with a direct effect of 0.49.

Such a model not only enriches the theoretical understanding of customer behaviour studies but also offers online shopping stores and platforms a quantitative benchmark and new perspective on the design of online shopping supply chains by considering product returns so as to improve the customer satisfaction.