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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.

The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.

Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.

Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.

Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.

The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

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.

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.

The purpose of this paper is to examine the strategic response to Industry 4.0 for Chinese automotive industry and to identify the critical factors for its successful implementation.

A technological, organizational, and environmental framework is used to build the structural models, and statistical tools are used to validate the model. The data analysis helps to determine which factors have impact on the strategic response and whether their relationships are positive or negative. Interpretive structural modeling method is applied to further analyze these derived factors for depicting the relationship.

The result shows that company size and nature do not increase the use of advanced production technologies, while other factors have positive impacts on improving the technology adoption among the companies surveyed.

A strategic response to Industry 4.0 not only helps in improving organizational competitiveness, but it also has social and economic implications. For this purpose, empirical data are collected to measure the understanding of Industry 4.0 in the Chinese automotive industry.

Despite the fact that the Chinese Government has proposed the “Made in China 2025” approach as a way to promote smart manufacturing, little empirical evidence exists in the literature validating company’s perspective toward Industry 4.0. This paper is to fill the research gap.

The advent of a dynamic and uncertain environment has shifted organizational focus from reactive to proactive approaches to develop resilience against disruptions. Organizations can strengthen their supply chain networks through strategic changes in structure and processes. In this connection, this study explores the extant literature on supply chain resilience (SCRE) concerning soft organizational factors (leadership, organizational culture and knowledge management) to analyse recent trends in this domain and propose future research directions.

It is a bibliometric analysis and systematic literature review of research articles from 2004 to 2024, collected from ISI Web of Science with keywords searches such as “knowledge management and supply chain resilience,” “leadership and supply chain resilience” and “organizational culture and supply chain resilience.” “VOS viewer” and “ATLAS.ti” were utilized for the co-occurrence and co-authorship analysis of the articles along with focusing on aspects such as theoretical and practical implications, the collaboration institutions and the countries involved in relation to the topic of interest.

The review shows that the development of studies on SCRE was slow from 2004 to 2015 but grew significantly from 2015 onwards and rose exponentially after 2020. Most studies were published in 2023. Results reveal the development of proactive strategies for SCRE in the recent literature by focusing on organizational factors. The study highlights exploring the contextual interplay between environmental, social and governance (ESG) and soft organizational factors for mitigation of supply chain risk and resilience in large-scale projects.

The COVID-19 pandemic became the precursor to highlighting the antecedents of SCRE, but the study of soft organizational drivers is still an ongoing area of research. There is a need to map the nascent literature on the link between organizational soft drivers and SCRE.

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.

This paper aims to examine the effect of customer knowledge management (CKM) on continuous innovation and firm performance in 35 private banks in Guilan (Iran). CKM emerges as an important and effective system for innovation capability and firm performance. However, the role of CKM in innovation and performance is not well understood.

Data have been collected via questionnaires from managers of private banks in Guilan. Feedback was received from 265 managers in 350 distributed questionnaires, and hypotheses were tested using the structural equation modelling.

The results of this paper indicate that knowledge from customers has a positive impact on both innovation speed and innovation quality as well as operational and financial performances. Also, our results demonstrate a different effect of knowledge about customer and knowledge for customers on various dimensions of innovation and firm performance. By using customer’s knowledge flows, firms will be aware of external environment and new changes in customers’ needs and so will be more innovative and perform better.

CKM is known as an important system to connecting internal environment to external environment to create novel ideas. The results of this paper shed light on the consequences of CKM on firms and provide support for the importance of CKM to enhance innovation capacity and firm performance.

This article is one of the first to find empirical support for the role of CKM within firms and its importance on innovation capability and firm performance. This study can provide valuable insights and guidance for researchers and managers as well.

The purpose of this paper is to explore the feasibility of adopting crowdsourcing for enhancing innovative problem solving through task design on task attributes. Task attributes have been proven to be an important factor influencing participation and engagement of crowdworkers.

A survey questionnaire was developed and data from potential and experienced crowdworkers was collected and analyzed to identify the influence of task attributes on the quantity and quality of innovative solutions from crowds. This study finds that extrinsic and intrinsic motivation of task attributes are linked to psychological participation attention and the contribution of innovative solutions.

It is found that crowdsourcing projects with higher awards and recognition as extrinsic motivation is positively associated with the quantity of solution. Competitive selection with performance feedback, problems with diversity of knowledge and job autonomy with more information from sponsors are positive associate with the quality of innovative solutions. These results can complement existing research studies on how task attributes influence the performance of existing crowdworkers in crowdsourcing and can also be a starting point for analyzing potential crowdworkers’ psychological perspectives.

The novelty of this paper is that it illustrates the influential effects of five selected task attributes – monetary rewards and recognition, competitive selection, knowledge diversity, task complexity and autonomy – to enhance extrinsic and intrinsic motivation toward crowdsourcing feasibility in terms of quantity and quality of innovative solutions.