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Developing strategic relationships with third-party logistics (3PL) providers has long been one of the key challenges in automotive supply chains. The purpose of this paper is to propose a new approach for evaluation and indexing 3PL providers using the Kano model.

The statistical population used in this research comprises managers from the Iranian automotive industry. The Kano evaluation approach is used to analyze the data collected and to classify the criteria used in selection of the preferred 3PL providers.

The results suggest that the proposed framework, based on the Kano classification, can be a powerful tool for the automotive industry in evaluating 3PL providers. Moreover, the analyses indicate that 3PL providers need to improve their service offering in aspects that are to be found in the Kano model’s requirements, namely, must-be, one-dimensional and attractive.

This study contributes to the supply chain management literature by being the first to classify selection indices of 3PL providers in the automotive industry using the Kano model.

Multilevel inverter (MLI) is an established design approach for inverter applications in medium-voltage and high-voltage range of applications. An asymmetric design synthesizes multiple DC input voltage sources of unequal magnitudes to generate a high-quality staircase sinewave comprising a large number of steps or levels. However, the implications of using sources of unequal magnitudes results in the requirements of a large variety of inverter switches and higher magnitudes of the total blocking voltage (TBV) rating of the inverter, which increase the cost. The purpose of this study is to present a solution based on algorithms for establishing DC source magnitudes and other design parameters.

The approach used in this study is to develop algorithms that bring an asymmetric cascaded MLI (ACMLI) design close to symmetric design. This approach then reduces the variety of switch ratings and minimizes the TBV of the inverter. Thus, the benefits of both asymmetric design (generation of a large number of voltage levels in the output waveform) and symmetric design (modularity) are achieved. The proposed algorithms can be applied to a number of ACMLI topologies, including classical cascaded H-bridge (CHB). The effectiveness of the proposed algorithms is validated by simulation in Matlab-Simulink and experimental setup.

Two new algorithms are proposed that reduce the number of variety of switches to just three. The variety can further be reduced to two under a specified condition. The algorithms are compared with the existing ones, and the results are promising in minimizing the TBV rating of the inverter, which results in cost reduction as well. For a specific case of four CHBs, the proposed Algorithm-1 produced 27% and Algorithm-2 produced 53% higher levels. Moreover, the presented algorithms produced minimum values of the TBV and resulted in minimum cost of inverter.

The proposed algorithms are novel in structure and have achieved the targeted values of minimized switch variety and reduced TBV ratings. Due to less variety, the inverter achieves a near symmetric design, which enables to attain the added advantages of modularity and reduced difference of power sharing among the DC sources.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantages and high efficiency by customers because of expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm's success. Therefore, paying attention to customer feedback in supply chain (SC) and logistics processes has recently increased, so manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumers’ purchasing behavior and to analyze the causality and prominence relations among them that can help decision-makers, policy planners and managers of organizations to develop a framework for customer-centric RL. These features are studied based on analysis of product review sites. This paper's special focus is on social media (SM) data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

This paper deals with identifying mobile phone features that affect consumer's mobile phone purchasing decisions. Using the DEMATEL approach and using experts' insights, a cause and effect relationship diagram was generated through which the effect of features was analyzed.

Eighteen features were categorized in terms of cause and effect, and the interrelationships of features were also analyzed. The threshold value is calculated as 0.023, and the values lower than that were eliminated to obtain the digraph. F6 (camera), F13 (price) and F5 (chip) are the most prominent features based on their prominent score. It was also found that the F5 (chip) has the highest driving power (1.228) and acts as a causal feature to influence other features.

The focus of this article is on SM data (Twitter), so that experts can understand the interaction between mobile phone features that affect consumer's decision on mobile phone purchasing by using the results. This study investigates the degree of influence of features on each other and categorizes the features into cause and effect groups. This study is also intended to help organizational decision-makers move toward a reverse customer SC.

This study addresses the prevailing complexities and limitations in estimating and managing construction overhead costs (COCs) in the existing literature, with the purpose of enhancing the accuracy of cost performance indicators in construction project management.

An innovative approach is proposed, employing the activity-based costing (ABC) accounting method combined with building information modelling (BIM) to assign real overhead costs to project activities. This study, distinguished by its incorporation of a real case study, focuses on an administrative building with a four-story concrete structure. It establishes an automated method for evaluating project cost performance through the detailed analysis of earned value management (EVM) cost indicators derived from ABC results and BIM data.

The results show that the ABC integration improves the accuracy of cost performance indicators by over 9%, revealing the project's true cost index for the first time and demonstrating the substantial value of the approach in construction engineering and management.

The current study highlights a notable gap in the existing literature, addressing the challenges in onsite overhead cost estimation and offering a solution that incorporates the state-of-the-art techniques.

The proposed method has significant implications for project managers and practitioners, enabling better-informed decisions based on precise cost data, ultimately leading to enhanced project outcomes.

This research uniquely combines ABC and BIM, presenting a pioneering solution for the accurate estimation and management of COCs in construction projects, adding significant value to the current body of knowledge in this field.

Sustainable supply chain management (SSCM) practices and green entrepreneurial preference (GEP) have gained increasing attention from academicians; however, their impacts on business' competitive performance (BCP) post-coronavirus disease of 2019 (COVID-19) remain unclear. Although SSCM is vital for supporting BCP, the previous publications indicate the absence of significant relationships among GEP, SSCM and BCP. This study tries to fill this literature gap by investigating if GEP and SSCM can shape BCP. This study also suggests the moderation effect of digital innovations such as artificial intelligence and big data analytics (AIBD) on those relationships from a COVID-19 viewpoint.

Data were collected from 245 Halal food firms in Yemen, and the research framework was assessed using structural equation modeling (SEM).

The empirical findings show that there are significant impacts of GEP on SSCM and subsequently on BCP. The findings also reveal that SSCM practice mediates GEP-BCP link. Besides, digital innovations such as AIBD positively moderate the link of GEP-SSCM.

This study is the first attempt that advises Halal food firms to formally adopt GEP, SSCM and digital innovations to boost BCP, especially in uncertain times like post-COVID-19. Unlike earlier studies that observe SSCM usage as a direct predictor of firm performance, this study delivers an innovative insight that digital innovations can assist in GEP and SSCM incorporation in the in-house operations of the firms post-COVID-19.

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.

This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.

The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.

The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.”

The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.

This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.

This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.

This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI).

Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model.

This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP.

This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

Despite increasing attentions to sustainable service supply chain management (SSSCM), a framework for performance evaluation of sustainable service only supply chain management (SSOSCM) is still missing. This paper tries to fill this gap and provides a novel conceptual framework.

The articles related to SSSCM are fetched from the databases of SCOPUS and Web of Science. Analysis of 174 articles identified by the systematic literature review is further carried out.

This research identifies the sustainable practices for service only supply chain (SOSC) as environmental management, social management, economic management, customer management, health, safety and risk management, technical sustainability, institutional sustainability, information and technology management as well as two performance measurement criteria as operational performance and organizational performance. This paper provides a novel conceptual framework for the performance evaluation of SSOSCM. The results call for future exploration in the following three broad directions-(1) customer's perception, involvement and their behaviour towards sustainability in SOSC context; (2) trade-off, incentive mechanism and multilevel evaluation for achieving sustainability in SOSC and (3) sustainability in SOSC from various point of views.

The managers can use the framework to assess the performance of the organization while researchers can explore the discussed research gaps.

This is the first paper that provides a novel conceptual framework for the performance evaluation of SSOSCM as well as potential future research directions.