Socially responsible operations in the Industry 4.0 era: post-COVID-19 technology adoption and perspectives on future research

A bibliometric analysis of IoT applications in logistics and supply chain management

The rapid integration of the Internet of Things (IoT) into logistics and supply chain management (SCM) marks a significant transformation towards enhanced efficiencies, security, and sustainability. Through a comprehensive bibliometric analysis of 2680 publications from the Scopus database, this study charts the evolution of IoT within logistics and SCM and reveals a shift from foundational explorations to mature implementations. The research unfolds a complex thematic structure, highlighting the revolutionary impacts of IoT and related technologies such as RFID, the synergy of Industry 4.0 with SCM through Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT), the strategic role of blockchain for enhanced traceability and security, and the advent of novel communication and encryption technologies for secure data exchanges. Further, the analysis categorizes the scholarly discourse into critical areas including big data and IoT optimization in SCM, IoT-driven innovation in the food supply chain, applications of blockchain and smart contracts, digital transformation through Industry 4.0, security advancements with intelligent systems, and the exploration of advanced technologies for Industry 4.0 and 5.0. This review not only delineates the intellectual landscape of IoT applications in logistics and SCM but also identifies emerging research areas such as blockchain integration, 5G potential, and AI-driven optimizations, suggesting pathways for future research to broaden the understanding in this dynamically evolving field. It serves as an essential resource for academics and practitioners, providing insights into the transformative role of IoT in logistics and SCM and proposing directions for future technological and academic endeavors.

Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.

A humanistic model of corporate social responsibility in e-commerce with high-tech support in the artificial intelligence economy

This paper aims to develop a humanistic model of corporate social responsibility in e-commerce, relying on high technology in an artificial intelligence economy. The research is based on the experience of the top 30 publicly traded e-commerce companies, the 16 most responsible companies in the retail industry in the USA, and the leading global and Russian e-commerce business structures in 2020-2021. Based on econometric modeling, it is substantiated that the humanization (qualitative criterion) of jobs provides an increase in revenues of e-commerce businesses to a greater extent than an increase in the number (quantitative criterion) of jobs. The high technology of the artificial intelligence economy (AI economy) makes it possible to maximize the contribution of responsible HRM of the e-commerce business in increasing its revenues. For this purpose, a humanistic model of corporate social responsibility in e-commerce based on high technology in the AI economy has been developed. The theoretical significance lies in proving the need to humanize jobs in e-commerce and revealing the essence of this process. The practical significance lies in the fact that the developed humanistic model will increase the profitability and, consequently, the resilience of businesses to future economic crises that arise against the backdrop of the COVID-19 pandemic.

The impact of corporate social responsibility decoupling on financial performance: the role of customer structure and operational slack

Purpose

Corporate social responsibility (CSR) decoupling indicates a misalignment between how firms report CSR and what firms actually practice with respect to CSR. The purpose of this paper is to examine the relationship between CSR decoupling and financial performance and the factors affecting this relationship.

Design/methodology/approach

This paper collects and combines secondary panel data from multiple sources of Chinese listed firms from 2008 to 2020 to test the direct impact of CSR decoupling on firms’ financial performance and the moderating role of customer structure and operational slack.

Findings

This paper finds that CSR decoupling is negatively associated with firms’ financial performance. These findings further suggest that the negative relationship can be suppressed by customer stability and operational slack, but amplified by customer concentration. These conclusions remain robust to alternate measures of independent and dependent variables and narrower samples.

Originality/value

In the literature, the effect of CSR on firms’ financial performance is inconclusive. This is the first study to examine the impact of CSR decoupling on firms’ financial performance and the factors affecting this relationship. This paper contributes to the CSR decoupling literature from an operations and supply chain management perspective.

Critical challenges of integrating OPEX strategies with I4.0 technologies in manufacturing SMEs: a few pieces of evidence from developing economies

Purpose

The study proposes to determine the impending challenges to lean integration with Industry 4.0 (I4.0) in manufacturing that aims at achieving desired operational performance. Integrating lean and Industry 4.0 as the two industrial approaches is synergetic in providing operational benefits such as increasing flexibility, improving productivity, reducing cost, reducing delivery time, improving quality and value stream mapping (VSM). There is an urgent need to understand the integrated potential of OPEX strategies like lean manufacturing and also to determine the challenges for manufacturing SMEs and further suggest a strategic roadmap for the future.

Design/methodology/approach

The current work has used a combined approach on interpretative structural modeling (ISM) and fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) approach to structure the multiple level analysis for the implementation challenges to integrate OPEX strategies with Industry 4.0.

Findings

The research has found that the indulgence of various implementation issues like lack of standardization, lack of vision and lack of trained support, all are the major challenges that inhibit the integration of OPEX strategies with I4.0 technologies in manufacturing.

Research limitations/implications

The research has investigated the internal factors acting as a roadblock to lean and Industry 4.0 adoption. Further studies may consider external factors to lean and Industry 4.0 implementation. Also, further research may consider other operational excellence approaches and extend further to relevant sectors.

Practical implications

This study provides the analysis of barriers that is useful for the managers to take strategic actions for implementing OPEX strategies with I4.0 in smart manufacturing.

Originality/value

The research determines the adoption challenges towards the integrated framework. This is the first study to explore challenges in integrating OPEX strategies with I4.0 technologies in manufacturing SMEs.

Switchover to industrial additive manufacturing: dynamic decision-making for problematic spare parts

Purpose

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

Design/methodology/approach

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

Findings

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

Originality/value

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.