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When is it more profitable for multinational manufacturers to manufacture in high-cost environments and when in low-cost environments? While the literature offers many cues to answer this question, too little empirical research directly addresses this. In this study, we quantitatively and empirically investigate the financial effect of companies' production footprint in low-cost and high-cost environments for different types of production networks.

Using the data of 770 multinational manufacturing companies, we analyze the relationship between production footprints and profitability during four calendar semesters in 2018 and 2019 (N = 2,940), investigating the moderating role of companies' production network type.

We find that companies with networks distinguished by both high levels of product complexity and process sophistication profit the most from producing to a greater extent in high-cost countries. For these companies, shifting production to low-cost countries would be associated with negative performance implications.

Our findings suggest that the production geography of companies should be attuned to their network type, as defined by the companies' process sophistication and product complexity. Manufacturing in low-cost countries is not always the best choice, as doing so can adversely affect profits if the products are highly innovative and the production processes are complex.

We contribute to the scarce empirical literature on managing global production networks and provide a data-driven analysis that contributes to answering some of the enduring questions in this critical area.

The purpose of this paper is to clarify the conceptual confusion in the extant literature about organizational agility and explore its role in different relationships in the context of digital transformation.

An integrative review of the relevant literature on agility was conducted. The literature on organizational agility and other variables in recent quantitative research was also examined to explore its role in different relationships.

Organizational agility is the ability to quickly respond and proactively embrace unanticipated changes in dynamic environments through effective resource reconfiguration and rapid decision-making. The role of organizational agility in achieving digital transformation has not been addressed from a holistic conceptual perspective. This paper addresses that gap and proposes that organizational agility is the underlying mechanism for an organization to fully use and engage its workforce, operation and network in the process of digital transformation.

This research is an integrative review of the existing literature on the concept of agility and its relationships. The next phase of research needed for theory building will be the operationalization of constructs.

Organizations should strive to strategically develop both the reactivity and proactivity sides of organizational agility in achieving digital transformation that involves fundamental changes at different levels of the organization.

This paper explores the role of organizational agility in digital transformation through an integrative review of the relevant literature.

This study focuses on (re-)introducing computer simulation as a part of the research paradigm. Simulation is a widely applied research method in supply chain and operations management. However, leading journals, such as the International Journal of Operations and Production Management, have often been reluctant to accept simulation studies. This study provides guidelines on how to conduct simulation research that advances theory, is relevant, and matters.

This study pooled the viewpoints of the editorial team of the International Journal of Operations and Production Management and authors of simulation studies. The authors debated their views and outlined why simulation is important and what a compelling simulation should look like.

There is an increasing importance of considering uncertainty, an increasing interest in dynamic phenomena, such as the transient response(s) to disruptions, and an increasing need to consider complementary outcomes, such as sustainability, which many researchers believe can be tackled by big data and modern analytical tools. But building, elaborating, and testing theory by purposeful experimentation is the strength of computer simulation. The authors therefore argue that simulation should play an important role in supply chain and operations management research, but for this, it also has to evolve away from simply generating and analyzing data. Four types of simulation research with much promise are outlined: empirical grounded simulation, simulation that establishes causality, simulation that supplements machine learning, artificial intelligence and analytics and simulation for sensitive environments.

This study identifies reasons why simulation is important for understanding and responding to today's business and societal challenges, it provides some guidance on how to design good simulation studies in this context and it links simulation to empirical research and theory going beyond multimethod studies.

This paper aims to investigate how manufacturing firms behave to mitigate business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global supply chain.

A systematic literature review for data mining was used to address the research objective. Multiple scientometric techniques (e.g. bibliometric, machine learning and social network analysis) were used to analyse the Lens.org, Web of Science and Scopus databases’ global supply chain risk mitigation data.

The findings show that the firms’ manufacturing supply chains used digitalisation technologies such as Blockchain, artificial intelligence (AI), 3D printing and machine learning to mitigate COVID-19. On the other hand, food security, government incentives and policies, health-care systems, energy and the circular economy require more research in the global supply chain.

Global supply chain managers were advised to use digitalisation technology to mitigate current and upcoming disruptions. The manufacturing supply chain has high uncertainty and unpredictable global pandemics. Manufacturing firms should consider adopting Blockchain technology, AI and machine learning to mitigate the epidemic risk and disruption.

This study found the publication trend of how manufacturing firms behave to mitigate the global supply chain disruptions during the global pandemic and business uncertainty. The findings have contributed to the supply chain risk mitigation literature and the solution framework.

This study aims to examine how external and internal conditions drive the impact of circular economy mechanism by decomposing into three policy networks in terms of reduce, reuse and recycle, to better understand the contingency model of climate change and effect of firm size on subsequent performance.

Drawing on circular economy network and resource-based view (RBV)-network-resilience strategy framework, a pooled longitudinal cross-sectional data model is developed using a sample of 4,050 Taiwanese manufacturing multinational corporations (MNCs) making foreign direct investment between 2013 and 2018. Structural equation modeling analysis is used to comprehensively examine and investigate each circular economy policy network in the context of climate change and firm size. Post hoc multigroup analysis (MGA) is also conducted.

MGA shows that the reduce policy network is positively and negatively related to manufacturing know-how and production size, respectively. The impact of reuse policy network can enhance the competence of large firms. The recycle policy network is more prominent in terms of competence enhancement of climate change.

MNCs are seeking to build circular economy policy networks to a greater extent, given climate change pressure and guidelines.

This study adds to the circular economy and RBV-network-related literature on climate change and interactions to enhance performance, echoing the recent call on the sustainability of the circular economy of MNCs.

This research aims to conduct a literature review of Lean Six Sigma (LSS) in the Indian context and related research publications and apply bibliometric analysis and the author's visualization to map research trends in this area.

This article conducts a bibliometric analysis of LSS-related research in the Indian context using data gathered from Scopus and Web of Science databases from 2011 to 2022. The review provides information on LSS-related research in the Indian context and evaluates performance based on primary sources, authors, keywords, countries, affiliations, and documents. The analysis employs the Biblioshiny app and Bibliometrix R-tool for data analysis and scientific mapping.

The results of the bibliometric analysis indicate that the LSS culture has widely spread in India. The International Journal of Lean Six Sigma and Production Planning and Control were found to be the most productive sources for publishing LSS-related research articles. Antony J. was identified as the most active author in this field, contributing the most over the years. Among all organizations, NITs have conducted the most comprehensive research on LSS, indicating their significant investment of resources and efforts in studying this methodology and its applications in India. Additionally, the study examined the intellectual, social, and conceptual structures to identify implicit gaps and future research opportunities.

The findings of this study can inform academicians, researchers, practitioners, and policymakers about the state-of-the-art and the specifics of the most prolific studies. This study will facilitate their exploration of emerging research areas in LSS.

To the best of the authors knowledge, this is the first bibliometric analysis of LSS in the Indian context, providing an overview of relevant publications published between 2011 and 2022. This study analyzed 194 articles on LSS in India, which can help researchers and academics identify emerging research areas, suitable collaborators, and relevant journals for future publications.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.

The purpose of this paper is to provide small and medium-sized enterprises (SMEs) in emerging markets with an updated Purchasing Portfolio Matrix (PPM) specifically for international sourcing. This data-driven PPM matrix is designed to provide a dynamic and process perspective that can help SMEs survive the disruptions caused by emergency situations such as the global COVID-19 pandemic.

This research reports on qualitative interviews with experienced informants from 15 SMEs in the manufacturing industry. The authors follow process-based research using a combination of retrospective and real-time case study approaches to gradually unveil the dynamics in segmentation and sourcing strategies in the international sourcing context during the COVID-19 pandemic.

The findings reveal the dynamics of segmentation and international sourcing strategies during global disruptions and unpack the underlying logic behind the dynamics that is specific to SMEs in emerging economies.

Existing literature on PPM predominantly focuses on static and normal sourcing circumstances. This paper addresses this gap by adopting a dynamic approach to study how sourcing strategies of SMEs from emerging economies evolve in a highly volatile environment from an international sourcing perspective.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).

The authors examine the effect of supply chain resilience (SCR) on supply chain performance (SCP) while considering the moderating effect of supply chain network complexity (SCNC) on the nexus between SCR and SCP of manufacturing firms.

The quantitative research approach and explanatory research design were utilised for this study. A sample of 345 manufacturing firms in the Accra metropolis was drawn. The partial least square structural equation modelling was employed.

Findings from the study revealed that SCR has a significant positive effect on SCP. However, SCNC had a significant negative moderating effect on the relationship between SCR and SCP.

The authors advocate that manufacturing firms are prone to stronger impact from complex networks that mitigate the already existing positive relationship between SCR and SCP and is dependent on the context in which the study is executed, and the extent to which resilience strategies are robust. Thus, the SCNC has an adverse impact on how well partners interact and how well the supply chain functions.

This is the first study that quantitatively investigates the SCR impact on SCP in the presence of SCNC of manufacturing firms in the context of a developing economy. The study redefines SCNC from earlier studies.