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The call for supply chain transparency (SCT), especially the environmental, social and governance (ESG) aspect, is getting increasingly louder. Based on the signaling theory, our study investigates the operational benefit of supply chain transparency in terms of ESG (SCT-ESG). To further clarify the signaling process, the moderating roles of digitalization of the firm and signal strength are also examined.

Longitudinal secondary data from multiple databases are matched and analyzed using ordinary least squares (OLS) regressions to validate the proposed hypotheses.

Results suggest that with SCT-ESG, firms have a weakened disparity between production variance and demand variance, and the supply chain experiences a reduced bullwhip effect. Further, digitalization of the focal company and signal strength reinforce the negative effect of SCT-ESG on the bullwhip effect.

The study integrates the SCT and ESG literature through SCT-ESG, extending benefits of ESG disclosure to the supply chain context. It extends the application of the signaling theory in OSCM by including contextual factors of digitalization and signal strength.

The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

One pertinent dynamic phenomenon in supply chains is the amplification of order variance, i.e. the bullwhip effect. Its continued significance is underscored in contemporary empirical research. While numerous publications have pinpointed various causes of the bullwhip effect, there remains a gap in their systematic consolidation. The purpose of this paper is to compile a comprehensive list of the causes of the bullwhip effect from existing literature and categorize them appropriately.

This study conducts a systematic literature review to offer a comprehensive overview of bullwhip effect causes addressed in the existing literature. The identified causes are categorized using a qualitative content analysis approach.

The study shows the diversity of the causes of the bullwhip effect and their interdependencies. In addition, this study demonstrates that, at the highest level of aggregation, causes of the bullwhip effect can be classified into four main categories: causes inherent in the system structure, causes related to uncertainty, causes related to misaligned incentives and causes related to inadequate cognition of the situation.

The work provides an extensive overview and categorization of bullwhip effect causes, offering valuable insights for both researchers and practitioners seeking a deeper understanding of this phenomenon. In addition, it underscores managerial implications and highlights future research opportunities.

This paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance.

The authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling.

The results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance.

This study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

This paper addresses a gap in task performance research, with a focus on supply chain operations, by exploring the role that defensive pessimism (DP)—a phenomenon sparsely studied in supply chain literature—has in the workplace. It investigates the roles that task complexity, perceptions of control and employee situatedness in the workplace play as predictors of DP, as well as addresses the relationship between defensive pessimism and supply chain performance.

Five hypotheses are developed and empirically tested employing the data-generating method, Monte Carlo simulation and then applying factor analysis and structural equation modeling (SEM) to survey data from practitioner members of the Council of Supply Chain Management Professionals.

The results reveal that task complexity and external locus of control heighten perceptions among employees that task completion could be outside their locus of control. The increased tendency to be defensively pessimistic about workplace commitments negatively impacts supply chain performance. This study found that task complexity and external locus of control encourage DP, negatively impacting supply chain performance (SCP).

This study explored underlying causes of defensive pessimism, a self-limiting behavior among supply chain professionals. In understanding the role of DP, it is possible to enhance SCP by managing task complexity, external locus of control and job autonomy—predictors of defensive pessimism in work commitments.

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.

Neo-institutional theories and their constructs have so far only received limited attention in supply chain management literature. As recent supply chain disruptions and their ripple effects affect actors on a broader institutional level, supply chains are confronted with multiple new and emerging, often conflicting, institutional demands. This study aims to unpack the notion of institutional complexity behind supply chain disruptions and present a novel institutional framework to lower supply chain susceptibility and increase supply chain resilience.

The authors identify the patterns of complexity that shape the supply chain susceptibility, namely, distance, diversity and ambiguity, and present three institutional responses to susceptibility to increase supply chain resilience, namely, institutional entrepreneurship, institutional alignment and institutional layering.

This paper analyses the current situational relevance to better understand the various and patterned ways how logics influence both supply chain susceptibility and the supply chain resilience. The authors derive six propositions on how complexity can be reduced for supply chain susceptibility and can be increased for supply chain resilience.

By expanding and extending research on institutional complexity to supply chains, the authors broaden how researchers in supply chain management view supply chain susceptibility, thereby providing managers with theory to think differently about supply chains and its resilience.

The purpose of the study is to analyze and measure the impact of disruption in demand which causes the bullwhip effect. The bullwhip effect impacts the performance of firm. Just like everything else, covid has had an impact on the disruption of supply chain too leading to the need of measuring the bullwhip effect of select Indian sectors. The comparison on bullwhip effect is drawn in pre- and during covid era in major sectors. The study helps to understand, analyze and measure the impact of covid and its challenges to supply chain.

The empirical study is carried out on five major select Indian sectors which have the largest market capitalization in Indian economy, namely, FMCG (fast-moving consumer goods), automobile, utility, consumer durable and IT (information technology). The disruption in the supply chain is measured in terms of bullwhip effect. The novel metric ratio of bullwhip effect is computed which is based on demand–supply mismatch and analyzed based on 10 years of observations. The data is analyzed twice, first from 2011 to 2019 (pre-covid era) and second from 2019 to 2021 (during covid era). Each time, Bombay Stock Exchange (BSE) sectoral indices are used to compute the bullwhip ratio, and empirical data is collected using Prowess. The firms listed in BSE represent most of the sector. Such panel data helps us to analyze inter- and intraindustry bullwhip effect. The changes in the bullwhip effect for various BSE listed firms are analyzed pre- and during covid era. These changes are specifically studied at the manufacturer end of the supply chain. Later regression analysis is performed to study the changes required in production based on the demand. The various strategies that cause or mitigate the impact of covid in intraindustry can be derived from the study. The disruption in production is analyzed based on the disruption in demand and profit before interest and tax (PBIT).

In pre-covid era, the percentage of demand disruption was low in select sectors but not exactly zero. Covid caused the disruptions in supply chain across the globe which resulted in bullwhip effect in Indian sectors too. Yet some of the sectors were able to cope better with the situation as compared to others. In the present study, same is analyzed statistically, and results are derived for practical significance.

The empirical data is having the observations of past 10 years to analyze the pattern of demand disruption in the firms and hence the sectors. The impact of covid is studied on performance, which is analyzed in terms of PBIT. The impact of other factors (political, social, marketing policies, etc.) that may cause disruption in the supply chain of a firm is not considered in the study.

Study is unique, as it measures disruption and provides a peerless way to study the inter- and intrasectors. To analyze the impact of bullwhip effect on sector performance, it is very much required to first measure the bullwhip; this measure of bullwhip as a ratio of the slopes of demand and supply is a novel approach. The study emphasizes that the impact of covid is not the same among the firms, and hence among the sectors. Also, it is found that the impact of such adversities can be mitigated, and performance of firm can remain intact in turbulent times too.

The purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.

To test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).

Several important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.

This research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.