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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.

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.

The purpose of this paper is to examine the nuanced effects of downstream complexity on supply chain resilience, based on portfolio theory and normal accident theory. Intelligent manufacturing is considered to clarify their boundary conditions.

The ordinary least squares regression was conducted, based on the data collected from 136 high-tech firms in China.

Horizontal downstream complexity has a positive effect on supply chain resilience significantly, while the negative impact of vertical downstream complexity on supply chain resilience is not significant. Contingently, intelligent manufacturing plays a negative moderating role in the relationship between horizontal downstream complexity and supply chain resilience, while it positively moderates the relationship between vertical downstream complexity and supply chain resilience.

This study disentangles the nuanced effects of both horizontal and vertical downstream complexity on supply chain resilience, based on portfolio theory and normal accident theory. It also clarifies their boundary conditions by considering the focal firm's intelligent manufacturing level as the contingent factor.

Climate change requires the reduction of direct and indirect greenhouse gas (GHG) emissions, a task that seems to clash with increasing supply chain complexity. This study aims to analyse the upstream supply chain complexity dimensions suggesting the importance of understanding the information processing that these may entail. Reducing equivocality can be an issue in some dimensions, requiring the introduction of written guidelines to moderate the effects of supply chain complexity dimensions on GHG emissions at the firm and supply chain level.

A three-year panel data was built with information obtained from Bloomberg, Trucost and Compustat. Hypotheses were tested using random effect regressions with robust standard errors on a sample of 394 SP500 companies, addressing endogeneity through the control function approach.

Horizontal complexity reduces GHG emissions at the firm level, whereas vertical and spatial complexity dimensions increase GHG emissions at the firm and supply chain level. Although the introduction of written guidelines neutralises the negative effects of vertical complexity on firm and supply chain GHG emissions, it is not sufficient in the presence of spatial complexity.

This paper offers novel insights by suggesting that managers need to reconcile the potential trade-off effects on GHG emissions that horizontally complex supply chain structures can present. Their priority in vertically and spatially complex supply chain structures should be to reduce equivocality.

The purpose of this paper is to examine the nature of supply chain complexity and extend this with literature developed within the project domain. The authors use the lens of ambidexterity (the ability both to exploit and explore) to analyse responses to complexity, since this enables the authors to understand the application of known solutions in conjunction with innovative ones to resolve difficulties. This research also seeks to investigate how managers respond to supply chain complexities that can either be operationally deleterious or strategically beneficial.

The authors develop a descriptive framework based on the project management (PM) literature to understand response options to complexity, and then use interviews with supply chain managers in six organisations to examine the utility of this framework in practice. The authors ask the research question “How do managers in supply chains respond to complexities”?

The case study data show first that managers faced with structural, socio-political, or emergent supply chain complexities use a wide range of responses. Second, over a third of the instances of complexity coded were actually accommodated, rather than reduced, by the study firms, suggesting that adapting to supply chain complexity in certain instances may be strategically appropriate. Third, the lens of ambidexterity allows a more explicit assessment of whether existing PM solutions can be considered or if novel methods are required to address supply chain complexities.

The descriptive framework can aid managers in conceptualising and addressing supply chain complexity. Through exploiting current knowledge, managers can lessen the impact of complexity while exploring other innovative approaches to solve new problems and challenges that evolve from complexity growth driven by business strategy.

This study addresses a gap in the literature through the development of a framework which provides a structure on ways to address supply chain complexity. The authors evaluate an existing project complexity concept and demonstrate that it is both applicable and valuable in non-project, ongoing operations. The authors then extend it using the lens of ambidexterity, and develop a framework that can support practitioners in analysing and addressing both strategically necessary supply complexities, together with unwanted, negative complexities within the organisation and across the supply chain.

The purpose of this paper is to conduct a large-sample empirical examination of how intangible supply chain complexity impacts firm performance in light of a firm's organizational structure.

The study uses panel data from 2,580 Indian manufacturing firms and constructs empirical proxy for intangible supply chain complexity, i.e. CHQ distance from major cities. The proposed conceptual model is grounded in the dynamic capability view (DCV) and social network theory (SNT). Multivariate regression analyses are performed to investigate the effect of intangible complexity on firm performance.

Results show that intangible supply chain complexity, as proxied by “CHQ distance from major cities”, negatively affects firm performance and a firm's organizational structure plays an important role in conceiving CHQ locational strategies. Firms with interconnected supply chain and social network (e.g. business group firms) have a higher propensity to locate their CHQs farther away from major cities, and business group firms that have more distantly located CHQs experience better financial performance compared to independent firms (with less network resources).

In light of the supply chain literature and relevant theories, the study conceptualizes intangible supply chain complexity as “CHQ distance from major cities” and deepens our understanding of the relationship between intangible complexity and firm performance in light of organizational structure. Further, it develops an objective understanding of intangible supply chain complexity by relying on secondary panel data.

The purpose of this paper is to investigate the effectiveness of supply chain integration in different contexts. More specifically, it aims to show that supply chain integration is only effective in buyer‐supplier relationships characterised by high supply complexity.

A survey‐based research design is developed to measure different dimensions or aspects of supply chain integration and supply complexity. Data were collected among manufacturers in The Netherlands and Spain.

This research shows that supply chain integration increases performance if supply complexity is high, while a very limited or no influence of supply chain integration can be detected in case of low supply complexity. The results also show that in high supply complexity environments the use of structured communication means to achieve supply chain integration has a negative effect on cost performance.

The limited sample size prohibits estimating and testing of more comprehensive models of the relationship between supply chain integration and performance. Specifically, the authors were not able to further investigate how different supply chain integration dimensions are inter‐related and mutually reinforce one another to improve performance.

The main managerial lesson is that, in contrast to what has been written in many books and other popular publications, high levels of supply chain integration are only necessary in environments characterised by high supply complexity.

This study helps to better understand context in supply chain management research. Specifically, it investigates the moderating effect of supply complexity on the integration‐performance relationship, a topic suggested by Bozarth et al. as a line for further research.

The objective of this paper is to develop a comprehensive model of supply chain and supply chain decision‐making complexity that provides an understanding of the drivers of supply chain complexity and strategies to manage supply chain and supply chain decision‐making complexity and outcomes.

Grounded theory methodology is employed to build a theory of supply chain and supply chain decision‐making complexity and develop propositions related to antecedents, moderators, and outcomes of supply chain complexity. In addition, extensive literature review and informal interactions with a number of supply chain professionals have been used to validate the theory.

In addition to identifying the antecedents of supply chain complexity, the authors explore strategic, human cognitive ability, and tactical moderators for managing supply chain complexity.

The comprehensive framework presented in the paper builds a theory of supply chain and supply chain decision‐making complexity that is grounded in empirical data. The research also incorporates disparate findings, constructs from multi‐disciplinary research on supply chain complexity and provides future research directions.

The research helps practitioners better understand the sources and outcomes of supply chain complexity and how to manage it. Various strategies to moderate the impact of supply chain complexity are presented.

An integrated, comprehensive theory of supply chain complexity is proposed along with definitions of supply chain complexity and supply chain decision‐making complexity. The proposed model is rooted in actual practice and supported by the existing literature.

Recent studies have argued that companies may actively implement practices to mitigate disruptions in their supply chain and reduce the extent of damage on performance. Other studies have shown that disruptions may propagate in supply chains, leading to consequences that are more negative and raising doubts on the effectiveness of mitigation strategies implemented downstream. This study investigates the influence of supply chain complexity on the two phenomena and their interplay, taking a focal company's perspective.

A systematic procedure for data collection, encoding and aggregation based on incident data mainly from secondary sources was used. Multiple regression models were run to analyse direct and moderation effects involving resilience, distance of impact location from trigger point, and supply chain complexity on weighted performance change.

Supply chain complexity is found to have positive moderation on the ripple effect of disruption. Resilience capability remains to have dominating direct positive effect in mitigating disruptions when supply chain complexity is taken into account.

This study extends the research discourse on supply chain resilience and disruption management with focus on the supply side. It demonstrates that, along with the severity of the disruption scenario, the ripple effect must also be considered when analyzing the benefits of resilience practices implemented by the focal company.

Complexity in the supply chain can only help to smooth-out the rippling effects of a disruption, which go largely beyond supply-demand unbalances and lead time fluctuations. To mitigate it better, the focal company has to act proactively with adequate resilience practices, which also connects to the importance of better visibility across multiple supply chain tiers.

To the best of the authors' knowledge, this is the first study that empirically tests the benefits of resilience practices and the ripple effect of disruptions under the moderation role of supply chain complexity.

The purpose of this paper is to understand and measure empirically the objective and perceived dimensions of supplier-induced complexity in supply chains.

An equal-weight, complementary mixed-method approach is used to investigate supplier-induced complexity and understand its impact on outcomes. Initial qualitative research and extant literature review allowed the identification of supplier characteristics that add complexity to supply chains and development of four research hypotheses. Subsequently, quantitative analysis was used for testing the hypotheses.

The results suggest that supplier-induced complexity is related to adverse outcomes, and both perceived and objective dimensions of complexity are valuable in understanding and measuring supplier-induced complexity.

This study employs a mixed-method approach to establish and test relationships among perceived and objective supplier-induced complexity, and their outcomes. The unit of analysis is the first-tier suppliers of one farm equipment manufacturing firm. This limits the generalizability of the results to similar industrial manufacturing firms.

This paper presents an approach to identify suppliers that add the highest levels of complexity to a supply chain and, thus, require closer monitoring. Specific supplier characteristics are identified for individual suppliers. Developing specific complexity-related measures helps better identify critical suppliers compared to traditional approaches (e.g. ABC analysis).

This paper contributes to supply chain management literature by comprehensively exploring supplier-induced complexity, incorporating the often-ignored perceived complexity dimension, and providing a managerially useful framework.