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Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.

In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.

A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.

The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

Changes frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry.

An exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs).

Dynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation.

The multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research.

In revealing antecedents of complexity’s effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment.

Building on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships.

This study aims to find the impact of supply chain certification (SCCert) on supply chain efficiency (SCEffi) with the inverted U-shaped moderator role of supply chain complexity (SCComp).

In order to test the conceptual model and the hypothesized relationships between all the constructs, the 307 useable survey responses were collected using the purposive sampling technique on a seven-point Likert scale. The SPSS26.0 and AMOS24.0 were used to analyze data, and the hierarchical regression analysis was used to test the model.

This study reached a set of interesting results where it was confirmed that there is a significant relationship between SCCert and SCEffi. It further confirmed the inverted U-shaped moderating effect of SCComp between SCCert and SCEffi: on the left side of the threshold, the increase of SCComp will enhance the promotion effect of SCCert on SCEffi, while on the right side of the threshold, excessive SCComp will rather weaken the promotion effect of SCCert on SCEffi.

The findings provide implications for supply chain efficiency enablers to introduce/promote certification upgrading actions. The study provides a framework for solving the power and constraint problem of supply chain efficiency change.

Findings provide deeper and new insights into threshold feature of supply chain complexity, analyzing how supply chain certification activity realize supply chain efficiency reform through the moderating role of supply chain complexity.

One of the major challenges for developing countries is the lack of mechanisms for the evaluation of critical success factors (CSFs) of quality initiatives, which hampers the journey toward sustainability. Lean Six Sigma (LSS) has been one of the most widely used initiatives supporting quality improvement with wastes reduction and facilitating sustainability. To expedite LSS and its spread, it is important to evaluate key CSFs. Accordingly, the purpose of this paper is to provide an approach for the evaluation of LSS-CSFs for Indian small and medium enterprises (SMEs).

The paper uses a graph theoretic approach and demonstrates the evaluation of LSS-CSFs by proposing an index. The development of index is illustrated using a set of seven prioritized CSFs based on the literature review paper (Lande et al., 2016).

This study guides about the translation of CSFs in the form of an index (number) and will benefit both researchers and practitioners, who wish to study the role of key CSFs for implementation and audit requirements for sustainability.

Authors remain confined only to Indian SMEs.

LSS possesses the potential to enhance the performance of manufacturing SMEs, but its evaluation is not easy. This attempt for offering a useful evaluation scheme involving CSFs, in the areas of LSS in developing country contexts, is the first. The approach also facilitates both quality audits and benchmarking between different sets of CSFs. The approach is generalizable and can be extended in other areas.

The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).

Through extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.

The authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.

In this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.

In today's globalized business environment, growing supply chain complexity (SCC) is arguably a major threat to the firm's business continuity with an adverse impact on the firm's competitive advantage and business performance. Researchers, though, investigated the impact of SCC drivers on a firm's operational performance, but the key question “Which supply chain complexity drivers severely impact the supply chain performance (SCP)?” remains largely unanswered from empirical research. The present study aims to decompose the SCC into four major constituting sub-categories (upstream, operational, downstream and external) to explore the causal impact of SCC drivers on SCP in direct and mediated manner.

The indicators applied for measuring constructs in the “Measurement model” are obtained from existing literature to increase the validity and reliability of the model. First, a pilot survey involving 25 SC managers from various manufacturing firms was conducted for indicator refinement and content validation. Second, the large-scale response data were collected through extensive surveys. This research explores the causality by testing the hypothesis applying Structural Equation Modeling (SEM) based on the responses received from 246 firms.

The study investigates the impact of SCC drivers on SCP through direct and mediation effect. The results indicate that upstream and operational SCC drivers play a mediating role in managing SCP. The findings reveal that upstream and operational SCC drivers adversely impact the SCP. Furthermore, the impact of downstream complexity on SCP is moderated through operational complexity drivers. The result explains the theoretical relation among SCC drivers supported by empirical validity.

The outcome offers practical relevance to supply chain (SC) managers in SCC and SCP management. Knowing the effect of SCC drivers among themselves and on SCP will facilitate the SC managers in devising the right strategies. The study provides a framework for prioritizing the resource in addressing the SCC issues among many.

The study addresses the apparent gap in the literature by modeling the impact of SCC drivers on SCP, which remained largely unexplored. First, it contributes to developing complex relationships among SCC drivers. Second, the direct and mediated causal effect of the SCC drivers individually and combinedly on SCP are explicated.

A literature review revealed that government of various developing economies have put an effort on health-care supply chain through the executing critical factors (CFs) directly. Although they have attained some significant benefits in this tactic, but it was not up to satisfactory level. One of the reasons can be attributed to the fact that government/policy makers are not quantifying the impact of CFs on health-care supply chain. This paper aims to propose a methodology to quantify and estimate the impact of CFs on government-supported health-care supply chain (GHSC).

The Graph Theoretic Approach is proposed for estimating the impact and utility of CFs on an Indian GHSC. This study is also extended to scenario analysis for comparing results with different performance situation.

The results obtained from this study show that performance of Indian GHSC is satisfactory, but performance gaps exist which need to be reduced. In this research work, 12 CFs are identified under two significant categories (SCs), i.e. enablers and barriers and the intensity of enablers and barriers have been calculated to show the impact or influence of CFs on GHSC. The value of intensity shows that the role or impact of enabler category (i.e. performance measurement, employee recognition and reward, technology adoption, training cell, inbuilt analytical tool for IT system) is higher on Indian GHSC in comparison to barriers category to enhance the performance of GHSC.

The obtained numerical results are completely in specific to the Indian perspective only; hence, they cannot be generalized for other countries. Simultaneously, this study is related to government supported health-care system; hence, the selection of expert panel was crucial due to the unavailability of doctors and other stakeholders of government system.

The proposed approach is aimed at providing a procedure for evaluating the impact of CFs on HSC in general and GHSC in specific. This study is an attempt to assist government and top management of GHSC to assess the impact of CFs on GHSC and accordingly define its course of actions.

Although various issues related to the CFs have been broadly identified and analyzed, no dedicated study has been reported in the field for quantification of impacts of CFs. Furthermore, this proposed model has an ability to recognize the specific contribution of each CF and overall contribution.

This study examines the motives and enabling factors regarding reverse factoring (RF) adoption in the automobile industry's supply chain.

This is a qualitative case study based on in-depth interviews with financial institutions in two countries having different statuses within the automobile industry global value chain: Mexico as a peripheral and Spain as a semi-peripheral country.

The RF is more widely deployed in Spain than in Mexico. The differences in the adoption of RF between the two countries stem from the availability of programs for suppliers at different supply levels, their efficient implementation and a robust regulatory framework, but especially from the cooperative approach adopted. The motives and enablers of RF adoption in the automobile industry can be explained under a framework of different supply chain management models. The RF programs driven by self-interest financial motives are characterized by an asymmetric distribution of benefits among supply chain participants. The RF programs that combine self-interest with cooperative motives are partially characterized by balanced benefits. In addition, they favor involvement practices and strengthen long-term relationships among supply chain participants. In this cooperative approach, trust, transparency and especially sharing information are considered relevant enablers. Finally, the specific automobile industry's features that determine RF adoption are linked to the structure and governance mode of the supply chain. The structure in terms of length – multiple supply levels – conditions the design of RF programs based on the buyer's position in the supply chain. The governance mode, particularly how the relationships are established, conditions the factors and requisites for efficient adoption of the RF programs.

This research analyzes the RF framed in the dynamics of buyer–supplier relationships and different models of supply chain management, allowing us to identify cooperation motives and their impact on RF adoption, beyond the traditional economic and financial motives highlighted by previous literature.

The purpose of this research is to systematically review the properties of supply chains demonstrating that they are complex systems, and that the management of supply chains is best achieved by steering rather than controlling these systems toward desired outcomes.

The research study was designed as both exploratory and explanatory. Data were collected from secondary sources using a comprehensive literature review process. In parallel with data collection, data were analyzed and synthesized.

The main finding is the introduction of an inductive framework for steering supply chains from a complex systems perspective by explaining why supply chains have properties of complex systems and how to deal with their complexity while steering them toward desired outcomes. Complexity properties are summarized in four inter-dependent categories: Structural, Dynamic, Behavioral and Decision making, which together enable the assessment of supply chains as complex systems. Furthermore, five mechanisms emerged for dealing with the complexity of supply chains: classification, modeling, measurement, relational analysis and handling.

Recognizing that supply chains are complex systems allows for a better grasp of the effect of positive feedback on change and transformation, and also interactions leading to dynamic equilibria, nonlinearity and the role of inter-organizational learning, as well as emerging capabilities, and existing trade-offs and paradoxical tensions in decision-making. It recognizes changing dynamics and the co-evolution of supply chain phenomena in different scales and contexts.

Although there have been considerable discussions on the business value of adopting blockchain in supply chains, it is unclear whether such blockchain-enabled supply chains (BESCs) can help firms mitigate the negative impact resulting from the recent COVID-19 pandemic. This study aims to answer this important question.

The authors conduct an event study to quantify the financial effects of the COVID-19 pandemic and compare the differences in such effects between treatment firms that have adopted BESCs and matched control firms that have not adopted BESCs. The authors also perform a regression analysis to examine how the role of BESCs in mitigating COVID-19's negative impact varies across firms with different levels of supply chain leanness and complexity. The analysis is based on 88 treatment firms and 88 matched control firms, all of which are publicly listed on the US stock markets.

The test results suggest that although both the treatment and control firms are negatively affected by the COVID-19 pandemic, the effect is less negative for the treatment firms compared to the control firms, demonstrating the role of BESCs in mitigating the negative impact caused by the COVID-19 pandemic. Moreover, the mitigating role of BESCs is more pronounced for firms with lean and complex supply chains.

This study is among the first to provide empirical evidence on the mitigating role of BESCs during the COVID-19 pandemic, highlighting the importance of adopting blockchain in supply chains with high uncertainties and disruption risks.