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The purpose of this paper is to propose a conceptualization of supply chain vs supply chain competition using the ecological niche approach. It suggests a probabilistic methodology for evaluating competition from time series data, using overlap in the utilization of services provided by critical providers as a source of competition.

Literature on ecological niche theory and competition is explored and given the uncertainty that surrounds the operation and management of supply chains, a probabilistic approach to the analysis of supply chain vs supply chain competition (via the Bayesian inference) is advocated. Simulated data are used to illustrate the methodology.

Should an area of overlap be identified, ecological niche theory provides a sensible approach to identifying the nature and extent of competition between supply chains. Applicability of the methodology is not limited to supply chain vs supply chain competition.

The data used for the analysis of competition between supply chains are computer generated and use a single niche dimension. Although this was done to merely test/validate the proposed model, the approach is somewhat oversimplified. However, the model is readily extendable to multiple niche dimensions.

The proposed approach offers a simple and straight‐forward method of estimating competition in general, and supply chains vs supply chain competition in particular. Attempts at using the niche theory of competition in this context are so far inconspicuous. Hence, approaching competition in this way contributes to furthering our understanding of competitive interaction especially in supply chains, whose prospect is yet to be pointed out in literature.

Although the supply chain (SC) competition concept has emerged during the past decade as the way firms will compete in future, there is scant academic research on actual mechanisms through which such competition can occur. The purpose of this paper is to proposes interaction as the means by which competition between supply chains may be undertaken.

The paper investigates a Swedish logistics center via case study methodology to develop the idea of interaction for SC vs SC competition.

Results suggest that interaction points along organizations ' supply chains may present enough breadth to assume a role in determining how SC vs SC competition may be played out in reality.

Interaction, as proposed here, implies an emphasis on all points at which supply chains meet to request goods and services, including various points where such supply chains converge, e.g. service providers, original equipment manufacturers, etc.

Most studies dealing with competition between supply chains fall short of exploring the link between theory and corresponding practice of this evolving competition mode. Such a link is provided with the use of logistics centers.

The purpose of this paper is to examine the use of V‐A‐T analysis in the management of supply chain networks.

The paper provides a conceptual overview of V‐A‐T analysis as a procedure for categorizing manufacturing facilities, and explores the use of V‐A‐T analysis as a technique for the management of supply chain networks.

There are several challenges to the effective management of supply chain networks. However, organizations can overcome these challenges by understanding the nature of network control points as revealed through the application of V‐A‐T analysis on their supply chains.

The paper is conceptual in nature and must be validated through empirical research studies.

The concepts presented can be used by supply chain professionals to increase the likelihood of effective supply chain management within their organizations, and by supply chain researchers to further explore the use of V‐A‐T analysis as a tool for examining supply chain networks.

The paper makes a start in filling a void in the literature concerning how V‐A‐T analysis can be used as a tool to facilitate improved supply chain management.

Supply chains can improve their performance by developing competitive priorities in a specified sequence: quality, reliability, flexibility, agility, and finally, cost efficiency. This paper extends Ferdows and De Meyer's (1990) sand cone model and Vokurka and Fliedner's (1998) sand cone model extension incorporating agility to supply chain management priorities. This work provides a framework for a cumulative and sustainable improvement process by which supply chains can build a strategic competitive advantage.

The purpose of this paper is to build and provide a step-by-step methodology to develop a speed-to-market (STM) process model for a fashion garment that can quickly respond to the marketing situation and shorten the cycle time from receiving orders to deliver the finished product. The improved STM process model to contrast the “before” and “after” scenarios in order to induce potential benefits such as reducing production lead-time and keeping low inventory.

First, collected garment business management systems and garment manufacture operational process. Second, according to the point-line-plane-volume work improvement to list the garment industry up and down stream improvement projects and to reduce the supply chain lead-time. Third, establish the STM model and use information technology to redesign the garment industry supply chain process. Fourth, amend the STM model process. Fifth, compare the supply chain lead-time of finished STM process and normal process.

After the garment industry implements STM and supply chain re-engineering, the clothing design to sale process can be simplified and reduced time to market. The garment order to buyer lead-time is shortened from 104 to 42 days totally.

This research is a practical business re-engineering process and work improvement. The improvement STM model can half the cycle time from receiving orders to deliver the finished garment product.

This paper provides a process with a step-by-step methodology to develop a successful speed-to-market model for the fashion garment industry and it is able to serve as a reference model for other industrial supply chain management.

As global supply networks proliferate, the strategic significance of supply chain risk management (SCRM) – defined as the identification, evaluation, and management of supply chain-related risks to reduce overall supply chain vulnerability – also increases. Yet, despite consistent evidence that firm performance is enhanced by appropriate fit between strategy and context, extant SCRM research focusses more on identifying sources of supply chain risk, types of SCRM strategy, and performance implications associated with SCRM than on the relative efficacy of alternative primary supply chain risk mitigation strategies in different risk contexts. Drawing on contingency theory, a conceptual framework is proposed that aligns well-established aspects of SCRM to present a rubric for matching primary alternative supply chain risk mitigation strategies (redundancy and flexibility) with particular risk contexts (severity and probability of risk occurrence). The paper aims to discuss these issues.

Conceptual paper.

The proposed framework addresses supply chain managers’ need for a basic rubric to help them choose and implement risk mitigation approaches. The framework may also prove helpful for introducing business students to the fundamentals of SCRM.

The framework and associated research propositions provide a theoretically grounded basis for managing the firm’s portfolio of potential supply chain risks by applying appropriate primary risk mitigation strategies based on the specific context of each risk rather than taking a “one size fits all” approach to risk mitigation. An agenda for progressing research on contingency-based approaches to SCRM is also presented.

The purpose of this paper is to introduce a methodology to identify sustainable supply chain key performance indicators (KPI) that can then be used for sustainability performance evaluation for suppliers.

Initially the complexity of sustainable supply chain performance measurement is discussed. Then, a two-stage method utilizing neighborhood rough set theory to identify KPI and data envelopment analysis (DEA) to benchmark and evaluate relative performance using the KPI is completed. Additional analysis is performed to determine the sensitivity of the KPI set formation and performance results.

The results show that KPI can be determined using neighborhood rough set, and DEA performance results provide insight into relative performance of suppliers. The supply chain sustainability performance results from both the neighborhood rough set and DEA can be quite sensitive parameters selected and sustainability KPI sets that were determined.

The data utilized in this study are illustrative and simulated. Only one model for the neighborhood rough set and DEA was utilized. Additional investigations using a variation of rough set and DEA models can be completed.

This tool set is valuable for managers to help identify sustainable supply chain KPI (from among hundreds of potential measures) and evaluate sustainability performance of various units within supply chains, including supply chain partners, departments, projects and programs.

Sustainability incorporates many business, economic and social implications. The methods introduced in this paper can help organizations and their supply chains become more strategically and operationally sustainable.

Few tools and techniques exist in the sustainable supply chain literature to help develop KPIs and evaluate sustainability performance of suppliers and the supply chain. This paper is one of the first that integrates neighborhood rough set and DEA to address this important sustainable supply chain performance measurement issue.

This paper presents an integrated conceptual model of supply chain flexibility. It examines flexibility classification schemes and the commonalities of flexibility typologies published in the literature to create a theoretical foundation for analyzing the components of supply chain flexibility. Even though there has been a tremendous amount of research on the topic of flexibility, most of it has been confined to intra‐firm flexibility concerns. As supply chain management goes beyond a firm’s boundaries, the flexibility strategies must also extend beyond the firm. This paper identifies the cross‐enterprise nature of supply chain flexibility and the need to improve flexibility measures across firms. Opportunities are identified for future cross‐functional research that builds on this theoretical foundation and leads to more effective formulation of supply chain strategies.

Supply chain management (SCM) proficiency is generally associated with superior business performance. Yet, SCM research continues to focus predominantly on the performance of individual firms, rather than on the collective performance of multiple supply chain participants as espoused by the extended enterprise (EE) concept. In response to calls for quantitative studies that examine the collective performance of multiple supply chain participants, this research study compares the combined performance of triads comprising focal firms recognized for their relative SCM proficiency and their upstream (supplier) and downstream (customer) supply chain partners with that of their close industry competitors' triads.

The triadic, longitudinal examination of multiple supply chain participants' collective performance utilized archival financial data of the period 2007–2017 from the Compustat database and the supply chain (SPLC) function of Bloomberg.

Findings of this study indicated that supply chain triads that included focal firms recognized for their relative SCM proficiency experienced significantly lower sales and general administrative expenses and significantly higher productivity, return on assets and profitability over time than their close industry competitors' triads. However, contrary to expectations, the performance advantages identified did not extend to revenue growth.

Supply chain triads cannot fully represent entire supply chains or EEs. However, this study’s triadic analysis can be viewed as a practically achievable proxy for further validating the EE concept. Moreover, based on assertions that triadic studies are suitable for SCM research and on empirical studies that consistently show individual firms recognized for their relative SCM proficiency outperform competitors, the authors contend that the study’s findings appropriately corroborate the value of the EE concept.

Because such empirical evidence is so rare, the consistent, collective performance advantages identified in this study should be highly significant to managers.

Robust, longitudinal evidence that supply chain triads which include focal firms recognized for relative SCM proficiency collectively outperform their close industry competitors' triads extends generally accepted associations between SCM proficiency and business performance, suggesting that the application of extended resource-based view (ERBV) in supply chain contexts warrants further examination and further substantiates the efficacy of the EE concept.