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The purpose of the study is to describe the implications of strategic lead times (SLTs) for return on investment (ROI).

This study was part of an interactive research project and is based on the logic of theory application leading to theory building. It uses a multiple case study with five holistic single cases. Empirical data (ED) have mainly been collected from interviews and focus groups.

The length of and uncertainty in SLTs have implications for companies' financial performance. These implications vary in strength and can be either direct or indirect. These findings are incorporated into a framework on SLTs' implications for ROI.

The presented array of SLTs' implications for ROI could be further investigated, focussing on their strength. Additionally, it would be interesting to substantiate the findings in the context of environmental and social sustainability (i.e. the triple bottom line).

The findings offer practitioners a rich description and understanding of SLTs' actual implications for financial performance in terms of ROI. This knowledge can support practitioners in analysing supply chain designs based on financial performance.

Using a combination of a relative financial performance measure (ROI) and a set of SLTs (systems perspective), this study focuses on SLTs' actual implications for ROI. The findings provide evidence that different sections of a supply chain can have different implications for revenue, cost and investment (i.e. the three absolute measures related to ROI).

The purpose of this paper was to investigate what types of supply chain strategies (SCS) are implemented within luxury fashion companies, according to the drivers that regulate competitiveness in this sector (brand positioning, distribution channel, type and line of product). Moreover, the objective was to define which key performance indicators (KPIs) should be measured according to the chosen strategy, and finally to evaluate the alignment of luxury fashion companies with the proposed indicators.

The literature review was the first step performed. Thereafter, a case study was conducted and the sample, composed of six companies, was selected, a questionnaire was then developed to guide the interviews, after which the data were collected. From the data, a primary case analysis was conducted, from which cross-case patterns were also researched.

From the results obtained, it was possible to state that companies involved in the case study adopted different SCS within the same company according to the drivers that regulate the sector competitiveness. As a result, the product line was shown to be the only driver that affected both the alignment between the expected and implemented SCS, respectively, and the alignment with the selected KPIs.

The paper provides valuable insights to companies that are trying to align SCS and KPIs. The close link between these aspects had not yet been explored previously. In particular, there were no indications about the KPIs that have to be measured for a specific SCS.

The purpose of this paper is to present a new strategic framework of Supply Chain Management (SCM) in the automobile industry. For our purpose, we first had studied about the structure of relationship between supplier and buyer in Korean automobile industry. With this study, we searched for factors which compose a strategy of SCM, and whether or not the companies’ performance that are influenced by specific SCM strategy factors. Of course, our study based on existing researches, especially Cox et al.(1995) and Venkatraman et al.(1992), but the existing researches is differentiated in this paper that is treated several power factors as resource, value, environment and relation. So, results of our paper are what a good strategy of SCM composed by these factors and how to adopt this strategy on global logistics. And our methodology has some using of statistics method by SPSS 14(v) such as factor analysis, reliability analysis, and SEM(Structural Equation Model) with AMOS.

A number of literature contributions have underlined the importance of developing value-added logistics activities or VALS in order to help improve customers’ satisfaction. However, there is usually very little attention given regarding where to perform these VALS. This study aims to: (1) identify a comprehensive set of factors which may influence the location of VALS, (2) to analyze to what extent those factors influence location decisions, and (3) to distinguish the determinants behind the location choices for distribution centers and for the kind of VALS that will be developed in these distribution centers.

In this paper, we will present a conceptual framework on the locations of VALS in view of the identifying determinants for assigning VALS to logistical centers. We argue that the optimal location of VALS is determined by complex interactions between the determinants at the level of the choice of a distribution system, distribution center location factors, and different logistical characteristics regarding products.

In the development processes of a product’s market life cycle, there are three phases of an enterprise’s innovation: new product development, production processes, and product management. In this article, the analyses of benefit and costs, value, and profit to companies are discussed in different stages. New logistics features that appear in an enterprise’s supply chain based on innovative modeling are discussed. Then a logistics model and its technical system based on the classified logistics center are established, which are appropriate for innovative modeling within an agile supply chain. Using the basic theory and techniques of ‘extenics’, the formal conception of innovative modeling-based manufacture in logistics is presented, and the matter-element models are established. Finally, a case study demonstrates the results.

This paper aims to investigate the conditional indirect effect of innovativeness on performance via supply chain agility (SCA) in the service industry at higher and lower collaborative relationships.

The hypothesised model is operationalised with survey data from 245 Australian service firms collected via LinkedIn and analysed using structural equation modelling and fuzzy set qualitative comparative analysis (fsQCA).

The analysis found that SCA significantly mediates the relationship between innovativeness and performance. Further, the conditional indirect effect of innovativeness on performance via SCA was significant when the collaborative relationship was high. Results also revealed that a configuration of both innovativeness and agility better predicts performance.

This study is an early attempt to investigate SCA in service industries by scrutinizing SCA from an innovative point of view. While previous studies have demonstrated the role of innovativeness in enhancing a firm's performance, this study explores this link further by investigating the conditional indirect effect of innovativeness on performance via SCA at different levels of collaborative relationships.

The aim of this paper is to review metrics and develop a framework for measuring leagile supply chain. Metrics that are applicable in the lean, agile and leagile strategies are identified in the literature and are then combined into a framework that can reflect both agile and lean strategies – the leagile supply strategy.

This work is based on the systematic literature review. Literature was collected, then lean and agile metrics were extracted, analysed, counted and grouped into the framework. Findings are compared against literature on leagile supply chain.

Findings indicate that there are sets of metrics specific to lean strategy, such as are process-focused, cost, productivity, inventory and delivery-based metrics, and specific to agile such as flexibility, responsiveness, information sharing and cooperation. There are also metrics common for both strategies; they are related to time, quality and customer satisfaction. Lean measures are tangible and focused on internal processes and products, while agile measures are targeted at external environment.

The framework could be used by practitioners as a starting point for performance system design.

There is a need to stop looking at lean and agile as separate and distinct supply strategies. Results of this research indicate that lean and agile are interlinked, both are focusing on customer satisfaction and quality. Applying a proposed set of metrics enables to design supply chain measurement system that reflects both strategies to measure leagile supply chain. The framework could be used by practitioners as a starting point for performance system design.

This paper aims to conceptualize supply market orientation (SMO) for the purchasing and supply chain management function and discusses how SMO capabilities are developed and how their application differs within and across firms. This research can thus be used as a blueprint for the development of a SMO capability that accommodates a firm’s unique contextual antecedents’ profile.

The qualitative research design comprises five in-depth case studies with 43 semi-structured interviews with large manufacturing and service firms.

SMO is defined as the capability to exploit market intelligence to assess, integrate and reconfigure the heterogeneously dispersed resources in purchasing and supply chain management in a way that best reflects the peculiarities of a firm’s supply environment. The empirical analysis shows that although SMO capabilities are configured similarly, their application varies across and within firms depending on the characteristics of a firm’s purchasing categories and tasks. Hence, reactive versus proactive SMO application is contingent upon firm-level and purchasing category–level characteristics.

The study uses the dynamic capabilities view as a theoretical background and provides empirical evidence and theoretical reasoning to elaborate and endorse SMO as a dynamic capability that firms need to have to compete in a complex and dynamic environment. The study provides guidance for supply chain managers on how to successfully develop and deploy a SMO capability.

The purpose of this paper is to define the “fast evolving industry” (FEI) and its supply chain management (SCM) challenges. The authors review and structure the literature regarding integration strategies and implementation methods to develop a strategic decision-making framework for SCM in the FEI.

The authors conduct a review of SCM literature, including supply chain strategy, supply chain integration (SCI), agile and responsive supply chain and SCM for innovative and fast-changing industries. The authors develop a conceptual model and a decision-making framework and use four mini cases to provide support for the model and framework.

The FEI, characterised by a high level of innovation and differentiation, short products/services lifecycle and high variety, is yet to be fully defined. Inherent uncertainty in FEI supply systems makes SCM in these industries a complex but strategic task for their managers. The framework and the model offered in this study, which employ a core competency concept and provide risk management strategies, offer a strategic tool for managers and scholars in the field to optimise their integration strategies and to operationalise integration decisions.

Little research has been published on transferable and cross-industrial SCM in FEIs. This paper defines the FEI and its resource-related concerns and then offers a conceptual model and a strategic decision-making framework for SCI in FEIs.

Due to increasing supply chain complexity, the supply chain uncertainty has become an imperative issue, which hinders the development of modern logistics and supply chain management. The paper attempts to conceptualize reverse logistics uncertainty from supply chain uncertainty literature and present the types of reverse logistics uncertainty in a triadic model.

The concept of reverse logistics uncertainty is developed based on a triadic model of logistics uncertainty and supply chain uncertainty literature. A desk research is conducted to develop a taxonomy of reverse logistics uncertainty. To better depict the reverse logistics uncertainty, we use case studies to discuss the types of reverse logistics uncertainty in the triadic model.

The study reveals four types of supply chain uncertainties in the reverse logistics. We call them reverse logistics uncertainty. Type-A and Type-B uncertainty are new types of supply chain uncertainty in the reverse logistics.

The types of reverse logistics uncertainty have not been empirically validated in industries. Especially, the two new types including Type-A and Type-B reverse uncertainty need further exploration.

Although reverse logistics has been discussed in the past decades, very few studies have been conducted on the supply chain uncertainty in returns management arena. The paper offers valuable insights to better understand the supply chain uncertainty in the reverse logistics. This also provides suggestions for both managers and researchers to reflect on the reverse logistics uncertainty management and business sustainability.