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The purpose of this paper is to investigate the impact of the salient task environment on reverse logistics (RL) practices and organizational performance in the context of Taiwanese computer, communication, and consumer (3C) electronics retail industry.

A hierarchical regression analysis was employed to test relationships between four constituents of the task environment and RL, as well as relationships between RL and environmental/economic performance. In addition, a regression analysis was used to examine the mediating effect of RL on relationships between the constituents of the task environment and environmental/economic performance. Data and information collected from a sample of 284 companies from the Taiwanese 3C retail industry were used for analysis.

Results suggest that three out of four constituents of task environment including government agencies, suppliers, and customers are associated positively with RL activities. In other words, as the salience of the constituents of the task environment increases, their level of influence on the firm’s RL also increases. This study also found the mediating effect of RL, indicating that superior performance emerges when a company’s RL matches the salient task environment.

The findings provide an insight into the relationships between the constituents of the task environment, RL, and environmental/economic performance which can assist firms within 3C retail industry in designing and developing appropriate strategy for RL. In practice, some retailers, especially SMEs, have outsourced their RL to professional recyclers. Investment in RL activity may be an option for some 3C retailers.

While previous research provides a strong foundation to further develop RL and subsequent policies, analysis of the factors affecting the decision processes to implement RL specially in the retail sector is scarce. This study fills this gap.

To propose and test an augmented collaborative planning, forecasting, and replenishment (A‐CPFR) model in a retailer‐supplier context with a view to improving forecasting accuracy and then reducing the “bullwhip effect” in the supply chain.

After a literature review, the paper presents a real case in which the present authors provided assistance. The description of the case includes: case company background; an “as‐is” model analysis; a “to‐be” (CPFR) model analysis; and a description of the results and potential benefits. The paper then proposes an A‐CPFR model for the case and performs a simulation of the new model for comparison with the existing CPFR model.

The results show that the mean absolute deviation of forecasting and the inventory variance are both better in the proposed model than in the existing CPFR model. The proposed model can thus improve the accuracy of sales forecasting, reduce inventory levels, and reduce the “bullwhip effect”.

In addition to information provided by the retailer, a logistics supplier should also obtain competitors' promotional information from the market as another factor for forecasting – thus enabling timely responses to demand fluctuations.

The proposed model is an original and useful development on the existing CPFR model. It could become a reference model for the retail industry in implementing CPFR in the future.

A growing body of literature has begun in the direction of supply chain performance measurement. However, selecting the appropriate set of key performance indicators (KPIs) for measuring supply chain performance have always remained a challenge. The purpose of this paper is to identify the KPIs and categorize them specifically for measuring retail supply chain performance.

A qualitative approach, based on literature has been adopted. Published literature from refereed journals on supply chain performance measurement has been considered and various approaches for developing KPIs have been studied to develop a theoretical framework for performance measurement in retail supply chain.

The paper identifies key indicators for performance measurement and classifies them into four major categories: transport optimization, information technology optimization, inventory optimization and resource optimization. These key indicators are arranged precisely for retail industry. A theoretical framework is proposed to link the performance of these constructs on financial performance of the firm.

Future research can be carried out to validate the relevance and applicability of identified indicators. The study can be further conducted to measure the interrelationships between the KPIs and their impact on financial performance of the firm.

This study proposes a list of indicators for retail industry, which are presented in appropriate categories so that it can be used by the focussed teams for further improvement.

To the best of authors’ knowledge, no other study has categorized the KPIs into groups, specifically for measuring retail supply chain performance. The researcher also intends to carry out further empirical study to test the proposed theoretical framework.

The purpose of this paper is to explain how buyers can more effectively and efficiently utilize technologies to improve their performance and to inform top executives in retail firms how these technologies can be used to improve the overall performance of the retail store.

Using a descriptive approach, the paper identifies current technologies that are being used and shows how technology can be used by retail buyers.

Technologies developed over the past 20 years have changed the way buyers execute their responsibilities with advancements in various technologies; faster transmission of data results in the ability of buyers to immediately react to inventory and pricing issues. However, given that technology investments can exceed millions of dollars, and that many retailers' margins and inventory productivity have been eroding over the last ten years, the stakes for information technology decisions have grown exponentially, so care must be taken in making these decisions.

This subject matter is most important for retail practitioners because it identifies areas where the performance of buyers can be improved. It is also valuable to college professors who teach retailing and buying and to their students because it provides current subject matter that should be incorporated into classes.

This paper identified the most current technologies being used by buyers in a variety of stores to improve their performance. In addition, this paper identified the future trends in technology adopted by innovative retailers.

Although many papers purport the significant value attributable to supply chain performance from the use of Collaborative Planning, Forecasting and Replenishment (CPFR), the question of “what are the main constructs and efficient framework for successful implementation of CPFR?” remains largely unanswered. This question will be addressed by identifying and analysing the main constructs for successful implementation of CPFR. The purpose of this paper is to attempt first to seek answers to this question. Second, to review the scope and value of CPFR using a devised state-of-the-art taxonomy for the classification of selected bibliographical references and third, to develop a conceptual framework by identifying areas which need more research.

The method underlying this paper followed the steps of a systematic literature review process outlined by Soni and Kodali (2011). The review is based on a total of 93 papers published from 1998 to 2013 on CPFR.

Four main constructs for successful implementation of CPFR have been identified: CPFR enablers, CPFR barriers, trading partner selection and IA. The findings indicate that there is a need for better understanding of the amount and level of information sharing as an important function of CPFR implementation. The paper also illustrates a number of shortcomings in the current literature and provides suggestions to guide future research on implementing CPFR in different industries.

This paper is of interest to both academicians and practitioners as it helps to better understand the concept and role of CPFR in supply chain integration and its implementation results, enablers and inhibitors. The proposed framework in this paper can be used to give insight for future research and practice.

The paper offers a framework for the review of previous research on CPFR and identifies the most important shortcomings that need to be addressed in future research. In addition, this review is both greater in scope than previous reviews and is broader in its subject focus.

In general, demand for functional products is dependent on a range of promotions offered in various retail outlets. To improve promotional sales many retailers collaborate with manufacturers for planning, forecasting and replenishment. The purpose of this paper is to hypothesize that collaborative forecasting will improve the forecast accuracy if all the partners can relate their demand forecast with underlying demand factors.

In this paper, the author uses a case study approach to study various demand factors of soft drink products of the UK based company which offers frequent promotions in retail outlets. The paper represents the case study findings in a conceptual framework called Reference Demand Model (RDM). Further, the case study findings are validated empirically by means of multiple linear regression analysis using actual sales data of the case company.

Surprisingly, some of the demand factors specified as very important by the case company are not found to be highly significant for actual sales. The paper uses the identified demand factors to suggest levels of collaboration.

Understanding the importance of product specific demand factors through regression models and incorporating the same in managerial decision making will aid managers to identify the necessary information to make accurate demand forecasts.

This approach unveils the presence of three levels of collaboration namely preparatory, progressive and futuristic levels among supply chain partners based on the information exchange. The proposed method will aid decision making on information sharing and collaborative planning among manufacturer and retailers for future promotional sales.

Successful implementation of supply chain collaboration (SCC) by Wal‐Mart has encouraged many manufacturing companies, such as Procter & Gamble, Hewlett‐Packard Co, and West Marine Products Inc., to initiate collaboration. Subsequently, collaboration between suppliers and retailers has become a common practice in many recent supply chains. However, measuring the benefits of collaboration is still a big challenge. Based on supply chain literature and practice, this paper aims to propose a conceptual framework and a standard set of metrics to evaluate the performance of SCC.

The authors discuss two case studies to validate the proposed model. The case study discussions are appropriate to understand the usage of different performance metrics in initial and advanced stages of collaboration.

From the case study it is recognized that the collaborating members in the supply chain are not able to visualise all possible benefits of collaboration. To surmount this issue, the paper proposes a framework to study the performance of companies involved in initial and advanced stages of collaboration.

The classification suggested in this paper on different stages of collaboration and related metrics can guide researchers and practitioners in manufacturing companies to evaluate the performance of SCC.

The purpose of this research synthesis is to gather and integrate findings on Collaborative Planning Forecast and Replenishment (CPFR) as a business process and as a management practice; and to assemble quantitative evidence of its impact on supply chain (SC) performance.

The researchers independently conducted a systematic review of 629 abstracts and 47 full-text papers. Original keywords were applied to four key electronic databases for operations management and information systems. Rigorous and verifiable selection criteria governed inter-coders reliability, review of steps and exclusion of papers. Resource and dependency-based view of the firm, contingency research and maturity models informed the analysis.

There is not a single “blueprint” for CPFR. Competing models emphasize the need for “trust and confidence” and reliable data systems. The type of products, scope, spatial diversity and number of partners in the network are important contextual variables. Firm resources that are unique and advantages from multiple and reciprocal dependencies are powerful levers. There is no consensus on maturity model and on required investment in data and communication systems.

Practical implications are implementation related: cost-benefit analysis and simulations should precede full-scale collaboration. There is a consensus on starting CPFR small and expanding gradually.

This synthesis applies a rigorous review method and attempts to assemble the dispersed literature in one study, utilizing explanatory operations management and information systems theories.