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Forecasting product returns plays an important role in the operations of reverse logistics (RL). However, their contribution to sustainability performance is yet to be explored. The purpose of this paper is to explore the product returns in Indian electronics industry and examine the relationship of forecasting product returns with triple bottom line performance of RL.

In this study, based on past literature review, four hypotheses, relating to forecasting of product returns and its association with performance, were developed. A questionnaire was sent to 700 respondents from the Indian electronics industry. Overall, 208 received responses were found suitable for the research. The necessary statistical analysis was carried out to ensure the reliability and validity of the questionnaire. In order to test different hypotheses, partial least square path modelling (PLSPM) technique of structural equation modeling was utilized.

Measurement model had shown sufficient data fit for the modeling. PLSPM results reveal that the accuracy in forecasting product returns is positively associated with operational performance of RL. It also plays an important role in the sustainability efforts of an organization.

Managers can utilize results of study for exploring and emphasizing issues of product returns for improving RL performance. One of the limitations is that data are collected only from Indian electronics industry. Another limitation is that only product returns are considered for the operational and TBL performance of RL. In future, study may be carried out considering different factors in other sectors and countries.

The intent of forecasting product returns is considered to be operational efficiency. It can make significant contributions to the sustainability efforts of an organization. Review of the past literature indicates that research in the field of RL is in developing stage, and issues related to forecasting product returns are under-represented. The paper adds value to the few available articles on product returns.

The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed‐loop supply chains.

The approach is based on Bayesian estimation techniques. It permits to forecast product returns on the basis of fewer restrictions than existing approaches in CLSC literature. A numerical example demonstrates the application of the proposed approach using return times drawn from a Poisson distribution.

The Bayesian estimation approach provides at least 50 percent higher accuracy in terms of error measures compared to traditional methods in all scenarios examined in the empirical part. Hence, more precise results can be obtained when predicting product returns.

The flexibility of the proposed approach allows for numerous applications in the field of CLSC research. Areas that depend on the results from a forecasting system, such as inventory management, can embed our estimation procedure in order to reduce safety stocks. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns.

The generic character of the proposed forecasting approach leaves degrees of freedom to the user when adapting it to a specific problem. This adaptability is enabled by the following features: first, an arbitrary function is allowed for capturing the customers' demand. Second, the stochastic timeframe between sale and product return may follow an arbitrary distribution. Third, by adjusting two parameters finite as well as infinite planning horizons can be incorporated. Fourth, no assumptions regarding the joint distribution of product returns are necessary.

The purpose of this paper is to develop a model for forecasting product returns to the company for recycling in terms of quantity and time.

Graphical Evaluation and Review Technique (GERT) has been applied for developing the forecasting model for product returns. A case of Indian mobile manufacturing company is discussed for the validation of this model. Survey conducted by the company and findings from previous research were used for data collection on probabilities and product life cycle.

Product returns for their recycling are stochastic, random and uncertain. Therefore, to address the uncertainty, randomness and stochastic nature of product returns, GERT is very useful tool for forecasting product returns.

GERT provides the visual picture of the reverse supply chain system and helps in determining the expected time of product returns in a much easier way but it requires probabilities of different flows and product life cycle. Both factors vary over a period, so require data update time to time before implementation.

This model is developed by considering all possible flows of sold products from customer to their reuse, store or recycle or landfill. First time this type of real life flows have been considered and GERT has been applied for forecasting product returns. This model can be utilized by managers for better forecasting that will help them for effective reverse supply chain design.

The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network.

The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted.

The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas.

In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other expert systems such as artificial neural networks can be integrated into fuzzy systems.

An application at an e-recycling facility is conducted for clarifying how the method is used in a real decision process.

It is the first study which aims to model an alternative forecasting by utilizing fuzzy expert system. Furthermore, a comprehensive factor list which includes predictors of the system is defined. Then, a dimension redundancy analysis is developed to reveal factors having significant impact on the return process and eliminate the rest.

This paper aims to propose a method for forecasting product returns based on reason codes. The methodology uses two approaches, namely central tendency approach and extreme point approach, and is developed for the consumer electronics industry.

The methodology presented here is based on the return reason codes (RC). The incoming returns are split into different categories using reason codes. These reason codes are further analyzed to forecast returns. The computation part of this model uses a combination of two approaches, namely extreme point approach and central tendency approach. Both the approaches are used separately for separate types of reason codes and then results are added together. The extreme point approach is based on data envelopment analysis (DEA) as a first step combined with a linear regression while central tendency approach uses a moving average. For certain type of returns, DEA evaluates relative ranks of products using single input and multiple outputs. Once this is completed, linear regression defines a correlation between relative rank (predictor variable) and return quantity (response variable). For the remaining type of returns the authors use a moving average of percent returns to estimate the central tendency.

Reason codes and consumer behavior in combination with statistical methods can be used to forecast product returns.

Consumer electronics retailers and manufacturers can effectively use this methodology to forecast product returns. This methodology effectively addresses and covers different product return scenarios.

This research paper shows the new way of forecasting product returns i.e. reason codes based forecasting by combining two approaches, namely extreme point approach and central tendency approach. Also, it shows a new way of translating the consumer behavior into meaningful data; that data can be fed to a model to forecast product returns.

End‐of‐life (EOL) products have become a major environmental issue among countries and manufacturers. This is due to the growing number of EOL products and their hazardous contents. Many collection strategies and pilot projects have been conducted to manage EOL products, especially Waste Electrical and Electronics equipments (WEEEs). However, as characteristics of a population are different to one another, a customized collection strategy is required. The purpose of this paper is to find an effective collection strategy which considers cost and environmental impact simultaneously.

This paper presents an integrated collection strategy which combines a Fuzzy Colored Petri Net forecasting method and collection network model to collect EOL products. Colored Petri Net is used in modeling the integrated collection strategy. To test the collection strategy, a case study on mobile phone collection in Australia is presented.

The integrated collection strategy developed in this paper finds that by providing demographic data and historical sales of a relevant product in a certain location, the best strategy to collect EOL products in that location can be determined. This paper finds that the best strategy that suits one location might be different to other locations.

This paper presents a model which provides a customized collection strategy that follows the characteristics of a population. This strategy allows government organizations or manufacturers to simulate the strategies to collect EOL products in different locations.

As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.

The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.

The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.

This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.

A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.

The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.

The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.

The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.

The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.

The purpose of this paper is to present the research carried out on the development of a conceptual framework termed as the reverse collaboration framework (RCF) to provide supply chain (SC) visibility and information sharing to practitioners in a reverse logistics (RL) operations.

The research methodology used in this research is a combination of concept mapping, and the extension of the work of other researchers (deductive approach) to develop a RCF that connects tools, techniques, systems and RL processes.

This research shows that by integrating tools, systems, tools and techniques with RL processes by means of the RCF will increase performance and productivity of a RL operations. This is demonstrated by applying the RCF to a consumer electronics business that proves that the time taken for the end to end RL operations is reduced by 20%.

The RCF has been demonstrated with the data from a consumer electronics organisation. Literature points out that there are many different mathematical models for RL across a number of industries. Thus, at this stage, it is not clear if the RCF developed in this research will work in other industries, such as the newspaper, plastic bottles and online retailers industry where product returns are high. This research work can be extended in developing an IT solution by future researchers that can be linked to the main ERP system of an organisation.

SC managers can use the RCF in the extended form of an IT solution to manage the RL operations of their organisations.

There is a lack of research in the space of reverse collaboration in the broader field of SC management. This paper has fulfilled that gap.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.