Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
Keywords
- Alert
- Algorithm
- Anomaly detection
- Case study: e-commerce
- Reverse logistics
- Supply chain management
Citation
Elia, G., Ghiani, G., Manni, E. and Margherita, A. (2024), "A system for anomaly detection in reverse logistics: an application into an e-commerce company", Measuring Business Excellence, Vol. 28 No. 2, pp. 222-242. https://doi.org/10.1108/MBE-01-2024-0002
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
1. Introduction
The environmental and economic concerns characterizing the current industrialized economy (Usman and Balsalobre-Lorente, 2022) have amplified the relevance of the circular economy, and its principles have gained considerable attention from supply chain scholars and practitioners. The circular economy generates benefits for all the stakeholders involved, combining economic advantages with environmental and social aims (Rosso et al., 2022), thus contributing to achieving a subset of the United Nations’ sustainable development goals (Morea et al., 2022). In this perspective, the circular economy can be a driver for the transition toward a more sustainable economic system (Mies and Gold, 2021). A vital element of the circular economy is reverse logistics (Ding et al., 2023; Mallick et al., 2023; Ahlén and Johansson, 2023), a process aimed at managing returned goods from the end customer traveling backwards in the supply chain to the source. The ultimate goal is to maximize the recovery of the residual value of products through effective and economically efficient flows that connect customers with suppliers (Sun et al., 2022).
The rapid diffusion of digital technologies and the exponential growth of e-commerce increased the relevance of reverse logistics, which focuses on the reverse flow of materials (and related information) through the maximization of their value by using online platforms (Han and Trimi, 2018). Dennis (2018) estimated a product return rate equal to 30% of the total e-commerce sales. The economic impact of reverse logistics in online marketplaces is between $800m and $1bn, and the returned products’ costs for retailers was about $260bn in 2016 (Morgan et al., 2018). Statista (2024) (Global retail e-commerce market size, 2021) reported a 228% growth in worldwide retail e-commerce sales in the period between 2014 and 2020.
The continuous growth of online commerce has generated an increase of returned goods, thus making reverse logistics a crucial process for guaranteeing the company’s performance and competitiveness. Reverse logistics is responsible for managing the flows of goods, money and information that are required when a customer decides to return a product. Actually, the more online sales increase, the more the demand for products to be returned increases. Therefore, if handled efficiently, reverse logistics can ensure a proper management of returned goods at acceptable costs without compromising customer loyalty (Röllecke et al., 2018; Ramanathan, 2011).
Besides, as firms respond to stricter regulations and increasing consumer expectations, reverse logistics programs supporting end-of-life product management strategies have become relevant for companies operating in the e-commerce domain not only to create benefits for the environment but also to generate societal and stakeholder value, augment and improve customer service and loyalty and increase market share and revenue capabilities.
Actually, in the e-commerce domain, where greater flexibility in the product research and selection often leads to mismatching between what is chosen and what is finally purchased, the management of returns takes on a significant dimension, both in terms of costs incurred and customer satisfaction, customer loyalty, brand strength and corporate image (Kannan et al., 2012). The reverse logistics flow travels backwards through a part of the supply chain and, therefore, requires careful organization and planning, given that the resulting economic impact can reach very significant proportions. Hence, the physical flows of goods and materials have to be integrated with associated informational flows, which represent a fundamental component for keeping the process under control through the understanding of the causes and the identification of possible guidelines to increase service efficiency and quality.
Despite a growing body of theoretical literature and conceptual research on this topic, many firms struggle to implement efficient and effective reverse logistics systems (Wilson and Goffnett, 2022). Most of the solutions proposed aims at optimizing the efficiency and costs of physical flows into traditional delivery-oriented industries, by introducing programs and actions at strategic level (e.g. secondary market forecasting), tactical level (e.g. modular design for recycling) and operational level (e.g. identification of collection centers or third-party logistics provider), as experienced by Cisco, Adidas or Dell (Wilson and Goffnett, 2022). The ultimate goal of these approaches is to maximize the value of remanufacturing, reusing, recycling and repairing (Agrawal et al., 2015).
On the contrary, the study of information and data flows in the e-commerce sector and online shopping represents a promising field of research (Dutta et al., 2020). Limited studies have been conducted to analyze the flow of data and information generated by the reverse logistics process to forecast product returns (Krapp et al., 2013) and reduce the number of returned products by reorganizing the supply chain and increasing the quality of decision-making. Moreover, the identification of key performance indicators to forecast product returns represents a promising area of research and investigation that requires more research efforts (Agrawal et al., 2015; Shaik and Abdul-Kader, 2012).
In such endeavor, this article, starting from the analysis of the defects found in the reverse logistics process of an e-commerce company, aims to answer the following research question:
RQ1.
What design logic and characteristics should have a reverse logistics management system to efficiently identify product anomalies and reduce the number of returned goods?
To address such research curiosity, the study describes a system encompassing an algorithm, a set of indicators and methodological guidelines to support the efficient and smart management of reverse logistics process. The system proposed identifies possible causes, suggest strategic choices and operational recommendations and define indicators for process monitoring and control. The developed approach, which grounds on new digital technologies, contributes to provide a systemic vision and a new mindset to the reverse logistics process, that represents a relevant research topic (Sun et al., 2022).
The remainder of the work is structured as follows. Section 2 illustrates the relevant background on reverse logistics in e-commerce. Section 3 presents the research method and the activities realized to generate findings. Section 4 illustrates the main outcomes. Section 5 discusses the obtained findings, whereas Section 6 concludes the paper, citing limitations and possible avenues for further research.
2. Theory background
Reverse logistics refers to the process of planning, implementing and controlling the efficient and cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for recapturing value or proper disposal (Morgan et al., 2018; Hawks, 2006).
In other terms, reverse logistics is the process that accepts previously shipped products and manages the return flows of materials, from the customer’s request to the firm’s decision on what to do in terms of possible recycling, remanufacturing, refurbishing, reselling or discarding (Dowlatshahi, 2000). It includes activities and procedures for returning after-sale and after-consumption products to obtain productive recycling by using reversed distribution channels (Chileshe et al., 2018).
Reverse logistics plays a crucial role for complementing the traditional forward logistics (Bernon and Cullen, 2007) at the point that forward logistics may include the reverse flow of products from the point of consumption back to the source (Rogers and Tibben‐Lembke, 2001). This would be in the direction to realize an integrated logistics system that aligns forward and reverse logistics operations (Guerra et al., 2021; Munaro and Tavares, 2021; Wibowo et al., 2022), which is particularly appreciated in the circular economy scenario (Seroka-Stolka and Ociepa-Kubicka, 2019). Reverse logistics refers to the effective and efficient management of activities required to retrieve a product from a customer to either dispose it or recover value (Erol et al., 2010), along multiple alternatives as maintenance, reuse, remanufacturing or recycling (Dhakal et al., 2017; Julianelli et al., 2020).
The activities of reverse logistics can be grouped in two categories: the first one relates to returning products from consumers to manufacturers due to low customer satisfaction, defective goods or wrong purchases, whereas the second one is aimed to recycle and recovery purposes (Batarfi et al., 2017).
If handled efficiently, reverse logistics could make the difference in firms, not only to maintain customer loyalty (Röllecke et al., 2018; Ramanathan, 2011) and enhance customer satisfaction (Bouzon et al., 2015; Pham and Ahammad, 2017; Mahindroo et al., 2018) but also to improve efficiency (Han and Trimi, 2018; Sirisawat and Kiatcharoenpol, 2018), increase profits (Xu et al., 2015; Huang et al., 2015; Li et al., 2018a, 2018b), experiment new revenue sources based on remanufactured or recycled goods (Larsen and Jacobsen, 2016), develop a good corporate image (Davidavičienė and Al Majzoub, 2021) and improve the impact of the economic activity along the environmental and social dimension (Dutta et al., 2020; Agrawal et al., 2016).
Reverse logistics plays a crucial role in several industries. Jim Wu and Cheng (2006) explored the benefits deriving from the implementation of reverse logistics in the publishing industry. Ravi and Shankar (2006) analyzed the paper industry by presenting a case study of an Indian manufacturing company. Biehl et al. (2007) focused on the carpet industry by analyzing the impact of the system design factors and environmental factors on the operational performance of reverse logistics. Kumar and Putnam (2008) investigated reverse logistics in the automotive, consumer appliances and electronic industries along three complementary dimensions, i.e. organization of the supply chain, the influence of European law on the closed-loop model of the supply chain and the market and regulation influence on recycling and reusing activities. Lau and Wang (2009) studied the problems encountered in the implementation of reverse logistics in the Chinese electronics industry. González‐Torre et al. (2010) studied the barriers hindering the implementation of reverse logistics practices in the automotive industry to mitigate the environmental impact. Bernon et al. (2011) explored reverse logistics in the retail industry by proposing a conceptual framework for managing the internal supply chain activities. Narayana et al. (2014) focused on the pharmaceuticals industry by presenting a systemic analysis of the complex interaction of factors affecting reverse logistics. Schamne and Nagalli (2016) analyzed both barriers and practices of reverse logistics in the Brazilian construction sector in the aim to reduce the volume of construction and demolition waste.
Most of the studies carried out to analyze the reverse logistics focused on the identification and mapping of the activities to increase the performance of goods collection, selection, examination, recovery, relocation and repair (Dekker et al., 2002; Agrawal et al., 2016; XiaoYan et al., 2012). Further studies have also been performed to combine the reverse logistics with the sustainability goals, in the aim to both optimize the cost function (e.g. raw materials procurement, end products transportation, resources usage and waste management) and reduce the negative effects on the environment (Paksoy et al., 2011; Chaabane et al., 2012; Silva et al., 2013; Bing et al., 2014; Zarbakhshnia et al., 2019) and society (Ramos et al., 2014).
The topic of reverse logistics in e-commerce is fairly new. Actually, global e-commerce sales are growing rapidly adding complexities to their supply chains, which include also the management of the returned goods. Factors such as environmental concerns, customer awareness and legal pressure have led the firms to pay attention to the circular economy and sustainability concepts while managing their reverse logistics (Han and Trimi, 2018).
Considering such issues, Davidavičienė and Al Majzoub (2021) identified a list of factors impacting the reverse logistics performance in the e-commerce domain. Such factors include the management of the company, the employees, the technology, the inventory management process, the return policy and procedures, the organizational structure and culture, the customer service and satisfaction and the quality management issue. By leveraging on these factors, the reverse logistics process increases its relevance and also contributes to enhance the overall company’s performance.
Ahlén and Johansson (2023) have identified barriers and possible solutions for overcoming the same to ensure a high-performing reverse logistics process. Based on their study, the most decisive barrier is at the management and organizational level, and it is represented by the lack of commitment from top management. To overcome such barrier, they propose to make top managers more aware about the benefits of reverse logistics, also by demonstrating them the positive impact at economic level. Afterwards, technological barriers are also relevant in terms of infrastructures to handle information and trace products. In this sense, the adoption of standards for interoperability reveals a good point to overcome such barriers. Political barriers also play a role because they indirectly influence reverse logistics. For example, legislation on return policies can obligate companies to adopt proper procedures and services to address properly such duty. To overcome such a barrier, the presence of incentives rather than penalties can be a good solution. Another typology of barrier is financial, because companies must make an initial investment to guarantee the correct organization and execution of reverse logistics activities. Such barriers can be properly overcome by making partnerships with other companies to share transportation services and warehouse usage. Finally, coordination barriers with the participants to the supply chain, including the customers, may also influence the performance of reverse logistics. At this purpose, a collaboration strategy and tools must be setup to facilitate communication, information exchange and trust building among all the participants.
Ding et al. (2023) have presented a new conceptual framework of circular logistics integration that consists of channel creation, network integration and inventory management to guide and inspire future research in tackling the systematic barriers that hinder materials and resource flow from forward to reverse logistics in construction life cycles.
Kazancoglu et al. (2023) focused on reverse logistics in sustainable food supply chains, which are particularly complex and vulnerable in terms of product variety, and investigated the adoption of blockchain technology to ensure information transparency and transaction traceability. In particular, the fact that information is constantly shared by each participant to the supply chain and is visible to all, contributed to increase trust and reduce risks (Wu et al., 2022).
Nanayakkara et al. (2022) proposed a three-stage reverse logistics framework for handling e-commerce returns. The first stage uses hierarchical clustering with geographical constraints on returns data to identify the return patterns. The second stage conceptualizes the network looking at the circular economy principles, by highlighting the importance of integrated decision-making and the commitment of all the parties involved. Finally, the third stage uses mathematical modeling to optimize the reverse logistics network by focusing on sustainability issues.
Chang and Zheng (Chang and Zheng, 2014) have proposed a method to reduce waste in nondefective reverse logistics. They used simulation and computation tests to show that the costs of consumer’s return transportation can be cut down by changing the consumer’s delivery address with another customer’s return address, in the aim to reduce the geographical distance of return transportation.
Das et al. (2020) have designed a reverse logistics network for an e-commerce firm operating in the fashion industry, by analyzing the trade-off between cost and responsiveness. They focused the attention on the identification of the positions of collection centers to collect and store periodically the returns from the customers before to send them to the final warehouses. The criteria to identify such centers were based on minimizing the total cost of collection, inspection, storage and disposition of returns.
Prajapati et al. (2022) presented a framework to address the complexity associated with forward and reverse logistics process to classify the products into refurbishing, recycling, remanufacturing and reusable. The framework includes multiple actors (e.g. customers, shippers, third-party logistics service providers, suppliers, local distribution centers and inspection centers) and is composed by five stages covering from the forward distribution and delivery of products to the customers, to the backward collection and return of products to the company. The first stage focuses on the products that move from the suppliers’ warehouse to the local distribution center. Then, the second stage focuses on the last-mile distribution, i.e. from the local distribution center to the customer locations. The third stage follows the returned products that go from different customer locations to the inspection center. Afterwards, the fourth stage focuses on the products inspection to identify the resellable ones that go from the inspection center to the supplier warehouse. Finally, in the fifth stage, the returned and inspected products that are refurbishable go from the inspection center to the refurbishing center, whereas those ones that are recyclable go from the inspection center to the recycling center.
Sinha et al. (2023) investigated the role of machine learning and artificial intelligence in reverse logistics within the scope of e-commerce and e-waste. Their study aims at overcoming the traditional techniques that optimize the cost, schedule and route of returned goods by highlighting how modern technologies exploiting machine learning and artificial intelligence can support reverse logistics process. Their study allows companies to reflect on both the economic performance and the environmental sustainability of the cited applications, so to optimize the flow of products from consumer to provider and generate value.
Qin (2022) described an inventory control system of reverse logistics for e-commerce packaging recovery based on a neural network to optimize each phase of returned goods. The system aims at increasing the utilization rate of recycled products by improving the management and optimizing the cost of reverse logistics inventory. The system coordinates four typologies of nodes (i.e. manufacturer node, supplier node, retail node and recycling processing node) to integrate forward logistics with and reverse logistics process.
Most of extant studies highlight the relevance of reverse logistics as a key activity to increase customer satisfaction and loyalty. Research studies also focus on the economic impact of reverse logistics on company performance and environmental sustainability, with the aim to propose solutions to minimize the costs of collection, transportation and handling of returned products, as well as to optimize the returning paths of goods. Instead, few studies have attempted to reduce returned goods by exploiting the value of data embedded into the logistics and warehouse management systems, thus strengthening the decision-making process of managers. This limitation found in extant literature represents a valuable research topic to be addressed, along with the identification of specific indicators to forecast product returns. In this way, the consolidated research devoted to optimize the execution of reverse logistics can be complemented by new research efforts devoted to prevent the use of reverse logistics.
Indeed, limited studies have been conducted to analyze the flows of data and information generated by the reverse logistics process to reduce the number of returned products by reorganizing the supply chain and increasing the quality of decision-making (Dutta et al., 2020; Krapp et al., 2013), especially by adopting artificial intelligence techniques (Sinha et al., 2023). Such preventive approach to reverse logistics allows to introduce new performance indicators, whose measuring and improvement is highly complex (Davidavičienė and Al Majzoub, 2021; Shaik and Abdul-Kader, 2018; Sudarto et al., 2017; Euchi et al., 2019). It may leverage artificial intelligence and advanced data analysis techniques to detect and identify anomalies almost in real time, in the aim to define a set of anticipatory actions targeted to diminish the reverse logistics flows of products. This approach is not widely applied and represents a gap in the academic research, especially in empirical ones, for experimenting new ways to manage reverse logistics (Sangwan, 2017; Huang et al., 2015).
3. Research method
3.1 Research context and case description
The study concerns the design and development of a reverse logistic system at LGH. LGH is a small-size logistics company operating in the South of Italy, and its main customers are e-commerce players dealing with home and garden furniture and equipment.
From a methodological perspective, a single-case study approach was adopted being LGH a “representative case” (Yin, 2009) of a company open to share data and information on product suppliers and logistics service providers. LGH operates under the control of a bigger Italian corporation, which is a leader in the market of online sales of bathroom and garden furniture and accessories. For the purposes of the study, the company provided an internal technical team committed to extract data from the information system, and the organization agreed to connect data of returned goods with product suppliers to observe the impact of recommendations provided after the analysis of data.
As with other companies operating in the e-commerce logistics sector, LGH experiences the event that a customer (who has not seen the ordered item in person) is not satisfied and wished to return the item back to the company. In fact, according to current regulations, the possibility of returns is a certified right (under given conditions and time limits) rather than an additional service provided by the company. Therefore, a strategy for the optimal management of reverse logistics activities is essential to limit the operational costs causes by the management of returns, which could be considerable. The problem is quite complex since “free” or extremely advantageous returns can generate so-called “serial returners,” i.e. customers who purchase many products without paying attention to the characteristics of goods. In LGH, reasons for returns are mostly the following:
Broken product;
Defected product;
Product not compatible (due to measures and/or technical characteristics);
Product of lower quality than customer expectations;
Buying decision change (within 14 days); and
Error during purchase.
Understanding and monitoring these events, especially in terms of quantitative impacts and dashboards displaying such factors like overall number of returns, reason and related % and the economic value, is a priority for LGH to minimize the returns while improving the shopping experience for customers. When a product returns to the company, it is examined, and a decision is made regarding the reprocessing activity, which has the objective of identifying the type of action to which the return should be subjected, with the following options:
Reintroduction in the warehouse if the product is still packaged and it was not opened by the customer;
Repair, to reconstruct the initial functionality by replacing damaged parts, thus obtaining a product similar to the original one;
Restoration, to create a new and improved product by replacing parts or modules with more updated versions and technological innovations;
Cannibalization, to proceed with disassembly by dismantling the product and selectively recovering spare parts or components that can be used to repair or update similar models or to obtain raw materials useful for the production cycle; and
Recycling, to regenerate the materials deriving from returned products after a cleaning process and reintroduction in the market (or into the production cycle).
In the case of repair or restoration, the product returns to the market through three channels, i.e. retailers, company outlets or donations to charities (benefiting from tax advantages and contributing to enhancing the company brand). LGH is experiencing an increasing importance of avoiding product returns and, if any, to efficiently and effectively manage reverse flows to ensure customer satisfaction and loyalty, brand strength and corporate image. The next sections show how the company has responded to such major operational challenges.
3.2 Research method
The study was based on a case study methodology (Yin, 2009), which is a preferred investigation strategy when the research questions are “how” or “why” and require a simple observation of the phenomena and an interpretation by the researchers (Ryan et al., 2009). To drive the research effort, extant literature was investigated to retrieve recent studies on reverse logistic, so to define the state of the art knowledge and retrieve other case experiences.
The process included five steps:
case study rationale and plan design, with the analysis of recent experiences;
study design and preparation, with the identification of study goals, case protocol and key informants;
data collection and integration, with the collection of different data sources and consolidation of the same in the study records;
data analysis and reporting, with the direct authors’ interaction with managers, and the triangulation of data using project documentation and the application of anomaly detection techniques (Chandola et al., 2009); and
results sharing and validation, with the preparation of a draft report to obtain managerial insights and validation. Figure 1 shows the research process conducted to undertake the case study.
The research focus is on the detection and analysis of anomalies in reverse logistics for an e-commerce player. Four key informants were involved, using a purposive sampling approach (Etikan et al., 2016), which is a nonrandom technique that allows researchers to identify and select individuals that are proficient and well-informed with a phenomenon of interest. Statistical significance was not a requirement for the specific nature of our research. Sampling, in this case, was not addressed to identify units of analysis but rather to define the company stakeholders providing support to the conceptual development and prevalidation phase of our approach/method. The four individuals were interviewed in April 2023 by following a three-step process aimed at:
sketching concepts and definitions based on direct knowledge and experience;
sharing the drafts of the research and asking company members to provide integrations and feedback; and
gathering and consolidating the feedback to improve the proposed model.
Data collection consisted in the transcription of the interviews, properly integrated by two further sources, i.e. a knowledge base containing scientific and technical documentation and reports about the application of innovative digital technologies in logistics, and company reports and data disclosing crucial figures and process measurements related to the previous two years. The choice to use multiple data sources and the opportunity to ask interviewees to review the information provided help to increase the construct validity of the research (Yin, 2013). The data analysis process included reading, coding and interpreting (Corbin and Strauss, 2008). More specifically, coding was implemented by identifying the key sentences, isolating the most representing concepts and linking them with the literature and technical documentation. Finally, results sharing and validation consisted in the presentation and discussion of the framework.
4. Findings: anomaly detection system
The system aims at implementing an efficient management of reverse logistics by using anomaly detection techniques and monitoring services. Through the system, a set of anticipatory actions based on continuous and automatic monitoring of returned goods is thus provided. The use of such a system would then suggest recommendations aimed at diminishing the number of future returns.
More specifically, a multidimensional analysis of data related to returned goods, carried out automatically and periodically (e.g. one time per day), may proactively activate a set of alerts that, if properly interpreted and managed, can dramatically reduce the volume of future returned goods. Such alerts, differentiated by typology of entity (e.g. entire line or batch, single product, specific product category, specific customer or supplier or carrier), arise from the identification of effects and their causes and suggest possible corrective actions.
Table 1 illustrates the list of alerts generated by the tool, the entities involved, possible problems, probable causes, potential indicators, and, finally, suitable corrective actions. For example, the system can calculate the frequency of returned goods from a specific customer over the total number of confirmed orders, so to determine if the customer is a “serial returner.” A set of indicators are calculated and compared (e.g. % of goods returned by the customer compared with the % of goods returned by other customers in the same category; trend of % of goods returned by the customer along the time). If the system verifies the presence of an anomaly, it may suggest corrective actions (e.g. to inhibit the customer from placing other orders in the future). The details of the indicators are provided in Table 1.
Concerning the calculation of the indicators, a module of Business Active Monitoring (Kim et al., 2018) based on a model for Anomaly Detection was defined and implemented. It allows to generate an early warning about potential anomalies related to products, customers or carriers, and it generates alerts integrated by correct information to be sent to the logistics manager. Such module executes automatically a test of hypothesis (Bethea and Rhinehart, 2019) by using a sample C of size n, and asking if it is acceptable that a specific feature p is “worse” than p0, which refers to the entire population, with a given level of confidence 1-α.
For example, by considering the generation of automatic alerts related to a lot that presents a high level of defects respect to previous lots, the data are:
C: current lot of products for sale (n products have been already sold);
P: total of lots of products already sold (entire population);
p: defective level for the lot (unknown variable);
p0: defective level for the product, estimated based on previous lots (by using historical data); and
1-α: level of confidence defined by the decision-maker (e.g. 0.9).
The model of Anomaly Detection aims to verify the null hypothesis, i.e. H0: p ≤ p0. To execute the test, the model considers the number X of returned goods belonging to the current lot C of products for sale. X follows a binomial distribution with the parameter p unknown, thus the probability that X is greater than a specific k (survival function) is given by the following formula:
This probability is increasing with p. Hence:
Therefore, the model will generate an alert if X > k*,
where k* is defined as follows:
Such model of Anomaly Detection can be integrated into a batch procedure of Early Warning and Recommendation that can be periodically executed to generate a set of recommendations aimed to reduce the number of returned goods, thus increasing the level of customer service.
In the following table, for each problem revealed and listed in Table 1, the model’s variables and parameters are identified and defined (C, P, p, p0, n, X), together with the actions to be implemented in case the alert is generated, and the frequency of verification.
For each of the problems listed in Table 2, the Business Active Monitoring module leverages the Anomaly Detection model and performs:
an estimation of the model’s parameters;
a check if generating or not the alert; and
a recommendation of the proper action to execute.
Such alerts, and the related recommendations, are sent via e-mail to the responsible of the reverse logistics, and the main metrics are visualized into a dashboard. The test of the Business Active Monitoring module has been carried out in the period May 2023–August 2023; by processing data of the past 90 days, the average number of alerts generated per day has been 35, with about 5 new alerts per day, as shown in Figure 2.
5. Discussions
Most of extant studies highlight the relevance of reverse logistics to increase customer satisfaction and loyalty, and they address the economic impact of reverse logistics on company performance and environmental sustainability. Few studies have attempted to reduce returned goods by exploiting the information generated within the logistics and warehouse management systems, thus strengthening the decision-making process of managers. This limitation represents a valuable research topic to be addressed, along with the identification of specific indicators to forecast product returns.
This article advances the research devoted to optimizing reverse logistics by bringing a novel approach and system mostly target at “preventing” the use of reverse logistics. Starting from the analysis of the defects found in the reverse logistics process of an e-commerce company, the article has addressed a research curiosity about how designing a system to reduce the number of returned goods. The system includes an algorithm, a set of indicators and methodological guidelines to support the efficient and smart management of reverse logistics. The developed solution identifies possible causes, suggests strategic choices and operational recommendations, and defines indicators for process monitoring and control. Such framework contributes to providing a systemic vision and a new mindset to the reverse logistics process. More specific scholarly and practitioner contributions are outlined in the next paragraphs.
5.1 Theory contribution
The recent advancement of digitalization provided opportunities for a smart logistics transformation. Despite studies having focused on improving the smartness, connectivity and autonomy of isolated logistics operations with a primary focus on the forward channels, there is still a lack of a systematic conceptualization to guide the coming paradigm shift of reverse logistics. The conceptual framework of smart reverse logistics transformation is proposed to link Industry 4.0 enablers, smart service and operation transformation and targeted sustainability goals. A smart reverse logistics architecture is also given to allow a high level of system integration enabled by intelligent devices and smart portals, autonomous robots and advanced analytical tools, where the value of technological innovations can be exploited to solve various reverse logistics problems. The contribution of this research lies in presenting a clear roadmap and research agenda for the reverse logistics transformation in Industry 4.0 (Sun et al., 2022). Besides, the alerts generated by the system, combined with the decision taken by the reverse logistics manager, can provide new insights to design a performance dashboard capable of calculating the impact of the correct management of reverse logistic processes along the financial, process, employee, stakeholder, innovation, environmental and social dimensions (Öz and Özyörük, 2021; Shaik and Abdul-Kader, 2012). In particular, the functions for forecasting product returns are relevant to design network and plan resources and facilities dedicated to reverse logistics (Xiaofeng and Tijun, 2009), which represents a valuable and rare practice for the effective monitoring of the entire process (Toktay et al., 2004; Krapp et al., 2013).
5.2 Practitioner implications
Despite a growing body of theoretical literature on this topic, many firms struggle to implement efficient and effective reverse logistics systems. In this article, we have presented an integrated methodology and tool which can bring significant benefits for the company, including:
Time savings, thus freeing employees for more valuable tasks and strategic activities;
Enhanced precision, by analyzing all data and highlighting all the possible critical events, so reducing the human failures that can have a relevant economic impact for the company;
Complete data analysis and full data interpretation, thus improving the quality of decision-making processes of logistics and procurement managers; and
Increase of efficiency, by optimizing logistics and transportation processes.
Besides, the automatic receipt of e-mails with complete reports, including the alerts generated by the Business Active Monitoring module, is useful because it supports logistics and procurement managers to focus their attention only on specific issues; thus, accelerating the problem analysis and the implementation of the proper corrective actions, including the opportunity to revise the entire procurement process making it more sustainable (Hald et al., 2021).
Another relevant implication stays in the fact that managers can leverage the indicators calculated by the module of Business Active Monitoring to feed a decision support system for supporting the responsible of reverse logistics to manage each request based on the following information:
number, typology and value of the products that customers intend to give back;
typology of customers (e.g. spot, professional and loyal), cumulated revenues and history of returned goods in the past;
transportation cost; and
possibility to obtain a reimbursem*nt from the carrier’s insurance.
Based on such information, the responsible of reverse logistics can decide if communicate to the customer several information including: if the return request has been approved and the money will be reimbursed (or the product will be substituted) without shipping back the product; if the return request has been approved and he/she is committed to send the product to the company or leaving it nearby a sales point; if the return request has been and he/she is committed to sending the product to the company by covering the shipping cost; if the return request has not been approved. Finally, a deep investigation of the processes strictly related to the measures and alerts generated by the system can shed light on defining proper and ad hoc strategies to reinforce and empower human resources, which play a key role in the implementation of reverse logistics (Ho et al., 2012).
Beyond the economic benefits at the company level, a smarter reverse logistics system also provides a positive contribution at the environmental (Islam et al., 2021) and social (Nikolaou et al., 2013) levels. For the environment, the decision support feature provided by the system helps to reduce the number of future returned goods, thus dropping the negative impact of transportation, packaging and waste disposal. From a social perspective, the proposed solution contributes to improving the working conditions and increasing safety, also educating people to save resources and energy.
However, beyond the advantages deriving from the adoption of the integrated reverse logistics system, organizations should also consider potential risks or challenges, here including:
the need to develop a new set of competencies required by the people using the system;
the continuous monitoring of information flows to ensure the alignment of data among the several digital systems involved;
the accurate management of data privacy to comply with current laws; and
the implementation of tools and procedures for ensuring systems’ protection.
Another critical area is the impact on service quality and customer satisfaction. Indeed, the optimization of information flows and enhanced awareness about reverse logistics operations should be supported by the introduction of a new culture of services offered to customers (Metz et al., 2020; Lee and Lee, 2020), with the ultimate goal of enhancing overall satisfaction even in the presence of request of returns.
Finally, the financial feasibility of the project can be a challenge for small and medium enterprises that usually have limited resources to invest in digital innovation. At this purpose, two opportunities for adopting reverse logistics systems while ensuring financial sustainability may derive from a more extensive use of open-source solutions that can be easily adapted to the company’s requirements, and from the creation of a consortium of small companies sharing the investment and the technological output.
The adoption of smarter reverse logistics is a goal for both technologically advanced companies and for less digitally mature organizations. The former may leverage their technological assets and competent workforce to exploit the value embedded in data and people, and experiment with new ways of organizing and executing activities. The latter may explore new forms of managing returned goods (although with a limited use of technology), by leveraging a strong network of relationships with partners, resellers and manufacturers that are interested in circular business practices.
6. Conclusions
This paper has presented the results of an analysis of return management within the LGH reverse logistics process. Using the data provided by the company, a statistical study was carried out, which resulted in a classification of returns by type and product category. This analysis was the starting point for identifying the actions to be taken and the strategic guidelines to follow to improve the returns management process.
Finally, the study allowed the definition of indicators to be used for monitoring and controlling the process. These indicators, constantly monitored through automated procedures to be defined in the subsequent phases of the project (development of a decision support system), have the objective of automatically monitoring returns data, generating alerts and proposing (semiautomatically) recommendations aimed at anticipating the onset of returns.
This study has some limitations. First of all, it needs further validation and implementation in other organizational settings to evaluate the impact, possibly by including both small-medium companies and large organizations located in Italy and outside. Then, new indicators could be defined for a more robust support to the analysis of reverse logistics issues. Finally, more vertical analyses on products, customers or carriers are required since they could bring complementary evidence and insights to define more innovative strategies for increasing the value extracted from returned goods.
Furthermore, a follow-up study could be aimed to integrate the dashboard with new forecasting data related to the number of returned products will be received in the next period (e.g. one week and one month) so that the responsible of the reverse logistics can organize the human resources required to manage the expected workload.
Figures
Figure 1
Case study process
Figure 2
Statistics on alerts
Alerts generated, probable causes, indicators and corrective actions
| Entity | Alerts/problems | Probable causes | Indicators | Corrective actions |
|---|---|---|---|---|
| Lot | Poor quality reported by customers | Poor quality of the lot that does not respect the standard agreed | - % of returned goods with poor quality for a specific lot, compared with the % of other lots of the same product | - Random check of product quality within the lot to decide if removing the lot from the market/catalog), so avoiding new sales - Evaluate the possibility to initiate a recourse against the supplier and obtain compensation |
| Customers complain that products do not meet the specifications | Lot not compliant with expected specifications | - % of returned goods that are not compliant for a specific lot, compared with the % of other lots of the same product | ||
| Product | Poor quality reported by customers | Poor quality of the product that does not respect the standard agreed | - % of returned goods with poor quality for a specific product, compared with the % of other products of the same category - % of returned goods with poor quality for a specific product, compared with the % of the same product along the time | - Random check of product quality to decide if removing the product from the market/catalog, so avoiding new sales - Evaluate the possibility to initiate a recourse against the supplier and obtain compensation - Evaluate the possibility to stop the future purchases of the product from the same supplier or others |
| Damages reported by customers | Damaged product | - % of damaged goods returned for a product, compared with % of other products of the same category - % of damaged goods returned for a specific product, compared with the % of the same product along the time | - Evaluate if contacting supplier to ask for a better and more robust packaging, possibly providing suggestions - Evaluate id adding additional packaging before the delivery to customers | |
| Customers complain that products do not meet the specifications | Lot not compliant with expected specifications or incomplete documentation | - % of not-compliant goods returned for a specific product, compared with the % of other products of the same category - % of not-compliant goods returned for a specific product, compared with the % of the same product along the time | - Revise the documentation by highlighting specific issues to be checked by customers before the purchase - Revise the documentation by updating the technical sheet | |
| Product category | Damaged products | Inadequate packaging | - % of returned goods for a specific category, compared with the % of the same category along the time | - Provide additional packaging for all the products of the category |
| Supplier | Poor quality of the product or weak compliance of the product specifications reported by customers and related to the products provided by the same supplier | Choice of a supplier that is unreliable | - % of returned goods for a specific supplier, compared with the % of other suppliers that provide the same categories of products - % of returned goods for a specific supplier, compared with the % of the same supplier along the time | - Evaluate if stopping the purchase of products from the problematic supplier - Identify alternative suppliers |
| Customer | High frequency of returned goods from a customer respect to the total number of confirmed orders | Customer behaves as a “serial returner” | - % of returned goods for a specific customer, compared with the % of customers of the same category - % of returned goods for a specific customer, compared with the % of the same customer along the time | - Evaluate if inhibiting a specific customer to purchase goods |
| Carrier | High frequency of broken products during transportation | Transportation standards not respected | - % of broken goods for a specific carrier, compared with the % of other carriers - % of broken goods for a specific carrier, compared with the % of the same carrier along the time | - Contact carrier to decide how to manage the problem (e.g. additional packaging) - Evaluate if substituting the carrier |
Source: Authors’ work
Logical architecture of the business active monitoring module and configuration of the parameters of the anomaly detection model
| Problems | Model’s variables (C, X) | Model’s parameters (P, p0) | Corrective actions (if alert is generated) | Frequency |
|---|---|---|---|---|
| Poor quality of a lot that is currently for sale | C = total number of goods belonging to the lot X = number of goods belonging to the lot that have been returned for poor quality | P = total number of goods sold (including previous lots) p0 = fraction of customers that returned the products for poor quality | - Execute a random check on the lot and then evaluate if removing the lot from sale - If so, evaluate the possibility to initiating recourse against the supplier | Daily |
| Lack of compliance of a lot that is currently for sale | C = total number of goods belonging to the lot X = number of goods belonging to the lot that have been returned for lack of compliance | P = total number of goods sold (including previous lots) p0 = fraction of customers that returned the products for lack of compliance | - Execute a random check on the lot and then evaluate if removing the lot from sale If so, evaluate the possibility to initiating recourse against the supplier | Daily |
| Poor quality of a product | C = total number of goods have been sold X = number of goods that have been returned for poor quality | P = total number of goods sold p0 = fraction of customers that returned the products for poor quality | - Execute a random check and then evaluate costs and benefits of removal the product from sale - Evaluate the possibility to initiating recourse against the supplier - Evaluate if stopping the purchase of products | Weekly |
| Product with more damages respect to other products in the same category | C = total number of goods have been sold X = number of goods that have been returned for damage | P = total number of goods in the same category that have been sold p0 = fraction of customers that returned the products due to the presence of damages | - Evaluate if asking the supplier for a better packaging, by providing suggestions - Evaluate if adding further packaging before the delivery | Weekly |
| Product not compliant | C = total number of goods have been sold X = number of goods that have been returned because not compliant | P = total number of goods in the same category that have been sold p0 = fraction of customers that returned the products because not compliant | - Update the technical sheet and highlight on the Web site the specific aspects that customers have to check before the purchase of product | Weekly |
| Category of products with more damages respect to other categories | C = total number of goods have been sold in the same category X = number of goods in the same category that have been returned for damages | P = total number of goods in all the categories that have been sold p0 = fraction of customers that returned the products because of damages | - Evaluate if adding further packaging to all the products in the category before the delivery | Monthly |
| A supplier provides products with a lower standard respect to the products of the same category (CP) provided by other suppliers | C = total number of goods (in the category CP) provided by supplier F that have been sold X = number of goods in category CP provided by supplier F that have been returned for any reason | P = total number of goods in the category CP that have been sold, provided by other suppliers F’≠F p0 = fraction of customers that returned the products of the category CP provided by other suppliers F’≠F | - Evaluate if stopping purchase of products from supplier F - Identify alternative suppliers | Weekly |
| The customer CC is a serial returner (i.e. the incidence of returned products from the customer CC is clearly higher that the incidence of products of the same category returned from other customers) | C = total number of goods purchased by the customer CC X = number of goods purchased by customer CC that have been returned | P = total number of goods that have been purchased by other customers with the same profile of the customer CC p0 = fraction of goods returned by customers with the same profile of the customer CC | - Evaluate if inhibiting the customer CC from future purchases or providing more information before new purchases | Weekly |
| The carrier T provokes more damages respect to other carriers | C = total number of goods transported by the carrier T X = number of goods transported by the carrier T and damaged | P = total number of goods transported by carriers T’≠T p0 = fraction of goods transported by carriers T’≠T and damaged | - Contact carrier T to decide how to manage the problem (e.g. additional packaging) - Evaluate if substituting the carrier | Weekly |
CP = category of product; CC = category of customer
Source: Authors’ work
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Further reading
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Acknowledgements
This paper was developed with the financial support of Apulia region (Italy) in the framework of the project “A.LO.SOL.” – Advanced LOgistics SOLutions.
Corresponding author
Gianluca Elia can be contacted at: gianluca.elia@unisalento.it
