One of the significant reasons for the study of RL is the large volume of goods that accumulates in the reverse channel, which again becomes the factor that brings adverse effect, both economically and environmentally (Rogers and Tibben-Lembke, 2001; Dias, Junior and Martinez, 2016). There has, however, not been adequate research to show how much of the goods can accumulate in various categories. Consequently, not having enough prior research to show exactly how much of the goods remain in excess or unsold in a company for a period of time can be a problem. However, as noted by Bernon and Cullen (2007), one of the main aspects that triggers an interest in RL is the volume of unsold and/or excess goods. Particularly in retail sector, these goods need to be moved back into the supply chain for keeping inventory fresh and in demand, which may lead to indirect economic gains (Tan, Yu and Arun, 2003).
Unlike for the volume of goods in the unsold and excess category, both in the past and in recent years, there have been numerous researchers who have emphasised the scale of goods accumulation in the customer return category. Hence, there have been various viewpoints on the volume of goods accumulation in the customer return category. Blumberg (1999) suggests that
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there will be a significant rise in after-sale service, as a repair service. Blumberg, based on the surveys and interviews carried out in more than 400 medium- and large-scale manufacturing companies, estimated the multiple annual growth rate for repair services in the United States to be 14.9%, and the compounded annual growth rate for repair services worldwide to be 15.8%. This shows how the volume of goods that may come back via the repair category is escalating. Higher customer return has been the main reason for firms undertaking RL (de Leeuw et al., 2016). Rogers and Tibben-Lembke (1999) report that return rates are very much industry-specific, and cite that the rates range from 5% to 50% in many industries. Likewise, Dowlatshahi (2012) suggests similar, at 3% to a maximum of 40% depending on the industry. Talking about the business type, Robbins-Gentry (1999) estimates that product return could range from 15% for a mass merchandiser to 35% for e-commerce retailers, as the return rates in e-commerce are, for instance, 35% (Robbins-Gentry, 1999) and 50% (Pogorelec, 2000). Moreover, among retail types, researchers have found that the return rates in e-commerce and catalogue retailing are the highest, at 18% (Robbins-Gentry, 1999) and 20% respectively (Daugherty, Autry and Ellinger, 2001). Likewise, in mail order, especially ladies fashion, return rates of 60% are common (Wheatley, 2002). Rogers and Tibben-Lembke (1999) identified returns for different industries, with figures being recorded at 50% (magazine publishing), 20-30% (book publishers), 18-35% (catalogue retailers) and 10-12% (electronic distributors). Similarly, Bernon and Cullen (2007) for UK context identified return rates of 30% for catalogue retailing, 4% for durable products, 10% for books, and 10% for music and entertainment. Returns are, and always have been, a fundamental part of retailing (Raimer, 1997). Firms to satisfy and consequently retain customers must provide a returns policy (Smith, 2005; Bernon and Cullen, 2011) and take the goods back from their customers (Skinner, Bryant and Richey, 2008; Cullen et al., 2013). Looking at the trend, it can be estimated that return rates are growing (Langnau, 2001; Stock, 2001; Guide et al., 2006). Some studies have analysed the return rates in terms of GDP, which can project the return figure as quite severe. In the US, consumers return products valued at more than $100 billion each year, which is more than the GDP of 66% of the countries in the world (Stock, Speh and Shear, 2002). The volume of customer-returned goods is escalating, and the total retail returns in the UK have been valued at £6 billion a year (Bernon and Cullen, 2009).
It is not just the returned goods that accumulate, but goods that have been damaged and broken (Ravi and Shankar, 2005; Cullen, Bernon and Gorst, 2010; Sarkis, Helms and Hervani, 2010;
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Rogers, Melamed and Lembke, 2012) also get accumulated. Goods also become obsolete (Lawrence et al., 2002; Verma, 2015) and expire (Goodwin III, 2003; Vaishnavi et al., 2016). This also indicates that the volume of goods in these categories are not less, however not having prior research on the volume within these categories is a problem.
There is also an indication that goods that may have to be taken back due to own initiative or due to legislation is escalating. The volume of electronic-related waste generation globally is anticipated to reach 93.5 million tonnes in 2016, up from 41.5 million tonnes in 2011, at a CAGR of 17.6% from 2011 to 2016 (MarketsandMarkets.com, 2011, cited by Hong, Lee, and Chang, 2014; Yu, Spiesz and Brouwers, 2014). Accordingly, out of the 20 largest US exporters, five specialise in shipping waste paper and one specialises in scrap metal (MacCormac, 2008, cited in Rogers, Rogers and Lembke, 2010). Proactive firms have now realised the benefits of RL. Even the firms who may not understand this as a problem are bound to take back goods anyway due to legislative reasons. In this regard, the volume of goods – either in the category of the own initiative take-back or in the legislative category – are growing rather than decreasing.
Companies may also have to recall faulty products that have already been sold. Research reveals that huge number of goods has been recalled in the past, and to maintain their status quo, companies will recall more goods in the future (Dai, Tseng and Zipkin, 2015), increasing the volume of the goods in this category.
Due to the complexity and costs attached to reversing the logistics, firms are trying their best to avoid accumulation in all categories. Stock, Speh and Shear (2006) suggest that returns could be divided into two categories: controllable returns, which can be avoided, and uncontrollable returns, which companies can do little or nothing about. Due to higher operating costs, firms should be more cautious about RL and can use some preventive measures to avoid or lessen the volume of accumulation, rather than let the situation escalate. For instance, to avoid customer returns, firms can tighten more liberal returns policies (Richey, Genchey and Daugherty, 2005) by gatekeeping and avoiding returns where possible (Rogers et al., 2002). To avoid goods accumulation in the unsold and excess or obsolete and expiry category, firms can analyse the product lifecycle and manage the goods accordingly (Tibben-Lembke, 2002). Again, training staff (Bernon, Rossi and Cullen, 2011); advising customers about the use of the product; repairing or repair facility; aftersales service; integrating marketing and logistics; proper forecasting; proper inventory management; integration of the activities related to collaboration, integration and
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evaluation; managerial decision making and know how; enhancing and improving the product quality; and better accounting systems (Bernon and Cullen, 2007) are just some of the measures that can be implemented to avoid or reduce the volume of goods accumulation, which is again believed to help reduce the cost of RL. These measures, however, may not always result in a win/win situation. For instance, avoiding and gatekeeping returns by making the return policy tighter may result in cost saving in the short term, but may create an adverse effect in the long run. A Harris Interactive survey, as cited in Sonya Hsu, Alexander, and Zhu (2009), indicates that 90% of the respondents’ purchasing decisions were based on their retailer’s return policy and process. Likewise, a survey conducted by Smith (2005) found that customers’ major purchasing decisions were based on the firms’ return policy. Hence, it is likely that a company’s tighter returns policy may distance their customers, resulting in a loss of market share.
The volume of the goods that are coming back are neither clear nor stable, and in some cases are unpredictable, creating a state of confusion to the businesses (Biehl, Prater and Realff, 2007; Lee and Chan, 2009). Once they know the volume of goods, firms can make decisions around how to manage, extract value, or dispose of these goods. For instance, a large volume of accumulation means that firms can enjoy the advantage of ‘economies of scale’ (Autry, Daugherty, and Richey, 2001). Besides, literature reveals that it is more practical for businesses who deal with the higher volume of goods to invest in RL-related activities (Johnson and Leenders, 1997). Also, businesses who experience higher return rates of products are more likely to gain expertise related to RL (Johnson, 1998). Smaller firms may have small volume of accumulation; however, they may equally need to worry about how to manage these products. Due to their resource constrained nature (Storey, 1996), these companies may have a devastating effect, both economically and environmentally, if they do not manage these goods, even if they are small in volume.
One of the factors required to calculate the operational costs of RL, among other factors, is to understand the volume of goods that has accumulated in the reverse channel. However, this volume of accumulated goods can only be known if the businesses kept their records: ‘… the
exact amount of reverse logistics activity is difficult to determine because most companies do not carefully track reverse logistics costs’ (Rogers and Tibben-Lembke, 2001, p.134). Cullen et al.
(2013) suggest that there was ‘a significant lack of visibility and management reporting in this
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is known only through estimation, as firms still do not see the management of RL as an important aspect of their operations (Rogers and Tibben-Lembke, 2001; Bernon and Cullen, 2007; Cullen et. al., 2013). Authors such as Vijayan et al. (2014) suggest that firms who understand the significance of RL are more likely to adopt this in their business strategy. Nevertheless, forecasting goods accumulation in the reverse channel is not always easy (Cheng and Lee, 2010; Das and Chowdhury, 2012). In a retail context, future planning and forecasting for reverse goods is difficult, because individual customers initiate the RL, and not the firms (Tibben-Lembke and Rogers, 2002). Experts, may have already come up with the various ways of forecasting future sales volumes but using techniques to calculate the volume of goods in the reverse channel is in its preliminary stage. For instance, Lee and Chan (2009) suggested developing the RFID (Radio Frequency Identification) based RL system which would help in calculating the volume of goods. Mutha and Pokharel (2009) suggested a calculated network design for RL which would help in optimizing the quantity of RL goods for the purpose of remanufacturing. Overall, it is always important for firms to understand the volume of goods accumulation, as this is one of the main aspects that motivates firms to perform RL (Autry, Daugherty and Glenn Richey, 2001; Richey et al., 2004; Bernon, Rossi, and Cullen, 2011), as this will also allow firms to understand the cost and expertise required to manage these.
Volume of goods and the way a firm manages these volumes may play a significant role in the processing of these goods. However, understanding the various steps the firm may need to go through to process these goods is equally important, which will be explained in the section below.