Table 3.13: The kind of elements to be considered in more detail
Decision making area Dominant dimensions Additional elements
Networks How & Who Value of the product;
Relationships Who The type of relation
be-tween the parties; Degree of control in acquisition;
Inventory Management Why-returning & What Why-returning + used vs.
not; What -characteristics (in detail);
Production planning How & Who How-processes (in detail);
Degree of involvement of the sender (bring and pickup systems); combined with forward, vs. not;
- why are they returned?
- why is the company involved in recovery?
- how are the products being recovered?
- who are the parties involved?
This review was also a means to improve the quality of the framework (Chap-ter 2). Both projects were carried out simultaneously and in an in(Chap-teractive way. This means that on the one hand, the case studies review is a means to ground the framework on empirical evidence, and on the other hand, the framework is a means to further analyze the case studies. The analysis of the case studies allowed us to establish relations between attributes of reverse logistics. We come back to this later in this section.
We presented the case studies according to the following decision-making focus: Network Structures, Relationships, Inventory Management, and Plan-ning and Control. We remarked particular issues and latent questions, for which we proposed topics for future research. For each group, some of the aforementioned questions gain a dominant role, as summarized below. Fur-thermore, there was an overview of Information and Technology (IT) for re-verse logistics.
Reverse Logistics Networks
The section on Reverse Logistics Network Structures was organized around the typologies on the how and the who (see Section 2). In more detail, we presented cases for
• Networks for Re-distribution/Re-sale,
• Networks for Remanufacturing,
• Networks for Recycling - Public Networks,
• Networks for Recycling - Private Networks.
Here, most of the cases fell under ‘Re-distribution’ and ‘Recycling - Public Networks’. Only a few cases relate to private networks, but at this time we do not know to which degree private networks for recycling are successful.
Reverse Logistics Relationships
The cases report quite different incentive tools to stimulate/enforce a certain behavior of other players in the supply chain (see Section 3.5):
• Deposit fees
• Buy back options
• Trade-in
• Acquisition price
• Timely and clear information
• Power
• Environmental responsibility
• Social responsibility
Some incentives are also part of sales contracts, while others require the cus-tomer to buy another product in exchange. There are also tools that are not directly coupled to a selling activity like a gift to a non-profit organization.
Furthermore, it seems that only deposit fees are specific for product recov-ery. The other mentioned tools are also used to attract or keep customers in general.
Inventory Management for Reverse Logistics
We have grouped the presentation of the cases according to the why-returning typology. The cases covered:
• Functional returns,
• Commercial returns,
• Service returns,
• End-of-use,
• End-of-life
We did not find cases for all the return reasons. Omissions can mainly be explained, because some return reasons are treated in different contexts, like production planning (see Section 3.6).
One of the complicating factors in Inventory Management is the uncer-tainty in the quality of reverse flows. Many have defended that product data are essential for the efficient handling of returns. This offers various research opportunities (see the next section).
Planning and Control for Reverse Logistics
We found case studies on Planning and Control of product recovery in the following categories:
• collection for recovery
- separate collection for recovery - combined collection and distribution
• recovery
- separate recovery
- recovery combined with production
In the literature, the supply of recoverables is often assumed to be autonomous, except for some literature on repair and remanufacturing. Besides this, un-certainty has been incorporated only as far as the arrival of products for recovery and the duration of recovery related activities are concerned. Uncer-tainty with respect to the result of the processing activities has hardly been taken into account. In practice, however, there is a lot of yield uncertainty.
IT for Reverse Logistics
We found cases reporting evidence that IT can be used in the following stages of the life-cycle of a product:
• Manufacturing - dfX - labels
• Distribution - software
- track & tracing
• Customer - software
- decision support systems
The technology to process and transmit information seems to be available with promising benefits for reverse logistics. The cases show, though, that the lack of appropriate data is still the bottleneck in the implementation of decision support systems.
For each of the above decision-making areas, some of the dimensions of the framework (see Chapter 2) are predominantly important (see Sections 3.4–
3.8). We summarize the predominant dimensions per decision-making area as follows
• Networks ⇔ Recovery option (How ) and Actor (Who)
• Relationships ⇔ Actor (Who)
• Inventory Management ⇔ Return reason (Why-returning) and Product type (What )
• Production Planning ⇔ Recovery option (How ) and Actor (Who) In the next section we give directions to explore this further.
Besides the analysis per decision-making area, we also presented the cases organized by return reason (why-returning) vs. recovery option (how ), and the cases organized by driver vs. recovery option (see Section 3.9). This analysis revealed some relations and issues. For instance, end-of-life returns appear to be highly correlated with recycling. If products or their modules would be designed for remanufacturing, however, remanufacturing would likely be more attractive, even at the end of life. The main driver for recycling is legislation. Actually, current legislation does not seem to be able to stimulate other forms of recovery. However, if legislation would also pro-actively focus on product design issues, rather than dealing with recovery in a reactive way, other recovery options than recycling would likely be more stimulated.
3.10.1 Research opportunities
If one wants to get more in-depth details, build-up theory, or explain phenom-ena, one has to go into more depth regarding some aspects of the framework.
In Table 3.13 we put forward the kind of elements that can be considered to further analyze the prominent dimensions of the framework per decision making area. In order to do this, a dedicated case study analysis should be conducted. Next we continue with specific research opportunities regarding the various decision making areas.
Reverse Logistics Networks
Commonly, the models to determine the location of reverse logistic facili-ties are based on deterministic integer programming models. However, it is
commonly accepted that reverse logistics is characterized by much more un-certainty than in forward logistics. With this in mind, there is room for other tools like stochastic programming modelling for reverse logistics network de-sign.
Reverse Logistics Relationships
We pointed out the lack of literature supporting the choice of incentive. For-tunately, there is plenty of literature that can be used as a starting point.
For instance, 1) literature on sales contracts with return options for unused products, and 2) research on the optimal acquisition price to realize a certain flow of products.
Inventory Management for Reverse Logistics
Knowledge about returns is seen to be essential for efficient inventory man-agement of returns. With respect to modelling, it is critical to question the validity of the common assumptions in the literature (see Chapter 6). Fur-thermore, it is important to investigate the impact on inventory management of having a priori information on product returns (see Chapter 7). Moreover, specialized forecasting techniques should be developed to deal with this issue.
Planning and Control for Reverse Logistics
In the literature, uncertainty with respect to recovery yield has hardly been taken into account. In future research, yield uncertainty should be mod-elled explicitly. Furthermore, return handling operations have been so far overlooked in the scientific literature (see Chapter 4). On this, Chapter 5 provides insights regarding splitting vs. combining forward and reverse flows during transport, actual handling and the storage.
IT for Reverse Logistics
Almost all the cases that focus on IT report on the benefits only, while dis-regarding the required investment. It is important to broaden such partially sighted reports.
In face of limited investment capacity, it would be helpful to know in which phase the investment would have most earnings. In addition, when alternative technologies are available, one can investigate which one is best.
To do so, one has to take into account the costs and benefits of collecting and managing data and the costs of investing and managing the technology.
The majority of the case studies deal with one aspect of a real reverse logistics situation but they do not give the overall business environment, which makes insights rather one-dimensional. Thus, there is a need for conducting
more integral case study research, by mapping the business context together with more broad information on critical factors, trade-offs and implications.
Besides this, the lack of theory for reverse logistics (see Dowlatshahi, 2000) or even for supply chain management (Croom et al., 2000) adds to companies’
inability of knowing what matters in reverse logistics. Therefore the devel-opment of theory should be on top of the research agenda to support reverse logistics decisions.
Return Handling:
decision-making and quantitative support
Consistently wise decisions can only be made by those whose wisdom is constantly challenged.
Theodore C. Sorensen