Purpose – For more than ten years, the value of additive manufacturing (AM) for after-sales service logistics has been propagated. Today, however, only few applications are observed in practice. In this paper, possible reasons for this discrepancy are discussed and a method is developed to simplify the identification of economically valuable and technologically feasible business cases. Design/methodology/approach – The approach is based on the Analytic Hierarchy Process (AHP) and relies on spare part information that is easily retrievable from the company databases. This has two advantages: first, the approach can be customized towards specific company characteristics, and second, a very large number of spare parts may be assessed simultaneously. A field study is discussed in order to demonstrate and validate the approach in practice. Furthermore, sensitivity analyses are performed to evaluate the robustness of the method. Findings – Results provide evidence that the method allows a valid prioritization of a large spare part assortment. Also, sensitivity analyses clarify the robustness of the approach and illustrate the flexibility of applying the method in practice. More than 1000 positive business cases of AM for after-sales service logistics have been identified based on the method. Originality/value – The developed method enables companies to rank spare parts according to their potential value when produced with AM. As a result, companies can evaluate the most promising spare parts first. This increases the effectiveness and efficiency of identifying business cases and thus may support the adoption of AM in after-sales service supply chains.
|Place of Publication||Eindhoven, the Netherlands|
|Publisher||TU Eindhoven, Research School for Operations Management and Logistics (BETA)|
|Number of pages||32|
|Publication status||Published - 2016|
|Name||BETA working papers|
|Publisher||TU Eindhoven, Research School for Operations Management and Logistics (BETA|
Knofius, N., van der Heijden, M. C., & Zijm, W. H. M. (2016). Selecting parts for additive manufacturing in service logistics. (BETA working papers; No. 515). Eindhoven, the Netherlands: TU Eindhoven, Research School for Operations Management and Logistics (BETA).