Pieter Colen

Optimization of a stochastic remanufacturing network with an exchange option

An international manufacturer of industrial equipment offers its customers a remanufacturing service consisting of a refurbishment of the most critical part in order to rejuvenate the equipment. Offering remanufacturing services is in line with a servitization strategy. We develop a strategic decision support tool to optimize the required remanufacturing network. Investment decisions have to be made, not only concerning the number and locations of remanufacturing facilities, but also concerning the appropriate capacity and inventory levels to guarantee specific service levels. These network decisions are influenced by the way remanufacturing services are offered. We consider two service delivery strategies, either a quick exchange of the used part by an available remanufactured one or re-installing the original part after it has been remanufactured. Given the high level of uncertainty, we build a stochastic, profit maximizing model to simultaneously determine the optimal network design and the optimal service delivery strategy for a multi-product, multi-level network for repairable service parts. The rapid modeling formulation with a non-linear objective function subject to non-linear constraints is solved by the differential evolution algorithm. We conduct the analysis for fast and slow moving part types. The model can be easily extended to more general settings, while the case-study provides valuable insights for practitioners.

Product service systems: exploring operational practices

Increasingly, original equipment manufacturers (OEMs) are offering integrated solutions of products and services (product service systems (PSS)). To be successful in a service strategy, the OEM has to be able to provide services at a lower cost than the do-it-yourself alternative of the customer. We identify five service operations guidelines to achieve operational excellence in the field of PSS. The guidelines are based on a structured return-on-invested-capital analysis in collaboration with an OEM in the compressed air and generator industry (due to confidentiality reasons the OEM is referred to as AirGen). Each of the guidelines is cascaded into operational practices.

Network and contract optimization for maintenance services with remanufacturing

Implementing comprehensive service contracts and sustainable supply chains are two recent trends that create the opportunity to develop maintenance contracts with an uptime guarantee for the customer and a remanufacturing process for removed parts. This involves management decisions on the design of the contract (price, uptime guarantee and overhaul interval) and the logistics network (facility locations, capacities and inventories with given service level). These two decision levels are interrelated: the number of contracts is a function of price and machine uptime, while this uptime is affected by the overhaul interval and network responsiveness. Steady-state queueing equations explicitly model the stochastic nature of the problem, e.g. the impact of resource utilization levels on lead time of the remanufacturing process. This approach results in a non-linear mixed integer model, which is solved by a differential evolution algorithm to find the maximum profit that simultaneously optimizes both problems. A real-life application reveals that price sensitivity is a critical determinant and that measures must be taken to tackle the problem of moral hazard.

Centralization or Decentralization of Remanufacturing Facilities in an After-Market Service Supply Chain

Equipment manufacturers are increasingly selling complementary services such as remanufacturing services to their equipment customers. This servitization trend mandates that the remanufacturing supply chain network is optimized accordingly. In order to set up such network, investment decisions have to be made, not only regarding the number, locations and types of remanufacturing facilities, but also with respect to the appropriate capacity and inventory levels in order to guarantee a specific service level. These network decisions are influenced by the way remanufacturing services are offered. We consider here two service delivery strategies: either a quick exchange of the used part by an available refurbished one or re-installing the original part when all corresponding remanufacturing processes are finished. Given the high level of uncertainty in this context, we build a stochastic, profit maximizing model to simultaneously determine the optimal layout and the optimal service delivery strategy for a multi-level logistics network with single indenture repairable service parts. Model results for this case study are obtained by the differential evolution algorithm.