How Yield Process Misspecification Affects the Solution of Disassemble-to-order Problems

Random yields from production are often present in manufacturing systems and there are several ways that this can be modeled. In disassembly planning, the yield uncertainty in harvesting parts from cores can be modeled as either stochastically proportional or binomial, two of these alternatives. A statistical analysis of data from engine remanufacturing of a major car producer fails to provide conclusive evidence on which kind of yield randomness might prevail. In order to gain insight into the importance of this yield assumption, the impact of possible yield misspecification on the solution of the disassemble-to-order problem is investigated. Our results show that the penalty for misspecifying the yield method can be substantial, and provide insight on when the penalty would likely be problematic. The results also indicate that in the absence of conclusive information on which alternative should be chosen, presuming binomial yields generally leads to lower cost penalties and therefore preferable results

On the alignment of lot sizing decisions in a remanufacturing system in the presence of random yield

In the area of reverse logistics, remanufacturing has been proven to be a valu- able option for product recovery. In many industries, each step of the products’ recovery is carried out in lot sizes which leads to the assumption that for each of the different recovery steps some kind of fixed costs prevail. Furthermore, holding costs can be observed for all recovery states of the returned product. Although several authors study how the different lot sizes in a remanufacturing system shall be determined, they do not consider the specificity of the remanufacturing process itself. Thus, the disassembly operations which are always neglected in former analyses are included in this contribution as a specific recovery step. In addition, the assumption of deterministic yields (number of reworkable compo- nents obtained by disassembly) is extended in this work to study the system behavior in a stochastic environment. Three different heuristic approaches are presented for this environment that differ in their degree of sophistication. The least sophisticated method ignores yield randomness and uses the expected yield fraction as certainty equivalent. As a numerical experiment shows, this method already yields fairly good results in most of the investigated problem instances in comparison to the other heuristics which incorporate yield uncertainties. How- ever, there exist instances for which the performance loss between the least and the most sophisticated heuristic amounts to more than 6%.reverse logistics, remanufacturing, lot sizing, disassembly, random yield

A new Silver-Meal based heuristic for the single-item dynamic lot sizing problem with returns and remanufacturing

In a recent contribution, Teunter et al. [2006. Dynamic lot sizing with product returns and remanufacturing. IJPR 44 (20), 4377-4400] adapted three well-known heuristic approaches for the single-item dynamic lot sizing problem to incorporate returning products that can be remanufactured. The Silver-Meal based approach revealed in a large numerical study the best performance for the separate setup cost setting, i.e. the replenishment options remanufacturing and manufacturing are charged separately for each order. This contribution generalizes the Silver-Meal based heuristic by applying methods elaborated for the corresponding static problem and attaching two simple improvement steps. By doing this, the percentage gap to the optimal solution which has been used as a performance measure has been reduced to less than half of its initial value in almost all settings examined.

Mathematical Decision Model for Reverse Supply Chains Inventory

In the reverse supply chain inventory theory, inventory models are concerned with the demand of reusable parts, stock replenishment, ordering cycle, delivery lead time, number of disassembled products, ordering costs. The particularity of these models consists in the occurrence of high uncertainties of the quantity and quality of the returned products and resulting parts. To overcome the problem, an inventory model that incorporates decision variables at proactive and reactive levels is derived and discussed in this paper

Integrating Closed-loop Supply Chains and Spare Parts Management at IBM

Ever more companies are recognizing the benefits of closed-loop supplychains that integrate product returns into business operations. IBMhas been among the pioneers seeking to unlock the value dormant inthese resources. We report on a project exploiting product returns asa source of spare parts. Key decisions include the choice of recoveryopportunities to use, the channel design, and the coordination ofalternative supply sources. We developed an analytic inventory controlmodel and a simulation model to address these issues. Our results showthat procurement cost savings largely outweigh reverse logistics costsand that information management is key to an efficient solution. Ourrecommendations provide a basis for significantly expanding the usageof the novel parts supply source, which allows for cutting procurementcosts.supply chain management;reverse logistics;product recovery;inventory management;service management

On the alignment of lot sizing decisions in a remanufacturing system in the presence of random yield

In the area of reverse logistics, remanufacturing has been proven to be a valu- able option for product recovery. In many industries, each step of the products\u27 recovery is carried out in lot sizes which leads to the assumption that for each of the different recovery steps some kind of fixed costs prevail. Furthermore, holding costs can be observed for all recovery states of the returned product. Although several authors study how the different lot sizes in a remanufacturing system shall be determined, they do not consider the specificity of the remanufacturing process itself. Thus, the disassembly operations which are always neglected in former analyses are included in this contribution as a specific recovery step. In addition, the assumption of deterministic yields (number of reworkable compo- nents obtained by disassembly) is extended in this work to study the system behavior in a stochastic environment. Three different heuristic approaches are presented for this environment that differ in their degree of sophistication. The least sophisticated method ignores yield randomness and uses the expected yield fraction as certainty equivalent. As a numerical experiment shows, this method already yields fairly good results in most of the investigated problem instances in comparison to the other heuristics which incorporate yield uncertainties. How- ever, there exist instances for which the performance loss between the least and the most sophisticated heuristic amounts to more than 6%

A new Silver-Meal based heuristic for the single-item dynamic lot sizing problem with returns and remanufacturing

In a recent contribution, Teunter et al. [2006. Dynamic lot sizing with product returns and remanufacturing. IJPR 44 (20), 4377-4400] adapted three well-known heuristic approaches for the single-item dynamic lot sizing problem to incorporate returning products that can be remanufactured. The Silver-Meal based approach revealed in a large numerical study the best performance for the separate setup cost setting, i.e. the replenishment options remanufacturing and manufacturing are charged separately for each order. This contribution generalizes the Silver-Meal based heuristic by applying methods elaborated for the corresponding static problem and attaching two simple improvement steps. By doing this, the percentage gap to the optimal solution which has been used as a performance measure has been reduced to less than half of its initial value in almost all settings examined

Integrating Closed-loop Supply Chains and Spare Parts Management at IBM

Ever more companies are recognizing the benefits of closed-loop supply chains that integrate product returns into business operations. IBM has been among the pioneers seeking to unlock the value dormant in these resources. We report on a project exploiting product returns as a source of spare parts. Key decisions include the choice of recovery opportunities to use, the channel design, and the coordination of alternative supply sources. We developed an analytic inventory control model and a simulation model to address these issues. Our results show that procurement cost savings largely outweigh reverse logistics costs and that information management is key to an efficient solution. Our recommendations provide a basis for significantly expanding the usage of the novel parts supply source, which allows for cutting procurement costs