The repair kit problem with imperfect advance demand information

Rippe, Christoph; Kiesmüller, Gudrun P.

doi:10.1016/j.ejor.2022.04.019

Cited by 9 publications

(2 citation statements)

References 31 publications

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“…When cost minimization is the goal, the model assumes holding cost and poor service costs (Teunter, 2006). Finally, Rippe and Kiesm€ uller (2023) present two heuristic approaches that address repair kit inventory policies when advanced demand information is available.…”

Section: Literature Searchmentioning

confidence: 99%

Repair part service level differentiation based on holding other parts shortage costs

Maleyeff,

2024

JQME

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PurposeThe article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.Design/methodology/approachDemand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.FindingsThe results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.Social implicationsThe proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.Originality/valueThe optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

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Section: Literature Searchmentioning

confidence: 99%

Repair part service level differentiation based on holding other parts shortage costs

Maleyeff,

2024

JQME

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“…The only attempt to date that uses ADI for the repair kit problem stems from an initial project (Rippe and Kiesmüller 2022) in which we considered unreliable and non-part-specific ADI when planning the repair kit. In this initial project we focussed on how to cope with the information obtained from a given imprecise and unreliable sensor system that is only able to communicate very few error codes.…”

Section: Introductionmentioning

confidence: 99%

The added value of advance demand information for the planning of a repair kit

Rippe

Kiesmüller

2022

Cent Eur J Oper Res

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In this paper the repair kit problem is studied, where technicians have to visit several customers to repair broken appliances (such as copiers or heating systems) and they can only take a limited set of parts with them (called the repair kit). In this problem, it has to be decided which spare parts to include in the repair kit. We consider a version of this problem in which partial advance demand information is available. That means we divide the set of parts into two subsets, where the condition of parts in one subset is monitored by sensors. In case an appliance fails and a repair job is requested by a customer the service provider is able to access this sensor data before a technician visits the customer. For this setting, we derive an expression for the job fill rate, which is used as a constraint in the optimization model, where holding and replenishment costs are minimized. We use a greedy heuristic to determine near-optimal repair kits. In a numerical study, we find that integrating advance demand information yields substantial cost savings. In order to find out for which parts having advance demand information is most valuable, we examine the effect of parts’ demand probabilities and their prices. We find that monitoring parts that are expensive and likely to fail leads to the largest cost savings. In particular, the price of the monitored parts and the achievable cost savings are strongly correlated.

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