The joint optimization of maintenance and spare parts inventory enables overall cost-effective decisions by integrating both system condition monitoring data and real-time inventory information. As sustainable manufacturing continues to evolve, remanufacturing is emerging as an essential strategy for extending component lifecycles and reducing environmental and economic costs. Nevertheless, recent studies have rarely integrated component remanufacturing into a dynamic decision framework for condition-based maintenance (CBM) and spare parts provisioning driven by system states, which will result in increased computational complexity. This paper investigates the joint optimization of CBM, spare parts inventory, and component remanufacturing for a multi unit system with degradation randomness. Firstly, we formulate this joint decision-making problem as a Markov Decision Process (MDP) that incorporates system states with degradation randomness and spare parts inventory accounting for multi-lifecycle remanufacturing. Building upon widely adopted threshold control methodologies, we design a heuristic Threshold-Based Policy to derive simple solutions to joint maintenance-inventory control with remanufacturing. However, the computational complexity induced by degradation randomness and remanufacturing severely limits the solution efficiency of the Threshold Based Policy. To further reduce computational time, we propose a Threshold-Optimized Deep Reinforcement Learning (TDRL) algorithm that integrates a threshold initialized policy, maintenance action filtering, and state standardization. A case study on a ball screw system demonstrates that the proposed TDRL algorithm outperforms the Threshold-Based Policy in terms of both cost and computational time. Sensitivity analyses are conducted to analyze the impact of key factors on the benefits of the remanufacturing policy within the joint optimization framework.

Increasing market competition and growing mechanical structural complexity have urged the application of complex systems comprising multi-component machines. Ensuring the economical operation and maintenance (O&M) for such systems is paramount in industry. To achieve this, the component maintenance policy should cover both the machine-level disassembly sequence among critical components and the system-level configuration setting connecting individual machines. However, integrating such hierarchical structural dependencies into maintenance decision-making poses a formidable challenge and remains unexplored in existing literature. Facing this key issue, our study presents a component-targeting opportunistic maintenance policy considering hierarchical structural dependencies (COMP-HSD) to provide real-time cost-effective maintenance schemes. Aiming at deriving the optimal component maintenance group at each maintenance opportunity, this policy builds a decision-making process including triple-level analyzing framework to comprehensively involve the component degradation and the structural dependencies analyses of machine level and system level. Specifically, sequential component-level predictive maintenance (PdM) intervals are initially optimized according to individual health conditions. Then, a maintenance cost savings model is developed to assess the economy of adjusting PdM actions for each component at the current maintenance opportunity, accounting for the impacts of hierarchical structural dependencies. Furthermore, we incorporate maintenance resource limitation into the decision-making procedure to enhance the adaptability to industrial practice. Numerical examples extracted from a partner engine cylinder head manufacturing enterprise certify the significant cost reduction of this COMP-HSD methodology.Note to Practitioners—This work is motivated by the critical problem of calculating a cost-effective component-targeting maintenance policy for a complex production system with multi-component machines. According to the real O&M management of a manufacturing company, it is intractable to reasonably decide when to maintain each critical component in multiple CNC machine tools of a production system, which restricts the maintenance planning economy. This is due to the difficulty of evaluating the impacts brought by hierarchical structural dependencies (i.e., machine-level disassembly sequence and system-level series configuration). Existing approaches are limited to be directly applied to practice since they either focus solely on individual multi-component machine or assume that each machine in a multi-machine system only contains one component, neglecting the holistic evaluation. Besides, the maintenance resource restriction is rarely considered in the literature. To fill this gap, we present the COMP-HSD policy, which simultaneously considers the individual component degradations, systemic maintenance opportunities and complex structural dependencies analysis in the maintenance decision-making process. Referring to this policy, the manufacturers could determine the component maintenance timetable to conduct timely PdM actions with lower cost. Compared with the traditional maintenance policy, our study greatly reduces the total cost, and also offers extending applications such as maintenance tool preparation and spare part ordering according to the component maintenance scheme.

This paper considers a service-oriented manufacturer providing products and associated after-sales maintenance service with a warranty period to customers. The manufacturer gains revenue from both product sales and after-sales maintenance service which are affected by the warranty period length. The arrival tendency of customers for after-sales maintenance service is higher during the warranty period than that after the warranty expires. The manufacturer repairs stochastic failures of products with both spare parts of the right kind and employed service engineers. If the spare part is not available upon repair demand, the manufacturer can resort to the emergency suppliers (e.g., OEMs). We establish an original decision model for the service-oriented manufacturer and analytically derive the arrival rates of service demand under and out of warranty, and the repair call rates for requiring regular and emergency replenishment based on queuing theory. Then, a computationally efficient algorithm is designed to obtain the joint multiple spare parts inventory, service engineer employment and warranty period decisions. Numerical studies show that both the manufacturer and customers can benefit from the longer warranty period when customers are more inclined to require maintenance service out of warranty from the manufacturer. A higher service level target or quality levels of spare parts reduce the after-sales service dependence on emergency suppliers.Note to Practitioners—After-sales maintenance services often generate significant profit for many service-oriented manufacturers that sell products bundled with after-sales services. To offer efficient after-sales maintenance services, efficient joint management of spare part inventory, service engineers and warranty periods, which often interact with each other, is crucial for service-oriented manufacturers. However, in practice, facing the stochastic arrival of repair calls, it is challenging for manufacturers to jointly optimize the decisions of spare parts inventory, service engineers and warranty periods. Most existing studies optimized them separately. This research investigates the joint decision problem considering the three aspects based on queuing model. Based on the model analysis, an efficient and easy-to-implement heuristic algorithm is proposed to obtain reasonable joint decisions. Practitioners can implement the proposed approach to improve the integrated planning of spare parts inventory, warranty and service engineers and enhance profitability. Besides, sensitivity analysis is conducted to show how the changes of parameters such as payment to the supplier per emergency shipment and rate of the component lifespan will affect the joint decisions. Some managerial insights obtained can be useful to practitioners. First, if customers become more willing to require maintenance service from the manufacturer out of warranty, the manufacturer can benefit more from extending the warranty period length which is also beneficial to customers, leading to a win-win situation. Second, if the emergency replenishment cost becomes higher, the manufacturer should extend the warranty period length. Third, a higher service level target or quality levels of spare parts decline the dependence of the after-sales service on emergency suppliers.

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method.Note to Practitioners—This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3).

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