Spare Parts Inventory Level Research Articles

A two-echelon two-indenture warranty distribution network development and optimization under batch-ordering inventory policy

The use of the warranty distribution network (WDN) to support after-sales service contributes to the stakeholders’ market competition and becomes popular in the modern business. However, there are two major challenges in the development of WDN: 1) locating repair centers and allocating corresponding customers, and 2) choosing the spare parts inventory policy for each repair center. In order to address these two challenges, this paper proposes a customized location-allocation WDN model under batch-ordering inventory policy where repair center acts as a two-layer system (two-echelon) to support a set of customers in a two-layer network (two-indenture). In this network, a depot repair center is selected to meet the orders through a high-capacity supplier in the first echelon and a certain number of sub-repair centers are determined to cover customers in the second echelon. To optimize the proposed model, a two-stage solution approach based on Monte-Carlo simulation and Variable Neighborhood Search is developed. The electric vehicle battery system case study is used to validate the proposed model and solution approach. The obtained results showed that depending on the budget available, spare parts inventory level in the operational repair centers is positively associated their distance to the depot repair center.

Joint Optimization Method of Spare Parts Stocks and Level of Repair Analysis Considering the Multiple Failure Modes

For the repair level and spare parts stocking problems, generally METRIC type methods and Level of repair analysis (LORA) are used separately. Since LORA does not consider the availability of capital goods, solving LORA and spare parts stocking problems sequentially may lead to suboptimal solutions. On these considerations, this study presents a joint optimization method to minimize the service logistics cost under the constraints of system availability. Maintenance capability factor and maintenance decisions are introduced into the joint optimization model to express the influence of multiple failure modes on repair level and spare parts stocking. Thus, we establish the bridge relationship between LORA and METRIC models. The joint optimization model is solved by an improved iterative algorithm, and a typical fleet system is taken as an example to verify the correctness and effectiveness of the model and the algorithm. Compared with the optimization of spare parts inventory and maintenance level independently, the joint optimization method could effectively reduce the service logistic system cost.

Research on Consumable Spare Parts Inventory Management System Based on Forecasting Method

Based on the analysis of actual consumption data of a large number of consumable spare parts and the application effect of the basic random inventory model, the current statistics of equipment spare parts consumption data in China, four forecasting methods are selected to obtain the forecasted demand and the standard deviation of the corresponding forecast error in each cycle and then find the smallest forecast error standard deviation and the corresponding forecast demand quantity. Finally, the forecast demand and the standard error of the forecast error are used to replace the corresponding values in the basic stochastic inventory model to obtain the demand for spare parts in the next cycle, which provides a basis for the preparation of spare parts procurement plans. A large number of application examples show that this method is more simple and practical, and the spare parts inventory level can be reduced by a large proportion.

A multi-skilled workforce optimisation in maintenance logistics networks by multi-thread simulated annealing algorithms

The sustainability of service and manufacturing operations rely heavily on the availability of equipment and assets. High availability of assets can be achieved with effective maintenance strategies. In this direction, we study a multi-skilled workforce planning problem to establish a resilient maintenance service network for high-value assets. We improve the efficiency of the maintenance network by optimising the workforce capacity in repair shops and achieving workforce heterogeneity by cross-training. As a solution strategy, we develop a two-stage iterative heuristic algorithm. At the first stage, the set of all feasible cross-training policies is effectively and systematically searched via a state-of-art multi-thread simulated annealing (MTSA) metaheuristic to find a policy(ies) that achieves the minimum cost. Further, the developed MTSA algorithm is enhanced with the multi-neighbourhood feature to escape from local optimality and implemented via parallel programming techniques. In the second stage, workforce capacity and spare parts inventory levels are optimised for the cross-training policy found at the first stage by a queuing approximation and a greedy heuristic. The MTSA obtains the lowest cost in 91 cases out of 128 compared to genetic algorithm (GA), variable neighbourhood search (VNS), an improved single-thread simulated annealing algorithm (SA) and integer programming-based clustering (IPBC) algorithms.

Spare parts or buffer? How to design a transfer line with unreliable machines

In this paper, we study the design of a transfer line consisting of two unreliable machines in series and an interstage buffer. In addition to the buffer capacity, the system planner can decide on the inventory levels for spare parts in order to reduce the time to repair failed machines. Our work aims to find cost-efficient combinations of buffer capacity and spare parts inventory. We formulate discrete-time Markov chains for two different situations in order to analyse the trade-off between buffer capacity, spare parts inventory and throughput. By means of numerical examples, we illustrate that the effect of a spare part on the efficiency of a transfer line is much greater than the effect of additional buffer places. We conclude that it is necessary to optimize buffer capacity and spare parts inventory levels simultaneously. For systems with identical critical components, our results show that not only is there a pooling effect of safety stocks but also between the buffer places and spare parts inventory levels. We conclude that planners who select a standardised component for the system design and optimize spare parts levels as well as buffer capacity can significantly reduce costs or increase the throughput of a transfer line.

A bi-objective model for optimizing replacement time of age and block policies with consideration of spare parts’ availability

Reliability and availability of critical systems play an important role in achieving the stated objectives of engineering assets. Preventive replacement time affects the reliability of the components, thus the number of system failures encountered and its downtime expenses. On the other hand, spare parts inventory level is a very critical factor that affects the availability of the system. Usually, the decision maker has many conflicting objectives that should be considered simultaneously for the selection of the optimal maintenance policy. The purpose of this research was to develop a bi-objective model that will be used to determine the preventive replacement time for three maintenance policies (age, block good as new, block bad as old) with consideration of spare parts’ availability. It was suggested to use a weighted comprehensive criterion method with two objectives, i.e. cost and availability. The model was tested with a typical numerical example. The results of the model demonstrated its effectiveness in enabling the decision maker to select the optimal maintenance policy under different scenarios and taking into account preferences with respect to contradicting objectives such as cost and availability.

The influence of spare parts provisioning on buffer size in a production system

ABSTRACTWe consider a discrete-part production line consisting of two machines with random processing and failure times. One option that can mitigate the effect of these uncertainties is the installation of a buffer between the two machines to avoid starving and blocking of the machines. In this article, we additionally allow the keeping of spare parts in stock to enable a fast repair and reduce machine downtime. We introduce a new model to support the optimization of the buffer size and the spare parts inventory level simultaneously. The model is based on a continuous-time Markov chain and our aim is to minimize the average costs, which are composed of costs for work in process and the stock-keeping of spare parts, subject to a minimum target throughput. Our numerical analysis reveals that the availability of spare parts can increase throughput enormously or reduce the required buffer size for a given target throughput. Using our approach, as opposed to a sequential approach, we can quantify the cost savings, which can be obtained by a joint optimization of the buffer size and the inventory level. Large cost savings can be obtained by the application of our new approach.

Cost-optimal spare parts inventory planning for wind energy systems

For safe and reliable machine operation, maintenance, repair and overhaul (MRO) activities are required. Spare parts demand forecasting and inventory planning, which is an important part of MRO activities, must be accurate to avoid costs because of surplus spare parts or machine downtimes. The restriction of reduced accessibility to wind turbines during the winter months also has to be taken into account when planning maintenance activities and spare part inventories for wind farms. The presented model provides the most economic stock quantity under given environmental conditions. It is based on the proportional hazards model, which is extended to calculate the remaining useful component life time and derive required spare parts inventory levels. The presented model is validated, using condition monitoring data and environmental data of an onshore wind farm. Comparison of the spare part inventory prediction to wind farm’s failure data proves the model’s accuracy. Parameter analyses show that the model can be applied for spare parts inventory planning under consideration of environmental conditions.

Demand Planning based on Performance Measurement Systems in Closed Loop Supply Chains

The operational readiness of an aircraft fleet is based on a reliable maintenance, repair and overhaul (MRO). In order to reduce the downtimes of aircrafts, damaged components are replaced on site. The need for immediate replacements associated with long lead times require spare parts stocking. To operate on a competitive basis, the optimization of the spare parts inventory level has to take place under consideration of financial and non financial aspects. In order to be able to evaluate and improve the spare parts supply process in the civil aviation industry, the formulation of a performance measurement system is subject matter of this paper. The performance measurement system presented in this paper is based on a balanced scorecard. Herewith, the complex coherences deriving from multiple participants in the closed loop supply chain can be considered by means of cause and effect relationships between the parameters used. The holistic approach leads to a performance measurement system that enables planning divisions to meet the requirements of complex closed loop supply chains and improve operating efficiency.

Optimization of component reliability in the design phase of capital goods

We introduce a quantitative model to support the decision on the reliability level of a critical component during its design. We consider an OEM who is responsible for the availability of its systems in the field through service contracts. Upon a failure of a critical part in a system during the exploitation phase, the failed part is replaced by a ready-for-use part from a spare parts inventory. In an out-of-stock situation, a costly emergency procedure is applied. The reliability levels and spare parts inventory levels of the critical components are the two main factors that determine the downtime and corresponding costs of the systems. These two levels are decision variables in our model. We formulate the portions of Life Cycle Costs (LCC) which are affected by a component’s reliability and its spare parts inventory level. These costs consist of design costs, production costs, and maintenance and downtime costs in the exploitation phase. We conduct exact analysis and provide an efficient optimization algorithm. We provide managerial insights through a numerical experiment which is based on real-life data.

A case study-based analysis of spare parts management in the engineering industry

Eroding margins for primary products have redirected many firms’ attention to the highly profitable spare parts business. These firms push for increased sales and more efficient spare parts supply chains. However, previous research in spare parts supply chain management has principally been limited to planning and operational aspects such as the determination of spare parts inventory levels and re-order policies. Taking the sellers’ perspective in the supply chain, the primary objective of the case study research presented in this article is to better understand the problems that engineering companies encounter and approaches that they adopt in their spare parts supply chains. The initial proposition, that engineering companies need to, and are eager to, adopt supply chain management practices in their spare parts businesses in order to capture revenue and profit potential, is supported. Among the key areas for improvement are the formulation of spare parts strategies, a better understanding of the installed base, and top management support for the spare parts business.

Performance Contracting in After-Sales Service Supply Chains

Performance-based contracting is reshaping service support supply chains in capital-intensive industries such as aerospace and defense. Known as “power by the hour” in the private sector and as “performance-based logistics” (PBL) in defense contracting, it aims to replace traditionally used fixed-price and cost-plus contracts to improve product availability and reduce the cost of ownership by tying a supplier's compensation to the output value of the product generated by the customer (buyer). To analyze implications of performance-based relationships, we introduce a multitask principal-agent model to support resource allocation and use it to analyze commonly observed contracts. In our model the customer (principal) faces a product availability requirement for the “uptime” of the end product. The customer then offers contracts contingent on availability to n suppliers (agents) of the key subsystems used in the product, who in turn exert cost reduction efforts and set spare-parts inventory investment levels. We show that the first-best solution can be achieved if channel members are risk neutral. When channel members are risk averse, we find that the second-best contract combines a fixed payment, a cost-sharing incentive, and a performance incentive. Furthermore, we study how these contracts evolve over the product deployment life cycle as uncertainty in support cost changes. Finally, we illustrate the application of our model to a problem based on aircraft maintenance data and show how the allocation of performance requirements and contractual terms change under various environmental assumptions.