A dynamic predictive maintenance model considering spare parts inventory based on hidden semi-Markov model

2012 ◽  
Vol 227 (9) ◽  
pp. 2090-2103 ◽  
Author(s):  
Qinming Liu ◽  
Ming Dong ◽  
Ying Peng
Keyword(s):  
Dynamic Programming ◽  
Markov Model ◽  
Transition Probabilities ◽  
Spare Part ◽  
Spare Parts ◽  
Stochastic Dynamic ◽  
Maintenance Strategies ◽  
Spare Parts Inventory ◽  
Optimal Maintenance ◽  
Maintenance Model

The maintenance strategies optimization can play a key role in the industrial systems, in particular to reduce the related risks and the maintenance costs, improve the availability, and the reliability. Spare part demands are usually generated by the need of maintenance. It is often dependent on the maintenance strategies, and a better practice is to deal with these problems simultaneously. This article presents a stochastic dynamic programming maintenance model considering multi-failure states and spare part inventory. First, a probabilistic maintenance model called hidden semi-Markov model with aging factor is used to classify the multi-failure states and obtain transition probabilities among multi-failure states. Then, spare parts inventory cost is integrated into the maintenance model for different failure states. Finally, a double-layer dynamic programming maintenance model is proposed to obtain the optimal spare parts inventory and the optimal maintenance strategy through which the minimum total cost can be achieved. A case study is used to demonstrate the implementation and potential applications of the proposed methods.

scholarly journals Preventive Maintenance Interval Prediction: a Spare Parts Inventory Cost and Lost Earning Based Model

2015 ◽  
Vol 5 (3) ◽  
pp. 811-817
Author(s):  
O. A. Adebimpe ◽  
V. Oladokun ◽  
O. E. Charles-Owaba
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Manufacturing Firms ◽  
Inventory Cost ◽  
Manufacturing Firm ◽  
Interval Prediction ◽  
Spare Parts Inventory ◽  
Optimal Maintenance

In this paper, some preventive maintenance parameters in manufacturing firms were identified and used to develop cost based functions in terms of machine preventive maintenance. The proposed cost based model considers system’s reliability, cost of keeping spare parts inventory and lost earnings in deriving optimal maintenance interval. A case of a manufacturing firm in Nigeria was observed and the data was used to evaluate the model.

scholarly journals Acquisition Pricing and Inventory Decisions on Dual-Source Spare-Part System with Final Production and Remanufacturing

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yancong Zhou ◽  
Xudong Guo ◽  
Xiaochen Sun
Keyword(s):  
Optimal Policy ◽  
Durable Goods ◽  
Optimization Problem ◽  
Market Competition ◽  
Spare Part ◽  
Spare Parts ◽  
Stochastic Dynamic ◽  
Management Problem ◽  
Production Order ◽  
Acquisition Pricing

The life spans of durable goods are longer than their warranty periods. To satisfy the service demand of spare parts and keep the market competition advantage, enterprises have to maintain the longer inventory planning of spare parts. However, how to obtain a valid number of spare parts is difficult for those enterprises. In this paper, we consider a spare-part inventory problem, where the inventory can be replenished by two ways including the final production order and the remanufacturing way. Especially for the remanufacturing way, we consider the acquisition management problem of used products concerning an acquisition pricing decision. In a multiperiod setting, we formulate the problem into a dynamic optimization problem, where the system decisions include the final production order and acquisition price of used products at each period. By stochastic dynamic programming, we obtain the optimal policy of the acquisition pricing at each period and give the optimal policy structure of the optimization problem at the first period. Then, a recursion algorithm is designed to calculate the optimal decisions and the critical points in the policy. Finally, the numerical analyses show the effects of demand information and customer’s sensitive degree on the related decisions and the optimal cost.

Bayesian degradation modelling for spare parts inventory management

2020 ◽  
Vol 32 (1) ◽  
pp. 31-49 ◽  
Author(s):  
Cesar Ruiz ◽  
Edward Pohl ◽  
Haitao Liao
Keyword(s):  
Inventory Management ◽  
Optimal Solution ◽  
Planning Horizon ◽  
Degradation Process ◽  
Spare Parts ◽  
Optimal Time ◽  
Model Parameters ◽  
Stochastic Dynamic ◽  
Spare Parts Management ◽  
Spare Parts Inventory

Abstract Decision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

An EOQ Based Multi-Storage Location of Spare Part Inventories: A Case Study

2013 ◽  
Vol 315 ◽  
pp. 733-738 ◽  
Author(s):  
Noor Ajian Mohd-Lair ◽  
Chuan Kian Pang ◽  
Willey Y.H. Liew ◽  
Hardy Semui ◽  
Loh Zhia Yew
Keyword(s):  
Inventory Management ◽  
Spare Part ◽  
Spare Parts ◽  
Inventory Level ◽  
Management Technique ◽  
Inventory Management System ◽  
Spare Parts Inventory ◽  
Storage Location ◽  
Manual Recording

Spare parts inventory management is very important to ensure smooth operation of maintenance department. The main objectives of inventory management of spare parts are to ensure the availability of spares and materials for the maintenance tasks and increase the productivity of the maintenance department. This research centred on the development of the Computerised Inventory Management System (CIMS) for the maintenance team at Weida Integrated Industries Sdn. Bhd. The inventory management technique used to control the spare parts inventory in this research was the basic Economic Order Quantity models (EOQ). However, the CIMS developed is unique as it has the ability in handling inventories in multiple-storage locations. The CIMS was written using the Visual Basic 2010 software. This CIMS has the abilities to keep records and process the spare parts information effectively and faster besides helping the user to perform spare parts ordering tasks compared to the current manual recording. In addition, the ordering quantity and frequency for the CIMS is determined through the EOQ technique. However, observation indicates that the overall average inventory level currently at the factory is lower than the expected overall average inventory level produced by the CIMS. This is due to the fact that the CIMS was unable to consider the opening stock in ordering the inventories. Therefore, further improvements are needed to optimize the performance of the system such as using the EOQ with the reorder point technique, the periodic or continuous review system.

scholarly journals LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP

2018 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Sinta Rahmawidya Sulistyo ◽  
Alvian Jonathan Sutrisno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Spare Part ◽  
Service Level ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Spare Parts Inventory

Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order to gain more understanding on the effect of the forecasting method on spare parts inventory management, inventory simulation using oil and gas company’s data is then conducted. Two inventory parameters; average inventory and service level; are used to measure the performance. The result shows that ANN is found to be the best method for spare part forecasting with MASE of 0,761. From the inventory simulation, the appropriate forecasting method on spare parts inventory management is able to reduce average inventory by 11,9% and increase service level by 10,7%. This result justifies that selecting the appropriate forecasting method is one of the ways to achieve spare part inventory management’s goal.

scholarly journals KEBIJAKAN PERSEDIAAN SUKU CADANG DI PT ABC MENGGUNAKAN METODE RCS (RELIABILITY CENTERED SPARES)

2018 ◽  
Vol 2 (1) ◽  
pp. 90-102
Author(s):  
Fransiskus Tatas Dwi Atmaji ◽  
Anak Agung Ngurah
Keyword(s):  
Heat Exchanger ◽  
Pressure Gauge ◽  
Spare Part ◽  
Spare Parts ◽  
Mechanical Seal ◽  
Inventory Policy ◽  
Machine Reliability ◽  
Spare Parts Inventory ◽  
Critical Components ◽  
One Year

Based on data from the production and maintenance department, the highest downtime happen in JDK machine at ABC company. High downtime caused by spare parts lacking or replacement parts when the machine damaged, it cause the engine stop longer. This study analyze and determine spare part inventory the policy, especially critical components in JDK machine. Reliability Centered Spare (RCS) method used in this study, this method essentially determines the optimal spare parts policy for a certain period. The result of research with RCS method shows that the spare parts inventory policy for all critical components of JDK machine in one year ahead by storing or stocking the following amount: packing valve: 469 pieces; teflon: 134 pieces; bearing pump: 10 pieces; mechanical seal: 26 pieces; motor pump: 10 pieces; packing pump: 141 pieces; motor driving reel: 9 pieces; bearing driving reel: 45 pieces; mechanical seal driving reel: 163 pieces; packing heat exchanger: 70 pieces; site glass: 29 pieces; and pressure gauge: 7 pieces.

scholarly journals Analisa Pengendalian Persediaan Suku Cadang Pada PT. XYZ Dengan Metode Analisis ABC

2020 ◽  
Vol 2 (1) ◽  
pp. 5-13
Author(s):  
Eka Sofia. A ◽  
Darno Darno ◽  
Mitha Otik Wiraswati ◽  
Dewi Agustya Ningrum
Keyword(s):  
Spare Part ◽  
Spare Parts ◽  
Analysis Method ◽  
Cumulative Percentage ◽  
Spare Parts Inventory ◽  
Group A ◽  
Abc Analysis ◽  
Group B

Inventory can be interpreted as a stock of goods to be sold or used at a certain time,without the inventory the company will run the risk and can not meet costomer demand. This research was conducted to analyze spare part inventory using ABC analysis method and EOQ method at PT. Adiprima Suraprinta, Gresik.The results of this study are there there are 4 spare parts inventory items in group A with a cumulative percentage of 8,59% by absorbing a budget of 56,78%, there are 5 spare parts inventory items in group B with a cumulative percentage of 18,47% by absorbing a budget of 24,15%, there are 17 spare parts inventory items in group C with a cumulative percentage of 72,92% by absorbing a budget of 10%.

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