MRO Spare Parts Management and Inventory Optimization

2022 ◽  
pp. 266-301
Author(s):  
Saeed Ramezani ◽  
Mohamad Reza Hoseinzadeh
Keyword(s):  
Inventory Management ◽  
Supplier Management ◽  
Spare Parts ◽  
Inventory Optimization ◽  
Inventory Management System ◽  
Spare Parts Management ◽  
System Supplier ◽  
Management Policies ◽  
Management Optimization ◽  
Comprehensive Framework

In this chapter, considering the importance of spare parts inventory management in the equipment life cycle, the excellence models in spare parts supply chain management are reviewed, and MRO-MMM framework based on the MMM maintenance excellence model will be presented in eight steps. In this comprehensive framework, all necessary actions are considered in terms of maintenance excellence, improvement, and optimization spare parts management. The steps include compiling strategies, spare parts management policies, and related indicators; determining criticality, classification, and spare parts data management; data and procedure preparation for analysis of inventory management; optimization of inventory management system; supplier management; integrity of automation and information system; continuous improvement; and risk based and smarting inventory management. This framework has been used in various industries and proved that the implementation of the MRO-MMM framework will optimize and significantly improve spare parts management.

Smart spare parts management systems in semiconductor manufacturing

2017 ◽  
Vol 117 (4) ◽  
pp. 754-763 ◽  
Author(s):  
Meimei Zheng ◽  
Kan Wu
Keyword(s):  
Inventory Management ◽  
Management System ◽  
Semiconductor Manufacturing ◽  
Big Data Analytics ◽  
Spare Parts ◽  
Content Type ◽  
Inventory Management System ◽  
Spare Parts Management ◽  
New Business ◽  
Real Time Information

Purpose The purpose of this paper is to propose a smart spare parts inventory management system for a semiconductor manufacturing company. Design/methodology/approach With the development of the Internet of Things and big data analytics, more information can be obtained and shared between fabs and suppliers. Findings On the basis of the characteristics of spare parts, the authors classify the spare parts into two types, the consumable and contingent parts, and manage them through a cyber-physical inventory management system. Originality/value In this new business model, the real time information from machines, shop floors, spare parts database and suppliers are used to make better decisions and establish transparency and flexibility between fabs and suppliers.

scholarly journals The Lean Management of Spare Parts in Automotive Manufacturing

2018 ◽  
Vol 214 ◽  
pp. 04005
Author(s):  
Dongdong Guo ◽  
Xingwu Yu
Keyword(s):  
Inventory Management ◽  
Spare Part ◽  
Spare Parts ◽  
Automotive Manufacturing ◽  
Purchasing Cost ◽  
Manufacturing Enterprises ◽  
Spare Parts Management ◽  
Purchase Process ◽  
Spare Part Management ◽  
Maintenance Process

Spare part management is one of the most important work for enterprises, especially for manufacturing enterprises; however, the spare part management problems trouble enterprise operators a lot. In this article, implementation methods of lean spare parts management are illuminated. Spare parts purchase process is declared to reduce the purchasing cost and inventory value. We had established a suitable lean spare parts inventory management model for consumable parts, wear parts, insurance parts and accident parts. In addition, methods of lean spare parts management had been created base on optimized supply chain, ERP and integrating repeated material inventory. We used SAP-iPro system and self-developed system to manage spare parts, so that warehouse management process, spare parts purchase process and maintenance process are standardized. According to theory analysis and practice, the remarkable economic benefit is created for enterprise by the means of optimizing spare parts distribution, standardizing and scientific spare parts management.

scholarly journals Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259284
Author(s):  
Hailan Ran
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Inventory Management ◽  
Management System ◽  
Prediction Method ◽  
Spare Parts ◽  
Collaborative Computing ◽  
Demand Prediction ◽  
Industrial Enterprises ◽  
Inventory Management System

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

An Integer Black-Box Optimization Model for Repairable Spare Parts Management

2021 ◽  
Vol 14 (2) ◽  
pp. 46-68
Author(s):  
Giuseppe Bernabei ◽  
Francesco Costantino ◽  
Laura Palagi ◽  
Riccardo Patriarca ◽  
Francesco Romito
Keyword(s):  
Inventory Management ◽  
Optimization Model ◽  
Spare Parts ◽  
Black Box ◽  
Expected Improvement ◽  
Selection Strategy ◽  
Management Problem ◽  
Repairable Systems ◽  
Spare Parts Management

Spare parts management affects significantly costs and service level for supply chains. This paper deals with an inventory management problem for multi-item repairable systems via a systemic perspective based on a new efficient integer black-box optimization model. With respect to the traditionally used marginal allocation that considers items individually, the proposed black-box optimization model is a holistic approach in the fact that it exploits relationships among items. The authors propose a derivative-free algorithm specifically tied to the application which exploits a new selection strategy for choosing entire subsets of items with the aim to get the best expected improvement in the objective function. The approach has been tested on a real case study for optimizing stocks in an airline's inventory network. The case study provides evidence about the good behavior of the exploratory geometry of the proposed approach in finding quickly a feasible and optimal solution for inventory control.

Spare parts inventory management: a new hybrid approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Leandro Reis Muniz ◽  
Samuel Vieira Conceição ◽  
Lásara Fabrícia Rodrigues ◽  
João Flávio de Freitas Almeida ◽  
Tãssia Bolotari Affonso
Keyword(s):  
Inventory Management ◽  
Hybrid Approach ◽  
Spare Parts ◽  
Mining Company ◽  
Content Type ◽  
Mineral Sector ◽  
Spare Parts Management ◽  
Spare Parts Inventory ◽  
Total Inventory ◽  
Criticality Analysis

PurposeThe purpose of this paper is to present a new hybrid approach based on criticality analysis and optimisation to deal with spare parts inventory management in the initial provisioning phase in the mining industry. Spare parts represent a significant part of mining companies' expenditures, so it is important to develop new approaches to reduce the total inventory value of these items.Design/methodology/approachThis hybrid approach combines qualitative and quantitative methods based on VED (vital, essential and desirable) analysis, analytical hierarchical process (AHP), and e-constraint optimisation method to obtain the spare parts to be stocked. The study was applied to a large mining company. The mineral sector was chosen due to the great importance to the emerging Brazilian economy and the lack of researches in this sector. In addition, the spare parts have a relevant weight on the total inventory cost.FindingsPresent a novel approach combining multi-objective optimisation and multi-criteria evaluation approaches to tackle the inventory decision in spare parts management. This work also defines and classifies relevant criteria for spare parts management in the mineral sector validated by specialists. The proposed approach achieves an average increase of 20.2% in the criticality and 16.6% in the number of items to be stocked compared to the historical data of the surveyed company.Research limitations/implicationsThis paper applies the proposed approach to a mining company in Brazil. Future research in other companies or regions should analyse the adequacy of the criticality criteria, hierarchy and weights adopted in this paper.Practical implicationsThe proposed approach is useful for mining industries that deal with a large variety of resource constraints as it helps in formulating appropriate spare part strategies to rationalise financial resources at both tactical and strategic levels.Originality/valueThe paper presents a new hybrid method combining the AHP a multi-criteria decision making (MCDM) approach coupled with e-constraint optimisation to deal with spare parts inventory management allowing for a better spare parts inventory analysis in the initial provisioning phase and providing managers with a systematic tool to analyse the trade-off between spare parts criticality and total inventory value.

A Computer‐based Inventory Management System for Spare Parts

1994 ◽  
Vol 94 (9) ◽  
pp. 22-28 ◽  
Author(s):  
Nagen N. Nagarur ◽  
Tai‐san Hu ◽  
Nirmal K. Baid
Keyword(s):  
Inventory Management ◽  
Management System ◽  
Spare Parts ◽  
Inventory Management System ◽  
Computer Based

A Risk Based Methodology for Spare Parts Inventory Optimisation

2008 ◽  
Author(s):  
Ujjwal R. Bharadwaj ◽  
Vadim V. Silberschmidt ◽  
John B. Wintle ◽  
Julian B. Speck
Keyword(s):  
Inventory Management ◽  
Optimal Level ◽  
Risk Profile ◽  
Spare Parts ◽  
Inventory Level ◽  
Machine Availability ◽  
Inventory Optimization ◽  
Spare Parts Inventory ◽  
Current Context ◽  
Capital Intensive

Spare parts inventories assist maintenance staff to keep equipment in operating condition. Thus the inventory level of spares has a direct bearing on machine availability, a factor that is increasingly important in capital-intensive industries. This paper presents a risk based approach for spare parts inventory optimization. At the outset, the paper highlights the unique features of maintenance inventories, such as spare parts inventories, compared to other inventories such as work-in-progress or finished product inventories. After a brief mention of the principles on which many of the current inventory management models are based and their limitations, the paper presents a risk-based methodology to spares inventory management. ‘Risk’ in the current context is the risk in monetary terms that arises when a component (spare) is not available on demand. It is the expected value of loss, i.e., the product of the likelihood of unavailability of the spare from the inventory and an estimate of the consequence(s) of that unavailability. Given a budgetary constraint and the risk profile of a number of spares, the model gives an optimal inventory of spares. By basing the inventory on the risk profile of spares, the model includes factors that are not normally considered in various other models. The ultimate aim of the methodology is to have an optimal level of spares inventory such that machine availability, to the extent it is dependent on the level of spares inventory, is maximized subject to constraints. The methodology is expected to benefit both, operational and financial managers.

scholarly journals A review of spare parts supply chain management

2019 ◽  
Vol 3 (2) ◽  
pp. 67
Author(s):  
Zineb Achetoui ◽  
Charif Mabrouki ◽  
Ahmed Mousrij
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Spare Parts ◽  
Classification Methods ◽  
Chain Management ◽  
Demand Fulfillment ◽  
Inventory Optimization ◽  
Forecasting Methods ◽  
Spare Parts Management ◽  
Organizational Maturity

The particular characteristics of spare parts have prompted several authors to provide substantial results for effective spare parts supply chain management. In this context, the purpose of this paper is to present the significant contributions that researchers have proposed, over time, for the management of spare parts supply chain. The literature has shown that the particular characteristics of spare parts have a significant impact on inventory performance and customer demand fulfillment. For this reason, most of the contributions were focused on spare parts classification methods, forecasting methods and inventory optimization.  The focus of researchers on some areas of spare parts management allowed us to identify some promising perspectives that were not developed in literature, such as the development of performance measurement frameworks for spare parts supply chain and the measurement of organizational maturity.

Sign in / Sign up

Export Citation Format

Share Document