Spare parts inventory management: a new hybrid approach

Author:Leandro Reis Muniz, Samuel Vieira Conceição, Lásara Fabrícia Rodrigues, João Flávio de Freitas Almeida, Tãssia Bolotari Affonso
DOI:https://doi.org/10.1108/IJLM-12-2019-0361
Pages:40-67
Publication Date:04 Nov 2020
Spare parts inventory
management: a new
hybrid approach
Leandro Reis Muniz
DEMEP Department of Mechanical and Production Engineering,
Universidade Federal de S~
ao Jo~
ao del-Rei,
S~
ao Jo~
ao del-Rei, Brazil, and
Samuel Vieira Conceiç~
ao, L
asara Fabr
ıcia Rodrigues,
Jo~
ao Fl
avio de Freitas Almeida and T~
assia Bolotari Affonso
Production Engineering Department,
Universidade Federal de Minas Gerais,
Belo Horizonte, Brazil
Abstract
Purpose The 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 companiesexpenditures, so it is important to
develop new approaches to reduce the total inventory value of these items.
Design/methodology/approach This hybrid approach combinesqualitative 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.
Findings Present 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/implications This 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 implications The 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/value The paper presentsa new hybrid method combining the AHP a multi-criteria decision
making (MCDM) approach coupled with e-constraint optimisation to deal with spare parts inventory
managementallowing for a better spareparts inventory analysisin the initial provisioningphase and providing
managerswith a systematictool to analyse the trade-offbetween spare partscriticality and totalinventory value.
Keywords Spare parts inventory management, Analytic hierarchy process, Optimisation model, Mining
industry
Paper type Research paper
IJLM
32,1
40
The authors would like to thank the Mining Company for its collaboration with this research, Federal
University of Minas Gerais - Brazil, Federal University of S~
ao Jo~
ao del-Rei - Brazil and the anonymous
reviewers for their constructive comments, which helped us to improve the manuscript.
Disclosure statement: No potential conflict of interest was reported by the authors.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0957-4093.htm
Received 20 December 2019
Revised 2 July 2020
Accepted 17 September 2020
The International Journal of
Logistics Management
Vol. 32 No. 1, 2021
pp. 40-67
© Emerald Publishing Limited
0957-4093
DOI 10.1108/IJLM-12-2019-0361
1. Introduction
Logistics management is a critical component in supply chain management (SCM), in which
one of the goals is to improve inventory management (Ripanti and Tjahjono, 2019). Inventory
management is particularly important since it impacts logistical operations (Konur et al.,
2017), mainly in the highly competitive environment faced by the mining companies. Beyond
that, the mineral sector is of great importance to the emerging Brazilian economy. In 2018, the
estimated production value in this sector was US$ 34 billion which accounted for 13.0% of
Brazils exports, particularly iron ore, which accounted for 62% of the sectors exports
besides provided 2 million direct and indirect jobs in this sector (IBRAM, 2018).
Regarding the costs of industrial plants, Hu et al. (2015) state that operational and
maintenance costs account for more than 60% of the overall costs, while the spare parts
related costs alone represent about 2530%. In Brazil, a research conducted by ABRAMAN
(2013) points out that the maintenance costs of Brazilian companies were 4.69% of the
countrys GDP in 201321.96% of this value was spent in materials used for maintenance
while the financial value of the inventories represented 13.41% of total maintenance costs.
This clearly indicates that better operations management of spare parts ensures plant
availability at an adequate cost.
The ability of inventory management is critical to maintaining competitiveness in the
SCM (Tatham et al., 2017). Inventory of spare parts allows for greater availability of
production equipment (Teixeira et al., 2018) while balancing spending with the stock of spare
parts and level of service to internal customers. In many organisations, spare parts inventory
costs are extremely high because several items are strategic for operation and a stock
shortage can directly affect production (Conceica
˛oet al., 2015;Zhang and Zeng, 2017). Owing
to this, many large companies keep thousands of spare parts in stock (Kennedy et al., 2002).
Hence, the decision of which parts to stock in each location and in what quantities are as
important as the management of these materials (Driessen et al., 2015).
One of the ways to deal with spare parts management is to group them into specific
categories, making it possible to establish specific policies for each group (Roda et al., 2014;
Hu et al., 2017). There are several methodologies for inventory classification in the literature
(see, for example, Botter and Fortuin, 2000;Suryadi, 2003;Braglia et al., 2004;Bosnjakovic,
2010;Roda et al., 2014;Stoll et al., 2015;Hu et al., 2017); most of them focus on the classification
of existing stocks and not on the initial provisioning when new equipment is introduced.
Despite this, the most common spare parts classification approach is the traditional ABC
method (Bacchetti and Saccani, 2012;Stoll et al., 2015), making it still necessary to develop
improved methods for spare parts management.
Regarding the mineral industry, specifically, Roda et al. (2014) present an extensive survey
and two case studies in spare parts management in the copper mining sector of Chile. They
found that 70% of the companies had implemented classification approaches and 31% of
these companies had adopted a quantitative ABC analysis based on a single criterion.
According to Hu et al. (2018), there are two main options to meet the maintenance demand
in the initial provisioning: wait until either the demand occurs or storage initial spare parts. In
the initial provisioning, time series forecasting methods cannot be used due to the lack of an
adequate length of demand history precludes the use of extrapolative time series methods
(Boylan and Syntetos, 2008). The forecast based on explanatory or judgment variables is used
in the initial phase and is based on qualitative information (Boylan and Syntetos, 2008;Hu
et al., 2018). It is also used to adjust the forecasting results when qualitative information is
available and is not considered by the quantitative model (Hu et al., 2018). However, acting
only in inventory management maybe not enough since the provisioning may be performed
inappropriately.
After initial provisioning and classification of spare parts, the supply department is
responsible to meet maintenance needs based on consumption data. Hu et al. (2018) point out
Spare parts
inventory
management
41

To continue reading

Request your trial