Spare parts inventory management: a new hybrid approach

Abstract

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.

Artificial intelligence for supply chain resilience: learning from Covid-19

“…Retail supply chains have also been impacted, wherein the demand for essential items has increased, but demand is subdued in other categories such as luxury items, and this further challenges operating margins and existing business models (Pantano et al ., 2020). On the consumer side, stocking essential supplies and over-the-counter medicines have stressed supply chains to their limits (Mahajan and Tomar, 2021; Muniz et al , 2020). This unnatural and unpredictable increase in demand and supply, and the continually fluctuating environment, makes it difficult to respond adequately to this type of bullwhip effect.…”

“…Big data helps to improve the decision-making capabilities of an organization and can therefore avoid redundancy throughout the supply chain (Baryannis et al , 2019; Dubey et al , 2020). Additionally, AI is capable of developing agent-based systems, genetic algorithms and expert systems to facilitate demand planning, order fulfilment, network design and inventory planning in conjunction with supplier systems (Muniz et al , 2020).…”

Spare parts management in industry 4.0 era: a literature review

“…A right balance between spare parts criticality and total inventory cost can be analyzed by applying a hybrid approach merging AHP and multicriteria decision making joined with e-constraints optimization. It creates the basis for devising spare parts strategies for rationalizing financial resources at strategic and tactical levels (Muniz et al, 2021). Table 5 shows the analysis of selected articles based on considered strategic performance indicators of the SPM.…”

“…For most of the consumer durables, SPM is a vital extent of apprehension for Original Equipment Manufacturers (OEM) especially after End of Lief (EOL) and End of Service (EOS) period (Ray and Mondal, 2016). The efficiency and effectiveness of inventory management needs to be balanced for deciding the organizational strategy with accomplishment of customer requirements (Sirova and Rodrigues, 2019; Muniz et al., 2021). The joint management of spare parts and maintenance may be achieved through evaluation of performance indicators (quality, cost and time) of integrated system, and using a multicriteria decision tool based on Fuzzy Analytical Hierarchy Process (AHP) (Ferreira et al., 2018; Bounou et al., 2020).…”

Spare parts control strategies for offshore wind farms: A critical review and comparative study

“…Digiesi et al (2015) used various terms in the objective function to measure the environmental impact of SPM. Other researchers worked on stocking strategies (Botter and Fortuin, 2000), heterogeneous environment (Barabadi et al, 2021; Chang et al, 2015), heuristic approach (Bülbül et al, 2019), stochastic demand classification (Conceição et al, 2015), uncertain demand (Gu et al, 2015), hybrid approach (Muniz et al, 2021), multiple components (Olde Keizer et al, 2017), binary decision fault tree (Remenyte-Prescott and Andrews, 2008), equivalent equipment exchange (Khademi and Eksioglu, 2018), optimizing spare parts necessity (Eruguz et al, 2018), spare parts demand planning (Kian et al, 2019) based on shelf degradation and without knowing the degradation level (Ghasemi et al, 2007; Moghaddass and Ertekin, 2018).…”