A risk‐based approach to manage non‐repairable spare parts inventory

Abstract

Purpose -The purpose of this paper is to propose a risk-based approach for spare parts demand forecast and spare parts inventory management for effective allocation of limited resources. Design/methodology/approach -To meet the availability target and to reduce downtime, process facilities usually maintain inventory of spare parts. The maintaining of non-optimized spare parts inventory claims more idle investment. Even if it is optimized, lack of attention towards the critical equipment spares could threaten the availability of the plant. This paper deals with the various facets of spare parts inventory management, mainly risk-based spare parts criticality ranking, forecasting, and effective risk reduction through strategic procurement policy to ensure spare parts availability. A risk-based approach is presented that helps managing spare parts requirement effectively considering the criticality of the components. It also helps ensuring the adequacy of spare parts inventory on the basis of equipment criticality and dormant failure without compromising the overall availability of the plant. Findings -The paper proposes a risk-based approach that used conjugate distribution technique with the capability to incorporate historical failure rate as well as expert judgment to estimate the future spare demand through posterior demand distribution. The approach continuously updates the prior distribution with most recent observation to give posterior demand distribution. Hence the approach is unique in its kind. Practical implications -Appropriate spare parts unavailability could have great impact on process operation and result in costly downtime of the plant. Following proposed approach the availability target can be achieved in process industry having limited maintenance resources, by forecasting spare parts demand precisely and maintaining inventory in good condition. Originality/value -Adopting the approach proposed in the paper, risk level can be minimized and plant availability can be maximized within the financial constraint. The resources are allocated to the most critical components and thereby increased availability, and reduce risk.

Optimal redesign decisions through failure rate estimates

“…The Bayesian method has been used in the maintenance literature for estimating critical components' remaining life distributions (Elwany & Gebraeel, 2008; Si, Wang, Chen, Hu, & Zhou, 2013; Yildirim, Sun, & Gebraeel, 2016). In addition, there are multiple other studies that rely on the Bayesian method to model the uncertainty in failure rates and, therefore, the uncertainty of demand for spare parts (Aronis, Magou, Dekker, & Tagaras, 2004; Hassan, Khan, & Hasan, 2012; Kamath & Pakkala, 2002; Li & Ryan, 2011). Our study contributes to the literature on reliability optimization by being the first study to optimize the system redesign decisions by combining two essential elements; using field data to update failure rate estimate and redesigning the system dynamically.…”

Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices

“…The prior function represents the probability of n or fewer failures during a time interval of (0, t), when all RIFs are equal to zero or absent, in the course of waste handling activities [13]. For instance, assuming that the components fail according to a Poisson process, the probability of n or fewer failures can be calculated as follows [29]: Step 2.6-Construct the likelihood function, based on the system failure rate data: After observing the RIFs' data and defining the prior probability function, the likelihood function has to be constructed. The likelihood function is generally the joint probability function and can be expressed as a product of conditional probabilities [30].…”

Dynamic spare parts transportation model for Arctic production facility

“…Spare parts and logistic support have a great impact on the availability of production facilities and on all types of maintenance activities (Hassan et al 2012). The importance of having spare parts on demand can be more significant for production facilities and systems installed in the environmentally sensitive and remote Arctic region (Ghodrati and Kumar 2005).…”

“…Therefore, to reduce the health, safety, and environmental (HSE) impact of the industrial activities such as oil and gas industry in the region, the need for high performance production facilities and systems is becoming imperative Barabadi et al 2014). As a result, production facilities are being designed incorporating non-traditional arrangements and unconventional technologies (Hassan et al 2012). That means the systems have become more and more specialised or tailor-made.…”

“…That means the systems have become more and more specialised or tailor-made. However, with the increased mechanisation and complexity in the production facilities, there is a rise in the number of component failure scenarios (Hassan et al 2012). Failure of components incurred downtime and unavailability of the system, which can cause substantial production losses and affect business performance (Gao et al 2010;Barabadi et al 2014).…”