Application of reliability models with covariates in spare part prediction and optimization – A case study

Barabadi, Abbas; Barabady, Javad; Markeset, Tore

doi:10.1016/j.ress.2013.09.012

Cited by 70 publications

(60 citation statements)

References 23 publications

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“…Barabadi et al [21] base their predictions on the statistics models of products' reliability, influenced by factors such as the means of management, maintenance policies, driver's skills, etc., while Lengu et al [22] propose to discard the supposition of normality in the time series and model the demand of the parts as Bernoulli processes, particularly in the Poisson distributions, taking advantage of the imminent nature of the spare-parts demand. Moon et al [23] decided to develop a selection model among the forecast methods, in order to predict the performance of each method when forecasting the demand for naval parts in South Korea.…”

Section: Spare-parts Demand Forecastingmentioning

confidence: 99%

Automobile spare-parts forecasting: A comparative study of time series methods

Vargas¹,

Cortés²

2017

Int. J. Automot. Mech. Eng.

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In Mexico, the automotive industry is considered to be strategic in the industrial and economic development of the country because it generates production, employment and foreign exchange. Good demand forecasts are needed for better manufacturing management. The time series modelling tools applied to the monthly demand forecasting of automobile spare parts in Mexico are assessed, for the case of a transnational enterprise, considering affordability. The classic methods of moving averages, final value and exponential smoothing, the prestigious autoregressive integrated models of moving averages (ARIMA), the rarely implemented artificial neural networks (ANNs) and the very little explored ARIMA-ANNs hybrid models are compared. A good performance of the models involving ANNs is observed, but they were not as steady as the ARIMA models in the post-sample periods. The mean absolute percentage error (MAPE) was reduced from an original 57% to 32.65%. The obtained results could help demonstrate the importance of improving industrial forecasting methodologies for better planning.

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Section: Spare-parts Demand Forecastingmentioning

confidence: 99%

Automobile spare-parts forecasting: A comparative study of time series methods

Vargas¹,

Cortés²

2017

Int. J. Automot. Mech. Eng.

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“…More generally, advanced PHMs have been proposed [15][16][17][18][19] to analyse the hazard rate behaviour in the presence of dynamically evolving covariates, such as the weather conditions, including changes in wind speed, occurrence of storms and lightning events, etc. [20][21][22]. Although these approaches seem attractive for their potential of providing more precise estimates of RAM, their application to practical Arctic offshore O&G case studies is still prevented from the lack of reliability and operating data for proper setting of the RAM models.…”

Section: Introductionmentioning

confidence: 99%

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Naseri

Baraldi²,

Compare³

et al. 2016

Reliability Engineering & System Safety

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We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions. Keywords: Dynamic weather conditions, failure rate, repair rate, equivalent age, preventive maintenance, availability, Monte Carlo simulation, oil and gas, Arctic offshore. , ′ ); = 1, … , ; 0 ′ = 0, partitioning the time horizon, during which the weather conditions remain unchanged at an intensity level of . In this study, the time interval length is taken to be equal to a day. Acronyms ALMReliability of a component operating under static weather conditions at WIL State of the system at WIL , = 0, … , ; = 1 and = 0 refer to the functioning and faulty states, respectively. Waiting downtime before commencing CM tasks in the base area, which includes the time required to shut down the unit, issue the work orders, wait for the spare parts, and start up the unit after repair.Weather element referring to either minimum daily air temperature or maximum daily wind speed, i.e., ∈ { , ′ } ( ) Maximum wind speed during in km/hr ′ ( ) Box-Cox transformed wind speed at the th day The factor by which the weather-dependent factor , changes due to modifications to plant design The factor by which the weather-dependent factor , changes due to modifications to the comfort of maintenance crew, or modifications to the plant design resulting in changes in component active repair time 0 Weibull shape parameter, estimated using the life data collected in the base area (i.e., normal weather conditions), partitioning the time horizon; during each time interval the weather conditions are assumed constant Weather-dependent multiplicative factor corresponding to the WIL , = 0, … , with 0 = 1, which accounts for the reductions in TTFs. Weather-dependent multiplicative factor corresponding to the WIL = , = 0, … , at the th time interval = 1, … , which accounts for the reductions in TTFs Weather-dependent multiplicative factor corresponding to the WIL , = 0, … , at the th time interval = 1, … , which accounts for the rises in TTRs Weather-dependent multiplicative factor corresponding to the WIL = , = 0, … , at the th time interval = 1, … , which accounts for the rises in TTRs * Modified weather-dependent multiplicative factor corresponding...

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“…potentially irreversible ecological and physical process) (Neff et al 1987;Schaanning et al 2008). 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).…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

Dynamic spare parts transportation model for Arctic production facility

Ayele

Barabadi

Barabady

2015

Int J Syst Assur Eng Manag

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Timely delivery of the required spare parts plays an important role in meeting the availability target and reducing the downtime of production facilities. Spare parts logistics is affected in complex ways while operating in the Arctic, since the area is sparsely populated and has insufficient infrastructure. It is also greatly affected by the distinctive operational environment of the region, such as cold temperature, varying forms of sea ice, blizzards, heavy fog, etc. Therefore, in order to have an effective logistic plan, the effect of all influencing factors, called covariates, on the transportation of the spare parts need to be identified, modelled and quantified by the use of an appropriate dynamic model. The traditional models, however, lack the comprehensive integration of the effect of covariates on the spare parts transportation. The purpose of this paper is to introduce the concept of a dynamic model for spare parts transportation in Arctic conditions by considering the time-independent and time-dependent covariates. The model continuously updates the prior probabilities according to the most recent time-dependent covariates to provide posterior probabilities. The application of the model is illustrated using a case study.

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Application of reliability models with covariates in spare part prediction and optimization – A case study

Cited by 70 publications

References 23 publications

Automobile spare-parts forecasting: A comparative study of time series methods

Automobile spare-parts forecasting: A comparative study of time series methods

Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Dynamic spare parts transportation model for Arctic production facility

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