Decision Support Models for Valuing Improvements in Component Reliability and Maintenance

Doerr, Kenneth H.; Apte, Uday; Boudreau, Michael W.; Kang, Keebom

doi:10.5711/morj.15.4.55

Cited by 10 publications

(8 citation statements)

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“…In order to verify our model we are using data that originate from [7,14], where the system of unmanned aerial vehicles consisting of four air vehicles, two ground-control stations, modular mission payloads, data links, remote data terminals and an automatic landing subsystem were observed. The concept of an unmanned aerial vehicle (UAV) is not new but it has not been utilized in civilian sector due to the insufficient level of reliability of current solutions that leads to high probability of failure occurrence [11].…”

Section: Numerical Resultsmentioning

confidence: 99%

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A stochastic model for estimation of repair rate for system operating under performance based logistics

Kontrec¹,

Panić²,

Petrović³

et al. 2018

Eksploatacja i Niezawodność – Maintenance and Reliability

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A stochAstic model for estimAtion of repAir rAte for system operAting under performAnce bAsed logistics stochAstyczny model do szAcowAniA intensywności nAprAw dlA systemu dziAłAjącego w wArunkAch logistyki wydAjnościowej Performance Based Logistics (PBL) concept has an aim to improve the system availability and it has been extensively researched in the recent years. These researches showed that inventory level does not impact system availability as much as component reliability and repair time in repairable system operating under PBL contract. Based on that, in this paper, we propose a new stochastic model for determination of annual repair rate for critical aircraft components in such system in order to achieve desired availability. The result obtained could be used for planning of base stock level and capacity of repair facilities. Keywords: repair rate, availability, stochastic model, performance based logistic. Koncepcja Logistyki Opartej na Wydajności (Performance Based Logistics, PBL), której celem jest poprawa gotowości systemów, została w ostatnich latach szeroko zbadana. Badania te wykazały, że w przypadku systemów działających w warunkach PBL, poziom zapasów nie wpływa na gotowość systemu w tak dużym stopniu jak niezawodność elementów składowych oraz czasy napraw. Opierając się na tej obserwacji, w niniejszym artykule proponujemy nowy model stochastyczny do określania rocznej intensywności napraw krytycznych elementów samolotu tworzących system tego typu. Model ten pozwala na osiągnięcie pożądanej gotowości. Uzyskany model może być wykorzystany do planowania bazowego poziomu zapasów oraz przepustowości zakładów remontowych.Słowa kluczowe: intensywność napraw, gotowość, model stochastyczny, logistyka oparta na wydajności.KoNtrec N, PANić S, Petrović M, Milošević H. A stochastic model for estimation of repair rate for system operating under performance based logistics. eksploatacja i Niezawodnosc - Maintenance and reliability 2018; 20 (1): 68-72, http://dx.doi.org/10.17531/ein.2018.1.9. Eksploatacja i NiEzawodNosc -MaiNtENaNcE aNd REliability Vol. 20, No. 1, 2018 69 sciENcE aNd tEchNology availability as much as reliability and repair rate. The authors advise to focus on the component reliability and repair system efficiency to improve system availability.Based on the aforesaid research, the model presented in this paper observes the repair rate as a stochastic process and has an aim to determine this parameter for preferred level of availability. The need for stochastic modeling of repairable systems has been justified and explained in paper [1]. Model for assessment of expected time to repairIn this paper we are observing system that alternates between two states -system is operative at certain time and non-operative otherwise. In the literature, this approach is known as alternating renewal process [4]. We assumed that at the start system is operative. It remains in that state for a period of time T (failure time), then it stops operating for time R (repair time) and after being repaired system is...

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Section: Numerical Resultsmentioning

confidence: 99%

“…Kang et al examined systems for inventory management under the PBL contracts [7]. They have developed a methodology which determines the system's availability based on reliability of its components/parts and maintenance possibilities.…”

Section: Introductionmentioning

confidence: 99%

A stochastic model for estimation of repair rate for system operating under performance based logistics

Kontrec¹,

Panić²,

Petrović³

et al. 2018

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…In this paper, we use a simulation model developed over several years as a decision support system to facilitate understanding of http://dx.doi.org/10.1016/j.cie.2014.04.009 0360-8352/Published by Elsevier Ltd. the relationship between Ao and LCC in a weapon system (Kang, Doerr, Apte, & Boudreau, 2010;Kang, Doerr, & Sanchez, 2006;Kang & McDonald, 2010). We extend that decision support tool to estimate readiness risk and cost risk, and then embed the tool in a search procedure.…”

Section: Introductionmentioning

confidence: 99%

Bi-criteria risk analysis of domain-specific and cross-domain changes in complex systems

Doerr

Kang

2014

Computers & Industrial Engineering

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“…According to Kang, Doerr, and Sanchez (2006) and Kang, Doerr, Apte, and Boudreau (2010), readiness risk measures the probability that A o will fall below, or between, identified ranges. In addition to being able to measure the benefit to improvements in the average A o , Crystal Ball's Monte Carlo simulation software allows us to dissect the distribution of A o .…”

Section: B Reduction Of Readiness Riskmentioning

confidence: 99%

Investigating the Relationship between Customer Wait Time and Operational Availability through Simulation Modeling

Thorn¹,

Hubbard²

2012

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACT (maximum 200 words)Customer Wait Time (CWT) measures all supply chain processes, from the time a customer places an order until the item is delivered. The Marine Corps intermediate supply activity, the Supply Management Unit (SMU), has the primary task of reducing the amount of time it takes for the operating forces to receive supplies by stocking items close to the warfighter. Such forward positioning of repair parts shields the operating forces from delays found at the wholesale inventory level, thereby increasing the material readiness of the operating forces. Intuitively, decreasing CWT increases operational availability (A o ), but the degree and magnitude of this relationship has yet to be quantified. This lack of understanding pertaining to the relationship between A o and CWT has led to arbitrary stock policies that do not account for the cost and benefit they provide. This project centers on monetizing the relationship between these variables through simulation modeling, and provides a tool whereby stock determination can be made based on desired end states. CWT has led to arbitrary stock policies that do not account for the cost and benefit they provide. This project centers on monetizing the relationship between these variables through simulation modeling, and provides a tool whereby stock determination can be made based on desired end states. SUBJECT TERMS vi THIS PAGE INTENTIONALLY LEFT BLANK vii

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Decision Support Models for Valuing Improvements in Component Reliability and Maintenance

Cited by 10 publications

References 0 publications

A stochastic model for estimation of repair rate for system operating under performance based logistics

A stochastic model for estimation of repair rate for system operating under performance based logistics

Bi-criteria risk analysis of domain-specific and cross-domain changes in complex systems

Investigating the Relationship between Customer Wait Time and Operational Availability through Simulation Modeling

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