Condition based spare parts supply

Xiang, Lin; Basten, Rob; Kranenburg, A.A.; Houtum, Geert‐Jan van

doi:10.1016/j.ress.2017.05.035

Cited by 37 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They propose heuristics for order and inventory allocation decisions, and find that the joint inventory requirement will be reduced due to the effect of risk pooling. Wang and Syntetos (2011), Topan et al (2018), Poppe et al (2017) and Lin et al (2017) pioneer new approaches towards spare parts inventory control driven by planned/foreseen maintenance, and as such they are arguably most closely related to our present work. Wang and Syntetos (2011) consider a block-based inspection policy.…”

Section: Literature Reviewmentioning

confidence: 66%

See 1 more Smart Citation

Spare parts inventory control based on maintenance planning

Zhu

Jaarsveld

Dekker

2020

Reliability Engineering & System Safety

View full text Add to dashboard Cite

For many maintenance organizations, on-condition maintenance tasks are the most important source of spare part demand. An uneven distribution of maintenance tasks over time is an important cause for intermittency in spare parts demand, and this intermittency complicates spare parts inventory control severely. In an attempt to partially overcome these complications, we propose to use the maintenance plan, i.e. the planned maintenance tasks, as a source of advance demand information. We propose a simple forecasting mechanism to estimate the spare part demand distribution based on the maintenance plan, and develop a dynamic inventory control method based on these forecasts. The value of this approach is benchmarked against state-of-the art time series forecast methods, using data from two large maintenance organizations. We find that the proposed method can yield cost savings of 23 to 51% compared to the traditional methods.

show abstract

Section: Literature Reviewmentioning

confidence: 66%

“…In particular, while our method needs more data than typical time-series methods, we need considerably less data and information than approaches based on the degradation process (e.g. Deshpande et al, 2006;Wang and Syntetos, 2011) or on system monitoring systems Topan et al (2018); Lin et al (2017); Poppe et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Spare parts inventory control based on maintenance planning

Zhu

Jaarsveld

Dekker

2020

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

“…Due to the variety of scenarios, further efforts to improve upon the proposed model, taking into account new variables [22] such as the level of degradation [23], failure and shock rates [24], delivery time of components [25], among others, are needed. Nonetheless, one should be aware that handling several parameters at the same time could result in complex solution systems [4].…”

Section: Discussionmentioning

confidence: 99%

Optimization of the replacement time for critical repairable components

Torres¹,

Oblitas²,

Castro³

2020

DYNA

View full text Add to dashboard Cite

Equipment-intensive industries must manage critical components due to their impact on the availability and high inventory carrying costs. In this context, this study seeks to assess mean times between interventions (MTBI) and mean times between failures (MTBF) to determine optimal replacement times for critical repairable components used in six EX5500 hydraulic excavators operating at an open-pit mining site. For these purposes, the authors compared a base policy using the MTBF values provided by the equipment manufacturer, against the proposed policy using the MTBI values obtained from equipment intervention records. The results from the study, revealed that the MTBI policy was able to streamline the replacement times for critical repairable components, thus, generating a cost optimization model at a higher level of reliability

show abstract

“…They found the optimal preventive maintenance threshold to satisfy the spare parts support requirements. Lin et al showed that condition-based inventory policy increases about twenty percent of the system efficiency [14]. Auweraer and Boute proposed a method to forecast spare part demand based on service maintenance information [15].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analyzing Supporting Systems for Smart Manufacturing Systems with Stochastic, Technical and Economic Dependences

Farsi

Zio

2020

IJE

View full text Add to dashboard Cite

Smart manufacturing systems are triggerring the next industrial revolution. They are intended to be collaborative manufacturing systems that respond in real time to meet the system's changing demands and conditions. Different types of dependencies among system components are introduced to enable this and to improve system performance, including structural, stochastic, technical and economic dependences. Supporting systems are also introduced to this aim, through specified interfaces. In this paper, the role of maintenance policy, spare part inventory and buffer size as supporting systems of smart systems is considered. Load-sharing dependence, adaptive control with feedback and economic dependence are specifically considered, and their effect is studied via Monte Carlo simulation. Results show that smart systems with properly designed supporting systems have undoubtedly increased system complexity and dependencies, but can indeed increase availability and production volume, and system efficiency overall, with total cost reduced.

show abstract

Condition based spare parts supply

Cited by 37 publications

References 29 publications

Spare parts inventory control based on maintenance planning

Spare parts inventory control based on maintenance planning

Optimization of the replacement time for critical repairable components

Modeling and Analyzing Supporting Systems for Smart Manufacturing Systems with Stochastic, Technical and Economic Dependences

Contact Info

Product

Resources

About