scholarly journals Maintenance Decision Support for Manufacturing Systems Based on the Minimization of the Life Cycle Cost

Procedia CIRP ◽  
2016 ◽  
Vol 57 ◽  
pp. 674-679 ◽  
Author(s):  
Alice Reina ◽  
Ádám Kocsis ◽  
Angelo Merlo ◽  
István Németh ◽  
Francesco Aggogeri
Keyword(s):  
Life Cycle ◽  
Decision Support ◽  
Life Cycle Cost ◽  
Manufacturing Systems ◽  
Maintenance Decision

scholarly journals Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 226
Author(s):  
Xuyang Zhao ◽  
Cisheng Wu ◽  
Duanyong Liu
Keyword(s):  
Life Cycle ◽  
Systems Engineering ◽  
Life Cycle Cost ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Cost Allocation ◽  
Industrial Robot ◽  
Case Analysis ◽  
Industrial Robots ◽  
Analysis Model

Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution.

Stochastic rail life cycle cost maintenance modelling using Monte Carlo simulation

2017 ◽  
Vol 232 (4) ◽  
pp. 1240-1251 ◽  
Author(s):  
Rick Vandoorne ◽  
Petrus J Gräbe
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle ◽  
Decision Support ◽  
Life Cycle Cost ◽  
Cost Model ◽  
Railway Track ◽  
Maintenance Costs ◽  
Relative Contribution ◽  
Inspection Interval

The need for decision support systems to guide maintenance and renewal decisions for infrastructure is growing due to tighter budget requirements and the concurrent need to satisfy reliability, availability and safety requirements. The rail of the railway track is one of the most important components of the entire track structure and can significantly influence maintenance costs throughout the life cycle of the track. Estimation of life cycle cost is a popular decision support system. A calculated life cycle cost has inherent uncertainty associated with the reliability of the input data used in such a model. A stochastic life cycle cost model was developed for the rail of the railway track incorporating imperfect inspections. The model was implemented using Monte Carlo simulation in order to allow quantification of the associated uncertainty within the life cycle cost calculated. For a given set of conditions, an optimal renewal tonnage exists at which the rail should be renewed in order to minimise the mean life cycle cost. The optimal renewal tonnage and minimum attainable mean life cycle cost are dependent on the length of inspection interval, weld type used for maintenance as well as the cost of maintenance and inspection activities. It was found that the distribution of life cycle cost for a fixed renewal tonnage followed a log-normal probability distribution. The standard deviation of this distribution can be used as a metric to quantify uncertainty. Uncertainty increases with an increase in the length of inspection interval for a fixed rail renewal tonnage. With all other conditions fixed, it was found that the uncertainty in life cycle cost increases with an increase in the rail renewal tonnage. The relative contribution of uncertainty of the planned and unplanned maintenance costs towards the uncertainty in total life cycle cost was found to be dependent on the length of inspection interval.

A Decision Support Framework for Cost-Effective and Energy-Efficient Shipping

2020 ◽  
Author(s):  
Khanh Q. Bui ◽  
Lokukaluge P. Perera
Keyword(s):  
Life Cycle ◽  
Decision Support ◽  
Energy Efficient ◽  
Life Cycle Cost ◽  
Performance Monitoring ◽  
Cost Effective ◽  
Life Cycles ◽  
Maritime Industry ◽  
Decision Support Framework ◽  
Maritime Operations

Abstract Stringent regulations regarding environmental protection and energy efficiency (i.e., emission limits regarding NOx, SOx pollutants and the IMO greenhouse gases reduction target) will mark a significant shift to the maritime industry. In the first place, the shipping industry has strived to work towards feasible technologies for regulatory compliance. Nevertheless, life cycle cost appraisal attaches much consideration of decision-makers when it comes to investment decisions on new technologies. Therefore, the life cycle cost analysis (LCCA) is proposed in this study to evaluate the cash flow budgeting and cost performance of the proposed technologies over their life cycles. In the second place, environmental regulations may support innovation especially in the era of digitalization. The industrial digitalization is expected to revolutionize all of the aspects of shipping and enable the achievement of energy-efficient and environmental-friendly maritime operations. The so-called Internet of things (IoT) with the utilization of sensor technologies as well as data acquisition systems can facilitate the respective maritime operations by means of vessel operational performance monitoring. The big data sets obtained from IoT should be properly analyzed with the help of Artificial Intelligence (AI) and Machine Learning (ML) approaches. Our contribution in this paper is to propose a decision support framework, which comprises the LCCA analysis and advanced data analytics for ship performance monitoring, will play a pivotal role for decision-making processes towards cost-effective and energy-efficient shipping.

Lebenszyklusbetrachtungen im Planungsprozess*/Life-cycle costing in the planning process - Integration of a life-cycle cost analysis for production technologies in the automotive body shop

2016 ◽  
Vol 106 (01-02) ◽  
pp. 89-93
Author(s):  
M. Bornschlegl ◽  
A. Müller ◽  
M. Bregulla ◽  
F. Mantwill ◽  
J. Franke
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Manufacturing Systems ◽  
Planning Process ◽  
Process Integration ◽  
Life Cycle Costs ◽  
Life Cycle Cost Analysis ◽  
Automotive Body ◽  
Applied Technology ◽  
Production Technologies

Die Betriebskosten von Fertigungsanlagen haben – abhängig von der jeweiligen eingesetzten Technologie – einen signifikanten Anteil an den Lebenszykluskosten. Aus diesem Grund ist es essentiell, diese bereits in der Planungsphase zu berücksichtigen, um nachhaltige Entscheidungen treffen zu können. Dabei liegt der Schwerpunkt insbesondere bei den Schulungs-, Energie- und Instandhaltungskosten. Dazu werden in diesem Fachartikel wesentliche Kostenelemente und Herausforderungen aufgezeigt.   The operating costs of manufacturing systems represent a significant part of the life-cycle costs, depending on the applied technology. For this reason, it is essential to take them into account during the planning phase in order to make sustainable decisions. The focus is mostly on costs for training, energy and maintenance. Therefore, the key cost elements and challenges are pointed out in this article.

Value Creation and Cost Management by Use of the New ISO 15663 Life Cycle Costing Standard

2021 ◽  
Author(s):  
Endre Willmann ◽  
Runar Østebø ◽  
Eduardo H. R. Montalvao
Keyword(s):  
Life Cycle ◽  
Decision Support ◽  
Value Creation ◽  
Life Cycle Cost ◽  
Net Present Value ◽  
Oil And Gas ◽  
Cost Management ◽  
Life Cycle Costing ◽  
Cycle Phase ◽  
Life Cycle Phase

Abstract The new edition of the ISO 15663 standard has been developed during the recent years and will strengthen the industry cost management for business value creation. This paper shows how such standardization can be used to further enhance and promote adoption of a common and consistent approach to life cycle costing in the offshore oil and gas industry. The new ISO 15663 edition maintains key principles from previous editions, but does also introduce an improved and revised management methodology for application of life cycle costing. The purpose is to provide decision support for selecting between alternative options (e.g., projects, operational and technical subject matters) across life cycle phases, also aligned with overall corporate business objectives such as HSE and sustainability. It also provides the means of identifying cost drivers and a framework for value optimization over the entire life of an asset. The international standard is providing an essential set of normative requirements on how to implement and apply the life cycle costing methodology and the decision criteria, supported by an exhaustive part of recommended practices. This includes the identification of common and specific contractual considerations for operators, contractors and vendors (e.g., complementary metrics besides expenditure, such as systems availability guarantee and risk-sharing clauses). It also includes the application in the life cycle phases of an asset, the techniques and data input, examples of application, and assessment and lessons learnt. Capital expenditure (CAPEX), operating expenditure (OPEX), revenue and lost revenue (LOSTREV) factors are addressed. The standard includes an unambiguous definition of the economic objectives of a project and application of the same business criteria when making major engineering decisions. The life cycle costing methodology is applicable to all asset decisions in any life cycle phase, but should be applied only when expected to add value for decision-support. The required extent of planning and management of the appropriate life cycle costing is depending on the magnitude of the costs involved, the potential value that can be created and the life cycle phase. This paper demonstrates how the new ISO 15663 can be utilized by providing new examples of life cycle costing, to give all participants in the process — oil and gas operators, contractors and vendors — an up-to-date and streamlined set of requirements and guidance, encouraging a fit for purpose application. The paper does also present unique key economic evaluation measures such as life cycle cost (LCC) and net present value (NPV).

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