Life Cost-Based FMEA Incorporating Data Uncertainty

2002 ◽  
Author(s):  
Seung J. Rhee ◽  
Kosuke Ishii
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle Cost ◽  
Failure Modes ◽  
Design Tool ◽  
Point Estimation ◽  
Data Uncertainty ◽  
Design Alternatives ◽  
Model Complex ◽  
The Cost

Failure Modes and Effects Analysis (FMEA) is a design tool that mitigates risks during the design phase before they occur. Although many industries use the current FMEA technique, it has many limitations and problems. Risk is measured in terms of Risk Priority Number (RPN) that is a product of occurrence, severity, and detection difficulty. Measuring severity and detection difficulty is very subjective and with no universal scale. RPN is also a product of ordinal variables, which is not meaningful as a proper measure. This paper addresses these shortcomings and introduces a new methodology, Life Cost-Based FMEA, which measures risk in terms of cost. The ambiguity of detection difficulty and severity is resolved by measuring these in terms of time loss. Life Cost-Based FMEA is useful for comparing and selecting design alternatives that can reduce the overall life cycle cost of a particular system. Next, a Monte Carlo simulation is applied to the Cost-Based FMEA to account for the uncertainties in: detection time, fixing time, occurrence, delay time, down time, and model complex scenarios. This paper compares and contrasts these three different FMEAs: RPN, Life Cost-based point estimation, and Life Cost-Based using Monte Carlo simulation for data uncertainty.

scholarly journals Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Electricity Sector ◽  
Cost Functions ◽  
Density Estimator ◽  
Monte Carlo Simulation Method ◽  
Commercial Building ◽  
Complementary Method ◽  
The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

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.

Interval estimation of operational availability of tree harvesting machines

1987 ◽  
Vol 17 (11) ◽  
pp. 1451-1454
Author(s):  
C. H. Meng ◽  
S. Z. Tang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Solutions ◽  
Analytical Approach ◽  
Interval Estimation ◽  
Pulp And Paper ◽  
Point Estimation ◽  
Interval Estimate ◽  
Point Estimates ◽  
The Mean

The Canadian Pulp and Paper Association has defined the operational availability of a piece of logging equipment as A = (T − M − D)/T, where T denotes total scheduled machine hours per day, M denotes maintenance hours per day, and D denotes machine downtime per day. The existing literature on logging machines contains only point estimates of the mean operational availability. This paper propounds interval estimation as a preferable alternative since, unlike point estimation, it provides an indication of the uncertainty involved. Two methods of interval estimation are developed: (i) an analytical approach derived from basic theories and (ii) a Monte Carlo simulation. A detailed example is given to demonstrate the application of both methods to the same logging machine. For situations in which theoretical distributions for downtimes and repair times can be assumed, analytical solutions provide general and exact answers for the interval estimate of machine operational availability. However, if theoretical distributions cannot be reasonably assumed and if the integration involved is difficult, the analytical procedures become difficult. In such cases, operational availability can be approximated by the method of Monte Carlo simulation.

Development of a Hybrid Life Cycle Cost Model for Estimating Product Design Alternatives in Cloud Computing Based Collaborative Design Environment

2013 ◽  
Vol 658 ◽  
pp. 614-619 ◽  
Author(s):  
Hong Kyu Kwon ◽  
Kwang Kyu Seo
Keyword(s):  
Cloud Computing ◽  
Life Cycle ◽  
Product Design ◽  
Collaborative Design ◽  
Life Cycle Cost ◽  
Cost Model ◽  
Design Environment ◽  
Cost Performance ◽  
Design Alternatives ◽  
The Cost

In a competitive and globalized business environment, the need for the sustainable product development becomes stronger. To meet these trends, the total cost during the product life cycle, called life cycle cost (LCC), should be considered as an important factor in new product development. In this paper, a hybrid life cycle cost model (HLCCM) is developed as a hybrid life cycle cost system (HLCCS) to estimate the cost performance of product design alternatives. It aims at improving the cost performance of products using genetic algorithms and artificial neural networks which consist of high-level product attributes and LCC results. The framework incorporated HLCCM is proposed in cloud computing based collaborative design environment and allows users to estimate the product data and other related information on a wide variety of application. This paper presents approximate LCC estimation of product design alternatives represented by solid models in cloud computing based collaborative design environment.

scholarly journals A Case Study for Business Integration as a Service

2014 ◽  
pp. 229-254 ◽  
Author(s):  
Victor Chang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Return On Investment ◽  
Cost Savings ◽  
Computational Results ◽  
Cloud Adoption ◽  
The Cost ◽  
The University

This chapter presents Business Integration as a Service (BIaaS) to allow two services to work together in the Cloud to achieve a streamline process. The authors illustrate this integration using two services, Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS), in the case study at the University of Southampton. The case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. Advanced techniques are used to demonstrate statistical services and 3D Visualisation services under the remit of RMaaS and Monte Carlo Simulation as a Service behind the design of RAaaS. Computational results are presented with their implications discussed. Different types of risks associated with Cloud adoption can be calculated easily, rapidly, and accurately with the use of BIaaS. This case study confirms the benefits of BIaaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management and stakeholders of University of Southampton.

scholarly journals Economic viability of implementing an infrastructure for recycling bedding sand from a free-stall facility for dairy cows

2021 ◽  
pp. 361-374
Author(s):  
Marcos Aurélio Lopes ◽  
◽  
Fabiana Alves Demeu ◽  
Eduardo Mitke Brandão Reis ◽  
André Luis Ribeiro Lima ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Operating Cost ◽  
Estimation Method ◽  
Cost Benefit ◽  
Minas Gerais ◽  
Point Estimation ◽  
Economic Viability ◽  
Total Production ◽  
Point Estimation Method

This study proposes to examine the economic viability of implementing the necessary infrastructure for the recycling of bedding sand from a free-stall facility in a milk production system in southern Minas Gerais, Brazil. In specific terms, the total production cost (TC), total operating cost (TOC) and effective operating cost (EOC) of a cubic meter of recycled sand were estimated in order to estimate the total sand consumption for the free-stall system and per bed year-1 as well as the equilibrium point of the amount of recycled sand, in cubic meters. The experiment was carried out on a farm located in the south of Minas Gerais from January 2016 to December 2017. Three scenarios were analyzed by the tree-point estimation method (MOP - most likely, optimistic, and pessimistic). Utilization of 85%, 95% and 75% of the recycled sand was considered for scenarios 1, 2 and 3, respectively. In all of them, the value charged per cubic meter of sand by a supplier close to the farm was considered. Monte Carlo simulation was also carried out with hurdle rates (HR) of up to 90%. Under the studied conditions, sand recycling showed to be economically viable in all scenarios, with positive net present values (NPV), internal rates of return above the HR, simple and discounted payback below the 10-year horizon, and satisfactory cost benefit-1 ratios (greater than 1). The EOC of one cubic meter of recycled sand was estimated at R$5.04, R$4.51 and R$5.72 for scenarios 1, 2 and 3, respectively, whereas the average TC, considering all scenarios, was R$6.84 (+0.81), which is less than the acquisition price of R$28.57 at the sand extraction site. The TC was R$37,219.51 and R$34,637.74 for the scenarios with HR of 8.50 and 6.99%, respectively, whereas TOC was R$22,572.08 in all analyzed scenarios. The estimated total annual sand consumption by the free-stall system was 526.44 m³, with an estimated average of 1.23 m³ (+0.28) bed-1 year-1. All Monte Carlo simulation models showed positive NPV as well as HR of up to 90%, which reflect a high probability of positive NPV.

scholarly journals Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

2021 ◽  
Author(s):  
Mohammad Ammar Alzarrad
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Possibility Theory ◽  
Resource Planning ◽  
Fuzzy Arithmetic ◽  
Fuzzy Variables ◽  
Research Outcomes ◽  
The Cost ◽  
Resources Planning ◽  
Selection Of

Resources planning and operations are essential concerns and specialty areas within industrial engineering and project management. Crew configuration plays a significant role in resource planning and operations. Crew configuration inefficiency is one of the most common reasons for the low productivity of manpower. Resources planning contains some inherent uncertainties and risks because it is an estimate of unknown values. Many factors affect resource planning. Some of these factors are fuzzy variables such as expert’s judgment, and some of them are random variables such as direct cost of equipment. The objective of this chapter is to present a method that combines fuzzy logic and Monte Carlo simulation (MCS) for the selection of the best crew configuration to perform a certain task. The model presented in this chapter is a joint propagation method based on both the probability theory of MCS and the possibility theory of fuzzy arithmetic. The research outcomes indicate that the presented model can reduce the duration and cost of a certain task, which will help reduce the cost and duration of the project.

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