Brennan Aircraft Division (BAD) Case Study By Elena White, Luigi DeAngelis & John Ramos.

Slides:



Advertisements
Similar presentations
Reliability McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Advertisements

Review Of Statistical Mechanics
1 Overview of Simulation When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions set for analytic.
11 Simulation. 22 Overview of Simulation – When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions.
Reliability Engineering (Rekayasa Keandalan)
MODULE 2: WARRANTY COST ANALYSIS Professor D.N.P. Murthy The University of Queensland Brisbane, Australia.
Q11: Describe how the effects of power supply failures on integrated luminosity will be mitigated. TESLA Response : –Mainly consider two types of magnet.
1 Non-observable failure progression. 2 Age based maintenance policies We consider a situation where we are not able to observe failure progression, or.
RELIABILITY Dr. Ron Lembke SCM 352. Reliability Ability to perform its intended function under a prescribed set of conditions Probability product will.
Introduction to Transfer Lines Active Learning – Module 1
CS433 Modeling and Simulation Lecture 11 Monté Carlo Simulation Dr. Anis Koubâa 05 Jan 2009 Al-Imam Mohammad Ibn.
SMJ 4812 Project Mgmt and Maintenance Eng.
James Ngeru Industrial and System Engineering
Reliability of Systems
Session 7b. Decision Models -- Prof. Juran2 Example: Preventive Maintenance At the beginning of each week, a machine is in one of four conditions: 1 =
Dependability Evaluation. Techniques for Dependability Evaluation The dependability evaluation of a system can be carried out either:  experimentally.
The Infeasibility of Quantifying the Reliability of Life-Critical Real-Time Software.
Maintenance and Reliability Ross L. Fink. Maintenance  All activities involved in keeping a system’s equipment in working order.
Simulation Basic Concepts. NEED FOR SIMULATION Mathematical models we have studied thus far have “closed form” solutions –Obtained from formulas -- forecasting,
1 Review Definition: Reliability is the probability that a component or system will perform a required function for a given period of time when used under.
Reliability Chapter 4S.
Simulation Basic Concepts. NEED FOR SIMULATION Mathematical models we have studied thus far have “closed form” solutions –Obtained from formulas -- forecasting,
Machine Shop Model. Lecture Two by: Lecturer/ Nada Ahmed.
1 Logistics Systems Engineering Availability NTU SY-521-N SMU SYS 7340 Dr. Jerrell T. Stracener, SAE Fellow.
Project & Quality Management Quality Management Reliability.
Chapter 6 Time dependent reliability of components and system.
9/10/2015 IENG 471 Facilities Planning 1 IENG Lecture Schedule Design: The Sequel.
Engineering Economy, Sixteenth Edition Sullivan | Wicks | Koelling Copyright ©2015, 2012, 2009 by Pearson Education, Inc. All rights reserved. TABLE 12-1.
Operations Management Maintenance and Reliability 保養維護與可靠程度 Chapter 17
1 Availability Modeling of Cooling Water Pumps to Assess if a Replacement Option is Economically Feasible. Dr. Salman Mishari.
Independent Demand Inventory Management
LSM733-PRODUCTION OPERATIONS MANAGEMENT By: OSMAN BIN SAIF LECTURE 26 1.
1 Sampling Distributions Lecture 9. 2 Background  We want to learn about the feature of a population (parameter)  In many situations, it is impossible.
TASK PACKAGING Module 1 UNIT IV ADDITIONAL TOPICS " Copyright 2002, Information Spectrum, Inc. All Rights Reserved."
Reliability Models & Applications Leadership in Engineering
10/25/2015 IENG 471 Facilities Planning 1 IENG Lecture Schedule Design: The Sequel.
Reliability Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
Reliability Study of Power Generation System Utilizing Wind Power Derek H. Geiger DSES-6070 HV5 Term Project 31-March-2008.
Outline of Chapter 9: Using Simulation to Solve Decision Problems Real world decisions are often too complex to be analyzed effectively using influence.
Analysis and Design of Asynchronous Transfer Lines as a series of G/G/m queues.
Safety-Critical Systems 7 Summary T V - Lifecycle model System Acceptance System Integration & Test Module Integration & Test Requirements Analysis.
An Application of Probability to
Equipment Replacement Four Really Great Models 1. Deterministic Age Replacement 2. Minimal Repair Model 3. Repair versus Replace 4. Block Replacement.
Reliability and availability considerations for CLIC modulators Daniel Siemaszko OUTLINE : Give a specification on the availability of the powering.
Reliability Failure rates Reliability
Reliability McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 3 System Performance and Models Introduction A system is the part of the real world under study. Composed of a set of entities interacting.
OPERATING SYSTEMS CS 3530 Summer 2014 Systems and Models Chapter 03.
Acceptance Sampling Systems For Continuous Production and Variables
Maintenance Management [14]
Mean Time To Repair
Stracener_EMIS 7305/5305_Spr08_ Systems Availability Modeling & Analysis Dr. Jerrell T. Stracener, SAE Fellow Leadership in Engineering EMIS 7305/5305.
Reliability Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 15-1 © 2006 by Prentice Hall, Inc. Upper Saddle River, NJ Prepared by.
CS203 – Advanced Computer Architecture Dependability & Reliability.
Chapter 4s Reliability. Learning Objectives You should be able to: 1.Define reliability 2.Perform simple reliability computations 3.Explain the purpose.
Tailoring the ESS Reliability and Availability needs to satisfy the users Enric Bargalló WAO October 27, 2014.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Supplement 4 Reliability.
OPERATING SYSTEMS CS 3502 Fall 2017
ANALYTICAL METHODS IN MAINTENANCE
Prepared by Lee Revere and John Large
Software Reliability Models.
Reliability Failure rates Reliability
Production and Operations Management
RELIABILITY Reliability is -
Operations Management
Chapter 6 Time dependent reliability of components and system
Model 4-2: The Enhanced Electronic Assembly and Test System
Model 4-2: The Enhanced Electronic Assembly and Test System
Presentation transcript:

Brennan Aircraft Division (BAD) Case Study By Elena White, Luigi DeAngelis & John Ramos

Overview of Presentation Executive Summary Data Analysis Basis of Simulation Conclusion

Executive Summary BAD operates large number of plotting machines  Consist of minicomputer system that directs 4 pens to move until desired figure is drawn  Connected to a 4 – by-5 foot table with series of ink pens suspended above it  Very reliable with exception of ink pens clogging, jamming, rendering plotter unusable

Executive Summary Cont… BAD replaces ink pen upon failure of each Alternative repair by service manager  Replace all 4 ink pens upon one failure  Ideally reducing the frequency of failures

Data Analysis The following data was provided by the case study:  Total cost of downtime $50/hr  Replacement time of 1 pen = 1 hr/pen  Replacement time of 4 pens 2 hr/set  Cost of each pen $8/pen

Data Analysis Cont… HoursProbability Probability Distribution Between Failures (each pen replaced as it fails)

Data Analysis Cont… HoursProbability Probability Distribution Between Failures (4 pens replaced as 1 fails)

Data Analysis Cont… Additional data (assumptions used in simulation to establish year utilization) 1 plotter year2500 Hours10hrs/day – 250 days

Basis of Simulation Simulated Brennan’s problem for two options  Case 1 : Replace ink pen as it fails  Case 2 : Replace all four ink pens as one fail Used “Next Event Increment Model” approach to carry out the simulation Split runs in “Year (of 2500 hrs each)” this helps in results analysis  Each run is arrested when “close enough” to 2500 hrs. A “While- cycle” would have been best approach. A spreadsheet works well as analysis is simple Used VLOOKUP to instantaneously look-up probability tables and determine hours between plotter failures

Basis of Simulation Cont… Computed total time adding downtime to TBF computed from Probability Distribution. Derived total cost of each failure  Cost of 1 pen plus cost of One hour of downtime (case 1) = 58 $  Cost of 4 pens plus cost of Two hour of downtime (case 1) = 132 $  Computed failures for the equivalent of 1 plotter year. Run repeated 5 times (reasonable life-cycle for a plotter).

Simulation: Results Case 2 is the most convenient choice evaluated as an average on a 5-Year simulation.

Analytical Results A different approach has been followed based on analytical considerations. The Mean for each distribution has been calculated, i.e. MTBF. We calculated number of failures X year as:  Numb. Fail. X Year= 2500 / (MTBF + MT) We calculated costs in 1 Year as:  1 Y Cost = [Numb. of Fail. X Year] * [Repairing Costs]  NOTE: Tot. Cost = [1 Y Cost] * [N Year]

Analytical Solution: Results Best Choiche is again Case 2. Note how close Analytical and Simulated results are evaluated as an average on a 5 Y time frame.

Conclusion Based on the results achieved with the.xls simulation we observed the progression of costs and maintenance times Determined that in Case 2, replacement of all 4 pens upon one failed pen, will minimize maintenance costs for BAD Analytical results reinforce our simulated study that Case 2 is indeed the best policy to implement. (or viceversa?)

Any Questions?