Program Description

The Master of Engineering degree program offers an area of study in Quality, Reliability & Statistical Engineering

An online engineering degree program.

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Overview:

The Master of Engineering degree program offers an area of study in Quality, Reliability & Statistical Engineering. This track offers specialized courses founded on basic engineering and statistics principles that are central to improving quality, reliability, and achieving meaningful business results in today’s modern business organizations. The program is designed to prepare graduates for a wide spectrum of opportunities in product and service realization, including engineering design, product and process development, manufacturing, distribution, logistics, and supply chain operations. Students will learn about how modern statistical and engineering techniques are deployed in a wide range of industrial and business settings. They will also learn the basic underlying methodology so that they can develop novel solutions to problems that are unique to their own environment. Some of the world-renowned faculty participating in this program include Dr. Douglas Montgomery, Dr. Esma Gel, and Dr. George Runger.

The MEng is designed for working professionals and delivered entirely online to meet the unique needs of professional engineers. It is a practice-oriented degree.

Degree Requirements

Students must complete 30 credit hours to earn the Master of Engineering degree. For students electing the Graduate Certificate Lean Six Sigma Black Belt Program option, they are required to complete IEE 581 Six Sigma Methodology and Six Sigma Capstone Project along with all required core courses, and one member of the project committee must be a practicing professional. Students electing this option will also receive a Lean Six Sigma Black Belt Certification along with the graduate certificate and MEng degree.

A culminating event is required and will be identified by the student’s advisory committee. The final report for a practice-oriented project (IEE 593:Applied Project or IEE585: Six Sigma Capstone) will serve as the capstone experience.

Courses

Required Core Courses (15 Graduate Credits):

• IEE 572 Design of Engineering Experiments
• IEE 578 Regression Analysis
• IEE 570 Advanced Quality Control
• IEE 573 Reliability Engineering
• IEE 571 Quality Management

Elective Courses (12 Graduate Credits):

• IEE 520 Data Mining
• IEE 579 Time Series and Forecasting
• IEE 581 Six Sigma Methodology *
• IEE 561 Production Systems
• IEE 582 Response Surfaces and Process Optimization
• IEE 512 Introduction to Financial Engineering
• IEE 552 Strategic Technological Planning
• IEE 545 Simulating Stochastic Systems
• IEE 574 Applied Deterministic Operations Research Models
• IEE 575 Applied Stochastic Operations Research Models
• IEE 534 Supply Chain Modeling and Analysis

* IEE 581 must be selected for students pursuing the Lean Six Sigma Black Belt Certificate.

Capstone (3 Graduate Credits):

• IEE 593 Applied Project – QRSE
• IEE 585 Six Sigma Capstone Project (capstone course for students pursuing the Lean Six Sigma Black Belt Certification)

Contacts

Jasmine Delaboin
Academic Success Coordinator
Global Outreach & Extended Education
Ira A. Fulton Schools of Engineering
Arizona State University

student-fseonline@asu.edu

For more information

send email 

visit website

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Program Description

Advance your maintenance management career and gain practical knowledge through five courses, including one required course and four elective courses.

A Maintenance and Reliability Management Certificate

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Overview:

New and experienced maintenance professionals in manufacturing and facilities settings will deepen their knowledge through problem-solving courses with UW–Madison’s Maintenance and Reliability Management Certificate program.

Faculty and industry experts team up to offer high-quality instruction and guidance to maintenance professionals, materials and purchasing managers, plant managers, and professionals with related product, process, or facility responsibilities.

The program equips you with strategies for confronting everyday problems and helps to increase your value to your employer. You choose four of the five classes offered to complete the certificate, providing a chance to tailor your curriculum to meet your individual needs and address the subject areas most useful to you.

By staying current with the latest maintenance practices and technologies, you’ll better understand how to attack complex challenges and contribute to your organization’s bottom line.

How you’ll learn

• Advance your maintenance management career and gain practical knowledge through five courses, including one required course and four elective courses.
• Each class runs two or three days.
• You can also attend as many courses as you wish, in any order, without obligation to the certificate.

Sample curriculum

Required Courses (Enroll in 1 of 2 options)

• Best Practices in Maintenance and Reliability
• Maintenance and Reliability-Centered Maintenance

Choose 4 Other Courses

• Effectively Managing Your Maintenance Team
• Best Practices in Maintenance and Reliability
• CMMS Setup and Utilization
• Root Cause Analysis Tools for Technical Applications
• Improving Equipment Uptime and Performance with TPM
• Business Basics for Maintenance and Operations
• Advanced World-Class Storeroom Principles
• Machinery Lubrication Fundamentals
• Using FMEA and RCM to Reduce Equipment Failures
• Self-Paced Maintenance and Reliability Best Practices

Contacts

Jon Wellik
Program Director

jwellik@wisc.edu

Maintenance and Reliability Management

For more information

send email 

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Program Description

The Subir Chowdhury School of Quality and Reliability offering Masters degree in the areas of Quality and Reliability Engineering.

The School focuses on overall improvement in engineering skills of students by exposing them theory and practices in Quality and Reliability Engineering through its academic programs, involving students in research and consultancy projects

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Overview:

In the present scenario of global competition, Quality and Reliability of products and processes play vital role in design, maintenance, operational safety/security and management of engineering systems.  Quality and Reliability Engineering is an inter-disciplinary area and plays an important role at different stages of a product life cycle starting from conceptual design to detailed design, manufacturing, operation, maintenance, and disposal. Quality and Reliability Engineering help to improve designs for operation, safety, easy/faster maintenance, understand the failure process, identify the root causes, suggests better fault diagnosis and maintenance, and quantifies the product performance over a period of its mission life. Subir Chowdhury School of quality and Reliability is established by upgrading the Reliability Engineering Centre (since 1983). It is the first and unique School of excellence in India offering Masters’ and research programs in the areas of Quality and Reliability Engineering.  The School focuses on overall improvement in engineering skills of students by exposing them theory and practices in Quality and Reliability Engineering through its academic programs, involving students in research and consultancy projects for Industries and R & D organizations, (viz., BARC, DRDO, NPCIL, L&T, AERB, Vodaphone, Indian Army, ECIL, ISRO, Crompton & Greaves, Robert Bosch, GE, Covedien, TATA Motors, Secure Meters, Indian Railways, Bosch etc.), exposing students to life testing/ prediction/ estimation/ demonstration of engineering items, and encouraging for extracurricular activities etc. There is great scope and demand of Quality and Reliability engineers with B.E. / B. Tech. in Mechanical, Electronics, Electrical, Chemical engineering, Computer Science, Production, Manufacturing, and other associated areas. Students of the School are well placed in MNC’s and reputed organizations like BARC, Bloom Energy,  Crompton & Greaves, DRDO, Eaton, ENTITY SOLUTIONS, GE, GM, Goodrich, HCL, Honeywell, IBM, INFOSYS, ISRO, LM Glass Fiber, Maruti Udhyog, NPCIL, SATYAM, TATA Steel, TCS, Time Tooth, WIPRO, TVS, Bosch and many more.

Message from the Head

Subir Chowdhury School of Quality and Reliability welcomes you all. Quality and Reliability Engineering is an interdisciplinary branch of study offered by our School currently located in old IIT building. We have faculty members specialized in quality and reliability engineering with basic degrees in different engineering disciplines. Similarly, our intake students are also from different engineering disciplines. In this unique School, our team of faculty members will be too happy to impart you the best standard of education in quality and reliability engineering that is available in our country. Infact, the standard of our programme is one of the best in this area and comparable with any international university offering similar specializations. The course curricula of this study are carefully designed to include latest developments, tools, techniques, as well as providing industrial exposure to our students. We offer post graduate degrees such as MTech, MS and PhD. Apart from these, the School also offers micro-specializations in System Reliability Engineering, and Quality Engineering to the UG students of the Institute. EDP/MDP courses are regularly offered to working professionals in industry and R&D organizations. The School is also actively involved in industrial consultancy and research. All our students get good placement in various organizations and the feedback we receive from them is very encouragingCourses

Contact

Prof. Siddhartha Mukhopadhyay
Chairperson of the School

akpradhan@ee.iitkgp.ac.in

+91-3222-82289

For more information

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Module 8 Non-Replicable Measurement Systems

Lesson M8-06

Text: Section 6, pages 23 – 25

Duration: 12 minutes

Dynamic Part/Sample Properties

The last lesson explores the scenario where part or sample properties change over time, preventing replicate measurements.  The two approaches for this situation (steady state testing and modeling) are described.  An example of modeling is presented

Module 8 Non-Replicable Measurement Systems

Lesson M8-05

Text: Section 6, page 11 – 22

Duration: 24 minutes

Dynamic Process Conditions

In Lesson 5, we explore the scenario where changing process conditions make the measurement process non-replicable.  One option is to use redundant measurement systems and use Grubbs procedure to estimate measurement error.  An example and exercise are included.  Another (better) option is fit a model to the data coming from the redundant measurement systems.  Finally, modeling in general is described to help estimate measurement error.

Downloads

MSA Grubbs Worksheet.xls

Exercise 1-4.15.xls

Module 8 Non-Replicable Measurement Systems

Lesson M8-04

Text: Section 6, pages 9 – 10

Duration: 8 minutes

Destructive Testing (Multiple Measurement Locations)

Lesson 4 introduces a method of analyzing data that comes from multiple locations on a part due to the measurement being destructive at the location being measured.  Assuming homogenous locations is the easiest approach, but methods that allow for non-homogenous locations exist and are briefly described.

Module 8 Non-Replicable Measurement Systems

Lesson M8-03

Text: Section 6

Duration: 15 minutes

Analyzing a Nested Gage R&R (Minitab) & Exercise

Here, we illustrate the analysis of nested Gage R&R in Minitab via an example.  Then you are given an opportunity to practice on an exercise.

Downloads

MSA Nesting Example-1.MTW

Exercise 1-4.14.MTW

Module 8 Non-Replicable Measurement Systems

Lesson M8-02

Text: Section 6, pages 4 – 8

Duration: 16 minutes

Destructive Testing (Homogenous Specimens & Split Specimens)

In Lesson 2, we cover methods for handling destructive tests.  The easiest approach is to identify homogenous samples that will be treated as though they are the same part and we provide guidelines for this method.  We also discuss the potential use of Split Specimens.  Situations that require nestedstudies and analysis are described.

Module 8 Non-Replicable Measurement Systems

Lesson M8-01

Text: Section 6, pages 1 – 3

Duration: 11 minutes

Non-replicable Scenarios & Overview of Methods

Lesson 1 describes scenarios that result in the inability to replicate measurements.  These include destructive testing, dynamic testing conditions, and dynamic part/sample properties.  Methods that will be covered in this module are introduced.

Module 8 Non-Replicable Measurement Systems

Lesson M8-00

Text: Section 6

Duration: 2 minutes

Non-Replicable Measurement Systems Module Introduction

In the types of assessments covered in this course, taking replicate measurements is a key part of quantifying the measurement error.  However, in some measurement systems, it is not possible to repeat the measurement with the same/part specimen.  While there is no perfect solution to this problem, several methods may be used to quantify measurement error in the absence of replication.  This module introduces several methods that may be used depending on the situation.

Module 7 Comparing Measurement Systems

Lesson M7-03

Duration: 29 minutes

Exercises

Two exercises allow you to practice using the methods presented in this module.

Downloads

Exercise 1-4.12.MTW

Exercise 1-4.13.MTW

Module 7 Comparing Measurement Systems

Lesson M7-02

Duration: 17 minutes

Direct Comparisons with Scatter Plots & Regression Modeling

Lesson 2 illustrates direct comparisons of two measurement systems using scatter plots with a 45-degree line.  Bias/Differences between the measurement systems may be easily observed based on the location of the paired observations vs. the 45-degree line.  Linearity may be assessed by comparing the 45-degree line to a fitted regression line.  We discuss how a value observed on one measurement system may be used to predict the value that would be observed on the other measurement system.  It may be a constant adjustment or variable adjustment depending on whether the bias/offset depends on the measured value. Prediction intervals are introduced which may be used to quantify the uncertainty in predicted values.

Module 7 Comparing Measurement Systems

Lesson M7-01

Duration: 25 minutes

Methods for Comparing Variability & Accuracy

The first lesson introduces some simple methods for comparing the variability and accuracy of multiple measurement systems.  The use of individual value plots to compare variation and averages is introduced.  Additionally, basic hypothesis tests for comparing variation and averages are described.

Module 7 Comparing Measurement Systems

Lesson M7-00

Duration: 4 minutes

Comparing Measurement Systems Module Introduction

Module 7 covers methods for comparing multiple systems.  Methods for comparing both accuracy and precision are presented.  Emphasis is on the use of graphical and modeling methods.

Module 6 Attribute Systems Assessment

Lesson M6-06

Text: Section 5

Duration: 11 minutes

Attribute Agreement Analysis Exercises

In the final lesson, you have a chance to practice analyzing data from an attribute agreement study.

Downloads

Exercise 1-4.11.MTW

Exercise 1-4.11.xls (Excel .xls format)