ASQ RRD courses on RAMS 2019

The Role of Reliability in Risk-Based Decision-Making

This 8-hour course answers the questions:

  1. What is Risk & Risk Management?
  2. What is the connection between Risk Management and Reliability?
  3. What type of data do I need for making a Decision under Risk?
  4. What’s the difference between Quantitative and Qualitative tools in deciding Risk?
  5. What should I expect from Managers & Customers when I present my analysis?

ISO 9001:2015 is a risk-based standard. In addition to the Quality Systems Management that must recognize risk and opportunities in all aspects of a business (Sections 4.1 & 4.2 of ISO9001-2015); Section 6 states that the organization shall “determine risks and opportunities that need to be addressed.” Thus we have arrived at the need for Risk-Based thinking and Risk Management.

But since there are typically too many risks, and not enough money to address all of them, how and what do we do? First you have to set a Risk “goal” (in terms of Reliability & –possibly Safety—depending on the product). Allocate this top-level Risk goal among the sub-systems (and lower if  that makes sense). This will set the Design Reliability (& Safety) goals.

Of the Risk Management processes this presentation will concentrate in the areas of

  • Qualitative risk analysis
  • Quantitative risk analysis

Examples using various Reliability & Statistical tools (FMEA, Weibull Analysis, Monte-Carlo Simulation, and others) will illustrate “calculating” risk and how to prioritize risks against a “standard.”… even when your data is sparse (or possibly non-existent).

In addition, you’ll see some methods to help in telling the Boss bad news: “We can’t do this project in the time frame (a budget – time risk to the company) as quickly as you want.”

While a knowledge of some elementary statistics is assumed, the presentation will briefly review Reliability and Statistical concepts before they are used. Also, EXCEL, MINITAB and Crystal Ball for illustrating parts of, or all of, some examples.

http://www.rams.org/the-role-of-reliability-in-risk-based-decision-making 

Communicating Reliability, Risk, and Resiliency to Decision Makers

Communication of concepts related to reliability, risk, and resiliency is frequently cited by technical professionals as the most challenging and overlooked aspects of their work.  Texts and guidance documents frequently reference the importance of better communication and education; however, there are few practical examples and limited practical guidance provided. Getting the boss’s boss to understand remains one of the most elusive aspects of serving as reliability professional.

This workshop will fill many of the gaps between the technical analysis and decision maker.  The workshop will be provided from the perspective of an individual who dually serves on decision-making bodies as well as who also provides reliability, risk, and resiliency analysis to decision makers.

A comprehensive list of references will be provided to provide tools, techniques, and approaches.  These will include communications best practices from a wide range of international sources. It will also include the book on the subject written by the facilitator.  However, the workshop argues that the presentation of reliability and risk information is different than manipulative practices frequently championed by marketing and political professionals.  Reliability and risk professionals must be able to truthfully, ethically, and effectively communicate what the data and analysis is concluding, and at the same time avoid being demoted or terminated.  The role of reliability and risk professionals as trusted advisors to executive management who does not understand, or does not care to understand, is indeed a tricky balance.

The learning objectives include:

  1. Practical approaches for communicating reliability, risk, and resiliency to subordinates, peers, senior management, and decision makers
  2. Basic understanding definitions of reliability, risk, resiliency, decision making and communications
  3. The major types of decisions and how communication approaches change with decision type (strategic, tactical, and rare events)
  4. Personality profiles and their impact on communication and decision making
  5. The role of ethics in communication of technical information
  6. Options and best practices for visual communication of reliability and risk information
  7. Impact of innumeracy, biases, and general population’s ability to understand probability
  8. Tips and best practices for building rapport and verbal communication
  9. Techniques for better communicating reliability, risk, and resiliency information in emails, conference calls, and Q&A sessions
  10. Tips and best practices for communicating to groups that advise decision makers

The interactive workshop will utilize case examples from the facilitator.  Participants will also be asked to bring in a real-word example on which to apply the workshop objectives when they return home.  An audience response system will be used to help solicit input and participation.  A role play will be utilized at the end of the workshop to demonstrate each participant’s improved ability to better communicate reliability, risk, and resiliency to decision makers.

http://www.rams.org/communicating-reliability-risk-and-resiliency-to-decision-maker 

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Essential Competencies for Improving Software Development – Webinar Slides

On Thu, Jul 12, 2018 Linda Westfall gave a webinar on Essential Competencies for Improving Software Development

Essential Competencies for Improving Software Development

Recorded webinar will be uploaded later.

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Introduction to “R” – Basic manipulations – Webinar Slides

On Thu, Aug 2, 2018, Helen Rogers gave a webinar to learn the basics of R – a free and powerful statistical language and computing software.

In this webinar we will cover how to install R on a personal computer, how to import data from Excel, how to find and access packages for R, and basic R syntax.
The goal of this webinar is to provide a path to start exploring R and its potential for basic data work.

ASQ-RRD-R-Basics

Recorded webinar will be uploaded later.

 

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ASQ RD Webinar Series – Software Requirements: Who are your Stakeholders?

Thu, Aug 9, 2018 12:00 PM – 1:00 PM EDT

By: Linda Westfall

Before you can effectively elicit, analyze and validate your requirements information, You must identify and involve your relevant stakeholders. identifying a complete list of product, stakeholders keeps requirements from being missed, and provides access to a broader experience base and more extensive domain knowledge. This webinar demonstrates how to identify a more complete list of stakeholders. The webinar also discusses determining who your key stakeholders are and how to define a stakeholder participation strategy for each of those key stakeholders.
Picture © B. Poncelet https://bennyponcelet.wordpress.com
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The Seven Reasons for Non-Linear Weibull Behavior

1. The Bath Tub Curve
A sample in question may be exhibiting one or more of the stages of the “bath tub curve” while it operates on test. This situation applies equally well to electronic or mechanical components or systems. A variety of failure modes are typically exhibited through the life history. The early life failure stage might exhibit a Weibull slope (Beta) of 0.6, followed by a mid-life stage with a slope of about 1.0. The wear phase has a Weibull slope typically greater than 1.5 and sometimes as high as 5.0. Some of the modes associated with early life failures, or infant mortality type failures, but other failure modes are usually associated with middle-life or end-of-life failures, the data can’t be linear. Further work and investigation is typically required to verify this situation, once a non-linear situation is recognized.
2. A Mixed Population
A sample in question may have been drawn from more than one sub-populations. These sub-populations often have distinct failure modes that are exhibited on Weibull graph as an “S-shaped curve” on the Weibull graph. Not all of the “S curves” may be visible due to sample size restrictions or even short test times. An example of the S curve follows.
3. Varying Environmental Conditions
This possibility may occur when test units or field systems operate in different environmental conditions. This is normally inadvertent or accidental and is often not discovered until data is plotted upon the Weibull graph and questions asked. The situation leads typically to a bimodal or multimodal result on the Weibull graph.
4. Mixed-Age Parts
Since most parts and systems do not carry a time clock, we cannot easily tell how old or how aged a part or system may be just by looking at it or putting it on test. Imagine for a moment a collection of aged (already have operated about 500 hours) mixed with new parts and all placed on life test or in operation. The test results would probably look a lot like a mixed population Weibull graph. The difference here is that the age difference is the main reason for differing sub-populations.
5. Three Parameter Weibull
Some parts (or systems) seem to have a natural bias concerning time-to failure. Examples include many material strength situations, car tires, or telephone poles. This situation leads to non-straight lines because of the natural offset present. Once this offset is recognized and corrected by a software program, the curves often straighten out into one smooth line.
6. Odd Distributions
Some distributions do not appear as straight lines on a Weibull graph. The most prominent example is the LogNormal distribution, which often appears as two straight lines which join at or very near 50% cumulative failures. Test this possibility by plotting data on Lognormal plot or another distribution.
7. Mixed Failure Modes
Very different failure modes, if operational during a test time or study period, may lead to unusual lines on the Weibull graph. Each line is usually associated with dominant failure mode. When modes are separated, as is customarily done, each failure mode usually appears as a straight line.

Published in Practical Weibull Analysis Techniques – Fifth Edition by James A. McLinn Published by  The Reliability Division of ASQ – January 2010 ISBN 0277-9633 (available as free download for ASQ Reliability Division Members)

“The Eight Reasons for Non-Linear Weibull Behavior”

 

Picture © B. Poncelet https://bennyponcelet.wordpress.com

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TECH SPOT: SAMPLE CRE QUESTIONS (Part 2)

 

1. The predicted reliability is higher than the long term realized probability. Which of the following is the MOST likely cause of this difference ?

A. Deterioration of the manufacturing processes and procedures.

B. Lack of adequate employee training and process audits.

C. The accumulation of random process variations.

D. A poor initial estimation of reliability.

 

2. In a corrective action system, trend analysis can be defined as :

I. Long-term movement. III. A cyclical component of a time series.

II. The short term status of problems. IV.. Seasonal variations.

A. I only

B. II and III only

C. I, II and III only

D. I, II, III and IV

 

3. The MOST valid source of failure rate data is :

A. Test data obtained under very closely controlled conditions.

B. Environmental test data.

C. The manufacturing process.

D. Operational data.

 

4. A comprehensive failure analysis and corrective action feedback loop must determine :

I. What failedII. How it failedIII. Why it failed.

A. I only

B. I and II only

C. II and III only

D. I, II and III

 

5. What is this system’s reliability at 700 hours?

Where component failure data is :

– Failure rate of A = 0.0007 failures/hrReliability of B = 0.92

– MTTF of C = 1400 hours – Reliability of D = 0.85.

A 0.986

B. 0.998

C. 0.994

D. 0.952

 

6. Given mean-time-to-failure of 200 hours for each of two components, what is the probability of system failure if both components operate in parallel for one hour ?

A. P = 0.010

B. P = 0.005

C. P = 0.001

D. P = 0.000025

 

7. Reliability prediction is :

A. A one time estimation process.

B. A continuous process starting with paper predictions.

C. More important than reliability attained in the field.

D. A popular method as simulation theory.

 

8. Ideally for a FRACAS to be effective, how many failures should be allowed to pass before corrective action is to be undertaken ?

A. First occurrence of a failure mode.

B. Second occurrence of a failure mode.

C. Third occurrence of a failure mode.

D. Fourth occurrence of a failure mode.

 

9. All of the following Boolean algebra expression are incorrect EXCEPT ?

A. 1 + 1 = 2

B. 1 – 1 = 1

C. 1 – 0 = 0

D. 1 + 0 = 1

 

10. What is the MOST accurate method to verify that the maintainability requirement of a design is being met ?

A. By analysis of the design.

B. By performing maintainability prediction.

C. By thorough design reviews.

D. By demonstration at the customer’s facility.

 

Picture © B. Poncelet https://bennyponcelet.wordpress.com

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ASQ RD Webinar Series – Introduction to “R” – Basic Manipulations

Thu, Aug 2, 2018 12:00 PM – 1:00 PM EDT

By Helen Rogers

Please join this webinar to learn the basics of R – a free and powerful statistical language and computing software. In this webinar we will cover how to install R on a personal computer, how to import data from Excel, how to find and access packages for R, and basic R syntax. The goal of this webinar is to provide a path to start exploring R and its potential for basic data work.

https://register.gotowebinar.com/register/2704627471495538177

 

 

 

 

 

More info on “R” https://cran.r-project.org/

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RECORDED WEBINAR: NEXT GENERATION SOFTWARE RELIABILITY PREDICTION USING CAUSAL LEARNING by Robert Stoddard

Software reliability practice continues to evolve from a early focus on the modeling of software test failures for reliability estimation to the modeling of pre-test activities and software attributes for reliability prediction.
The speaker believes the next major evolutionary step in software reliability research and practice will come with the application of causal learning.
Causal learning has become a practical and exciting field rooted in matching methods employed long before Ronald Fisher created Designed Experimental methods in the 1930s and 1940s.
This webinar will share the recently matured landscape of causal learning consisting of causal discovery and causal estimation.
A brief description of causal methods, algorithms and modern publications will be shared along with recommendations on how reliability engineers might pursue learning and adopting causal learning.

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Recorded webinar: BASIC QUANTITATIVE RMA FOR THE PRACTITIONER by Tim Adams

RMA stands for Reliability, Maintainability and Availability.

This presentation targets the practitioner working basic quantitative Reliability, Maintainability, and Availability (RMA).
The presentation’s sequence is:
1. RMA concepts are described, and five central questions in RMA are stated to describe basic competencies in probabilistic RMA.
2. Each central question is illustrated with an example, and each example is worked in Microsoft Excel.
3. Three of the questions and associated examples pertain to forecasting reliability for the following scenarios:
a. New item with no downtime
b. Used item with no downtime
c. New item with scheduled downtime for idealized preventive maintenance.
4. The three mentioned reliability cases are compared as a means to summarize principles pertaining to when break-in and preventive maintenance provide a benefit to the reliability measure.
5. A process that transforms data to a math model for reliability and maintainability is described.
6. Sources for life data and tips for making a data collection program are summarized.

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Reliability analysis using Reliability Block Diagram (RBD) – WEBINAR SLIDES

On April 12, 2018 Frank Thede presented “Reliability analysis using Reliability Block Diagram (RBD)”
Below a link with the slides of this webinar.

Frank Thede brings 20+ years experience in all aspects of asset management, from capital project management, maintenance management and reliability improvement, to the end of life replacement programs. Frank is the Principal Reliability Engineering at Reliability Works. He has worked on a large variety of projects in Power Generation and Transmission, Oil and Gas, Aluminum, Marine, Transportation and Telecommunications. Frank’s extensive background in electrical engineering, combined with his specialization in reliability and maintenance management provides him with the necessary skill set and experience to effectively manage any group of physical assets.

Reliability analysis using Reliability Block Diagram (RBD).

The recorded webinar will be released later.

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