The Intersection of Quality Management and Project Management

Hosted by ASQ Philadelphia and PMI-DVC

Valley Forge Sheraton April 24, 2014

Participants

Moderator: Chris Connors, PhD

Panelists:

-  Robert Bleau, CQE, CQA, CQM/OE -  Jack Merritt, CSSBB, CSSMBB -  Jason Wilds, CSQ -  Jay Armstrong, CSSBB -  John Connors

The Propagation of Risk Management Methods to Other Business Processes

Robert Bleau, CQE, CQA, CQM/OE Quality Assurance Manager

Moog, Inc., Components Group

The Intersection of Quality Management and Project Management

Presentation Overview

Risk Management, Defined:

The purpose of managing risk is to proactively plan for possible events or circumstances that – should they occur – could affect the outcome of a process.

Risk Management entails working to minimize the likelihood and/or impact of negative events (i.e. “risks”) and working to maximize the likelihood of positive events (i.e. “opportunities”).

Typical Uses:

• As a Program Managers tool, to capture the initial risks and opportunities from an overall perspective, and strategize a Risk Management Plan to deal with each Risk and Opportunity

• As a Project Design Managers tool, to identify risks associated with technology challenges in a new design

•  In significant contracts, is often one of many Supplier Data Requirements List (SDRL) items deliverable at Preliminary Design Review, with periodic updates expected

•  It is a “Living Document”

As applies to an Aerospace Quality Management System (QMS)

AS9100 rev C took the subject to a new level, by adding Project Management (7.1.1) and Risk Management (7.1.2) as new required sections under Planning of Product Realization.

7.1.1 “…the organization shall plan and manage product realization in a structured and controlled manner to meet requirements at acceptable risk, within resource and schedule constraints.”

As applies to an Aerospace QMS…

7.1.2 “The organization shall establish, implement and maintain a process for managing risk…that includes…” a)  Assignment of responsibilities b)  Definition of risk criteria c)  Identification, assessment and communication of

risks throughout product realization d)  Identification, implementation and management of

actions to mitigate risks that exceed the defined risk acceptance criteria

e)  Acceptance of risks remaining after implementation of mitigating actions

Also added to AS9100 rev C

Under Customer-Related Processes, Review of Requirements Related to the Product: …”The organization shall…ensure that…” d)  Special requirements of the product are

determined, and e)  Risks (e.g., new technology, short delivery time

frame) have been identified (see 7.1.2)

Under Purchasing, Purchasing Process: ”The organization shall… f)  Determine and manage the risk when selecting

and using suppliers (see 7.1.2)

What the Registrars Went Hunting For

Evidence of formal Risk Management tool use in Customer-Related processes and Purchasing processes.

What we showed them Evidence of training in Risk Management

Evidence of use of formal Risk Management tools in:

•  Contract/Sales Order Review •  Purchasing (Quotation) Decisions

How was the training received??

First reaction: “Yet another thing we have to do”

Later realization: “Why have I been the only one staying awake at night, hoping things will turn out OK? It’s a shared responsibility.”

Examples of Use in Purchasing Requesting Quotations Standard Risk Reduction Techniques:

•  On printed circuit boards, requiring that a layout plot be provided for approval prior to fabrication of boards.

•  Where drawing requires Special Processing, that customer (or NADCAP) approved SP providers be used. Further, heat treat suppliers are to submit hardness readings with each lot.

Examples of Use in Purchasing Requesting Quotations Mandated Use:

•  A formal Risk Management Report must be generated and maintained for any single procurement in excess of $250,000.

Example of Risk Report for Instrument Glass Procurement

Example of Risk Report for Major Contract Review

Summary

• As you revise documentation to implement Risk Management in other processes, remember to keep it simple •  The challenge is to satisfy the requirement in an effective way,

based on your business needs

•  Take the time to sell the idea •  Facilitate the first few attempts at Risk Management

• Publish your successes •  Nothing catches on like a winner

Questions?

•  SAE Aerospace Standard AS9100 http://www.sae.org/technical/standards/AS9100C

RESOURCE:

Quality Processes & Tools – They’re not just for Black Belts

Jack Merritt, CSSBB, CSSMBB Practice Director

Magic Hat Consulting

The Intersection of Quality Management and Project Management

Agenda:

Present some thoughts on how to adjust your perception of the way Business Process Improvement and classic Project Management fit together in a typical business situation.

Deliverables vs. Tools

•  A DELIVERABLE is the output of a process

•  The output may be tangible such as a document or file …

•  … or it may be intangible such as information

•  In order to prove that an intangible was created, there should be some tangible evidence

•  A TOOL may be the means to produce a deliverable …

•  … or it may be the tangible evidence that an intangible deliverable was produced

Components of a Project Charter:

•  Business Case

•  Problem Statement

•  Scope & Goal

•  Guidelines for the Team

•  Roles & Responsibilities of the Players

•  Preliminary Plan

Kaori Ishikawa Created the “Fishbone” Cause & Effect Diagram

He also identified 7 basic quality tools:

–  Checksheets –  Control Charts –  Histogram –  Pareto Diagrams –  Flow Charts –  Run Charts –  Scatter Diagrams

To which Jack Merritt adds: –  Boxplot –  FMEA

Often a root cause is readily apparent using a Failure Mode and Effects Analysis

How to know when to ask for help from a BPI Practitioner

Remember the DARE model?

Utilize the skillsets within the organization to identify root cause

Key Takeaways

•  All the professionals in the organization are working to improve business situations

•  There are more similarities than differences in the methodologies deployed by the different professional groups

•  Cycle time reduction and successful project outcomes are best gained when professionals within an organization work together

•  Key driver of collaboration is understanding that the first step in change management is that I must change

QM Tools & PM Tools: The Sum of the Whole is Greater than the Parts

The Intersection of Quality Management and Project Management

Jason Wilds, CQE Program Manager Lean Six Sigma

Church & Dwight Co. Inc.

3 Disciplines?

QM LSS PM

Structure

• Methodology • DMAIC • DMADV •  I,P,E, M&C, C • PDCA • S.M.

•  Question, Hypothesis, Prediction, Test, Analysis

• Tools • Risk Analysis • Scoping • Stakeholder Analysis •  Team Dynamics

•  N,F, S, P

• Process Maps/Flow Charts

•  7 Quality Tools

QM

Quality Planning

Corrective & Preventative Action

ISO/FDA

Process Optimization

Data/Stats

PM

Scope

Budget/Capital

Timeline

Procurement

Clear Start/Finish

Soft Skills

Facilitating Leading

• with Authority • w/out Authority

• Which One? • QM or PM or LSS

• All 3?

Dream Team

http://sportsillustrated.cnn.com/2012/writers/jack_mccallum/07/25/dream-team-interview/index.html

Decision Analysis – Making better PM decisions

The Intersection of Quality Management and Project Management

Jay Armstrong, CSSBB Director

GlaxoSmithKline

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Introduction – Decision Analysis - DA

•  DA is a rigorous methodology based on probabilities, which facilitates high-quality, rational thinking to deliver clear and positive actions by the decision maker.

•  Given sufficient data/information, DA can recommend an optimal decision AND provide better insights for:

The issue under consideration Amount of uncertainty/risk Ultimate goal(s) of the decision Potential alternatives

This is valuable, structured thinking that can assist project teams in crafting better solutions!

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Why are decisions difficult to make?

•  You are uncertain - Uncontrollable elements will always impact your decision(s). The outcome of decisions can never be certain. You can only control the decision, NOT the outcome.

•  You have choices – You always have at least two alternatives, either of which you could take. If you have no choices, you have no decision to make.

•  Life is complex – Numerous factors to effectively consider, hidden interrelationships between and among factors, not knowing what you don’t know, etc….

•  Time is short – Decisions are usually made under severe time constraints.

The 4 elements of any decision event:

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Good decision making has these traits…..

• Always develop a strong decision frame • Always identify uncertainties in the process and use

factually based probability to describe the uncertainty • Always create viable alternatives • Always avoid conceptual biases, especially “Sunk

costs”

A decision is generally considered to be “good” if you would make the exact same decision again, under the same circumstances, if you were still unaware of the outcome….

“Good” means good for you, given the alternatives/data that YOU have and YOUR preferences….

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How to improve project type decisions…. A formal, more involved process than that used for quick decisions, it

requires these groups working in concert:

+ +

Decision makers

Identify the issue, allocate resources

Understand the solutions required

Make the decision and lead the

implementation

Decision staff

Drive the process

Collect information & data

Develop analysis

Content experts

Define certainties

Implement decision to ensure value

May be the same people

Analysis Team

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Project type decisions – Dialog process1

Corporate Leadership

Decision makers

Decision Staff

Content experts

Identify issue

Make Decision

Confirm alternatives

Confirm frame

Implement Review Alternatives

Create Alternatives

Initial Review

Develop frame

Generate Alternatives

Short list Alternatives

1 – Carl Spetzler (2007), Advances in Decision Analysis pp. 451-468

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Decision Tools

In attribute selection, you must be completely honest with your selections and the weights assigned

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Multi-attribute utility or “Which software to buy?” Tool applies to decision making either under risk or under uncertainty

Suppose a project team is considering a major new software purchase (or developing a new drug, selecting a new manufacturing site…..)

You have narrowed your alternatives down to five software systems:

Alpha, Beta, Gamma, Delta, Epsilon

You picked these attributes as being critical in your decision: Price , Reliability, Training, Reputation, After sale support

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Multi-attribute utility - Which software to buy?”

Attributes Weight Alpha Beta Gamma Delta Epsilon

Actual Calc Actual Calc Actual Calc Actual Calc Actual Calc

Price .8 94 75 98 78 100 80 25 20 10 8

Reliability .6 82 49 94 56 88 53 92 55 96 58

Training .4 88 35 93 37 82 33 92 37 98 39

Reputation .3 88 26 97 29 76 23 90 27 96 29

Support .2 80 16 88 18 82 16 90 18 94 19

TOTALS 201 218 205 157 153

High score wins!

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Decision trees - Introduction

Decision trees use a visual approach to compare alternatives and assign values to alternatives by blending uncertainties, payouts, and costs, into a specific, comprehensible quantity.

Decision trees identify an optimal decision strategy – that strategy which provides the highest Expected Monetary Value (EMV), if we wish to maximize profit and the lowest Expected Monetary Value if we wish to minimize cost.

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Decision trees 1) Decision nodes are square = what we control. Chance nodes are circles = beyond our control 2) Decision trees are evaluated from left to right 3) Only 1 alternative can be chosen after each decision node 4) No more than 1 outcome can happen at once, and 1 outcome WILL

happen 5) Decision trees represent all possible futures and the nodes occur in a logical

time sequence

Do not invest

Invest

Fail

Success

Outcomes To invest or not to invest……

Savings Interest /2%

Total financial loss

Large financial return

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Expected Monetary Value (EMV)

Project decisions and outcomes are almost invariably related to cost issues – reducing costs, reducing risks to investments, saving money

– or time.

Expected monetary value (EMV), is a useful tool in project management, because it incorporates the probability of success (or failure), of choices into the decision. EMV allows us to characterize

decisions as “potential money.”

EMV is the average return we would expect to achieve if we had multiple, identical choice opportunities – it is a probability

weighted average value – or the payoff you would expect over choosing that option many times. It is determined by multiplying each outcome by the probability of it occurring and then adding

those products

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Expected Monetary Value - example

Suppose we decide to gamble on predicting a coin toss with these conditions – If you guess correctly, you will receive $50, an incorrect

guess will cost you $25. What is the EMV?

guess

No guess

right

wrong

0.5

0.5

Outcome

$50

-$25

$0

EMV = ($50 x 0.5) + (-$25 x 0.5) = $12.50 This is an expected average payout over time – but for each trial,

you can only receive either $50 or lose $25.

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Project decision example A project team has been tasked with deciding whether to risk building a new facility or just update the existing one. The cost to build new is $4.5M. If the economy is good (p=0.4), projected profits will be $13M, if bad (p=0.6), the profits will be $3M. Updating the facility will cost $100K and the profits are projected to be $4M in a good economy and $1M in a poor economy. What should they do?

Cost - $4.5M

Cost - $100K

0.4

0.6

0.4

0.6

$13M

$3M

$4M

$1M

EMVbuild = [ .4(13) + .6(3)] – 4.5 = $2.5M EMVdon’t build = [.4(4) + .6(1)] – 0.1 = $2.1M Build the facility!

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Summary – Decision Analysis •  Incorporates Uncertainty – assigns mathematical values to uncertainty •  Manages Complexity – considers different perspectives through a structured

approach •  Evaluates tradeoffs and risks – defines attitudes toward risk and provides

objectives to evaluate alternatives •  Develops a consistent approach – reduces dependence on key individuals,

avoids hunches, crystallizes knowledge •  Creates insights – allows us to make better decisions about future data

gathering •  Software is available

•  Crystal Ball •  DPL Professional •  TreeAge Pro

•  Decision analysis is not a perfect discipline – probabilities are not magically accurate – the final decision is only as reliable as the data used to produce it