Opportunity Based Thinking; Is this a Paradigm Shift for a Quality - - PowerPoint PPT Presentation

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Opportunity Based Thinking; Is this a Paradigm Shift for a Quality Professional? Angelo Scangas Quality Support Group, Inc. QSG Meeting 03.29.19 1 @ 2019 Agenda Risks & Opportunities - What we learned during ISO 9001:2015?

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“Opportunity Based Thinking; Is this a Paradigm Shift for a Quality Professional?”

Angelo Scangas Quality Support Group, Inc.

QSG Meeting – 03.29.19 1

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Agenda

  • Risks & Opportunities - What we learned

during ISO 9001:2015?

  • Risks and Opportunities
  • Traditional vs Evolving Quality Environment
  • Where can a Quality Professional make an

Input

  • Conclusions

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Risks & Opportunities - What we learned during ISO 9001:2015 Implementation?

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Quality System Planning

Risk & Opportunities Context External Issue Context Internal Issue

(ISO 9001 Clause 6.1) (ISO 9001 Clause 4.1) (ISO 9001 Clause 4.2)

Interested Parties’ Needs

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Quality System Planning(cont.)

Risk & Opportunities

(ISO 9001 Clause 6.1)

Plan: Determine Action Plans to address Risks and Opportunities (Clause 6.1.2) Do: Integrate and implement actions into its quality management system processes (Clause 4.4) Check: Evaluate effectiveness of actions taken (Clause 9.3) Act: Learn from results and reassess Context and Interested Parties (Clause 10.3)

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ISO 9001:2015 Structure

4 Context of the

  • rganization

5 Leadership 10 Improvement 6 Planning for the quality management system

7 Support

8 Operations 9 Performance and evaluation Understanding

  • f the
  • rganization

and its context Understanding the needs and expectations

  • f interested

parties

Scope of management systems QMS Leadership and commitment

Quality policy

Organizational roles, responsibilities and authorities Action to address risk and opportunity Quality

  • bjectives

Planning of changes

Resources Competence Awareness

Communication

Documented Information

Release of products and services Operations planning and control Requirements for products and services Design and development of products and services Control of externally provided products and services Production and service provisions Control of non- conforming

  • utputs,

Monitoring, measurement. analysis and evaluation Internal audit Management review Nonconformity and corrective action Continual Improvement

Plan Do Check Act

General

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What is Risk?

Risk: the effect of uncertainty on an

  • rganization’s ability to meet its objectives

(ISO 31000:2018)

  • r

Risk is the possibility of events or activities impeding the achievement of an organization’s strategic and operational objectives.

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Risk Definitions

Risk can often be defined by two parameters

– Severity

  • This is the Seriousness of the harm

– Probability

  • This is the Likelihood that the harm will occur

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Risk Assessment - Quantitative

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Risk Guidance (ISO 9001)

Options to address risks can include:

  • avoiding risk,
  • taking risk in order to pursue an opportunity,
  • eliminating the risk source,
  • changing the likelihood or consequences,
  • sharing the risk, or retaining risk by informed

decision. Quality Professionals have done a good job guiding businesses in identifying, mitigating and evaluating the effectiveness of actions taken.

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What is Risk?

Risk is the possibility of events or activities

impeding the achievement of an organization’s strategic and operational objectives.

What is Opportunity?

Opportunity is the possibility of events or activities enhancing the achievement of an organization’s strategic and operational objectives.

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Risk Definitions

Risk can often be defined by two parameters

– Severity

  • This is the Seriousness of the harm

– Probability

  • This is the Likelihood that the harm will occur

Opportunity Definitions

Opportunity can often be defined by two parameters

– Benefit

  • This is the Positive Effect of the event

– Probability

  • This is the Likelihood that the event will occur

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Risk & Opportunity

5 5 4 4 3 3 2 2 1 1 5 4 3 2 1 1 2 3 4 5 Risks Opportunities Probability Probability Negative Impact Positive Impact

Increasing Revenue and Profits Best in Class Reputation Positive Customer Satisfaction Growth in Day to Day Operations Employee Security and Rewards Declining Financials and Profits Loss of Reputation Lower Customer Satisfaction Day to Day Operation Disruption Employee Discontent

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Opportunity Guidance (ISO 9001)

Opportunities can lead to:

  • the adoption of new practices,
  • launching new products,
  • opening new markets,
  • addressing new customers,
  • building partnerships,
  • using new technology and
  • other desirable and viable possibilities to

address the organization’s or its customers’ needs.

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Opportunity Guidance (ISO 9001 –cont.)

  • Opportunities for Improvement (Mgmt.

Review -Clause 9.3.3)

  • The organization shall determine and select
  • pportunities for improvement…….

(Improvement – Clause 10.1)

Examples of improvement can include correction, corrective action, continual improvement, breakthrough change, innovation and re-organization

So where will a Quality Professional make an Impact?

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Where Does the Quality Professional Focus?

Traditional Quality (Product & Service)

– By the Numbers – 3P, 5S, 6σ, 8D – By the Colors – White, Yellow, Green, Black – By the Acronyms – JIT, SPC, DOE, DMAIC, etc. – System Approach/Data Driven Decisions/ Continual Improvement/Customer Focus

Future Quality (Product & Service & Data)

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Environmental Scan

Today’s decisions require large quantities of data but…….

  • The right data is not always available
  • The data is not formatted for easy analysis
  • The data is incomplete
  • The data is not current and out dated
  • Tools to make good decisions are not always

available

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Environmental Scan (cont.)

New tools and technologies being developed to improve quality of the data

  • Artificial Intelligence
  • Block Chain
  • Machine Learning
  • Data Analysis Tools
  • Pattern Recognition
  • Predictive Maintenance
  • Others…

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Environmental Scan (cont.)

Bigger data may not be the ultimate solution to decision making, enabling new tools and techniques or improving operational efficiencies Maybe the ultimate solution to small, medium, or large data sets is the “Quality of the Data” How can a Quality Professional demonstrate their value in this new and evolving environments? (Quality 4.0)

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Opportunity

Data Quality

Increasing Revenue and Profits Best in Class Reputation Positive Customer Satisfaction Growth in Day to Day Operations Employee Security and Rewards

How?

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Dilemma & Opportunity

Dilemma:

Large corporations are well on their way to leveraging big data – They have intact IT organizations (Keeper of the Data) – They have Data Analysts and Tool Developers in multiple organizational functions – The Quality Function is not necessarily in the Data/Information Stream

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Dilemma & Opportunity (cont)

Opportunity: Need Leadership to see the need for systems to control data quality – Process Approach/System Thinking – Data Driven Decision Making – Customer Perception and Satisfaction – Continual Improvement – Future - Simple tools and techniques that can be delivered to smaller organizations

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Process

(Major Elements & Boundaries) Start End Process Owners:

Outputs Customers

(for Whom?)

Inputs Suppliers

(By Whom)

Process Approach (SIPOC)

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Process Artificial Intelligence Block Chain Machine Learning Data Analysis Tools Pattern Recognition Predictive Maintenance Outputs Information Customers

(for Whom?)

Inputs Data Suppliers

(By Whom)

Process Approach (Data)

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Monitor & Measure Data Quality Accurate Relevant Timely Complete Format Info Quality Accurate Relevant Timely Complete Format

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Process Artificial Intelligence Block Chain Machine Learning Data Analysis Tools Pattern Recognition Predictive Maintenance Outputs Information Customers

(for Whom?)

Inputs Data Suppliers

(By Whom)

Process Approach (Data)

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Monitor & Measure Data Quality Accurate Relevant Timely Complete Format Info Quality Accurate Relevant Timely Complete Format Process Design/Definition Implement under Controlled Conditions Effective & Efficient & Rapid Adaptability

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Process Artificial Intelligence Block Chain Machine Learning Data Analysis Tools Pattern Recognition Predictive Maintenance Outputs Information Customers

(for Whom?)

Inputs Data Suppliers

(By Whom)

Process Approach (Data)

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Monitor & Measure Data Quality Accurate Relevant Timely Complete Format Info Quality Accurate Relevant Timely Complete Format Customer Satisfaction Customers’ Needs and Expectations have been fulfilled. Transparency with Customers . Rapid Continual Improvement

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Process Artificial Intelligence Block Chain Machine Learning Data Analysis Tools Pattern Recognition Predictive Maintenance Outputs Information Customers

(for Whom?)

Inputs Data Suppliers

(By Whom)

Process Approach (Data)

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Monitor & Measure Data Quality Accurate Relevant Timely Complete Format Info Quality Accurate Relevant Timely Complete Format External Provider Management Conform to Requirements Evaluate, Select, Monitor Performance, and Re-Evaluate

Transparency with Customers .

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Process Artificial Intelligence Block Chain Machine Learning Data Analysis Tools Pattern Recognition Predictive Maintenance Outputs Information Customers

(for Whom?)

Inputs Data Suppliers

(By Whom)

Process Approach (Data)

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Monitor & Measure Data Quality Accurate Relevant Timely Complete Format Info Quality Accurate Relevant Timely Complete Format

New Opportunity Working with functional counterparts lead an effort to improve quality data transformation to information quality for the benefit of all

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Intelligent Machines

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Today- IBM Elevator Commercial

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Predictive Maintenance when elevator calls the repair man

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Today - MIT Cheetah

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Machine Learning/AI Teaching a machine to jump over a hurdle.

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Conclusions

  • Industries in all markets are increasing their

need to make “Data Driven Decisions”

  • Machines are getting smarter and learning.

And

  • The transition from “Data” into “Information” is a

“Process”.

  • The “Opportunity” is for the Quality Function to

work with their organizational counterparts applying traditional tools to the expanding demands of big data and its applications.

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Disclaimer

The author is not advocating that Quality Professionals evolve into Data Analysts unless you want your career take this direction But It might not hurt if you inform yourself with brief tutorials in Artificial Intelligence, Machine Learning, or Analytics

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Questions?

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Thank You

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References

  • ISO 9001:2015 Quality management systems –

Requirements

  • “Quality of the future: Managing the Digital

Disruption to Improve the Analog World”, Willy Vanderbrande, ASQ Quality 4.0 Summit, Dallas TX, November 12, 2018

  • “Let’s get Digital”, Nicole Radziwill, Quality

Progress, October 2018

  • “QUALITY 4.0 IMPACT AND STRATEGY

HANDBOOK”, Dan Jacobs, LNS Research

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References

  • “Good vs. Bad Data – Laying a Foundation

for Successful Analytics, Dr. Nathan Soderberg”, ASQ Quality 4.0 Summit, Dallas TX, November 12, 2018

  • ISO 31000:2018
  • IBM Elevator Commercial Video
  • MIT Leaping Cheetah Video

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