Leading in a Competitive Environment:  Integrating Statistical Engineering, Lean, and Quality
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Leading in a Competitive Environment:  Integrating Statistical Engineering, Lean, and Quality Bill Rodebaugh Honeywell, Inc. Discussion Points. Need for Leadership Multiple and Maximized Objectives Management Systems and Continuous Improvement Activities Tools to Lead to the Goal.

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Leading in a Competitive Environment:  Integrating Statistical Engineering, Lean, and QualityBill RodebaughHoneywell, Inc.


Discussion Points Statistical Engineering, Lean, and Quality

  • Need for Leadership

  • Multiple and Maximized Objectives

  • Management Systems and Continuous Improvement Activities

  • Tools to Lead to the Goal


The Big Issue Statistical Engineering, Lean, and Quality

  • Excellent Summary from Suárez-Barraza, Bou, and Cataldo. 2007

    • Organizations need to cope with strong competition and a dynamic market environment with demanding customers

    • Organizations are required to be innovative…to capitalize on the knowledge and skills of employees

    • Work processes need to be managed efficiently and strictly

    • Three questions assist in understanding the challenges

      • How do organizations achieve competitive advantage?

      • Why do some organizations respond better to environmental change?

      • How can organizations be flexible, predictable, innovative, and efficient?


Competing Priorities Statistical Engineering, Lean, and Quality

  • Corporate Level – Organizations need to cope with strong competition and a dynamic market environment with demanding customers

  • Factory Level – Requirement to make the right product at the right time with good cost structure and in keeping with all Environmental and Safety considerations while having an engaged workforce

  • Project Level – Complete each project in the required timeframe using the preferred processes…assuming that optimizing the variable of choice will make the overall process better


Competing Priorities Statistical Engineering, Lean, and Quality

Health, Safety,

And Environmental

Production Rates

At Rate,

High CpK,

No Incidents

Product Quality &

Customer Satisfaction


Defining the Journey Statistical Engineering, Lean, and Quality

  • Understanding organizational routines, standards, and non-routines

    • Organizational routines can be defined as “a repetitive, recognizable pattern of interdependent action, involving multiple actors” (Feldman, 2003). There is a sense of stability here.

    • “Standards are applied to activities that are repetitive in an identical fashion” (Takeyuki, 1995). The business process is where these standards exist….SOP. Follow the SOP meticulously yields results.

    • If the business process is variable, then the process difference must be viewed and understood, and specific procedures are activated.

    • Non-routine processes has vague input and are difficult to classify, so this is where non-predictable, surprising, and unfamiliar events exist.


Defining the Journey Statistical Engineering, Lean, and Quality

STANDARDS

SOP

ROUTINES

5S, ISO,

Six Sigma

NON-ROUTINES

Experimentation

Observation


The Tools – the Standard Statistical Engineering, Lean, and Quality

  • Our Standards

    • Name our repetitive processes

    • Understand the way these processes should be done

    • Develop the standard

    • Train the standard

    • Chronicle the standard (SOP)

  • We can develop standard plans as well

    • Business Continuity

    • Disaster Recovery

    • Emergency Responses

    • Any return to standard


The Tools – the Routines Statistical Engineering, Lean, and Quality

  • Our Routines – Systems that Guide Us

    • Toyota Production System, TPS, for Honeywell, HOS

    • 5S

    • Lean / Six Sigma (CI)

    • Statistical Engineering

    • ISO

    • RCA (Back to Standard)

    • Horizon Planning

    • Strategy Deployment (X-Matrix)

    • SWOT


The Tools – the Routines – Where I Am Statistical Engineering, Lean, and Quality

  • Need to do three things

    • Bring back to Standard – TER / UOR

      • Total Event Report / Unusual Occurrence Report, RCA)

    • Improve from Standard (Short Term) – Statistical Engineering

    • Improve from Standard (Long Term) – Horizon / X-Matrix / SWOT


TER / UOR Statistical Engineering, Lean, and Quality

HES

Steering

Action

Mgmt

Process

Issue or

Upset

Closeout

/Audit

UOR / TOPS

Review

COMMS

TER

Written

(by multiple peoplemostly supervisors)

Completed

Report

TOPS Investigation

Super Gatekeepers

0930

TER

Meeting

Review

Resolved

At

Mtng?

UOR

Meeting Attendees

PSM / HES

Actions to the TER board


Statistical Engineering Statistical Engineering, Lean, and Quality

  • Continuous Improvement systems

    • Think about variation broadly

    • Engulfs our Lean & Six Sigma tools

      • Still battles with the methods

      • Key is to improve the problem

      • Favorite Tools?

      • More you use a hammer, themore everything looks like a nail


Horizon Assessments Statistical Engineering, Lean, and Quality

  • Forces improvement

  • Horizon 1 is doing what you do well now (SOP)

  • Horizon 2 is building on Horizon 1 in the next few months (opportunity for DFSS)

  • Horizon 3 is years ahead, but you need to prepare now…big time strategy thinking


Strategy Deployment – X-Matrix Statistical Engineering, Lean, and Quality

  • More Detail than the Horizon planning

  • Adds metrics and people

  • Able to do year over year analysis

  • Not easily presented… super complex


Easy to understand evaluation tool Statistical Engineering, Lean, and Quality

Definitions vary by output question

Definitions (Internal/External Focus)

S trengths -- What are the good things about the program?

W eaknesses -- What are the possible issues with the program?

O pportunities -- What benefits result from the application of the program?

T hreats -- What issues arise from the application of this program?

SWOT Analysis

Internal

Internal & External


SWOT Analysis Statistical Engineering, Lean, and Quality

Strengths & Weaknesses Refer to Process Input Factors

Opportunities & Threats Refer to Additional Metrics or Customer Voices


The Non-Routines Statistical Engineering, Lean, and Quality

  • CI Culture

    • Everyone observes how things could be better

    • Need to drive people to want to do it

    • Need to make it in their DNA

  • Safety Culture

    • Observing behaviors

    • Observing risks

  • Escalation

    • When is bad, really bad?


Conclusion Statistical Engineering, Lean, and Quality

  • Need to balance

    • Standards, Routines, and Non-routines

  • Need to understand

    • Events that just happened, Improvements for tomorrow, Improvements for next year, and New Processes for 5 years

  • Need to be able to

    • Bring back to standard, Bring away from standard

  • Need to understand the behaviors that go with this


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