Transcript Document 7163180

Metrics
Define
Measure Analyze Improve Control
LEAN
SIX SIGMA
RD120101
Business and
Process Metrics
Learning Objectives
Measure
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The importance of measures (metrics) in helping processes
to become consistent and stable as well as forming a basis
to improve.
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Strategies for leveraging measures to assist and sustain
the improvement process.
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How measures drive behavior, and the importance of
balancing effectiveness, risk and cost.
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The importance of linking the customer’s requirements to
the operations of the process and associated inputs from
outside the organization.
Module 6,7,8
How do You Know…
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That the Overall Business You Participate In is Healthy?
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Measure
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Now … How Would Other People Know?
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Module 6,7,8
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How do You Know… (con’t)
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That the Process You Perform is Working Properly?
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Now … How Would Other People Know?
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Module 6,7,8
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Measure
Notable Quotables – Importance of Metrics
Measure
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If you don’t know how well you are doing, how do you
know what to keep doing and what to change?
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If you don’t keep score, you are only practicing.
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Be careful what you measure – you might just get it!
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Words without verifiable numbers are simply just opinions.
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What gets measured, gets done!
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In God We Trust – all others bring data!
Metrics tell the world
what you think is important.
Module 6,7,8
Metrics Defined
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Measure
A metric is a verifiable measure stated in either
quantitative terms (’95% inventory accuracy’) or
qualitative terms (‘very high customer satisfaction ratings’
from a recent survey).
Metrics tell the world
Types of metrics include:
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Module 6,7,8
what you think is important.
Organizational metrics (market share, rate of growth, employee
retention rates, net income, etc.)
Product metrics (cost per unit, product profitability or profit
contribution, sales levels, etc.)
Functional metrics (purchasing, fabrication, assembly,
engineering, marketing, etc.)
Activity metrics (time to make one unit at a specific machine,
machine speed, etc.)
Metrics Defined (con’t)
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Metrics provide:
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Measure
Control (over processes, equipment and employee performance)
Reporting (of actual performance relative to expectations)
Communication (of what constitutes value and key success
factors)
Opportunities for Improvement (by showing gaps in
performance)
Expectations (to both our personnel and our customers)
Metrics can be used to display information about:
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Module 6,7,8
Outcomes (outputs after the completion of activities; too late to
adjust this run = lagging indicators), or
Processes (in-process predictions on the chances of attaining
specific goals or objectives; opportunity to adjust this run = leading
indicators).
Metrics tell the world
what you think is important.
Measure
Metrics – Example
Assembly Area 51 Direct Labor Productivity
(DL Productivity = std hrs earned / total hrs paid)
DL Productivity (%)
80%
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Productivity
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Goal
Linear (Productivity)
Measurements
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Measure
Characteristics of a Good Measurement:
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Objective / Quantifiable (everyone knows how to calculate it)
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Non-Conflicting & Relevant (important to doing a good job)
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Achievable & Verifiable (motivational in nature)
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Monitored Frequently / Timely (feedback on recent actions)
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Visible (accessible to all who can make an impact)
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Comprehensible (everyone knows what it means)
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Actionable (information to know what to do now)
Module 6,7,8
What Does HBD Track?
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Measure
At the corporate level (for GMs and the Dublin office), we
track on a monthly performance metrics ‘dashboard’:
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Shipment and Order Levels and Percentage Sales Growth
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Sales and Operating Income Levels per Employee
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Operating Profit Margin (% of Net Sales)
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Inventory Turns
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Cost of Quality (defects, returns, rework, scrap, inspection, etc.)
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On-Time Delivery
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Order-to-Shipment and Manufacturing Lead-Times
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Credit Memos (customer satisfaction issues)
Module 6,7,8
Key Operational Indicators
Measure
 Flows
Avg. Production Lead-time (hours to manufacturing completion)
 Productivity Index (units per man-hour)
 Attainment/Fulfillment of Commitments (deadlines, quantity, on-time
delivery %, fill rate)
 Volume of WIP/Semi-Finished Items (units in process)
 Throughput/Cycle Time (hours from initial order to actual delivery)
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 Materials
and Inventories
Inventory Volumes/Levels and Inventory Turns
 Quantity of Material Needed to Build One Unit of Good Product
 Unavailable Items (stock-outs, shortages, back-orders)
 Inventory Accuracy (perpetual vs. actual; MRP vs. actual on-hand)
 Warehouse Response Times (hours to actual shipment)
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Module 6,7,8
Key Operational Indicators, con’t
 Technical
Measure
Resources
Machinery Availability (capacity, uptime, downtime)
 Yield Level (% good products to total volume)
 Set-up/Changeover Times (time between runs of good product)
 Maintenance Costs
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 Quality
Rework/Scrap/Defect Rate
 Cost of Poor Quality
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 Clients
and Suppliers
Sales/Purchasing Volume
 On-Time Delivery
 Customer Satisfaction Levels
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Module 6,7,8
Key Operational Indicators, con’t
 Employees
Headcount
 Number of Suggestions Proposed/Implemented
 Hours of Training
 Level of Skill Diversity (cross-training)
 Absenteeism
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 Work
Environment
Housekeeping Score (workplace organization)
 Safety Issues
 Work Accidents
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Module 6,7,8
Measure
Key Operational Indicators, con’t
Measure
 Miscellaneous
Automatic Devices on Equipment
 Machines Monitored with Statistical Process Control (SPC)
 Level of Standardization of Components
 Workstations Covered under Predictive Maintenance Schedules
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Module 6,7,8
Key Metrics Design Principles
 Metrics
Measure
should be gathered by the persons doing the work!
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frequency and depth of performance measurement
(and display / feedback) should be:
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Balanced between the desire for action/learning cycles and the level
of effort/cost
Based on how often the key parameters change, using the shortest
practical interval
Highest at the lowest level (where the key drivers are, i.e., at the
operators’ workstations)
 Feedback should be visible to all and easily understandable.
 A picture (or graph) is worth a thousand words!
 Avoid simply posting output reports that are printed by the AS/400.
Module 6,7,8
Key Metrics Design Principles (con’t)
Measure
 Metrics
can be leading or lagging – i.e., they can tell you
about your performance before (leading) or after (lagging)
the fact.
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Module 6,7,8
Leading metrics are preferable, since they can stimulate corrective
action before it is too late. Leading indicators are always preferred.
Unfortunately, it is often difficult to find good leading metrics (inprocess metrics).
Often the most accurate and easily implemented metrics are
lagging metrics (outcomes or output-based measures).
For example, the weather / temperature in Florida orange groves
and concentrate inventory are leading measures; the lagging
measure might be orange juice concentrate prices.
Statistical Process Control (SPC) charts will give a warning to
operators prior to a process going ‘out of control’.
Displaying Metrics
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Metrics should ALWAYS include:
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Measure
Baseline (recent historical performance)
Goal Line (expectations)
Primary Data
Titles and Legends (to improve comprehension, esp. for multiple data sets)
Units of Measure
If possible, the following should also be included on the display:
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Equation for calculated data:
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Explanation of non-common data or data definition:
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Module 6,7,8
Example: (WIP is valued at 1/2 all open work orders at standard cost)
Bulleted (or hand-written) explanations of any non-conforming data points
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Example: (Labor Productivity = hours earned / hours paid)
Example: (Year-end inventory adjustments)
Trend lines for measured data
Takeaways
Measure
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The measures used in any process have to be aligned with
those of other impacted processes and the overall
organization – as well as reinforce the behavior sought.
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Correctly identified and appropriately monitored measures
will enable consistent production and help in finding and
isolating irregularities as they occur.
Module 6,7,8
Metrics
Define
Measure Analyze Improve Control
LEAN
SIX SIGMA
RD112501
Quality – Everybody’s
Job
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In Process Indicators
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Constant Visual Inspection
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Process Variation
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Owning the Process
Learning Objectives
Measure
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Review the purpose of quality indicators within our
processes.
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Become familiar with the quality indicators throughout our
plant.
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Review variation and how it affects our processes.
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Discuss the effect of poor quality in the small run
environment.
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Taking the step beyond.
Module 6,7,8
Quality Indicators
Measure
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In process measurements (leading) which provide the
operator with the information needed to complete their
work correctly.
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Work instructions include process measurements.
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Tools may be used to indicate that the “process” in in
control.
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Visual inspection is also a part of in process measurement.
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The Small Run Environment
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Measure
In the “Small Run” environment:
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Module 6,7,8
We have to be able to react quickly to our customers needs.
Variation is magnified.
There are no airbags.
Set ups need to be correct…the first time.
Each individual has a direct impact on our ability to produce good
product.
Taking the Step Beyond
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Measure
Reporting problems………..
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We can’t fix what we don’t know is broken.
If it doesn’t get fixed…..report it again, and again, and again!
Quality in Process
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Warehouse/Shipping
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Attendance
Accuracy
Last chance to visually inspect our product
Administrative
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Module 6,7,8
Attendance
Work smart
Be open to changing current practices
Support the hourly employees
Measure
Takeaways
Measure
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We have many quality indicators built into our processes.
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Variation is the enemy.
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We can’t improve and out-of-control process.
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The small run environment does not allow for mistakes.
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All employees need to think quality at all times.
Module 6,7,8
Metrics
Define
Understanding Process
Variation
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Variation = The Enemy
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Common Cause vs. Special Cause
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Tampering
Measure Analyze Improve Control
LEAN
SIX SIGMA
‘Variation is the root of all process evil.’
Learning Objectives
Measure
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Recognize that variation in processes can cause great harm
in results and quality
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Understand (at a high level) the difference between
‘common cause’ and ‘special cause’ variation
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Gain the ability to identify and measure variation
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Understand how process tampering can lead to
excessive variation
Module 6,7,8
Variation = The Enemy
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Measure
As a customer, the worst experience I can imagine is being
a casualty of process variation. 'It doesn't seem that bad,'
you may be thinking to yourself…except, just remember
back to the last time you:
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went grocery shopping, only to select the slowest cash register
lane in the entire store at check-out;
received a haircut that was shorter (or longer) than usual, and
definitely not what you asked for;
decided to go clothes shopping, but got stuck with the most
ignorant and rudest salesperson available;
found yourself building with a piece of 2x4 lumber, that was far
from straight;
ordered a meal, was told “it’ll take just a few minutes,” and found
yourself still waiting to eat, more than 40 minutes later.
Can We Tolerate Variation?
Measure
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There will always be some variation present. (ALWAYS!)
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We can tolerate this variation if:
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The process is on target.
The variation is small compared to the process specifications.
The process is stable over time.
We need to recognize that variation should be minimized.
Module 6,7,8
The Consequences of Variation
Look at the two images to the right.
Which picture represents an
accurate process?
Which picture represents a precise
process?
Which is easier to correct?
Module 6,7,8
Measure
Variation
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Measure
The elements of a process responsible for variation are
what have been called the “6M’s,” which adequately
describe the basic elements of all processes:
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Man (People)
Machine (Equipment)
Material
Method
Measurement
Mother Nature (Environment)
Knowing which of these 6M elements
are controllable and consistent can
help eliminate lots of guesswork
when problems arise and need to be
investigated.
Types of Variation
Measure
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Special Cause Variation is not random and changes
over time. It is a local workforce issue and can be handled
on the floor with the proper tools.
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Common Cause Variation is random, stable, and
consistent over time. It is a system issue and is the
responsibility of management. Management owns and
creates the system (i.e., the selection of machines, parts,
product design, process paths, priorities, etc.), and only
management can intervene to change the system.
Why is the distinction between common cause and special
cause variation important to continuous improvement efforts?
Module 6,7,8
Measure
The Signature Exercise
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First, sign your name 5
times.
1.
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2.
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3.
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4.
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5.
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Next, sign your name 5
times using your other
hand.
1.
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2.
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3.
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4.
_________________________
5.
_________________________
Is the variability between sets of signatures
common cause or special cause?
Module 6,7,8
Measure
Special Cause Variation
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Examples of special cause variation (or, ‘assignable
causes’) are:
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Machinery
Use of wrong tool
 Tool wear, material, angle
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X-Bar Chart for Process B
Feed rate
 Operator setup/error
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Office
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Workers out for holiday
UCL=77.27
Sample Mean
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80
LCL=64.70
60
Special cause variations are determined
50
by statistically comparing actual results to
the past variation seen within a process.
(i.e., “outside the limits” noted by the redcolored upper and lower control limits)
Special Causes
Module 6,7,8
X =70.98
70
0
5
10
15
Sample Number
20
25
Measure
Common Cause Variation
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Some examples of common cause variation are:
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Machinery
Vibration of machines or tools
 Temperature or humidity fluctuations
 Variability in material hardness
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Office
Experience of individual workers
 Workers out sick
 Internet server or AS/400 crashes
 AS/400 speed fluctuates
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Common cause variations are seen as actual
results “within the limits” of past variation noted by
the red-colored upper and lower control limits.
Module 6,7,8
X-Bar Chart for Process A
UCL=77.20
Sample Mean
75
X =70.91
70
65
LCL=64.62
0
5
10
15
Sample Number
20
25
Tampering
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“There will always be some variation
present in a process and its results.”
Measure
Tampering, or over-control, occurs when adjustments are
constantly made to a process based upon sampling, individual
measurements, test results, etc.
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Examples: Machine settings, thermostats, line speeds, material
placements, etc.
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Tampering is a form of over-reacting to data.
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One of the most common sources of special cause variation is
tampering. Studies show that total variation can be cut in half
by eliminating tampering (by operators, supervisors, managers).
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This is a huge improvement opportunity available to most
companies. Today! Simple! No-cost!
Train individuals to understand what degree of variation is
acceptable (spec limits) and what is natural (control limits).
Module 6,7,8
What to Do? (con’t)
Measure
First, determine whether the experienced variation is
within the range expected from the system (compare to
control limits) and then whether the results are customeracceptable (compare to specification limits).
 Variation from ‘common-cause’ systems should be left
alone during a production run. Don’t tamper! (Really!!)
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Address common-cause variation “off-line,” with process
improvements. Develop new methods for doing the work daily.
Variation identified as ‘special cause’ (outside of the
expectations from prior experiences) should be identified,
researched and quickly eliminated.
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Module 6,7,8
Look for “what’s different” in the process when special cause
variation appears, and prevent that temporary change from
happening again.
Reading a (Modified) Control Chart:
Is This Good, or Bad?
Measure
Any ideas?
Is this process stable?
Is this process creating scrap?
Control
Limits
I Chart for Length
11.4
UCL=11.34
11.3
Individual Value
11.2
11.1
Mean=11.01
11.0
Spec
Limits
10.9
10.8
10.7
LCL=10.68
(specially
drawn in)
10.6
0
10
20
30
Observation Number
4 units will be scrap
Module 6,7,8
Reacting to Performance Results:
Tampering Worsens Overall Results!
Measure
What happens if we adjusted settings downward by 0.1” after
seeing the first piece of scrap? Even more scrap gets created!
I Chart for Length2
11.3
Control
Limits
UCL=11.27
Individual Value
11.2
11.1
11.0
Mean=10.93
10.9
10.8
(specially
drawn in)
10.7
10.6
LCL=10.59
Adjustment
10.5
0
10
20
Observation Number
Module 6,7,8
Spec
Limits
30
7 (not 4) units will be scrap
Takeaways
Measure
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Variation is the enemy of quality and cost control.
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However, there is always variation present in a process.
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Identify when variation can be tolerated and when it
exceeds tolerable limits (and thus must be addressed).
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Tampering, or over-control, creates more scrap and rework
than if a stable process is left to its own devices.
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Variation must be separated into common cause vs. special
cause to determine when corrective action is truly needed.
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Control Charts help separate the random variation from
assignable causes that must be identified and addressed.
Module 6,7,8