Quality and the Toyota System

1
Topic 9 Quality and the Toyota System

• Quality Costs
• Statistical Process Control
• Six Sigma
• Just in Time Production

2
Philip Crosby

• Former VP of quality control at ITT corp.
• Wrote Quality is Free The Art of Making Quality
  Certain
• Proposed Zero Defects as the goal for quality
• Consider the AQL you would establish on the
  product you buy. Would you accept an automobile
  that you knew in advance was 15 defective? 5?
  1? 1/2? How about nurses that care for newborn
  babies? Would an AQL of 3 on mishandling be too
  rigid?
• Mistakes are caused by lack of knowledge and
  lack of attention

3
Crosbys Quality Postures

• Uncertainty
• We dont know why we have problems with quality
• Awakening
• It is absolutely necessary to always have
  problems with quality
• Enlightenment
• Through management commitment and quality
  improvement we are identifying and resolving our
  problems
• Wisdom
• Defect prevention is a routine part of our
  operation
• Certainty
• We know why we dont have problems with quality

Cost of Quality as a of sales
4
Categories of Quality Costs

• Cost of yield loss
• cost to send your employees to quality training
• warranty costs associated with unplanned product
  repair
• cost of a new automated quality testing device
• cost of rework
• loss of market share due to a national product
  purity scandal
• litigation cost due to product defect

• Prevention costs
• Costs associated with preventing defects
• Appraisal costs
• Costs associated with assessing quality within a
  productive system
• Internal failure costs
• Costs associated with losses from disposal of or
  fixing quality problems
• External failure costs
• Costs associated with releasing poor quality into
  the demand stream

5
Rework / Elimination of Flow Units
Rework Defects can be corrected by same or
other resource Leads to variability
Loss of Flow units Defects can NOT be
corrected Leads to variability To get X units,
we have to start X/y units
6
Calculation of Yield Loss

• B(1-d1)(1-d2)(1-d3)(1-dn) m
• Thus Bm/(1-d1)(1-d2)(1-d3)(1-dn)
• Where
• di proportion of defectives generated by
  operation i
• n number of operations
• m number of finished products
• B raw material started in process

Example 1000 finished product needed from a
flow cell 4 operations generating 2,3,5,3
proportion defective respectively. How many
units must be started in the process?
7
Quality Costs
1086
1119
1142
1031
2
5
3
3
1000
55
33
23
31
8
The Concept of ConsistencyWho is the Better
Target Shooter?
Not just the mean is important, but also the
variance Need to look at the distribution
function
9
Two Types of Causes for Variation
Common Cause Variation (low level)
Common Cause Variation (high level)
Assignable Cause Variation

• Need to measure and reduce common cause
  variation
• Identify assignable cause variation as soon as
  possible

10
W. Edwards Deming

• Quality is first a management responsibility
• There are two keys to ongoing quality improvement
• Employee training
• Reacting to process data in real time
• Variation is the disease and SPC/SQC tools are
  the cure

11
SPC Objectives

• Insure high quality production by reducing and
  controlling process variation.
• Identify types of process variation.
• Common cause variation small, random forces that
  continually act on a process
• Special cause variation that may be assigned to
  abnormal, unpredictable forces
• Take action whenever a process is judged to have
  been influenced by special causes.

12
A General SPC Procedure

• Periodically select from the process a sample of
  items, inspect them, and note the result.
• Because of common or special causes, the results
  of every sample will vary. Determine whether the
  cause of the variation is common or special.
• Take action depending on what was determined in
  step 2.

This procedure is enacted through the use of
control charts
13
Statistical Process Control Control Charts
Process Parameter

• Track process parameter over time - mean -
  percentage defects
• Distinguish between - common cause variation
  (within control limits) - assignable
  cause variation (outside control limits)
• Measure process performance how much common
  cause variation is in the process while the
  process is in control?

Upper Control Limit (UCL)
Center Line
Lower Control Limit (LCL)
Time
14
Charting Continuous Variables

• The Xbar-R Chart tracks the mean and range of a
  variable calculated from a fixed sample
• The Xbar-S Chart tracks the mean and standard
  deviation of a variable calculated from a large
  sample

15
The Xbar-R Chart

• Collect sample data by sub-group (normally
  containing 2 - 5 data points) record the
  continuous variable under study.
• Compute the mean and range for each sub-group
• Calculate average mean and average range
• Compute and draw control limits
• Plot mean and range for each subgroup.

16
Parameters for Creating X-bar Charts
17
Example of an Xbar-R Chart
Each data point is the pulling force applied to a
glass strand before breaking
For 5 obs. D30 D42.114 A20.577
18
Example (cont)
14
Mean
For this example, the control limits reduce to
13
12
Sub-group
3
Range
2
1
Sub-group
19
The Xbar-s Chart

• Similar to Xbar-r chart except that a larger
  sample is taken.
• The calculation of control limits may include a
  sample standard deviation as an estimate of the
  population standard deviation.
• Control limits are calculated

20
The Statistical Meaning of Six Sigma
Process capability measure
Upper Specification Limit (USL)
Lower Specification Limit (LSL)
Process A (with st. dev sA)
x? Cp Pdefect ppm 1? 0.33 0.317 317,000 2? 0.67
0.0455 45,500 3? 1.00 0.0027 2,700 4? 1.33 0.00
01 63 5? 1.67 0.0000006 0,6 6? 2.00 2x10-9 0,00
3?
Process B (with st. dev sB)
21
Control Limits and Specification Limits

• Control limits of a quality characteristic
  represent natural variation in a process
• Specification limits indicate acceptable
  variation set by the customer
• The process capability index is useful in
  comparison
• The capability index may be adjusted to to
  consider how well the process is centered
  within the limits

K2 design target - process average /
specification range
22
Process Capability Example
USL10 LSL9.5 ? .02
9.5
10.0
K2 9.75 - 9.95 / .5 .8
23
PC Example (cont)
USL10 LSL9.5 ? .02
9.5
10.0
K2 9.75 - 9.79 / .5 .16
24
Charting Discrete Attributes

• Charts that track the number of units defective
• P Chart fraction of a sample that is defective
  given different sample sizes
• NP Chart fraction of a sample that is defective
  given constant sample sizes

25
Attribute Based Control Charts The p-chart
Period n defects p

• Estimate average defect percentage
• Estimate Standard Deviation
• Define control limits
• Divide time into - calibration period
  (capability analysis) - conformance analysis
  

0.013
0.091
0.014
26
Attribute Based Control Charts The p-chart
27
Example of a P Chart
Quantities of light bulbs are tested to see if
they function
Note control limits calculated assuming z3
28
Example of a P Chart (cont)
For this example, the control limits reduce to
25
20
Percent Defective
15
UCL
p
10
LCL
5
Sub-group
29
The NP Chart

• Similar to the P Chart except assumes constant
  sample size
• Calculation of the control limits must be
  performed only once

30
Discrete Attributes (cont)

• Charts that track the number of defects in one or
  more units
• U Chart defects in a variable sized sample
  volume
• C Chart defects in a fixed sized sample

31
The U Chart

• Collect sample data for each sample record the
  number of units sampled (n) and the number of
  defects (c)
• Compute the number of defects per unit for each
  sample sub-group (u c/n)
• Calculate the mean defects per unit
• Compute and draw control limits
• Plot u

32
The C Chart

• Similar to the U Chart except assumes constant
  sample size
• Calculation of the control limits must be
  performed only once

33
Example of a C Chart
In this example, a data point represents the
number of rips found in 5 yards of nylon fabric
Note control limits calculated assuming z3
34
Example of a C Chart
For this example, the control limits reduce to
10
UCL
Defectives
5
C
Sub-group
35
We assume the process is in an in control state
when

• Points are within the control limits
• Consecutive groups of points do not take a
  particular form.
• Runs on one side of the central line (7 out of 7,
  10 out of 11, or 12 out of 14)
• Trends of a continued rise or fall of points (7
  out of 7)
• Periodicity or same pattern repeated over equal
  interval
• Hugging the central line (most points within the
  center half of the control zone)
• Hugging the control limits (2 out of 3, 3 out of
  7, or 4 out of 10 points within the outer 1/3
  zone)

36
Statistical Process Control
Capability Analysis
Conformance Analysis
Investigate for Assignable Cause
Eliminate Assignable Cause

• Capability analysis
• What is the currently "inherent" capability of
  my process when it is "in control"?
• Conformance analysis
• SPC charts identify when control has likely been
  lost and assignable cause variation has
  occurred
• Investigate for assignable cause
• Find Root Cause(s) of Potential Loss of
  Statistical Control
• Eliminate or replicate assignable cause
• Need Corrective Action To Move Forward

37
How do you get a Six Sigma Process? Step 1 Do
Things Consistently ISO 9000 can be very
helpful Step 2 Reduce Variability in the
Process Taguchi Even small deviations are
quality losses. It is not enough to look
at Good vs Bad Outcomes. Only
looking at good vs bad wastes
opportunities for learning especially as
failures become rare (closer to six sigma)
you need to learn from the near
misses Step 3 Accommodate Residual Variability
Through Robust Design Double-checking and
Fool-proofing
38
A Systems View of Total Quality Management
MANAGEMENT COMMITMENT LEADERSHIP
CONTINUOUS IMPROVEMENT
PLANNING
CUSTOMER
FOCUS
MGT BY FACT
EMPOWERMENT
ANALYTICAL PROCESS THINKING
EMPLOYEE INVOLVEMENT
TRAINING
39
Toyota Production System

• Pillars
• 1. just-in-time, and
• 2. autonomation, or automation with a human touch
• Practices
• setup reduction (SMED)
• worker training
• vendor relations
• quality control
• foolproofing (baka-yoke)
• many others

40
JIT Implementation

• Adopt goal to eliminate all forms of waste
• Improve workplace cleanliness and order
• Promote flow manufacturing
• Level production requirements
• Improve and standardize all process steps

41
The Seven Zeros

• Zero Defects To avoid delays due to defects.
  (Quality at the source)
• Zero (Excess) Lot Size To avoid waiting
  inventory delays. (Usually stated as a lot size
  of one.)
• Zero Setups To minimize setup delay and
  facilitate small lot sizes.
• Zero Breakdowns To avoid stopping tightly
  coupled line.
• Zero (Excess) Handling To promote flow of parts.
• Zero Lead Time To ensure rapid replenishment of
  parts (very close to the core of the zero
  inventories objective).
• Zero Surging Necessary in system without WIP
  buffers.

42
Cross Training and Plant Layout

• Cross Training
• Adds flexibility to inherently inflexible system
• Allows capacity to float to smooth flow
• Reduces boredom
• Fosters appreciation for overall picture
• Increase potential for idea generation

43

• Plant Layout
• Promote flow with little WIP
• Facilitate workers staffing multiple machines
• U-shaped cells
• Maximum visibility
• Minimum walking
• Flexible in number of workers
• Facilitates monitoring of work entering and
  leaving cell
• Workers can conveniently cooperate to smooth flow
  and address problems

44
U-Shaped Manufacturing Cell
45
Kanban

• Definition A kanban is a sign-board or card in
  Japanese and is the name of the flow control
  system developed by Toyota.
• Role
• Kanban is a tool for realizing just-in-time.
  For this tool to work fairly well, the production
  process must be managed to flow as much as
  possible. This is really the basic condition.
  Other important conditions are leveling
  production as much as possible and always working
  in accordance with standard work methods.
• Ohno 1988
• Push vs. Pull Kanban is a pull system
• Push systems schedule releases
• Pull systems authorize releases

46
One-Card Kanban
Outbound stockpoint
Outbound stockpoint
Completed parts with cards enter outbound
stockpoint.
Production cards
When stock is removed, place production card in
hold box.
Production card authorizes start of work.
47
The Lessons of JIT

• The production environment itself is a control
• Operational details matter strategically
• Controlling WIP is important
• Speed and flexibility are important assets
• Quality can come first
• Continual improvement is a condition for survival