Lean Manufacturing
Minus-Cost Principle
Cost + Profit = Selling Price
Toyota Production System
(Lean Manufacturing)
Taiichi Ohno (1912-1990), Toyota
executive pioneered the concept
To do more and more with less and less
Less human effort
Less equipment
Less time
Less space
Less capital
Lean Production
(Levels of Abstraction)
Lean production has been described at three levels
1. Philosophical perspective A. Elimination of waste (Womack and
Jones,1996)
2. Implementation of tools and techniques
3. System design using three rules
What is Waste?
Waste is defined as any activity that does
not add value to a product from
customers perspective
Fujio Cho, Toyotas president defined
Anything other than the minimum amount of equipment, materials, parts, and workers
essential for production
Seven Categories of Muda
Muda means Waste
1. Overproduction
2. Unnecessary Inventory
3. Transportation
4. Over Processing
5. Waiting
6. Unnecessary motion
7. Product Defects
Overproduction
Unnecessary Inventory
The Rocks in the Stream Concept
Production Problems
Lowering the Water Level
Production Problems
Inventory
Reductions
Transportation
Waiting
Unnecessary Motion
Overprocessing
Defects
How Time is Spent by a Typical Part in a
Batch Production Machine Shop
Time on M/c
5%
Moving and Waiting Time in factory
Time on m/c
30% 70%
Cutting Loading, positioning, gauging
95%
Class Exercise
Students identify waste in their
organization
Wastes in IT Sector
How long did you wait to start a scheduled
meeting?
How many reports you created that nobody
read?
How much time you waited for a decision from
your superior?
How much rework you did while developing the
software?
Activities
Value-added
Makes a product more complete
Non-value-added
Does not add value in the customers eyes and customer unwilling to pay
Required non-value-added
Value-added Activity
An activity that makes a product a more complete product, in the eyes of the customer
The value is defined from customers point of view
End result is the receipt of cash for our actions
Required Non-Value Added
Activity
Activity for which the customer is likely to
pay
We can change and improve the method
of performing these activities
Non-Value Added Activity
The activity that consumes time and resources but does not advance the product to a more complete or finished state. Adds no value in the customers eyes and that customer is unwilling to pay for
Seven categories of waste Overproduction, unnecessary motion, transport,
process, waiting, unnecessary motion
Basic Words
Seven forms of waste composed of non-
value added activities, add cost
The value added activities, generate
revenues
Conversion Time
Value-added
Start of production
for a single item
Components of
Lead Time
Nonvalue-added
Wait Time Move Time Down Time
End of production
for a single item
Total Lead Time
Relationship Between Setup
Times and Lead Times
Long
Setup
Times
Large
Batch
Sizes
Large
Inventory
Longer Lead Times
Value Stream Mapping
Process mapping tool that enables all
stakeholders of an organization to
visualize and understand a process
To differentiate value from waste
Eliminate waste
Chinese Proverb: One picture is worth ten thousand words
Value Stream Map
Walking and drawing the processing steps
(material and information) for one
product family from door to door in your
plant
Value Stream Mapping
To maximize value and eliminate waste
1. Form inter-disciplinary team
2. Mapping the current key process how it actually operates
Identify value added and non-value added activities
Eliminate non value added activities using Kaizen (continuous incremental improvement)
3. Develop future state value stream map
Takt Time
Available work time
per day
Takt Time = --------------------------------
Customer demand rate
per day
TPS Terminologies
Ways to Eliminate NVAs
Rearranging sequence
Consolidating process steps
Changing work methods
Change type of equipment
Redesigning forms and documents
Improving operator training
Eliminate unnecessary steps
Toyota Production System
Multiple explanations for Toyotas success:
Elimination of waste
Using specific tools for production
Design Rules
Lean Tools and Techniques
1. Pull Systems
2. Cellular Layout
3. Uniform Plant Loading (Heijunka)
4. Small lot sizes
5. Minimized set-up times
6. Kanban Systems
7. Quality at source (Poka-Yoke)
8. Flexible Resource
9. Total Productive maintenance
10. 5S
1. Traditional Production
1. Continuous Flow
(One-piece flow)
1. Traditional vs. Lean
Approach
1. Pull vs. Push (Traditional)
Pull Method: A method where customer demand activates the production of service or item. Work releases are authorised
Push Method: A push method where the production of the item begins in advance of customer needs. Work releases are scheduled
1. Pull Systems
Push vs. Pull
Show Video
VTS_02_1.VOB
2. Cellular Layouts
Cells group dissimilar machines to process
parts with similar shapes or processing
requirements
2. Cellular Layout
Process (Functional) Layout Group (Cellular) Layout
Similar resources placed
together
Resources to produce similar
products placed together
T T T
M M M T
M
SG CG CG
SG
D D D
D
T T T CG CG
T T T SG SG
M M D D D
M M D D D
A cluster
or cell
3. Uniform Plant Loading
(Heijunka) Mixed model production
LEVELLED PRODUCTION
Levelled production means producing various models on the same production line to cater
the customer demand. See the following diagram. The various products are shown in the
form of different geometrical shapes. Assume they are different models of vehicles being
produced on the same production line.
Production leveling is done by finding the ratio of demand of various models. Instead of
producing batches of the same model, mix models are produced on the same production
line according to the ratio of their demand in the market.
This is how customers do not have to wait for long and throughout the month all the
customers are served equally well
Uniform Plant Loading (Maruti)
Tata Motors Plant
4. Small Lots
Use lot sizes as small as possible
Advantages
Average level of inventory less
Pass through the system faster
Quality problems are detected fast
Easier to schedule
Disadvantage
Multiple set ups
5. Minimized Set up Times
Small lot sizes to make mixed models Japanese workers: 800 T, time: 10 mins
US workers time: 6 Hrs
German workers time: 4 Hrs
Set Ups Internal (Done when m/c is stopped); disruptive
External (Done when m/c is running)
Convert internal to external set ups
Abolish the setup itself (uniform product design)
Single Minute Exchange of Dies (SMED)
Video
Internal and External set up
VTS_02_2.VOB
6. Kanban System
Kanban post
7. Quality at Source
Emphasis on eliminating defects at their origination points
Workers act as inspectors
Jidoka (the authority of the workers to stop the line if quality problems encountered)
Andons or call lights
Each worker given access to andons to seek help.
Visual control of quality
Poka-Yoke
Are either warnings that signal existence of a problem or controls that stop production until the problem is resolved
Minimize human errors
http://facultyweb.berry.edu/jgrout/everyday.html
Errors in Service
Service Error
Server Errors Customer Errors
(67%) (33%)
Classifying Service Poka-Yokes
Server Errors
Task:
Doing work incorrectly
Treatment:
Failure to listen to
customer
Tangible:
Errors in physical
elements of service (dirty
waiting rooms, unclear
bills)
Customer Errors Preparation:
Failure to bring necessary materials before the encounter
Encounter:
Failure to follow system flow
Resolution:
Failure to signal service failure
Failure to execute post encounter actions
8. Flexible Resource
Multifunctional workers
General purpose machines
9. Total Productive Maintenance
(TPM)
Eliminating causes of machine failure
Maximizing effectiveness of machine throughout
its entire life
Involving everyone in all departments and at all
levels
TPM develops a maintenance system
Central to TPM is the concept of Overall
Equipment Effectiveness (OEE)
Overall Equipment
Effectiveness(OEE)
OEE = Availability Rate * Performance Rate * Quality Rate
OEE captures six big losses which result
in reduced effectiveness of using an
equipment
OEE
Availability: % of scheduled time that the
operation is available to operate. Often
referred to as Uptime.
Performance: Speed at which the work
centre runs as a % of designed speed
Quality: Good units produced as a % of
total units produced
OEE
Can be applied to any individual work
centre or rolled up to department or plant
levels
OEE
10. 5 Elements of 5S
1. Sort: Remove all unnecessary material and equipment
2. Straighten: Make it obvious where things belong
3. Shine: Clean everything, inside and out
4. Standardize: Establish policies and procedures to ensure 5S
5. Sustain: Training, daily activities
Note: Some add 6thS for safety
4S: Place for Cleaning Supplies
4S: Equipment Storage Area
4S: Peg Board for Tools
4S: Hazardous Waste
Measuring and Tracking 5S
Toyota Production System
Multiple explanations for Toyotas success:
Elimination of waste
Using specific tools for production (SMED,
Poka Yoke)
Design Rules
TPS
Spear and Bowen article
Toyota Production System
(Design Rules)
Design Rules to design work processes
Activity
Connections
Pathways
Rule 1: Activity
All work shall be highly specified as to
Content
Sequence
Timing
Outcome
Specified Tasks
Rule 2: Connections
Every customer-supplier connection must
be direct and there must be an
unambiguous yes-or-no way to send
requests and receive responses
Streamlined communication
Rule 3: Pathways
The pathway for every product and service
must be simple and direct
Simple process architecture
Rule 4: Scientific Problem Solving
Any improvement must be made in
accordance with the scientific method,
under the guidance of a teacher, at the
lowest possible level in the organization
Hypothesis-driven problem solving
What usually happens
Time
Impro
vem
ent
Actual
If worked to the
new standards
Innovate
Innovate
Adapted from: Imai, Kaizen
Continuous Improvement
A3 Problem Solving: The Toyota Way
5 Whys Approach
A workstation starved for work
Why starved? A pump failed
Why pump failed? It ran out of
lubricant
Why it ran out of lubricant? A leaky
gasket not detected
Why leaky gasket not detected? Lack of training
Quality Management and SPC
What is Quality?
Degree to which performance of a product or
service meets or exceeds customer
expectations
Performance - Expectations>0, performance has
exceeded customer expectations
Performance = Expectations, expectations have
been met
Performance Expectations
Performance and Conformance
Quality Different kinds of quality
Performance quality
Refers to the ability of the product to excel along one
or more performance dimensions (attributes)
Conformance quality
Because of inherent variability in production
processes, nothing is produced exactly according to
specifications. The degree of match between
specifications and the actual product or service is
what we call as conformance quality
Quality Article
Competing on eight dimensions of quality (Harvard Business Review, Nov-Dec
1987) by David Garvin
Dimensions of Quality:
Manufactured Products
Product quality is often judged on eight dimensions:
Performance primary product characteristics Features secondary characteristics that supplements the
primary characteristics
Reliability How often does the product fail? Consistency of performance
Conformance to standards meeting design specifications Durability How long the product lasts; its life span before
replacement
Serviceability ease of repair, speed of repair Aesthetics sensory characteristics (sound, feel, look) Perceived Quality past performance, reputation, recognition
Article Key Points
Eight dimensions of quality
Companies need not pursue all eight
dimensions
If pursued, products become costly
Companies need to find what dimensions
customers care for and work on those
dimensions
Proper market research is key
Some Quality Issues in Recent
Times Indian companies
Safety features in Indian made passenger
vehicles
Five Indian made hatchbacks failed in New
Car Assessment Program (NCAP) Test
Banning of Indian drugs in US for some Indian
pharmaceutical companies for poor
manufacturing practices
Ranbaxy, Wockhardt, RPG Life Sciences and
many
Quality Gurus
Walter Shewart
W. Edward Deming
Joseph Juran
Armand V. Feigenbaum
Philip Crosby
Kaoru Ishikawa
Taguchi
Key Contributors to Quality
Management Shewart Control Charts
Deming 14 points, special vs. common cause
variation
Juran Quality is fitness-for-use
Feigenbaum Customer defines quality
Crosby Quality is free, zero defects
Ishikawa Cause-and-effect diagrams
Taguchi Taguchi loss function
Ohno and Shingo Continuous improvement
Modern Definition of Quality
Quality is inversely proportional to variability
Reduction of variability is the fundamental idea in quality
control.
Describing Variability
Measures of variability (or spread out)
Range
Variance and the standard deviation
Stem-and-leaf plot
Histogram
Box Plot
Coefficient of variation
Quality Improvement
Quality improvement is the reduction of
variability in processes and products
Describing Variability
Stem-and-leaf display (Graphical display about a data set) Shape
Spread
Central tendency
Box Plot (Graphical Display) Central tendency
Spread or variability
Departure from symmetry
Identification of outliers
Histogram Same as above
Histogram
Coefficient of Variation
Coefficient of Variation, c = /
Where = standard deviation
= mean
If c
Where in the Process to Inspect?
Raw materials and purchased parts
Finished products
Before a costly operation or where
significant value is added to the product
Before an irreversible process
Before a covering process
Note: Inspection is an appraisal activity that compares goods and services to a
standard
How Much to Inspect and How
Often? The amount of inspection can range from no inspection
whatsoever to inspection of each item.
Low cost, high volume items require less inspection
High-cost, low volume items require intensive inspection
Majority of the quality control applications lie somewhere
between the two
Most require some inspection, but it is neither possible nor
economically feasible to examine every part of a product or
every aspect of a service for control purposes
As a rule, operations with high proportion of human involvement necessitate
more inspection than mechanical operations
Costs of Quality
Prevention Costs (costs associated with tasks intended to prevent defects from occurring)
Quality Planning (developing & implementing quality management program)
Process monitoring
Training
Purchasing better equipment that produces less variation
Working with vendors to increase the quality of input materials
Process redesign to reduce errors
Quality data acquisition and analysis
Quality improvement projects
Costs of Quality
Appraisal Costs (assessing the condition of materials and processes at various points in process)
Inspection and testing of incoming materials
Product inspection and test at various stages
Maintaining accuracy of test equipment (calibration)
Laboratory testing
Costs of quality estimated to be between 15%-20% of sales at most companies
Crosby
Cost of Quality
Internal failure costs (defects discovered before shipment)
Scrap
Rework
Process downtime
Retest
Failure analysis
Disposition
External failure costs (defects discovered after shipment)
Customer complaint
Warranty charges
Liability costs
Returned product/material
External and internal failure costs together accounted for 50%-80% of COQ
Juran
The Costs of Quality
Cost of Quality
Quality Cost Trend Prediction
as a Function of Time
Reporting Quality Costs
A motor company produces small motors for use
in lawn mowers and garden equipment. The
company instituted a quality management program
in 2006 and has recorded the cost data and
accounting measures for four years
An Evaluation of Quality Costs 2006 2007 2008 2009
QUALITY
COSTS
--Prevention $27,000 41,500 74,600 112,300
--Appraisal $155,000 122,500 113,400 107,000
--Internal
failure costs
$386,400 469,200 347,800 219,100
--External
failure Costs
$242,000 196,000 103,500 106,000
TOTAL $810,400 $829,200 $639,300 $544,400
ACCOUNTING
MEASURES
--Sales $4,360,000 4,450,000 5,050,000 5,190,000
--Mfg. costs $1,760,000 1,810,000 1,880,000 1,890,000
Quality Index Number Year Quality Sales Index Quality Mfg. Cost Index
2006 18.58 46.04
2007 18.63 45.18
2008 12.66 34.00
2009 10.49 28.80
Quality Index = (Total quality costs / Base)100
Cost of Quality
It is estimated that the cost to fix a problem at the customer end is about 5 times the
cost to fix a problem at the design stage
Cost of Quality
Ce + Ci + Ca + Cp Cost of Quality= --------------------------------------
Cb + Ce + Ci + Ca + Cp
Ce = External failure cost
Ci = Internal failure cost
Ca = appraisal cost
Cp = prevention cost
Cb = measured base production cost ( no costs for quality)
Consequences of Poor Quality
Loss of business
Liability
Productivity
Costs
Process Control
Starts with measuring an important
variable. This can be a
Product attribute
Diameter of a metal component, weight of a bag of
potato chip
Process Attribute
Temperature in a restaurants oven, length of waiting time in a ticket booth, pressure applied in a
molding process
Statistical Process Control
(SPC) A statistical process control involves testing a random
sample of output from a process to determine whether the process is producing items within a pre-selected range
SPC uses statistical tools to observe the performance of the production process in order to detect significant variations before they result in the production of a sub-standard article.
SPC is about monitoring consistency and repeatability of a process
Major Objectives of SPC
Quickly detect the occurrence of
assignable causes of process so that
investigation of the process and corrective
action may be undertaken before non-
conforming units are manufactured
Reducing variability in the process
Why Quality Problems?
Variation in output is due to two reasons:
Common Cause or random variation
Assignable or Special cause or controllable
variation
Statistical Process Control Tools
(SPC)
Variation in output is due to:
Common causes (also known as natural variation)
Inherent variation present in every process
Causes may difficult to distinguish or wholly
unidentifiable
Resulting degree of variation is minor
Assignable causes (known as special variation)
Variations due to specific causes
A process subject to assignable variation is out of control
Statistical Process Control
Tools (SPC)
Control (or in control or stable)
A process that exhibits only common cause
variation is said to be in control or stable
A process is said to be out of control when
it exhibits assignable variation
Examples: less experienced worker has
replaced an experienced worker, machine
malfunctioning, change of machine settings
Natural and Assignable Variation
Process Control: Three Types of Process Outputs
Relationship Between Population and Sampling
Distribution
Control Charts
A control chart is a time ordered plot of sample statistics
Sometimes called the voice of the process
Graphical display of a quality characteristics (for example, level of beer in each bottle in a bottling plant)
Distinguish between random and non-random variability
Chart contains a center line and two limits
Upper control limit
Lower control limit
If the process is in control, all sample points will fall between them
As long as points fall within control limits the process is in statistical control
However, any point outside limits investigate the assignable causes
Control Charts
If all the points plot inside the limits, but
behave in a nonrandom manner indication that process is out of control and
needs investigation
A Control Chart
In Statistical Control
A process that is operating with only
chance cause of variation present is said
to be in statistical control
If the process is in control, all the plotted
points should have an essentially random
pattern
Reasons for Popularity of Control
Charts
Proven technique for improving
productivity
Effective in defect prevention
Prevents unnecessary process adjustment
Provides diagnostic information
Provides information about process
capability
Statistical Process Control Tools
Control Charts for variables (Characteristics that are expressed on a
numerical scale: density, weight, diameter, resistance, length, time, volume)
X-bar Chart and R-Chart
X-bar chart for process average
R-chart for process variability
Control Charts for attributes (characteristic that cant be measured on a numerical scale: smell of cologne acceptable or not acceptable, color of a
fabric acceptable or not)
p-chart and c-chart
p-charts for percent defective in a sample
c-charts for counts (e.g. # of defects)
SPC Tools
Control Charts for variables (X-chart, R-Chart)
Variables data are measured on continuous
scale
Length
Width
Weight
Voltage
Viscosity
Amount of time needed to complete a task
Mean Control Chart (x-bar
chart)
A mean control chart or x-bar chart can be
computed in one of the two ways.
Choice depends on what information is
available
If process standard () is known from past experience or historical data
Mean Control Chart (x-bar
chart)
Mean Control Chart (x-bar
chart)
If the process standard deviation is not known, a
second approach is to use the sample range as
a measure of process variability. The
appropriate formulas for control limits are
R-Chart Control Limits
D3, D4 = constants that provide 3 standard deviations (3) limits
for a given sample size
X-bar Chart Limits
A2 = constant to provide three sigma limits for the sample mean
Steps in developing X-bar and R-
chart
Collect data on the variable measured (time, weight, diameter). Collect at least 20-25 samples randomly. Sample size should be of 4 to 5 units.
Compute range for each sample, and average R-bar
Calculate the UCL and LCL
Plot the sample ranges. If all are in control, process is in statistical control
Calculate UCL and LCL for x-bar chart
Plot the sample means. If all are in control, process is in statistical control.
Control Limits are Based on
Sampling Distribution
Zones for Identification of
Nonrandom Pattern
Control Chart Patterns
Control Chart Patterns
Pattern Recognition in Control
Charts
Recognizing non-random patterns on the control
chart
One point plots outside 3 limits
Two or three consecutive points plot beyond
2 limits
Four out of 5 consecutive points plot at a
distance of 1 or beyond from the centre line
Eight consecutive points on one side of centre
line
p-chart
Control charts for attributes
p-chart measures % defective items or proportion defective
items in a sample
Total # defects from all samples
p-bar = ----------------------------------------
# samples Sample size
Appropriate when data consists of two categories of items
Good or bad, pass or fail
Examples: # bad light bulbs and good light bulbs in a given
lot
# of bad glass bottles and good glass bottles
P-chart Limits
c-chart
Appropriate when number of defects are counted because not possible to compute proportion defective
Examples
Number of accidents per day
Number of crimes committed in a month
Blemishes on a desk
Complaints in a day
Typo errors in a chapter of the text book
# customer invoice errors
C-chart Limits
C-bar = average no. of defects per unit = Total number of defects No of samples
Process Capability
Specifications: A range of values imposed by designers
of the product or service based on customer
requirements
Control limits and based on production process, and they
reflect process variability
Process variability: Natural or inherent variability in a
process due to randomness
Process capability: The inherent variability of process
output relative to the variation allowed by the design
specifications
Measures of Process Capability
Measures of Process Capability
Process Capability Ratio
Process Capability Index
Process Capability Ratio
Cp = (Upper Spec Lower Spec) / 6
If Cp < 1, process range > tolerance range
Process not capable of producing within design specifications
If Cp = 1, Tolerance range and process range are same
If Cp > 1, Tolerance range > process range
A desirable situation
Ideally Cp > 1.33
Process Capability
Process Capability
Process Capability
Cp does not take into account where the
process mean is located relative to the
specifications
Cp simply measures the spread of the
specifications relative to the six sigma
spread in the process
Process Capability Index
Generally, if Cp = Cpk, the process is centered at the midpoint of the specifications
When Cpk < Cp, the process is off center
Process Capability
(Sequential Steps)
1. Calculate Cpk to check centrality
2. Calculate Cp to check whether the
process variation are within design
specifications
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