Lean Mfg and Quality

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


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

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


Multiple explanations for Toyotas success:

Elimination of waste

Using specific tools for production (SMED,

Poka Yoke)

Design Rules

TPS

Spear and Bowen article


(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


chart)


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

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

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

Lean Mfg and Quality

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