THE BUSINESS CASE FOR
IMPLEMENTING MACHINE
VISION
Vision Systems International
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Established in 1984
Consultancy concentrating on machine vision
Services include:
 Training
 Application related:
 Application engineering
 Specification writing
 Vendor identification/evaluation
 Market related
 Market research
 strategic development and planning
 partnering activities
 market analysis/competitive analysis
 due diligence
 Technology transfer
Introduction
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Electronic Imaging
Where is Machine Vision Used
Why Machine Vision Now
Machine Vision Industry/Market
Compared to Human Vision
Why Consider Machine Vision
Applications
Systematic Deployment
What is Machine Vision
Electronic Imaging vs.. Machine
Vision
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Computers generating images
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CAD
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Animation
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Scientific Visualization
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GIS
Computers operating on acquired images - Computer vision
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Security/surveillance
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Security/baggage handling
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Retail security
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Biometric/access control
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ATMs/OCR/security
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ITA/IVHS
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Biomedical/scientific/microscope
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Radiology - CAT/MRI/PET
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Automotive - autonomous vehicles
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Automotive aftermarket
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2D symbology/bar code
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Document/form reading/OCR
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Machine Vision
Where is Machine Vision Being
Used
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Machine Vision is in use in virtually all
manufacturing industries
In some industries one can no longer
produce without machine vision
Why Machine Vision Now
Technology Readiness
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Underlying technology for machine vision has evolved
Components developed with features required to
succeed in machine vision applications
Lighting - LED - stable, long life
Cameras - solid state, progressive scan, asynchronous
scan, exposure control, color, high resolution
Optics - telecentric, computer controlled zoom
Compute power - PCs, DSPs, etc.
Software - GUI - Windows - Standard
PCI Interface, IEEE 1394
Technology Pull
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Quality emphasis (ISO 9000, 6 sigma,
etc.)
Productivity gains sought/downsizing eliminates eyes/requires substitute
sensing
Government regulations
Machine Vision Industry/Market
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Not homogenous
Segmented
 supply side
 GPMV/IPBS
 ASMV
 VAR
 demand side
 by industry
 process end
 package end
 applications that cut across industries
 e.g. web scanners
GPMV Application Specific
Modules
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PRINTING: INSPECTION, REGISTRATION CONTROL, COLOR CONTROL
PHARMACEUTICAL: BLISTER PACK, VIAL/AMPULE, SOLID DOSAGES, OCR/OCV
WELDING
WEB PRODUCT
OFF-LINE GAUGING
MECH ASSY VERIFY
CONSUMER PKG INSP FILLED
CONTAINER: METAL, PLASTIC, GLASS, CLOSURES
2D LOCATION ANALYSIS
ELEC. PKG. INSP: INSPECTION, QUALITY OF MARKINGS, CO-PLANARITY, BALL GRID ARRAY,
OCR/OCV
ELECTRICAL/ELECTRONIC CONN
OCR/OCV
1D BAR CODES/2D BAR CODES/SYMBOLOGY
EMPTY CAVITY INSPECTION
COMPACT DISC APPLICATIONS
CRT
ELECTRONIC DISPLAYS
DATA STORAGE
Container Market
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Glass
 glassware manufacturer
 filler
Can
Plastic
Closure
For glass and can in late majority phase; for plastic in
early adopter phase; for closure in early majority phase
Pharmaceutical Market
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Process end
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Packaging end
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vials, filled/unfilled
solid dosages
label issues
In process end in early adopter phase; in
packaging in early/late majority phase
Compared to Human Vision
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Machine vision does not compare well!
We use 1011 neurons to perform about
1015 operations per second
2 billion years of evolutionary
programming
So Why Machine Vision?
Humans only 70 85% effective!
People
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Attention span/distractions
Eye response
Relative gauging
Availability (breaks, vacations, sick, etc.)
Consistency
 individual
 between individuals
 from day-to-day
People
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Overload
Boring
Detect anomalies
Adapt/make adjustments
Interpret true nature of condition
Machine Vision vs. People
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Speed
Accuracy
Repeatability
Production Errors
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System
Random
Machine Vision vs. Human Vision
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Machine vision: best for quantitative
measurement of structured scene
Human vision: best for qualitative
interpretation of complex unstructured
scene
Why machine vision works
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Because variables can be controlled
parts can be presented consistently
scene can be constrained
MACHINE VISION
Technology to
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improve quality
reduce scrap/rework
reduce cost
improve productivity
improve product reliability
increase customer satisfaction
increase market share
Why Consider Machine Vision
Technology to
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lower inventories
avoid equipment breakdowns
eliminate adding value to scrap
avoid inspection bottlenecks
yield
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consistent and predictable quality
Machine Vision Applications
Throughout a manufacturing facility
incoming receiving
 forming operations
 assembly operations
 test
 packaging operations
 warehousing
 etc.
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Generic Applications
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Inspection
 2D, 3D Metrology
 surface flaw/cosmetic analysis
 mechanical/electronic assembly verification
location analysis
 visual servoing (2D and 3D)
 robot guidance
pattern recognition
 character recognition
 part recognition
 2D symbol reading
Systematic Deployment
Success Requires
Senior management must
 foster atmosphere to encourage change
 support change agents
 demonstrate buy-in to change
 encourage plant and line to take
ownership
 establish realistic schedule for changes
Success is more likely
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People assigned are interested in new
techniques and welcome change
begin with easy, non-critical application
define the parameters of the project and avoid
creeping expectations
select applications not critical to labor issues
be supportive during learning process
plan for replications
Success is more likely
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Obtain people involvement
Avoid technology leap that is too far
Make certain project is part of an overall
plan
Implementation Process
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Assemble task force and study production
process
task force should develop understanding of what
machine vision is
define need and evaluate alternatives
investigate - select specific applications
assess technical feasibility and cost feasibility
write comprehensive specification
Implementation Process
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Install and run-in.
Conduct acceptance test
Provide shop floor support
Evaluate system’s performance against
goals
Look for another machine vision
opportunity
Implementation Process
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Solicit 4 - 6 vendors with appropriate expertise
Visit vendors to review proposals, policies,
expertise, QC procedures
Systematically select vendor
Purchase
Acceptance test at vendor
Train all personnel Involved
What is Machine Vision?
As defined by the AIA:
A system capable of acquiring one or more
images, using an optical non-contact
sensing device, capable of processing,
analyzing and measuring various
characteristics so decisions can be made.
Relevance of Pixels
512 X 512
1300 X 1200
AP Wire Photo
35 mm color film
Pixels
1/4M
1.4M
2.5M
20.0M
Steps to Take When Buying a
Machine Vision System
Steps to Take When Buying a
Machine Vision System
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Identifying Machine Vision Opportunities
Assess Application Feasibility
Understand the Application
Understand the Vendors
Responsive Proposals
Systematic Buy-off Procedure
Mistakes in Buying Automation
Project Justification
Identifying Machine Vision
Opportunities
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Quality concerns
Productivity/mechanization
Process control
Rework
Inventory build-up - inspection bottleneck
Equipment jams
Warranty issues - field returns
Employee turnover
Identifying Machine Vision
Opportunities
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Lowest value added
Expensive fixturing
Lengthy set up times
100% inspection required to sort bad parts
Hazardous environment
Contaminants
Capital expansion
Operator limitations
Profile of Good Machine Vision
Opportunity
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Perceived value
Cost justifiable
Recurring concern
Can do something about it
Straight forward
Technically feasible
Profile of Good Machine Vision
Opportunity
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User friendly potential
Dedicated line
Long line life
Operation champion
Management commitment
Global Competition Requires
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Higher manufacturing productivity
Increased demand
Higher product quality
Better customer service
Flexible manufacturing
Greater return on manufacturing assets
Changing standards of manufacturing
performance
Computer Aided Inspection
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Provides traceability - records
Statistical data base - isolate production
problems
Real time machine correction/adaptive
control
Automatic QC data collection and analysis
Remove drudgery of humans
Hidden Costs Machine Vision
Can Help
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Lost business because product not
produced on time
Shipment of wrong products
Excess inventory
Idle labor because parts are not available
Doing a job over
Loss of valuable information
Machine Vision and Factory
Automation
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Data driven automation
Machine vision = data !
Statistics
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Measurements
Parts recognized
Classification
Types of defects
Trend analysis
Performance assessment
Record keeping
Process Control
Successful Application Requires
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Comprehensive understanding of needs
Proper application process
Good equipment and performance
specifications
Comprehensive understanding of machine
vision system capability
Steps to Take When Buying a
Machine Vision
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Machine Vision is in Widespread Use
Best Justification is Process Control
Infrastructure
Resources: AIA and MVA
How To Select Machine Vision Equipment
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Understand the technology
Assess application feasibility
Understand the application
Understand the vendors
Responsive proposals
Systematic buy-off procedure
Applications in pharmaceuticals
Understand the technology
Steps to Take When Buying a
Machine Vision System
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Become Informed
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Conferences
Books
Bibliography
Assess Application Feasibility
Steps to Take When Buying a
Machine Vision System
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Assess Feasibility
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Basis rests with size of a pixel/FOV
 MVA slide rule
Typical system handles 500 pixels
Function of generic application:
 verification
 gauging
 part location
 flaw detection
 OCR/OCV/pattern recognition
Verification
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Function of contrast - real or artificial
high contrast - feature should cover
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3 X 3 pixel area
low contrast - feature should cover more
pixels
Gauging
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500 marks on a ruler = resolution
subpixel interpolation - factor of 4 to 10
requirements driven by tolerance
rules of thumb:
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repeatability: 1/10th of tolerance
accuracy: 1/10th to 1/20th of tolerance
sum of accuracy + repeatability = 1/3
tolerance
Gauging
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Discrimination - smallest change in
dimension detectable with measuring
instrument
Discrimination = sub-pixel resolution
Repeatability = +/- Discrimination
Accuracy - determined by measurement of
calibration standard = Discrimination
Gauging - Example
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2” part and 2” FOV
tolerance: +/- 0.005”, total range 0.010”
repeatability: 1/10 X 0.010” or 0.001”
discrimination/accuracy: 1/20 X 0.010” or
0.0005”
with 500 X 500 pixel camera, resolution = 0.004”
with sub-pixel resolution 1/10, discrimination =
0.0004” = accuracy, so
repeatability is = 0.0008”
Part Location
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Analogous to gauging
Can expect to achieve sub-pixel
resolution: repeatability and accuracy
Flaw Detection
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Contrast! Contrast! Contrast!
Detection Vs. Classification
Detection: High Contrast, normalized
background (no pattern), can detect a flaw
that covers 3 X 3 pixels
Classification: flaw should cover 25 X 25
pixels
OCR/OCV
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Stroke width - 3 pixels wide
Character should cover 25 X 25 pixels
Spacing between characters - 2 pixels
Single font style - bold
Result - 99.9% read rate effectiveness
Linear Array Image Capture
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2000 - 8000 pixels
Scanning rates up to 2 - 20 KHz
Speed should be well regulated
Resolution in direction of travel function of
speed and sampling rate of camera
Understand the Application
General
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Defect prevention is better than the cure!
Study application site personally!
Consider vision to enhance people!
Expect productivity to decline!
Steps to Take When Buying a
Machine Vision System
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application issues:
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generic application
variables: part, presentation, etc.
material handling
operator interface
machine interfaces
environmental issues
system reliability/availability
miscellaneous: documentation, warranty, training,
software, spares, service
acceptance test/buy off procedure
responsibilities
Tools
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Job descriptions
Present specifications
Part drawings
Floor space drawings
Samples
Photos/videos
Personnel
Steps to Take When Buying a
Machine Vision System
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Write functional specification
Use “Machine Vision Requirements
Checklist” - available from MVA - forces
examination of:
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production process
justification issues
application issues
System Spec
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Defines “what” system is and “how”
system will work
involves examination of implementation
details
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programming standards
style
control methods
System Specification
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The spec is not what the customer wants!
Creeping expectations!
Variables - Gotchas!
System Specification
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Adhere to factory standards
Adhere to engineering standards
Use conventional jargon for part
descriptions and to describe the process
Use existing frames of reference to
develop acceptance test
Before RFP
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Prepare preliminary conceptual design
Develop schedule - be realistic
Assess cost
Determine technical and cost feasibility
Developing Functional
Requirements
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What does the system do?
What specific function do you want the MV
value adder to do?
What goals do you expect to achieve with
MV?
Will the MV system be for a retrofit or next
generation product?
Developing Functional
Requirements
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Defines “what” system is and “how”
system will work
involves examination of implementation
details
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programming standards
style
control methods
Developing Functional
Requirements
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Does the application involve:
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One object at a time
Multiple objects
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How many different objects
What are the part numbers?
Is it a batch operation or continuous dedicated
process?
What are the changeover times and frequency
of changeovers?
Developing Functional
Requirements
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What are the skill levels involved in
changeover?
How is function currently being
performed?
Can new variations to the part be
expected? What might they be?
Where do parts come from? What is
material handling surrounding MV?
Developing Functional
Requirements
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Can rejected parts be repaired?
Where do pass and fail objects go?
When does the project have to be
completed?
How many shifts is the equipment used?
If machine vision fails, what is the option?
Developing Functional
Requirements
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How many MV systems will be required
annually?
What are the consequences of a failed MV
sequence?
What are the consequences of a false
reject?
Developing Functional
Requirements
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Describe the application
Generically, does the application involve
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Gauging
Assembly verification
Flaw inspection
Pattern recognition
Developing Functional
Requirements
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If Gauging
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What are the tightest tolerances?
What is the accuracy design goal?
What is the repeatability design goal?
Are there reference features?
What are calibration requirements?
Developing Functional
Requirements
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If assembly verification
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Dimensions of assembly
Is it presence/absence
Orientation verification
What is the smallest piece to be verified and
dimensions of that piece?
Is part correctness also required?
Developing Functional
Requirements
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If flaw inspection
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Describe flaw types
What is the smallest size flaw?
Does the flaw affect surface geometry?
Does the flaw affect surface reflectance?
Is it more of a stain?
Is classification of flaws required?
Developing Functional
Requirements
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If location analysis
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What is the design goal for accuracy?
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For repeatability?
What is the area over which the “find” is
required?
Will angular as well as translation correction
be required?
Will scale change?
Describe calibration requirements
Developing Functional
Requirements
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If pattern recognition
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What is the size of the pattern?
Describe difference between patterns?
Is there a background pattern?
Does pattern involve color? Geometry?
Number of different patterns?
Is objective to identify? To sort?
Developing Functional
Requirements
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If specifically OCR/OCV
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Fixed font? Variable font? What is font?
What is the height of the characters?
What is the stroke width?
What is spacing between and around characters?
How many characters in string? How many lines?
Color of print?
Describe background – color, “busyness”
Developing Functional
Requirements
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Object dealing with
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What is material?
What is finish (texture) like? Dull, glossy,
specular?
Is surface finish the same on all surfaces?
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For all part numbers? Production runs?
Any platings, coatings, films, paints?
Markings?
Developing Functional
Requirements
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Object dealing with –
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Shapes – flat, curved, gently curved, other?
Irregular, grooved, sharp radii, mixed
geometric properties?
Part orientation variation?
Part sizes?
Part colors? (hue, saturation, brightness)
Part temperature?
Developing Functional
Requirements
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Object dealing with –
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Possibility of warping, shrinking, bending,
etc?
Any change in appearance over time?
Any markings?
General appearance variables?
Sensitivity to light?
Developing Functional
Requirements
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Material handling
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Present handling or being considered?
Production rates? Currently? Future?
Parts static? Moving continuously? Speed?
If indexed
How long stationary?
 Total in-dwell-out time?
 Settling time?
 Acceleration?
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Developing Functional
Requirements
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Material handling
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Maximum positional variations – translation,
rotation?
More than one stable state?
Volume envelope for MV?
Any restrictions or obstructions?
What triggers action?
What is result of MV?
Developing Functional
Requirements
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Operator interface
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Operators themselves (education, familiarity
with machinery, electronics, computers, etc.)
Operator interface requirements?
Personnel access requirements?
Enclosure requirements?
Object display requirements?
Image condition storage requirements?
Developing Functional
Requirements
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Operator interface
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Fail-safe operation?
Program storage requirements?
Data storage requirements?
Power failure requirements?
Reporting requirements?
False reject and escape rates?
Developing Functional
Requirements
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Machine interfaces
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Alarms desired?
Other machine integration?
What event triggers MV action? How
detected? How communicated to MV?
Machine interfaces: part in position, sensor
type, PLC, Ethernet, etc.
Hierarchical interfaces anticipated?
Developing Functional
Requirements
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Environmental issues
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Factory – clean room?
Air quality? Corrosive?
Ambient lighting?
Part conditions?
Wash-down?
Temperature? Humidity? Radiation? Shock &
Vibration?
Utilities available: power, air, water, vacuum?
Developing Functional
Requirements
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System availability/reliability
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Number of hours per week? Hours available
for maintenance?
Calibration procedures?
Challenge procedures?
MTBF? MTTR?
Developing Functional
Requirements
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Other issues
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Special paint?
Installation?
Warranty?
Spare parts?
Documentation?
Training?
Software ownership?
Questions?
Good RFP
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Describes project in detail
Describes operation’s business
Reviews why the project is being solicited
Reviews schedule
RFP Should Request
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Schedule
Training
Service
Warranty
Software ownership
Documentation
Installation support
Steps to Take When Buying a
Machine Vision System

Identifying Vendors
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AIA - Directory
MVA - Directory
Opto*Sense database
Vendor type:
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image processing board
general purpose machine vision system
application specific machine vision system
system integrator
Understand the Vendors
Machine Vision Industry
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Image Processing Board Suppliers
General Purpose Machine Vision suppliers
Machine Vision Software Suppliers
Smart Cameras Suppliers
Application Specific Machine Vision Suppliers
System Integrators
OEM
System Integrator
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Look for
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application competency
industry competency
technological competency
professional competency
technology independence
schedule/cost
System Integrator
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Questions to ask:
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Have you done anything like this before?
What do other clients think of you?
Do you understand my requirements?
Are your skills consistent with my
requirements?
Need a Consultant?
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Time an issue and corporate resources
are lean
Consultant can:
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write specifications
write bid package
identify vendors
evaluate proposals
prepare acceptance test plans
Need a Consultant?
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Consultants
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conserve resources
bring technology knowledge
bring vendor knowledge
bring objective counsel
bring negotiating prowess
Steps to Take When Buying a
Machine Vision System
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Evaluate vendors systematically
 Use Decision Matrix technique to assess proposals
 Visit the 2 - 3 “best” vendors to assess:
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application engineering skills
quality control procedures
software practices
training materials
documentation
policies
references
Responsive Proposals
Proposals Should Include

Review of implications of variables:





staging
image processing
image analysis
Implication of organization/lack of
organization of parts
Time budget to demonstrate confidence
throughput can be met
Proposals Should Include


Position/temperature error budgets
Interfacing issues:



people
machine/line
Miscellaneous issues:




enclosures
battery back up
diagnostics
reports
start up/changeover
maintenance
calibration
Proposals Should Include


Exceptions to the spec
Responsibilities:



installation
specifically what is required for system to be
successful
Acceptance testing


validation procedure
challenge set
Proposals Should Include

Policy review







training
installation
warranty
field service
spares
software upgrades
documentation
Proposals Should Include


Schedule
Cost
Proposals Should Reflect






Familiarity with processes
Grasp of problem
Completeness and thoroughness
Responsiveness
Evidence of good organization and
management practices
Qualifications of personnel
Proposals Should Reflect





Experience in similar or related field or
application
Record of past performance
Project planning
Technical data and documentation
Geographic location
Proposer Evaluations







Assess staying power
Technical resources
Design philosophy
Capital/human resources
Physical facilities
Documentation
Policies
Proposer Evaluations




Schedules
References
Quality control practices
Vendor skills:




optics
TV
Mechanical engineering
Quality engineering
Sizing up Vendors Differentiators



How long before a service call is made or
phone support is obtained
Are software upgrades included in the
price? Is upgrade notification automatic?
What is the company’s annual sales
revenue in the specific product/application
Finalize Evaluations



Make sure vendor understands
Visit most responsive vendors
Visit up and running installations
Vendor Decision





Previous work
Quality of work
Reputation
Ability to meet schedule
Understanding of your business and
application
Reference Checks





Quality of work
Ability to meet schedule
Policies
Support
Would they do it over !!!
Systems




No system should be more complicated
than it need to be!
Good application engineering is critical!
Contrast, Contrast, Contrast!
Staging is important, if not more important
than image processing algorithms!
Customer






Software and hardware should be transparent!
Tinkering should be discouraged!
Should not specify equipment, rather function!
Samples furnished should be representative of
all variables expected!
Training is critical!
A little knowledge is dangerous!
Vision Company






Room lighting is a No - No!
Vision company should have all disciplines
required!
Beware of “Piece of cake!”
Look for relevant experience!
Verify quality practices!
Verify policies: training, documentation,
etc.!
Systematic Buy-off Procedure
Application Engineering

Material handling



Must avoid jamming regardless of deformities!
Murphy’s Law - If it can go wrong, it will!
Lighting



Lighting is not a constant!
Never use software to compensate for poor
lighting!
Shrouds are cheaper than software fixes!
Application Engineering

Optics



There are limits to resolution!
Nothing exceeds the speed of light!
Image resolution



Nyquist’s theorem does apply!
More resolution means more compute power!
A pixel is not a fixed size! - Magnification
issues
Steps to Take When Buying a
Machine Vision System


Write an acceptance test plan/buy-off
procedure
Different for:


attribute inspection system - based on Thorndyke
Chart to arrive at sample size - to test for both
escapes and false rejects
gauging/location analysis - repeatability/accuracy
performance at upper limit, nominal and lower limit of
tolerance
Acceptance Testing
Includes evaluation of








operator interface
basic operation
calibration
accuracy & repeatability
throughput
sensitivity
maintainability
availability
Acceptance Testing





Test at system level
Test at other than nominal
Test failure modes
Test everything in system spec
Don’t put anything into spec that can not
be tested!!!
Buy-off at Supplier





Simulate external equipment
Generate reports
Run through all screen functions
Simulate alarms and failure modes
Power up/down system and components
Steps to Take When Buying a
Machine Vision System

Using the Thorndyke chart




from chart np - 3.0



e.g.. for 0 defects
95% confidence
400 PPM (reliability)
n = 3/400 x 10
n = 7,500
-6
for every factor: color, finish, size, etc.
Steps to Take When Buying a
Machine Vision System

Create a challenge to verify
performance
Working With The System In The
Factory



Should not deteriorate production speed!
Ideally, avoid having to re-engineer the
manufacturing process to accommodate
machine vision!
System should have the capacity to be
reconfigured!
Training


Basic principles of operation
Normal operating procedures




screen functions
power up/down
reports
Alarm conditions and recovery procedures
Training



Back-up procedures
Normal and emergency maintenance
Calibration
Mistakes in Buying
Automation
Mistakes in Buying Automation
1. No equipment specification
2. Requesting quotes before visiting
prospective suppliers
3. Incorrect cost estimate
4. Insufficient in-house machine support
5. No input from production people
Mistakes in Buying Automation
6. Poor communication with vendor
7. Acceptance of inadequate equipment
8. Failure to supply latest drawings and
parts with specifications
9. Failure to design for automation
10. Using the wrong technology
per E. Martin, Lanco/NuTec, Assembly March 96
Reasons Why Automation Fails
Per Automation Research Corp. Study
 Unclear or false expectations regarding
what is to take place and the results that
are to be achieved
 Lack of commitment by user management
 Over dependence on technical solutions
Reasons Why Automation Fails



Lack of acceptance by the user
organization
Poor project management
Not properly taking into account the
human resources issues
SI Difficulties With Users







Inadequate specifications
Lack of technical knowledge
No management commitment
Internal policies
Separating needs from wants
Inability to take over system
Changes in midstream
SI Difficulties With Users







No one person in charge
Tight project constraints
Lack of communication
Price constraints
Inability to take risks
Manpower shortages
Rigid specifications
Project Justification
Benefits of Machine Vision








Scrap reduction
Scrap disposal costs
Rework
Inventory reduction associated with rework
Avoiding value added
Improving machine uptime - capital productivity
Avoiding return and warranty costs
Improving customer satisfaction
Project Justification

Tangible benefits:







increase productivity
reduce scrap
reduce rework time/inventory
avoid adding value to scrap
avoid product returns - warranty issues
avoid liability issues
avoid field service
Project Justification

Tangible benefits:





avoid freight costs on returns
avoid equipment breakdowns/improve
machine uptime
improve product fabrication cycle and impact
on inventory
save indirect labor cost
save floor space to store rework inventory
Project Justification

Tangible benefits:





training/labor/turnover/recruiting costs
out of cycle costs due to schedule upsets
waste disposal costs
costs of overruns to compensate for yield
personnel/payroll costs per employee:



average worker’s compensation
average educational grant per employee
tooling/fixturing savings
Project Justification

Intangible benefits






improve quality - consistency of quality
predictability of quality
information automation
flexibility
people effectiveness/limitations
sample inspection only monitors system
errors, not random errors
Project Justification

Intangible benefits:






process control
environment
consumer/government pressure
“eyes” for automation
expansion needs
seasonality
Project Justification
Because some things appear to be
intangible does not mean they have zero
value !!!
In final analysis, justification of technology is
a management issue - not an accounting
issue !!!!
Project Justification

Data required:




How many pieces are produced per month
per line?
How many production lines make the piece?
What is the current inspection time per piece?
(minutes/piece)
What is the inspection labor rate? ($/hr
including benefits)
Project Justification

Data required:





How many rejects per month (%)?
What is the value of a reject - $ -?
What is the value of the raw material in the
piece - $ -?
What percent of the rejects are reworked per
month?
What is the average rework time/piece
(minutes/piece)?
Project Justification

Data required:


What is the monthly warranty cost - $? includes costs of field service, field returns,
repairs, shipments to and from plant,
paperwork, etc.
Product liability costs per month - $? includes liability claims, lawyer fees,
insurance, paperwork, etc.
Project Justification

Data required:


What percent of the rejects are scrapped per
month? - the difference between the number
of rejects per month and the number of
rejects reworked per month and returned to
inventory
What are the monthly waste disposal costs
due to the scrapped pieces?
Project Justification

Data required:


What are the scrap and rework inventory
costs per month? - eg. Calculate based on
average number of units scrapped and in
inventory per month multiplied by the value
(cost) of the piece divided by 10 (factor that
assumes any such unit will only be in
inventory an average of two days)
How many shifts does the line operate?
Project Justification

Data required:




Total hours operating per shift?
Hours worked per month/shift/person? - paid
hours
Number of units sold per month? Average
selling price of the piece? - not cost Indirect
(supervisory) labor rate ($/hr with benefits)?
What is the profit per piece produced? ($)
Project Justification

Data required:


Current cost of money? Prime rate + 1%?
If sample inspection, hours per month for
specific piece?
Project Justification

Calculated values:


annual direct cost of inspection per piece =
inspection labor rate X hours worked per
month/shift X number of shifts line operates X
12
annual indirect cost of inspection per piece =
indirect labor rate X hours worked per
month/shift/person X number of shifts X 12
Project Justification

Calculated values:


cost of rejects scrapped = percent rejects/
month X value of a reject X % pf rejects
scrapped/ month X number of pieces
produced/month X 12
cost of rework = percent of pieces
reworked/month X number of pieces
produced per month X rework time X rework
labor rate X 12
Project Justification

Calculated values:





warranty costs = monthly warranty costs X 12
liability costs = monthly product liability costs
X 12
scrap disposal costs = monthly cost X 12
scrap and rework inventory costs = monthly X
12
training costs - based on turnover experience
Project Justification

Assigning values:


value of reliable data = sum of annual direct
and indirect labor costs X 0.05
value of improved customer satisfaction =
average selling price of the piece X number of
units sold per month X 12 X 0.001
Project Justification

Assigning values:


percent uptime line improvement anticipated an estimated value
value due to gain in line uptime = cost of
machine vision system X number of systems
required X 0.05
Project Justification

Costs



cost of machine vision system (or systems)
launch costs (training, etc.) - estimate 10% of
machine vision system costs
annual service contract - estimate 10% of
machine vision system costs
Project Justification

Costs:

opportunity cost - function of the cost of
money = cost of the machine vision system X
number of systems + launch costs + annual
service contract X number of systems X
current cost of money
Project Justification

Costs:


total equipment costs = cost of machine vision
systems X number of systems + launch costs
+ annual service contracts X number of
systems + opportunity cost
average annual cost over four years = total
equipment costs/4
Project Justification


Return on investment = (average annual
savings/total equipment costs) X 100
Payback (years) = total equipment
costs/(average annual savings + average
annual costs with machine vision)
Average Payback Period by
Company Size
Per Automation Research Corp.
large
2.85
medium
3.06
small
3.27
2.6
2.8
3
Years
3.2
3.4
Average Payback Period in Years by
Industry
Per Automation Research Corp.
2.8
2.4
3
2.8
2.3
2
1
ry
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al
s
cs
ni
tro
ec
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ec
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ca
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el
e
ac
sp
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ae
to
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ive
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Years
3.7
3.6
4