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THE BUSINESS CASE FOR IMPLEMENTING MACHINE
VISION
Vision Systems International
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
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 Computers generating images
CAD Animation Scientific Visualization GIS
Computers operating on acquired images - Computer vision Security/surveillance Security/baggage handling Retail security Biometric/access control ATMs/OCR/security ITA/IVHS Biomedical/scientific/microscope Radiology - CAT/MRI/PET Automotive - autonomous vehicles Automotive aftermarket 2D symbology/bar code Document/form reading/OCR Machine Vision
Where is Machine Vision Being Used
Machine Vision is in use in virtually all manufacturing industries
In some industries one can no longer produce without machine vision
CHART 2 NORTH AMERICAN MERCHANT MACHINE VISION MARKET UNITS
0
10000
20000
30000
40000
50000
60000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
CHART 7 NORTH AMERICAN MACHINE VISION MARKET DISTRIBUTION BY MAJOR END USER INDUSTRIES - UNITS
0
2000
4000
6000
8000
10000
12000
14000
SEMICON ELECTRONIC CONTAINER FOOD WOOD TRANSPORT FAB METAL PLASTIC PRINTING PHARM + MED
DEV
MISC
2001
2002
Why Machine Vision Now
Technology Readiness
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
Quality emphasis (ISO 9000, 6 sigma, etc.)
Productivity gains sought/downsizing - eliminates eyes/requires substitute sensing
Government regulations
Machine Vision Industry/Market
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 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
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
Process end vials, filled/unfilled solid dosages
Packaging end label issues
In process end in early adopter phase; in packaging in early/late majority phase
Compared to Human Vision
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
Attention span/distractions Eye response Relative gauging Availability (breaks, vacations, sick, etc.) Consistency
individual between individuals from day-to-day
People
Overload Boring Detect anomalies Adapt/make adjustments Interpret true nature of condition
Machine Vision vs. People
Speed Accuracy Repeatability
Production Errors
System Random
Machine Vision vs. Human Vision
Machine vision: best for quantitative measurement of structured scene
Human vision: best for qualitative interpretation of complex unstructured scene
Why machine vision works
Because variables can be controlled parts can be presented consistently scene can be constrained
MACHINE VISION
Technology to improve quality reduce scrap/rework reduce cost improve productivity improve product reliability increase customer satisfaction increase market share
Why Consider Machine Vision
Technology to lower inventories avoid equipment breakdowns eliminate adding value to scrap avoid inspection bottlenecks yield
consistent and predictable quality
Machine Vision Applications
Throughout a manufacturing facility incoming receivingforming operationsassembly operationstestpackaging operationswarehousingetc.
Generic Applications
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
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
Obtain people involvement Avoid technology leap that is too far Make certain project is part of an overall
plan
Implementation Process
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
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
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
Pixels
512 X 512 1/4M
1300 X 1200 1.4M
AP Wire Photo 2.5M
35 mm color film 20.0M
Steps to Take When Buying a Machine Vision System
Steps to Take When Buying a Machine Vision System
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
Quality concerns Productivity/mechanization Process control Rework Inventory build-up - inspection bottleneck Equipment jams Warranty issues - field returns Employee turnover
Identifying Machine Vision Opportunities
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
Perceived value Cost justifiable Recurring concern Can do something about it Straight forward Technically feasible
Profile of Good Machine Vision Opportunity
User friendly potential Dedicated line Long line life Operation champion Management commitment
Global Competition Requires
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
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
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
Data driven automation Machine vision = data !
Statistics
Measurements Parts recognized Classification Types of defects Trend analysis Performance assessment Record keeping Process Control
Successful Application Requires
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
Machine Vision is in Widespread Use Best Justification is Process Control Infrastructure Resources: AIA and MVA
How To Select Machine Vision Equipment
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
Become Informed Conferences Books Bibliography
Assess Application Feasibility
Steps to Take When Buying a Machine Vision System
Assess Feasibility 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
Function of contrast - real or artificial high contrast - feature should cover
3 X 3 pixel area low contrast - feature should cover more
pixels
Gauging
500 marks on a ruler = resolution subpixel interpolation - factor of 4 to 10 requirements driven by tolerance rules of thumb:
repeatability: 1/10th of tolerance accuracy: 1/10th to 1/20th of tolerance sum of accuracy + repeatability = 1/3
tolerance
Gauging
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
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
Analogous to gauging Can expect to achieve sub-pixel
resolution: repeatability and accuracy
Flaw Detection
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
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
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
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
application issues: 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
Job descriptions Present specifications Part drawings Floor space drawings Samples Photos/videos Personnel
Steps to Take When Buying a Machine Vision System
Write functional specification Use “Machine Vision Requirements
Checklist” - available from MVA - forces examination of: production process justification issues application issues
System Spec
Defines “what” system is and “how” system will work
involves examination of implementation details programming standards style control methods
System Specification
The spec is not what the customer wants! Creeping expectations! Variables - Gotchas!
System Specification
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
Prepare preliminary conceptual design Develop schedule - be realistic Assess cost Determine technical and cost feasibility
Developing Functional Requirements
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
Defines “what” system is and “how” system will work
involves examination of implementation details programming standards style control methods
Developing Functional Requirements
Does the application involve: One object at a time Multiple objects
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
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
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
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
Describe the application Generically, does the application involve
Gauging Assembly verification Flaw inspection Pattern recognition
Developing Functional Requirements
If Gauging 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
If assembly verification 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
If flaw inspection 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
If location analysis What is the design goal for accuracy?
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
If pattern recognition 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
If specifically OCR/OCV 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
Object dealing with What is material? What is finish (texture) like? Dull, glossy,
specular? Is surface finish the same on all surfaces?
For all part numbers? Production runs? Any platings, coatings, films, paints? Markings?
Developing Functional Requirements
Object dealing with – 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
Object dealing with – Possibility of warping, shrinking, bending,
etc? Any change in appearance over time? Any markings? General appearance variables? Sensitivity to light?
Developing Functional Requirements
Material handling 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?
Developing Functional Requirements
Material handling 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
Operator interface 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
Operator interface Fail-safe operation? Program storage requirements? Data storage requirements? Power failure requirements? Reporting requirements? False reject and escape rates?
Developing Functional Requirements
Machine interfaces 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
Environmental issues 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
System availability/reliability Number of hours per week? Hours available
for maintenance? Calibration procedures? Challenge procedures? MTBF? MTTR?
Developing Functional Requirements
Other issues Special paint? Installation? Warranty? Spare parts? Documentation? Training? Software ownership?
Questions?
Good RFP
Describes project in detail Describes operation’s business Reviews why the project is being solicited Reviews schedule
RFP Should Request
Schedule Training Service Warranty Software ownership Documentation Installation support
Steps to Take When Buying a Machine Vision System
Identifying Vendors AIA - Directory MVA - Directory Opto*Sense database
Vendor type: image processing board general purpose machine vision system application specific machine vision system system integrator
Understand the Vendors
Machine Vision Industry
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
Look for application competency industry competency technological competency professional competency technology independence schedule/cost
System Integrator
Questions to ask: 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?
Time an issue and corporate resources are lean
Consultant can: write specifications write bid package identify vendors evaluate proposals prepare acceptance test plans
Need a Consultant?
Consultants conserve resources bring technology knowledge bring vendor knowledge bring objective counsel bring negotiating prowess
Steps to Take When Buying a Machine Vision System
Evaluate vendors systematically Use Decision Matrix technique to assess proposals Visit the 2 - 3 “best” vendors to assess:
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 start up/changeover battery back up maintenance diagnostics calibration reports
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 e.g.. for 0 defects 95% confidence 400 PPM (reliability)
from chart np - 3.0 n = 3/400 x 10 -6
n = 7,500 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 technologyper 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 SizePer Automation Research Corp.
3.27
3.06
2.85
2.6 2.8 3 3.2 3.4
small
medium
large
Years
Average Payback Period in Years by IndustryPer Automation Research Corp.
2.42.8
2.32.8
3.6 3.7
0
1
2
3
4
Ye
ars