Cost Justification Strategies for Machine Vision

With the tremendous growth the machine vision market has experienced in the last couple of years - 30% more units were shipped in 1999 than in 1998 - justifying machine vision must be easy. The cost of an average system has declined dramatically to about $35K. This includes the cost of the hardware, software, application engineering and system integration! Some implementations, such as smart cameras and vision sensors and embedded vision systems, while somewhat limited in the applications they can perform, cost under $10K. Interestingly, for $10K one gets the performance of a machine vision system that only five years ago sold for $35 - 45K.

Justification can still be a challenge when it is based solely on labor savings. With a three-shift operation, just based on labor savings one can justify a typical machine vision system in less than a year. Given all the other savings that might accrue, a machine vision investment can be recovered in less than a year.

Of course, every application has to be examined and justified on its own merits. Savings that might apply under one set of circumstances may not apply under another. There are strategic and tactical reasons for justifying machine vision. From a strategic perspective, there could be installations justified strictly on the basis that it is time for a plant to take advantage of the latest technology. Machine vision could be a factor in enhancing a company's competitive advantage by eliminating customer concerns. It might be reasonable to invest in machine vision as part of an integrated plan to modernize a line.

From a tactical perspective there are many potential justification reasons, such as: increasing productivity, improving quality, reducing scrap and rework costs, cost avoidance such as reducing the value added to scrap, avoiding product returns/warranty issues (including cost of shipping associated with returns), avoiding liability issues, avoiding field service costs, improving capital productivity by increasing machine uptime by avoiding jams, etc., reducing waste disposal costs, etc.

When one looks at labor costs it is possible that ultimately, given enough capital substitution for labor, that this will also include indirect labor cost as well as direct labor cost. Incidentally, labor costs, besides hourly rate and fringe benefits including vacations/holidays/etc., should also include the cost of recruiting, given inevitably there is turnover, as well as the costs of training and such costs as average cost of worker's compensation and average educational grant per employee. In other words, direct labor costs can easily amount to 1.5 times the actual hourly rate of an employee. In the case of training there are two issues. On the one hand there is the actual cost to train an operator to perform the equivalent inspection task. On the other hand, the use of a machine vision system may even result in the requirement for less training in conjunction with other line related jobs by eliminating some of the training related to material handling for the inspection tasks.

Since the parts are 100% inspected the scrap and rework costs should decline appreciably. The machine vision system will be able to signal trends leading up to reject conditions before they are experienced and flag a line operator to take corrective action to avoid the production of rejects or conditions that will require rework. A secondary benefit is derived from avoiding the need to reinspect a part that has been reworked.

So what kind of data do you need to develop a comprehensive justification? As suggested the actual data will be dependent on the specific application. What follows is meant to give you some ideas and, in many cases, may be relevant to a specific application. The concept was originally developed to justify the purchase of a machine vision-based populated printed circuit board inspection system. What follows are typical questions that need to be answered.

1. How many pieces are produced per month? This should be the number of units coming off a single line where the machine vision system is being proposed.
2. How many production lines make the piece? If more than one line is producing the same product, how many are there and specifically how many will be equipped with machine vision?
3. What is the current inspection time - minutes/piece? If someone is assigned to the line as a full time inspector, calculate this figure based on the amount of time the inspector spends inspecting each unit. If sample inspection is now the case, estimate the amount of time an inspector is engaged to inspect the samples per month.
4. What is the inspection labor rate - dollars per hour including benefits? If the benefit-based rate is not available, multiplying the direct labor rate per hour by 1.35 will provide a reasonable estimate.
5. How many rejects are experienced per month? This should be the average percent over some period of months.
6. What is the value of a reject - $? 
7. What is the value of the raw material in a piece - $? This should be used where the actual value of a reject is not known.
8. What percent of the rejects are reworked per month? Again, use an average figure for several months.
9. What is the rework time in minutes/piece? What is the average time it takes to rework a reject so that it can be put back into inventory?
10. What is the rework labor rate - dollars per hour? 
11. What are the monthly warranty costs? This should include the average monthly costs of field service, field returns, repairs, shipments to and from plant, paperwork, etc.
12. What are the product liability costs per month? This should include the average monthly costs of liability claims, liability insurance, paperwork, legal fees, etc.
13. What percent of the rejects are scrapped per month? This is actually a calculated value: the difference between the number of rejects per month and the number of rejects reworked per month and returned to inventory.
14. What are the monthly waste disposal costs associated with the scrapped pieces? It is reasonable to estimate that a percentage of your total waste disposal cost is associated with the scrap from a given line. Base on that estimate, one can calculate the monthly waste disposal cost accordingly.
15. What are scrap and rework inventory costs per month? This can be calculated based on the average number of units scrapped and in inventory per month multiplied by the value (cost) of the piece divided by 10. The 10 factor assumes that any such unit will only be in inventory an average of two days.
16. How many shifts does a line operate? 
17. What are the total hours per shift? Be sure to take into consideration line down time due to breaks, lunches, etc.
18. What are the hours worked per month per shift per person? This should be the actual number of hours that a person is getting paid for during a month.
19. What is the number of units sold per month? This should be the actual number of units shipped per month.
20. What is the average selling price of a piece? This should be the selling price and not the cost.
21. What is the indirect (supervisory) labor rate - dollars per hour with benefits? 
22. What is the profit per piece produced and sold?
23. What is the current cost of money? If unknown, a good estimate might be the prime rate plus 1%.
24. In a sample inspection scenario, what are the hours spent per month inspecting the specific products produced on the line contemplating machine vision?

With this data one can create a simple spreadsheet to assist in the cost justification analysis. This should include the following calculations:

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1. The annual cost of inspection for a piece: inspection labor rate X hours worked per month/shift X number of shifts X 12. In the case of sample inspection this would be the number of hours spent in inspection of the specific piece per month X inspection labor rate X 12. This represents the present costs. Using a machine vision system one could estimate that the inspection labor content would be about 10% of that of the present manual inspection techniques. The savings are the difference between manual and machine vision-based inspection.
2. The annual indirect cost of inspection per piece: indirect labor rate X hours worked per month per shift per person X number of shifts X 12. This represents the present costs. Using a machine vision system one could estimate that the inspection labor content would be about 10% of that of the present manual inspection techniques. The savings are the difference between manual and machine vision-based inspection.
3. The cost of rejects scrapped: the percent rejects per month X value of a reject X percent of the rejects scrapped per month X number of pieces produced per month X 12. This is the cost associated with the present methods. In the case of machine vision one could estimate that rejects would decline to some percentage of what is now being experienced 20%, for example. The savings are the difference between present manual techniques and machine vision-based inspection.
4. Cost of rejects based on raw material cost: percent rejects per month X percent rejects scrapped per month X value of raw material in a piece X number of pieces produced per month X 12. Again assuming that machine vision would reduce this to 20% of what is now experienced, one can calculate the savings between the existing procedure and the machine vision-based inspection procedure.
5. Cost of rework: percent of pieces per month X number of pieces produced per month X rework time X rework labor rate X 12. In the case of a machine vision-based inspection scenario one can estimate that the rework rate would decline to 20% of that presently experienced. The savings are the difference between present manual techniques and machine vision-based inspection.
6. Warranty costs: monthly warranty cost X 12; alternatively an estimate could be derived based on the number of units sold per month X average selling price per unit X 12 X 0.001 (estimated percentage). Again, with the adoption of machine vision this should be reduced to less than 20% of the present cost and the savings would be the difference.
7. Liability costs: monthly product liability cost X 12; alternatively an estimate could be derived based on the number of units sold per month X average selling price per unit X 12 X 0.0001 (estimated percentage). Again, with the adoption of machine vision this should be reduced to less than 20% of the present cost and the savings would be the difference.
8. Scrap disposal costs: monthly waste disposal costs per month X 12. Again, with the adoption of machine vision this should be reduced to less than 20% of the present cost and the savings would be the difference.
9. Scrap and rework inventory costs: scrap and inventory costs per month x 12. Again, with the adoption of machine vision this should be reduced to less than 20% of the present cost and the savings would be the difference.
10. Training costs: annual cost of inspection per piece (sum of direct and indirect labor costs) X 0.05 (estimated percentage). In the case of a machine vision-based inspection approach, this is estimated to be proportional to the direct labor savings or 10% of the present cost. The savings are the difference between the present and machine vision-based inspection approach.

Are there other benefits that can be quantified? Most assuredly! For example, machine vision is an automatic data collector. This data will be very reliable. One will never experience transcription errors for example. Measurements will always be taken the same way. For example, there will be no differences in measurements taken between operators or by an operator or by the use of more than one measuring instrument. This could be assigned a value of 1% of the annual cost of inspection for a piece.

One can expect customer satisfaction to improve. Again an attempt should be made to quantify this value. One approach might be based on the average selling price: average selling price X number of units sold per month X 12 X 0.0001.

Another benefit could be improved line uptime. If one can avoid downtime as a result of trying to handle foreign or misshapened parts, for example, one can improve the overall productivity of the manufacturing equipment. The line downtime should be known. Using machine vision could improve this by 20%. Given five lines, this could yield a production gain equivalent to a whole new line. The justification might even include the value of 20% of a new production line.

The spreadsheet should also include the cost analysis for the machine vision system. The costs should include not only the cost of the equipment itself, but also installation and training as well as the cost of an annual service contract.

Calculating the costs and the savings, it should be straightforward to calculate a return-on-investment. Inevitably, given the lower costs of a typical machine vision system today, one can often expect quantitative savings that will result in a one year or less payback.

Are there other savings that could be included? Some might be reaching a little, but they nevertheless exist. For example, what about the floor space to store rework inventory or inventory that has to be reworked? If a machine vision system reduces rework by 80%, the floor space saved will be proportional.

Other benefits include the opportunity for process control. In many applications, machine vision can furnish data that can be automatically interpreted to reflect process variables and conditions related to those variables that will ultimately lead to rejects if the conditions are not corrected in a timely manner. In some cases, a machine vision system could avoid the shipment of wrong parts. It could also contribute positively to reducing the amount of idle labor because reject parts are made and quality parts are now available for subsequent operations.

All in all machine vision systems go a long way to reducing what have been classified as the costs of quality: the cost of prevention and the cost of failure. Prevention costs are generally associated with inspection costs or the costs of existing practices in place to avoid quality problems. The cost of failure include both internal failure costs that arise from nonconforming materials before they are shipped and external failure costs or the costs incurred when a customer has problems with the product after it has left the manufacturer.  Machine vision virtually guarantees the consistency and predictability of quality.