Ultimate Guide to Industrial Robotics -- Why Automate the Small- and Medium- Sized Manufacturers (SMMs)? (part 2)

Justifying Robotic Automation

There are many drivers that are considered to be important when justifying a robotic project. All end-users establish their own unique criteria for judging whether a robotic project is good or bad. In terms of justification, the most popular explanation for automating an existing or new process is because of the lack of skilled labor.

Interestingly, the lack of skilled labor has been the most popular explanation for at least fifteen years, and the response is the same regardless of the geographic region. For example, Mexico has a skilled-labor shortage. The lack of skilled labor is a topic of extreme importance and influence on US national policies affecting manufacturers. Even China has a lack of skilled labor, emphasizing the term skilled as meaning someone who has been trained. Training of operators for running industrial robotic work-cells, or running any type of machinery in a production environment, is absolutely critical.

Another driver that is certainly on the minds of owners as well as managers is the economic justification. In evaluating a robotic project the estimation of payback needs to be considered almost immediately. There are many ways to examine this payback. Using the complex or the simple return on investment (ROI) calculator will bring similar conclusions. The 80/20 rule applies to the economical evaluation of a project, meaning that 20 percent of the criteria being judged contribute to 80 percent of the value. There are certainly reasons in some instances to continue to examine the remaining areas for savings. However, for a quick barometer or gage of payback, the exercise is not necessary. The total cost involved with a manufacturing task or process is called the total ownership cost (TOC). The characteristics that contribute to the 80 percent of the TOC or the important "tangible" economic drivers for justification of a robotic project are as follows:

- Direct labor, expressed in terms of labor hours for each shift of production including overtime, is typically accumulated on an annual basis. A single production shift consists of 2050 available annual hours, assuming two weeks of vacation.

There are 8760 total available hours annually, when twenty four hours per day, 365 days per year is considered.

- Asset utilization expressed in terms of machine percentage efficiency. The asset utilization affects productivity and available capacity. The lower the efficiency of an asset, assuming the asset is available to be used, the lower the productivity.

- Process on-time, described, for example, as "arc-on-time" for welding, is important for process-intensive tasks such as welding, de-burring, grinding, or where an operator is adding value to the work-piece. This category is another measure of productivity.

The three categories listed should be considered in the payback evaluation. Other considerations to be included in the ROI analysis at some point, are the effects of the robotic work-cell on the immediate up- and down-stream operations. This principle is important because automating one step in the value stream, when the other steps have problems, makes little sense. Problems with the manufacturing flow should be fixed before automating. This aspect illustrates another reason why robotics is complementary to the principles of lean manufacturing. Robotic systems sometimes fail to meet expectations because operations prior to, or after the robot work-cell were not properly con figured for automation in the manufacturing sequence. Some examples of how robotics can drive the cost of a work-piece lower, in addition to the aforementioned three categories, are as follows:

- Robotics are inherently ideal poka-yoke devices (a go no go gage) because they can prevent defects moving through a process where the costs to repair the defect can compound when they occur in downstream processes. This concept can be called quality at the source.

- Transferring raw material into the work-cell, and discharging finished material. Efficient material flow is a vital requirement for a robotic system, and efficient material flow is a prime essential for lean manufacturing. A robot work-cell that is starved, either because it’s not fed efficiently, or finished parts can't be removed at once, isn't making money.

- Building an adaptable process that eliminates scrap, and provides a statistically consistent procedure. Every manufacturing operations guide explains that quality saves money.

- Providing a flexible, programmable, and re-deployable operator that is available to perform a task for 8760 hours per year.

- Long cycle times drive down the cost of automation per unit of measure, such as automation cost/spindle in machining, or in automation cost/production line in palletizing. Examples are as follows: Robot servicing multiple machine tools decreasing cost of automation/spindle; Robot servicing multiple case conveyor lines and palletizing multiple unit loads, reducing overall costs The longer the process cycle time, the greater the ROI of the automation.

- Peripheral cost reductions can be achieved in terms of the following examples:

Welding --- Reducing shielding gas, welding wire and other consumables by means of an overall reduction in over-welding or cutting, etc..

Material removal -- Reduction in costs of grinding media because of an overall reduction in media wear

Machine tending--Consistent and predictable tool life, that permits improved process control ; Consistent loading, reducing scrap and improving quality

Generic --Reductions in costs of protective safety gear, uniforms, and tools necessary to support a manual process

In cost-justification models, cost of consumables will inherently increase for a robotic solution versus a manual operation, for the prime reason that productivity will increase, requiring additional raw material inputs, and consumables usage. Power requirements, and costs of other utilities will also increase as a function of the increased throughput.

An important question for a manufacturer to ask during the justification exercise is "what does it cost to not automate?" For example, injuries, operators not showing up for work, qualifying, testing, and training of employees in the event of turnovers, and unpredictable capacity levels, all cost money. The success of the Toyota Production System (TPS), which is the model for manufacturing, is certainly challenged to demonstrate a higher productivity for manual versus robotic operations. Integrated robotic cellular manufacturing is today's ticket. An article titled, "Machining Today and Tomorrow," printed by Modern Machine Shop (2007) is a great testimonial, describing the productivity improvements achieved by automated machining work-cells, at Roberts Tool Company, which is a tier 1 supplier to Boeing and other aerospace companies. The owner, Brad Hart is quoted as saying, "cells are the only way to make healthy profits", and then goes on to further state, "adopting lean and cellular manufacturing is essential if we are to keep this kind of manufacturing in the United States." At Roberts Tool Company, automation uses linear pallet pool systems, not industrial robotics, primarily because of the specialized products being machined, but either way, a form of automation was utilized in a cellular environment.

Not every work-piece or application should be automated, but the bottom line is to use the correct form of automation for the application and use manual labor where it makes sense. A big advantage of automated processes is that they eliminate the human variability that is introduced every time a person touches a process.

Other questions that owners and managers should be asking is how best to operate a given process. If there is no motivation to automate, then the following additional questions need to be answered: How reliable are your operators, shift over shift, everyday? What kind of efficiency are you getting out of the process? Efficiency can be determined by dividing the throughput into the total available time for a given interval.

Are you running production during breaks, lunch, and shift changes? Do your operators sometimes walk away to talk to someone, or do they simply appear to work slower one day versus another? Does your throughput vary from one shift to another in a multi-shift setting? Does your operator make mistakes, with the result that rework or downtime is incurred? Have you been in a position where production could not be run because an operator wasn't available? Do you find defects in your product downstream that should have been caught earlier? Have you shipped non-compliant product to a customer because a process such as rust inhibitor wasn't applied? Have you had to schedule emergency overtime or simply over time to try to meet a shipment for a customer?

Answering these questions should provoke thought about the flaws in the way you're running your process regardless of how well you designed it. You will probably realize you're not achieving the efficiency you thought you were. Responses to the questions above will show clearly whether manual operators introduce variables to the process.

The ROI Power of Industrial Robotics

Never automate a bad process is rule number one, because not every project is a good robot project. That being said, this section of Section one is meant to be thought provoking. In other words, if the lights are on and parts are being produced on a single shift basis, then why not compare the total cost of ownership to a robotic solution and see what happens.

Think about the cost savings resulting from running even a single-shift, lights-out operation, versus paying an operator $10 or more an hour to perform a redundant task. The ability to keep a robot system busy for approximately 1400 hours annually is the milestone for getting into the 25% annual return and three year payback. Obviously more working hours contribute to better ROI and vice versa. Looking even beyond the conventional one-, two-, or three-year ROI requirements used by many for yes/no decisions, examine the simple cost per hour to run a robot system. For example, most firms are hard pressed to find labor for much lower than $10 an hour in the US, including uniforms, training, benefits, and other costs. A $150,000 investment in automation will cost approximately $17/hr to run on a single-shift basis using a three year lease calculation and basing usage on 8 hours per day and 22 days per month. On a two-shift basis the $150,000 investment will cost $9/hr to run. The conventional ROI may not provide enough information to make a decision, so the decision making may be changed to comparison of robot vs. operator in terms of the cost per hour, and ultimately the profit per hour.

Running a plant unmanned for two shifts per day will significantly increase the profit per hour based on productivity gains and labor savings. Imagine going from $25/hr profit to $125/hr profit making the same part. That part is now being made more efficiently and with less labor cost. Fgr. 6 illustrates an example of the power of labor savings and increased productivity.

Assuming that manufacturing engineers participate in 40 1 K funds, and are concerned about saving for the future, then rate of return becomes important to them. Whenever money is invested in anything, the return on investment is important to the decision about whether there is value in making the investment. A financial planner will advise that, over time, the best investment vehicle in which to place personal money is the stock market, or a fund that includes the stock market index of companies. Fgr. 7 represents the periodic table of investment returns for virtually every type of mutual fund from the period of 1986 to 2005.

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Compare the cost of ownership "manual" versus an unmanned work-cell

Profit per hour ($/hr) --- Annual salary (in $000).

Profit per hour (man vs. machine) --- Annual labor costs (man vs. machine)

Assuming a single operator, and $5 profit per part

Assuming operator feeding system works 10% of time at the work-cell

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What is interesting about this table is that the average rate of return for the best performing funds over the time period is 13 percent. This 13-percent return is very good, and most people would be satisfied with an annual 13 percent return on their 401K's. Using the rule of 72 to calculate the number of years required to double an investment requires dividing 72 into the interest rate of 13, which shows that the investment would take 5.5 years to double in value.

Using 13 percent as the benchmark of a great rate of return, industrial robotics exhibiting a 13 percent return would never be justified, because waiting 5.5 years to earn the payback of the investment in full would never fly at most manufacturing companies. Qualified and well-defined robotic projects typically achieve payback periods of 1-2 years or less, and worst cases 3 years. Using the rule of 72, robotic investments deliver rates of returns of 25 percent and certainly in the 1 to 2-year payback period, 36 percent and greater. Where can a manufacturer invest funds and earn that kind of return? Let's apply these thoughts to a manufacturing chain, and try to understand how industrial robotics can impact the overall cost of producing a product.

The example in Fgr. 8 illustrates the point of how removal of significant direct labor costs from a process results in large savings.

This example illustrates a main reason why the direct labor component is one of the first categories to examine. Normally, the examination of robotic justification is on a micro level, often in terms of comparing the manual process to the robotic process for a specific task like welding or machine loading/unloading, grinding, or palletizing. The basic initial data inputs required to examine the economic feasibility for any application type across any industry are as follows:

Number of hours per shift, shifts per day, or hours per year, Number of operators per shift Throughput of manual operation in terms of work-pieces per Cycle time per piece for manual operation worked in the process hour

Amount of time used for breaks, vacations, and other scheduled downtime for manual operators in units of minutes Capital investment of automation including ancillary costs such as installation, training, rigging of equipment, freight, utilities, other Operator wage with full burdened benefits for insurance, uniforms, protective gear, vacation, disability, and other costs Estimated profit per work-piece

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Aluminum die cast component

Assumptions:

Direct labor; All other manufacturing cost; Total cost per piece; Annual volume of 80,000 pieces per year; Two shifts (16 hours/day)

*Assume 250 days per year and 16 hours per day This equals 4000 hours per year to produce 80 000 pieces

Fgr. 8 Industrial Robot Cost Savings Example

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Application

[Arc welding (including cutting)

Material removal (de-burring, polishing, grinding, buffing)

Machine tending 5 (conventional lathe, HMC, VMC ) Press tending Pick/place

Palletizing/De-palletizing ]

Manual Efficiency (%)

[ 30-35 25-30

55-60 40-50 70-75 70-75 65-70 ]

Robotic Efficiency (%)

[ 90 + 90 + 90 90 90 90 90 ]

Fgr. 9 Typical Efficiency Ratings by Application for Manual vs. Robotic (not including time for changeover setup equipment)

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The inputs will compare robotic costs with manual process costs in terms of productivity. Automation is exploiting the ability to produce more output with the same assets because of the efficiency gains that can be achieved, as well as producing more output with less labor. A critical component of the efficiency side of the comparison is that robotics exhibit 98 percent uptime, meaning they are always available to perform a task. Industrial robots are very reliable, and published mean time between failure (MTBF) data exceeds 70,000 hours. The efficiency of the robotic process is still a function of setup, changeover, delivering raw material, and discharge of finished material. The robot cannot control these parameters of the process. A good efficiency to use for most robotic applications factoring in the uncontrolled parameters is 90 percent.

The example in Fgr. 8 shows that $2.40 per piece can be saved due to reductions in the direct labor component that happened to contribute 30 percent of the machining and deburring/cleaning process costs. The deburring/cleaning, as well as the machining, contributed 50 percent of the overall manufacturing cost. If the goal is to make 80,000 pieces per year, then the labor savings is approximately $192,000 annually. The other area for significant savings is the hours saved to produce the 80,000 pieces. The example is using 4000 hours to produce 80,000 pieces per year. If the productivity gain between the manual labor and the robotic labor is approximately 30 percent, the 80,000 pieces can be produced in 2800 hours with the same equipment, versus 4000 hours. Each hour would be worth $320 because 80,000 x $16/piece cost, divided by 4000 equals $320 per hour, to produce about twenty parts. So the 1200hour capacity gain enables the customer to save another $384,000 annually. That extra capacity could be used to produce a different part or more of the same part, if more than 80,000 pieces can be sold. This is only an example, but it shows the power of labor and productivity savings when a process is automated.

Fgr. 9 describes efficiency that is typically derived from robotic applications, compared with the manual efficiency counterpart.

Efficiency erosion due to changeover, setup, and downtime, whether machine- or material flow- related, has other parameters that must be considered when justifying a robotic system. The rule is that, if the robot is inhibited in its ability to achieve a significant increase in process efficiency, the project may not be a candidate for robotics. The efficiency gain that has become notorious for industrial robotics is a reason why engineers should not be concerned with the exact cycle time for a task to be completed from start to finish. If you measured the cycle time from start to finish for a task manually and with a robot, the time for the operator to complete the task will usually be less. The efficiency gains made over an hour, shift, day, and annually, is what will drive the increased productivity, because an operator cannot continually repeat the same cycle over and over in the allotted time frame. The important benchmark is finished pieces at the end of a time period, not the length of the individual cycle. Looking at the influence of efficiency, Fgr. 7 illustrates how a robotic solution makes sense in the automation of a single machine work-cell, in a single-shift application.

The example in Fgr. 10 is designed to allow the user to input information about how production is run for the targeted process as well as enter efficiency ratings, cost of investment, and profit per part. Each application type will have additions and subtractions regarding the inputs, but for the most part, Fgr. 10 shows an ROI calculator that can be used for all types of applications.

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Fgr. 1 0 ROI Calculator Example for Machine Tending

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The justification side of Fgr. 10, shows the productivity comparisons, and how they relate to increased gross profit.

Additionally, for this example, the justification describes the tonnage that the operator has to handle manually on an annual basis.

OSHA regulations limit the amount of weight that can be handled by an operator. The threshold is typically around 35 lb. before an operator is required to use a lifting device. Ergonomics is a big deal to many firms because of the cost of employee injuries, so that asking someone to lift tons of materials a day is not acceptable in many workplaces, Lifting devices help, but they reduce the throughput.

Generally, the heavier the component, the larger the safety risk, so that ergonomic benefits further justify robotics.

The following example helps illustrate the usage of the ROI calculator. Figure 11 described five parts starting as forgings that are machined in two operations (turning and drilling). The annual part volume is as follows:

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Mixed family of part types Operations: Turning in lathe Drilling in Vertical Machining Center

Fgr. 11 Example of Sample Part For a Machining Cell

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Other pertinent information about these applications is that there are 2050 hours available annually for a single shift, and the operator costs $12 an hour, including wages and total cost burden. The machines exist, and they are going to be redeployed for the six new part styles. The total number of machine hours required to achieve the annual demand is 1245 hours at 100 percent efficiency.

Evaluating the real efficiency, the operator spends 60 percent of time in loading and unloading parts. Including cutting tool changes and work-holding setups for the five parts that the robot solution is estimated to run at 90 percent efficiency. Single-shift production is planned.

The conclusions derived from this example show significant advantages to the user from automating the six work-pieces. The comparison of manual vs. robotic are as follows:

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Criteria

[ Capacity requirements (hours)

Overtime required Cost of automation Extra capacity in one year Cost per hour for employee Cost of ownership Cost of operator after 1.085 years (employee-specific items)

Cost of increased process consumables Tax Benefits ]

Manual R (60% eff.)

[ 2075 Yes NIA (-25)

$12/hr

$ 25001yr

$ 12hr ]

Robot (90% eff.)

[ 1383 667 No

$ 150,000

$ 17/hr NIA

$0/hr 15% increase TBD ]

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The comparison indicates that the robot cell will produce the parts in 692 hours less than is needed for the manual operation.

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Fgr. 12 Concept Robotic Work-cell for Machine Tending Guarding w/ interlocks 5 drawers part queue with part trays Part turnovers I Mori Seiki 2550 lathe with a robot interface (door and 110)

Mori Seiki Dura-Vertical 5100 with Robot interface ( door and I/O) FANUC M16iC RJ30iA

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Those 692 additional available hours on the machines are extremely valuable. The data also indicate that overtime would be required for manual production to keep up with demand, and more overtime further will be needed every time the operator fails to achieve the 60% efficiency. When the investment is factored into a cost per hour calculation, this type of automated system ends up costing less than paying an operator to run a work-cell on a per hour basis. The example for the parts detailed in Fgr. 10 yields a 1.085 year payback for a single-shift application. If the application included additional production shifts, the payback period would be even shorter because more labor would be saved by use of the robot.

Based on elimination of labor and the additional throughput, the ROI calculator indicates the robotic cell is paid for at the end of the 1.085-year payback. The robot operator will be usable for at least 15 years, and will always be re-deployable. The main limitations of the robot are its reach, and the payload capacity while carrying the gripper and parts. An important advantage is the ability for the robot cell to run "lights-out" at the end of a shift, when an operator can fill the raw material queue and let the robot cell run until the queue is empty. The lights-out concept works very well for machine- tending operations because typically, a queue of parts is staged for the robotic cell.

---Additional Benefits of Implementing Industrial Robotics ---

One drawback of labor savings and increased throughput is the requirement to run the robotic work-cell with an operator standing at the cell full-time. Standard welding cells are useful, but they typically require an operator to stand at the cell fulltime, to feed and empty the system of parts. Most systems in general, but specifically welding, will utilize at least two stations for loading and unloading, and the operator is often performing a setup such as tack welding, or loading/unloading parts into and out of fixtures while the robot is welding. This concept allows the load/unload time or the setup time to be included in the robot welding cycle.

It’s an advantage if the welding cycle is long enough to allow the operator to walk away from the system. However, it must be kept in mind that there will always be some component of labor in feeding and removing product from a robotic work-cell, although innovations in robotic bin picking now allows robots to randomly pick piece parts from a bin for pick and place, or machine tending. There also may be value in paying the operator to load and unload parts for the welding cell. The robotic process is intended to provide higher levels of consistent quality and consumable usage than a manual process. Additionally, there are savings to be achieved in the arc-on time versus manual welding of the work-piece. The issue comes down to having someone always nearby, to feed the cell.

Applications that are cycle-time intensive, whether in welding, machining, or material removal, are excellent candidates for automation because of the "on-process" efficiency gains previously mentioned. The increased throughput is also sometime misrepresented. In the earlier example, the user may not care about the additional capacity of the robot cell because the user can't find additional demand for machining work. Even though the robot yields a higher throughput than the manual operation, if there is no demand for the additional throughput, then the ROI aspect of additional productivity needs to be considered. The benefit of robotics is that they only need to run on demand, and don't require to be run all the time to achieve some ideal operational result, as if there were concerns in shutting the robot on and off at a regular frequency. The robot system allows for users to have flexible capacity without having to manage temporary workers for peaks and valleys of production.

Managing and training temporary employees is a huge task and there is a cost. This worry can be eliminated by introducing the new machine operator in the form of the industrial robot. The goal in the author's opinion for any robotic system is to strive for "lights out" meaning unmanned production.

Arc-welding ROI calculators will exploit information about arc-on time and not machine efficiency. Arc-on time is defined as how much time the welding arc is on, depositing filler metal, measured over 10-minute durations and expressed as percent efficiency. Thirty percent for a manual operation versus greater than 90 percent efficiency, validates the rule of thumb that one robot can match the productivity of three operators. Material removal operations are regarded as being similar to welding in terms of how the ROI analyzes the on-process time of manual vs. robotic efficiency.

Other areas of exploiting cost savings that contribute to the total ownership cost (TOC) are: Scrap rates Cost of overtime Cost of personal items to enable operator to perform task Tax rate Hurdle rate Cost of increased utilities, consumables related to process Depreciation method of capital investment Arranging material flow into cells, reducing WIP and increasing throughput One of the last advantages that are difficult for ROI calculators to validate, as been previously mentioned, is that the robot is a re- deployable asset over the course of its useful life. A robot should be viewed as a 15-year plus employee that always shows up to work, never gets sick, never complains, and is ready to go 8760 hours per year. Should the original, intended project disappear, the robot can be re-deployed for whatever comes next. The constraint is the robot reach and payload. Robots often are converted in terms of the process, such as material handling to welding, or vice versa. Assuming the original payback of the robot was under 3 years, that leaves at least 12 years to deploy an asset with the only remaining costs for electrical usage and maintenance.

As a point of interest, there are tax incentives for capital equipment, one of which is specifically called Section 179.

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Cash Flows

- Find the present value of each period's cash flows, either by using a financial calculator or by referring to a present value table. In the example below, the discounted value of $50,000 at a 10% cost of capital is

$45,400. The other values are also found using a present value table

* Discount the cash flows at each time period by the cost of capital.

Total the cash flows, and include the initial investment ($ 100,000 ) in the equation. The calculated cash flow number is the net present value (NPV). Year 1 Year 2 Year 3 Year 4 Time line In years NPV = -100,000 + 45,400 + 33,000 + 22,500 + 13,800 = 14,700

Fgr. 1 3 Net Present Value (NPV) as a Robotic Justification Tool

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Applying Present Values to Evaluate the Robot Invest men t

Another exercise would be to select characteristics that might guide the choice among the many projects that manufacturing engineers might have on their plates. Finance managers will want to examine the total cost of ownership inputs when the project is evaluated for final approval. A cash flow stream is used to calculate the payback period using net present value. The hurdle rate, or cost of capital, is typically included as the rate to be used in the net present value calculations. The hurdle rate allows the cash flows in each year to be factored along with the rate of return required for the firm, based on the cost of borrowing money. Fgr. 13 shows an example of the present and net present value financial tools that are commonly used.

When the Net Present Value exceeds zero, the project is considered to be good. The stronger the NPV value, the stronger the program is thought to be.

The purpose of Fgr. 13 is to show how common financial tools can become a barometer in determining whether a robotic project is a good investment and when the payback will occur. The first step is to examine the savings from labor, productivity gains, and all the other criteria previously reviewed. At the end of this first step there is a savings in terms of dollars. The example in Figure 13 indicates that there was a savings of $50,000 the first year.

Below is an example of where the savings came from for that first year:

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Labor Savings (single shift) $23,000 2000 more parts produced at $ 10/part profit $20,000 Overtime reduction $ 4,000 Reduction in scrapped parts $ 3.000 Total $ 50,000

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This step needs to be repeated for each of several years, the number of years being discretionary. Usually four (4) or more make sense. It’s necessary to select at least the number of years beyond the payback period that occurs when the initial investment equals the sum of the savings each year. In the example above, the payback period will be around 2.5 years, so it’s necessary to extend the cash flow stream from the savings beyond three years. The initial total investment, including the robot system, installation, training, and shipping costs also must be included. The analysis will start with a negative ($100,000) because at time zero, the user purchased the system with total costs of $100,000. Year one savings was determined to be $50,000, and year two was $40,000, and so on for the example in Fgr. 13. A net present-value (NPV) table found on calculators or in financial books is used to cross-reference the number of years, whether it be year one, two, three, four, etc., and the discount factor, which is a percentage.

The present-value table is important because it allows an equally valid comparison of the value of the investment made now, and the realized savings in the future, that are paid at different times. The value of a dollar today is always worth more than the value of a dollar in the future because the dollar can be invested today and a year from now it will be worth more than a dollar. The discount rate used from the present-value chart is whatever interest rate the firm thinks could be earned if the money was invested elsewhere. For example, the 13 percent earned in a mutual fund, which would then become the discount rate used in the present-value tables.

The discount rate is multiplied by the savings for each year to arrive at a new value for each year called the discounted cash flow.

This process of multiplying the discount rate by the savings at the end of year one, two, etc. enables all the dollars through the time line to be represented in a present-value sense. This step is repeated for as many years as the cash flows to be analyzed. The sum of the total investment made today, plus the discounted cash flows from each year, will provide the NPV value.

Resources that Manufacturers Need to Become Familiar With

Despite the volume of robot sales, one of the commonly-held beliefs of the robot industries is that 90 percent of the manufacturers in the US have yet to purchase their first industrial robotic system. That is sort of a staggering statistic, given the potential return on investment data, as well as all the other benefits that organizations can achieve by automating. Traveling in southern Indiana a few weeks ago, the author visited two potential clients, to review possible robotic projects. Both clients had no automation installed but thought it was time to do something. During a meeting with the manufacturing engineer and the plant manager the conversation was classic, relative to about every client that has yet to automate. The stereotypical discussion goes as follows: We have to automate but don't know exactly where We have a lot of changeover, so a robot won't work for us What does the robot cost We really haven't started looking at our product mix, and we will have to hunt for part prints, but we will find them We can't find reliable operators so we have to do something In general, many engineers and managers get locked up in taking the first steps to identify good or bad automation projects. There is a huge dichotomy in manufacturing between those that embrace and use technology and those that have stuck to doing things the way they always have. The most successful firms have integrated robotics into their manufacturing strategy across the entire manufacturing value stream from fabrication to paint. Typically, several years after the first robot is installed, the factory has several, and often many, robotic work-cells. Moving past the first step or road block in implementing automation involves education and a plan.

Education is available in many forms and from many organizations. Several agencies offer educational support through consulting and websites, with free published articles about manufacturing.

Organizations such as the Robotic Industries Association, and the National Association of Manufacturers (NAM) are committed to the welfare of their industry constituents. NAM is an organization that focuses on the manufacturer and policy issues related to taxes, OSHA, EPA, trade, tort, regulations, energy, pensions, health care, and the workforce. Industry- focused organizations such as the American Welding Society (AWS, http://www.aws.org), the Fabricators & Manufacturers Association, International (FMA, http://www.fmanet.org), and others, provide industry contacts, case studies, and local consulting.

NACFAM is a policy research organization that focuses more on the manufacturing issues of technology and innovation, workforce preparedness, and supply chain value creation. One of the more recent initiatives of NACFAM is the concept of Network-Centric Manufacturing. The concept involves the OEM developing intense collaboration with its supplier network to focus on driving down costs, improving delivery, releasing products faster to market, and raising quality, among other benefits. The concept is powerful when every supplier is focusing on what they do best to provide as much value as possible to the OEM that supplies the end-product to the marketplace.

The definition of value chain in terms of the Network-Centric concept will change the relationship of suppliers and OEM from a coordinated transactional arrangement to an intense collaboration involving product design, and joint product development. An example of Network-Centric Manufacturing in practice is the Supplier Excellence Alliance (SEA), which is a membership organization of prime contractors, tier-one suppliers, and other supplier companies in the aerospace, defense, and space industries. SEA was formed in 2004 to promote the adoption of lean manufacturing, and other techniques that bolster supply-chain efficiency and competitiveness.

The FANUC Robotics America Save Your Factory website provides information about the hidden and real costs of off-shoring manufacturing, as well as encouraging manufacturers about embracing automation and lean strategies. The Society of Manufacturing Engineers (SME, http://www.sme.org ), and the RIA organizations, are resources from whom to obtain the latest information on the use of technology, emerging technologies, and case studies, as well as networking with other manufacturers. These organizations also offer various training capabilities. It’s essential to have the employees within the manufacturing firm participate in some way with these various trade organizations, especially from the educational standpoint.

Owners and managers of small and medium-sized manufacturers have a lot of tools available for assistance in driving productivity and performance, as discussed in just some of the examples discussed in this section. The text in subsequent sections will provide guidance to implementing the robotic plan, relying on the best practices used today.