The Consequences of “Offshoring”
Six years ago, I wrote a book called The Causes & Consequences of Exporting Our Manufacturing Jobs. At the time, the U.S. was not experiencing an obvious crisis, and we couldn’t convince a publisher to print the book. Now, people acknowledge an economic crisis—but don’t seem to understand the root causes.
In just the past 10 years, the United States has lost 32% of its manufacturing jobs. In June of 1999, there were 17.3 million manufacturing employees in the United States. In June of 2009, there were just 11.8 million manufacturing employees (source: Bureau of Labor Statistics) despite the fact that the U.S. population has grown over that same time period (source: U.S. Census Bureau).
Although I have a book and blog on the causes of our industrial decline, I have not blogged on the consequences of this decline.
10 Consequences of Exporting Manufacturing Jobs
1. The industrial sector was largely responsible for both the economic prosperity of the United States and the jobs and spending power it created. It was industries such as automotive, electronics, pharmaceutical, furniture, etc. that provided millions of jobs for Americans. Without these jobs, people consume and invest less, so most other businesses are negatively affected.
2. The government has lost tax dollars from corporations and individual income taxes. With sky-rocketing unemployment and more uninsured families, the government needs more money than ever—but there are far fewer individuals and corporations contributing.
3. Products are easy to export—and can bring money into the United States. Services, on the other hand, are more difficult to export, so even if our service base grows, we cannot expect to gain the same global consumer base that manufacturing offers.
4. Without the industrial sector, decent opportunities for the unskilled or uneducated are scarce. The manufacturing base provided high-paying jobs to disadvantaged adults—and gave them a chance to prosper and care for their children.
5. With the exportation of our industrial jobs, we leave fewer future opportunities for our children. Unless our children become “super-specialized,” they will have few options in the U.S. marketplace.
6. Once we export these jobs, it’s difficult to bring them back. The nation loses expertise in industrial technologies.
7. When the labor costs rise in some of these “low-wage” countries, the cost advantage disappears.
8. Although the costs of production are much lower in other countries, the costs of poor quality, reliability, transportation, and inadequate safety have often eclipsed the labor savings.
9. The factories in low-wage countries often experience high rates of internal scrap and inefficiencies. These end up costing corporations millions of dollars, but much of the cost hidden by plant personnel and unaccounted for.
10. It took the United States decades to develop production and business systems for manufacturing. Now, the U.S. simply transfers our technology, knowledge, and secrets to low-cost foreign competitors.
Conclusion
We must make our industrial base more competitive, so we can keep jobs and wealth in the United States. If we do not make the necessary improvements, we leave little for our children other than massive debt. Is that the legacy we will choose to leave?
Tuesday, July 14, 2009
Monday, February 23, 2009
Government Ignores Root Causes
The current economic situation in the United States should come as no surprise. Those aware of the root causes anticipated this crisis long ago. The steady and steep decline in our manufacturing sector and education levels over the past 30 years left the U.S. in a weakened position. The collapse of the financial system simply exposed the more fundamental deficiencies in our economy.
Now, the U.S. government is scrambling to institute a variety of “stimulus packages” to ameliorate the situation. Unfortunately, many of these programs do not address root causes—and will put our children into an even deeper hole. Our inability to compete globally did not result solely from the financial meltdown and will not be remedied by massive government spending.
Americans can pull ourselves out of this crisis, but we must understand the causes of our decline. The right decisions require fortitude, discipline, and the American people taking responsibility for their own futures. The right decisions will not be popular, even though they would be effective and ultimately provide our children with economic strength and opportunities.
Most politicians do not appear to understand the behaviors of the manufacturing, education, and financial sectors responsible for our economic collapse. This article briefly describes only a few root causes. These are not the only causes, but they are significant and generally unacknowledged.
Manufacturing
The United States has been steadily losing manufacturing jobs over the past 30 years. In fact, 35% of our high-paying manufacturing jobs have disappeared (Bureau of Labor Statistics). More amazing is that U.S. demand for products has nearly tripled during that same period (U.S. Bureau of Economic Analysis).
Sending jobs overseas has weakened the United States considerably. Manufacturing jobs have traditionally been high-paying jobs, and with the loss of those jobs, Americans have less spending power and the government fewer income tax dollars.
Manufacturing executives have been exporting our jobs to “lower-wage” countries. Their excuses have included high labor and benefit costs and strict regulations. However, an inside look would reveal that American manufacturers have been staggeringly inefficient in producing parts “right the first time.” The amount of money spent on waste, rework, and inspection has cost manufacturers dearly. Executives opted to export jobs rather than to optimize manufacturing systems and address the gross internal waste.
To make matters worse, manufacturers spend 2% of revenue on warranty costs—even as they lose money. The deaths, injuries, and property damage associated with consumer products cost the U.S. over $700 billion annually (Consumer Product Safety Commission). There have been over 76,000 separate vehicle recalls over the past 40 years and each recall may involve hundreds of thousands of vehicles. The billions of dollars spent on vehicle warranty annually have devastated the former “Big 3.” Had our manufacturing sector truly optimized their designs and production systems, they would have remained highly profitable while employing American workers.
Manufacturing executives have collected obscene compensation while running American companies into the ground. Rather than being rewarded for long-term corporate health, market share growth, and developing innovative products that are competitive with global leaders, they have been grossly compensated while destroying our brands. Long-term optimal strategizing has been replaced by short-term, reactionary decisions in product development and manufacturing.
Education
We have an education crisis in this country. Consider the following statistics from the National Center for Education Statistics:
1. Our 15 year-olds rank 25th worst in standardized mathematics testing among the 30 participating countries!
2. Of the Ph.D. degrees we issue in science and engineering, over 60% go to foreign nationals. The majority of graduate students (in technical disciplines) in our publicly funded universities are not Americans.
3. The number of American 18 – 24 year-olds who receive scientific degrees has fallen to 17th in the world. We were 3rd in the 1970s.
Children in the U.S. spend less time in school than children in the high-ranking countries. Most parents and teachers do not comprehend the criticality of mathematics and science, but these are the skills necessary for innovation and technological achievement—the areas needed to fortify our economy.
While millions of Americans are looking for jobs, many technical fields still lack qualified American candidates, so the work is exported. A strong U.S. economy requires the dedication to excel in demanding but vital disciplines needed to stimulate future growth (e.g. engineering, science, math, information technology).
Conclusion
There are many root causes responsible for our economic collapse. This recession will be deeper than necessary due to the steady decline of our manufacturing base and technical achievement over the past 30 years. It is time for frank discussions about the values and work ethic that led to America becoming an economic powerhouse and the apathy that has led to its decline. Unfortunately, our children will face debilitating debts to pay for our attempts to “quick-fix” the economy.
Author: Allise Wachs, Ph.D., President, Integral Concepts, Inc.
http://www.integral-concepts.com/, allise@integral-concepts.com
Now, the U.S. government is scrambling to institute a variety of “stimulus packages” to ameliorate the situation. Unfortunately, many of these programs do not address root causes—and will put our children into an even deeper hole. Our inability to compete globally did not result solely from the financial meltdown and will not be remedied by massive government spending.
Americans can pull ourselves out of this crisis, but we must understand the causes of our decline. The right decisions require fortitude, discipline, and the American people taking responsibility for their own futures. The right decisions will not be popular, even though they would be effective and ultimately provide our children with economic strength and opportunities.
Most politicians do not appear to understand the behaviors of the manufacturing, education, and financial sectors responsible for our economic collapse. This article briefly describes only a few root causes. These are not the only causes, but they are significant and generally unacknowledged.
Manufacturing
The United States has been steadily losing manufacturing jobs over the past 30 years. In fact, 35% of our high-paying manufacturing jobs have disappeared (Bureau of Labor Statistics). More amazing is that U.S. demand for products has nearly tripled during that same period (U.S. Bureau of Economic Analysis).
Sending jobs overseas has weakened the United States considerably. Manufacturing jobs have traditionally been high-paying jobs, and with the loss of those jobs, Americans have less spending power and the government fewer income tax dollars.
Manufacturing executives have been exporting our jobs to “lower-wage” countries. Their excuses have included high labor and benefit costs and strict regulations. However, an inside look would reveal that American manufacturers have been staggeringly inefficient in producing parts “right the first time.” The amount of money spent on waste, rework, and inspection has cost manufacturers dearly. Executives opted to export jobs rather than to optimize manufacturing systems and address the gross internal waste.
To make matters worse, manufacturers spend 2% of revenue on warranty costs—even as they lose money. The deaths, injuries, and property damage associated with consumer products cost the U.S. over $700 billion annually (Consumer Product Safety Commission). There have been over 76,000 separate vehicle recalls over the past 40 years and each recall may involve hundreds of thousands of vehicles. The billions of dollars spent on vehicle warranty annually have devastated the former “Big 3.” Had our manufacturing sector truly optimized their designs and production systems, they would have remained highly profitable while employing American workers.
Manufacturing executives have collected obscene compensation while running American companies into the ground. Rather than being rewarded for long-term corporate health, market share growth, and developing innovative products that are competitive with global leaders, they have been grossly compensated while destroying our brands. Long-term optimal strategizing has been replaced by short-term, reactionary decisions in product development and manufacturing.
Education
We have an education crisis in this country. Consider the following statistics from the National Center for Education Statistics:
1. Our 15 year-olds rank 25th worst in standardized mathematics testing among the 30 participating countries!
2. Of the Ph.D. degrees we issue in science and engineering, over 60% go to foreign nationals. The majority of graduate students (in technical disciplines) in our publicly funded universities are not Americans.
3. The number of American 18 – 24 year-olds who receive scientific degrees has fallen to 17th in the world. We were 3rd in the 1970s.
Children in the U.S. spend less time in school than children in the high-ranking countries. Most parents and teachers do not comprehend the criticality of mathematics and science, but these are the skills necessary for innovation and technological achievement—the areas needed to fortify our economy.
While millions of Americans are looking for jobs, many technical fields still lack qualified American candidates, so the work is exported. A strong U.S. economy requires the dedication to excel in demanding but vital disciplines needed to stimulate future growth (e.g. engineering, science, math, information technology).
Conclusion
There are many root causes responsible for our economic collapse. This recession will be deeper than necessary due to the steady decline of our manufacturing base and technical achievement over the past 30 years. It is time for frank discussions about the values and work ethic that led to America becoming an economic powerhouse and the apathy that has led to its decline. Unfortunately, our children will face debilitating debts to pay for our attempts to “quick-fix” the economy.
Author: Allise Wachs, Ph.D., President, Integral Concepts, Inc.
http://www.integral-concepts.com/, allise@integral-concepts.com
Friday, January 30, 2009
Accelerating Lithium-Ion Battery Development
The race is on to develop and introduce alternative energy technologies. Despite the recent relaxation of oil prices (due to the global recession), the thrust toward adopting new energy sources continues and is about to receive a further boost from Congress. Yet, while the opportunities abound, there will be winners and losers. Only those companies quickest to deliver reliable and high performing batteries utilizing cost-effective manufacturing will thrive.
Some Challenges
The challenges in lithium-ion battery development include finding the best electrolyte and electrode materials that boost capacity. Formulation problems for the electrolyte are challenging—although optimization methods for experimenting with mixtures do exist.
These materials must also behave safely in a wide variety of environments. The notorious safety problem involving laptop batteries is still fresh in consumers’ minds. A better job in robust design and/or manufacturing—or possibly the consideration of better separator materials could have prevented these problems.
Material expansion and contraction must be managed. The efficiency of cell charging must be optimized. Potential for cell damage must be minimized. The ability to manage internal and external heat sources must be refined. Aging and its consequences must be considered. Electrical and mechanical interfacing must be managed. Consumer misuse and abuse must be accounted for. A host of additional regulatory requirements will also constrain development.
Once the product has been successfully designed, another set of challenges emerges—notably, the ability to manufacture the product in large quantities or scale.
Many scientists and engineers are working feverishly to overcome the numerous obstacles inherent in alternative energy technologies and applications. But, just as with any product and process development plan, many steps are required to conceptualize, design, develop, test, and launch products.
Scientists and engineers spend years studying physical sciences, life sciences, and engineering disciplines such as chemistry, physics, mechanics, biology, biochemistry, thermodynamics, fluid mechanics, material science, etc. The underlying knowledge of these disciplines and their experience guides their research and development efforts.
However, few scientists and engineers specialize in optimization techniques that drastically reduce the time to develop optimal products (optimal in the sense of performance, reliability, and safety). These methods are found in the fields of Mathematics, Statistics, and Operations Research. Researchers should leverage the expertise in these areas by including mathematicians who apply optimization methods to engineering and product development challenges. Unfortunately, partnerships between researchers and optimization specialists are rare.
A Solution
There are many quantitative methods that not only shorten the research & development process, but also result in optimal solutions (best performance, cost-effective, safe, reliable, and robust products). These methods can minimize the development time by strategically hunting down potentially fruitful combinations to experiment with. Rather than trying dozens or even hundreds of design/material combinations, these optimization methods identify just a few calculated combinations. From the data acquired through shortened experimentation, mathematical models are produced. These math models indicate solutions or an improved direction to proceed along.
Optimal solutions are found only by quantitatively understanding how design or process factors (that the designer has control over) impact the ultimate performance requirements. More importantly, design and process factors interact with each other to impact performance and safety, but many types of experiments do not reveal these critical interactions.
Ultimately, mathematical models that predict performance, reliability, and safety, are vital to the success of the development process. With these math models, all aspects of the design can be refined so that the best performance is achieved—while meeting all other safety and regulatory requirements.
Without these mathematical models, optimizing a single performance requirement (such as maximizing the energy density of a lithium-ion battery) is a challenge. To make matters worse, most products must meet multiple performance requirements, and the ability to simultaneously optimize over a large number of performance requirements is critical.
In addition to meeting a host of performance and regulatory requirements, the products must be safe, affordable, “manufacturable,” reliable, serviceable, etc. Tradeoffs between features and costs are inevitable. Optimizing one feature (such as minimizing lithium-ion battery charging time) may result in degradation of another performance characteristic (such as energy density of the cell).
Multi-response optimization is a technique commonly used to identify materials, dimensions, geometries, compositions, and manufacturing settings that will satisfy a large number of performance and cost requirements simultaneously. Essentially, it ensures feasibility and manages the inherent “tradeoffs” or compromises prevalent in the design and manufacture of products.
Hopefully, engineers and researchers in alternative energy development will consider the advantages of including mathematicians who specialize in product optimization on their teams. The potential gains include drastic reductions in development time and risk associated with safety and reliability.
Some Challenges
The challenges in lithium-ion battery development include finding the best electrolyte and electrode materials that boost capacity. Formulation problems for the electrolyte are challenging—although optimization methods for experimenting with mixtures do exist.
These materials must also behave safely in a wide variety of environments. The notorious safety problem involving laptop batteries is still fresh in consumers’ minds. A better job in robust design and/or manufacturing—or possibly the consideration of better separator materials could have prevented these problems.
Material expansion and contraction must be managed. The efficiency of cell charging must be optimized. Potential for cell damage must be minimized. The ability to manage internal and external heat sources must be refined. Aging and its consequences must be considered. Electrical and mechanical interfacing must be managed. Consumer misuse and abuse must be accounted for. A host of additional regulatory requirements will also constrain development.
Once the product has been successfully designed, another set of challenges emerges—notably, the ability to manufacture the product in large quantities or scale.
Many scientists and engineers are working feverishly to overcome the numerous obstacles inherent in alternative energy technologies and applications. But, just as with any product and process development plan, many steps are required to conceptualize, design, develop, test, and launch products.
Scientists and engineers spend years studying physical sciences, life sciences, and engineering disciplines such as chemistry, physics, mechanics, biology, biochemistry, thermodynamics, fluid mechanics, material science, etc. The underlying knowledge of these disciplines and their experience guides their research and development efforts.
However, few scientists and engineers specialize in optimization techniques that drastically reduce the time to develop optimal products (optimal in the sense of performance, reliability, and safety). These methods are found in the fields of Mathematics, Statistics, and Operations Research. Researchers should leverage the expertise in these areas by including mathematicians who apply optimization methods to engineering and product development challenges. Unfortunately, partnerships between researchers and optimization specialists are rare.
A Solution
There are many quantitative methods that not only shorten the research & development process, but also result in optimal solutions (best performance, cost-effective, safe, reliable, and robust products). These methods can minimize the development time by strategically hunting down potentially fruitful combinations to experiment with. Rather than trying dozens or even hundreds of design/material combinations, these optimization methods identify just a few calculated combinations. From the data acquired through shortened experimentation, mathematical models are produced. These math models indicate solutions or an improved direction to proceed along.
Optimal solutions are found only by quantitatively understanding how design or process factors (that the designer has control over) impact the ultimate performance requirements. More importantly, design and process factors interact with each other to impact performance and safety, but many types of experiments do not reveal these critical interactions.
Ultimately, mathematical models that predict performance, reliability, and safety, are vital to the success of the development process. With these math models, all aspects of the design can be refined so that the best performance is achieved—while meeting all other safety and regulatory requirements.
Without these mathematical models, optimizing a single performance requirement (such as maximizing the energy density of a lithium-ion battery) is a challenge. To make matters worse, most products must meet multiple performance requirements, and the ability to simultaneously optimize over a large number of performance requirements is critical.
In addition to meeting a host of performance and regulatory requirements, the products must be safe, affordable, “manufacturable,” reliable, serviceable, etc. Tradeoffs between features and costs are inevitable. Optimizing one feature (such as minimizing lithium-ion battery charging time) may result in degradation of another performance characteristic (such as energy density of the cell).
Multi-response optimization is a technique commonly used to identify materials, dimensions, geometries, compositions, and manufacturing settings that will satisfy a large number of performance and cost requirements simultaneously. Essentially, it ensures feasibility and manages the inherent “tradeoffs” or compromises prevalent in the design and manufacture of products.
Hopefully, engineers and researchers in alternative energy development will consider the advantages of including mathematicians who specialize in product optimization on their teams. The potential gains include drastic reductions in development time and risk associated with safety and reliability.
Monday, January 12, 2009
How to Regain American Manufacturing Jobs (Part III)
This blog is broken into three parts. Part I describes costly manufacturing sector problems. Part II explains the primary causes of the problems. Part III offers solutions.
The Solution
The fields of applied mathematics and statistics are well-armed with quantitative methods available for predicting product performance and for predicting the output from manufacturing processes. If a manufacturer can predict performance, it can also prevent poor performance.
Unfortunately, manufacturers suffering from failed new product launches, high internal scrap, and premature failures in the marketplace are NOT predicting or preventing. As a result, they add even more cost by purchasing inspection equipment to try to detect defective product (so it doesn’t get to customers).
Many companies argue that they do have predictive models for predicting product performance. Unfortunately, most are computer-based models (with only theoretical relationships included), and clearly, many are inadequate—otherwise, there wouldn’t be such devastating dollar losses due to internal scrap, warranty costs, recall costs, litigation costs, losses in customer loyalty, high inspection costs, excessive equipment down-time costs, etc.
Physical models are required for predicting product performance. Physical models are equations developed from actual data (collected under a variety of strategically determined conditions/scenarios). .
Collecting physical data from testing can be expensive, but there are many optimization methods for minimizing the amount of data required while maximizing the amount of information produced.
Physical models can predict product performance under infinite sets of conditions, so engineers understand how their products will perform in a wide variety of environments (i.e. identify robustness). Furthermore, the predictive models can uncover design flaws quickly, so design adjustments can readily be made.
Process performance and tool wear can be best predicted by Statistical Process Control (SPC). A plant without proper SPC is a plant that reacts to problems—rather than preventing them. Unfortunately, I have rarely seen SPC applied properly during the past 20 years (while working in numerous plants world-wide).
Many quantitative methods can be used to make U.S. manufacturing plants competitive and highly profitable. Unfortunately, we generally do not see these methods being used—at least not properly. These quantitative methods are mastered by applied mathematicians and industrial statisticians, but rarely do we see highly credentialed mathematicians or statisticians working in manufacturing environments.
If our manufacturers continue to ignore these predictive modeling and optimization techniques, U.S. manufacturing will continue its precipitous decline.
The Solution
The fields of applied mathematics and statistics are well-armed with quantitative methods available for predicting product performance and for predicting the output from manufacturing processes. If a manufacturer can predict performance, it can also prevent poor performance.
Unfortunately, manufacturers suffering from failed new product launches, high internal scrap, and premature failures in the marketplace are NOT predicting or preventing. As a result, they add even more cost by purchasing inspection equipment to try to detect defective product (so it doesn’t get to customers).
Many companies argue that they do have predictive models for predicting product performance. Unfortunately, most are computer-based models (with only theoretical relationships included), and clearly, many are inadequate—otherwise, there wouldn’t be such devastating dollar losses due to internal scrap, warranty costs, recall costs, litigation costs, losses in customer loyalty, high inspection costs, excessive equipment down-time costs, etc.
Physical models are required for predicting product performance. Physical models are equations developed from actual data (collected under a variety of strategically determined conditions/scenarios). .
Collecting physical data from testing can be expensive, but there are many optimization methods for minimizing the amount of data required while maximizing the amount of information produced.
Physical models can predict product performance under infinite sets of conditions, so engineers understand how their products will perform in a wide variety of environments (i.e. identify robustness). Furthermore, the predictive models can uncover design flaws quickly, so design adjustments can readily be made.
Process performance and tool wear can be best predicted by Statistical Process Control (SPC). A plant without proper SPC is a plant that reacts to problems—rather than preventing them. Unfortunately, I have rarely seen SPC applied properly during the past 20 years (while working in numerous plants world-wide).
Many quantitative methods can be used to make U.S. manufacturing plants competitive and highly profitable. Unfortunately, we generally do not see these methods being used—at least not properly. These quantitative methods are mastered by applied mathematicians and industrial statisticians, but rarely do we see highly credentialed mathematicians or statisticians working in manufacturing environments.
If our manufacturers continue to ignore these predictive modeling and optimization techniques, U.S. manufacturing will continue its precipitous decline.
How to Regain American Manufacturing Jobs (Part II)
This blog is broken into three parts. Part I describes costly manufacturing sector problems. Part II explains the primary causes of the problems. Part III offers solutions.
The Manufacturing Problem—Product and Process Failures
Manufacturing plants typically take some raw materials (steel, plastic, powders, etc.), process them (heat, press, stamp, mold, form, etc.), and then assemble different pieces together. Unfortunately, many of the products made are unacceptable—that is, they do not conform to requirements.
The parts produced by a manufacturing process are supposed to be identical, however, no two parts are exactly the same, and that is the biggest problem that manufacturers face (though many don’t realize this fact). The variation from part to part means that some of the parts won’t conform to customer requirements. More importantly, the ones that do conform will still vary in their performance. For example, all 2002 Model X washing machines do not fail with the exact amount of usage. Some will fail early—and some may last for a long time.
Imagine yourself trying to make a stew. You buy a soup stock, vegetables, and meat, put it together and heat it. Suppose that your family enjoys the stew very much, and they ask you to make it again the following week. Will the second stew be identical to the first? Might the stock be slightly more or less salty? Might the vegetables be more or less ripe? Might the meat be more or less tender? As a result of the variation, your family may have a different reaction to your second stew.
Most manufacturers cannot afford to tolerate much variation. When parts vary, they do not fit together the same way. For example, we bought a two-pack of spaghetti sauces, in which one lid was sealed properly, and the other was not. The improperly sealed container had a strong foul odor of plastic, and the contents were unsafe and discarded.
Products sometimes fail internal testing (at production facilities), but products also fail in the hands of consumers. In fact, data on product failures in the marketplace abounds. Several agencies collect such data (such as cpsc.gov and nhtsa.gov). Many websites provide outlets for consumers to review and complain about products (such as CNET.com, bizrate.com, amazon.com, consumerreports.org, JDPower.com among numerous others).
There are three primary reasons for product failures:
1. inadequate engineering (design shortcomings)
2. variation in production (so parts perform differently for consumers) and
3. customer abuse (misuse of a product)
The first two reasons are far more prevalent—as seen in the data. Part I of this series illustrated the tremendous costs of these failures. Part III of this series explains how manufacturers can prevent these product failures and their expensive consequences.
The Manufacturing Problem—Product and Process Failures
Manufacturing plants typically take some raw materials (steel, plastic, powders, etc.), process them (heat, press, stamp, mold, form, etc.), and then assemble different pieces together. Unfortunately, many of the products made are unacceptable—that is, they do not conform to requirements.
The parts produced by a manufacturing process are supposed to be identical, however, no two parts are exactly the same, and that is the biggest problem that manufacturers face (though many don’t realize this fact). The variation from part to part means that some of the parts won’t conform to customer requirements. More importantly, the ones that do conform will still vary in their performance. For example, all 2002 Model X washing machines do not fail with the exact amount of usage. Some will fail early—and some may last for a long time.
Imagine yourself trying to make a stew. You buy a soup stock, vegetables, and meat, put it together and heat it. Suppose that your family enjoys the stew very much, and they ask you to make it again the following week. Will the second stew be identical to the first? Might the stock be slightly more or less salty? Might the vegetables be more or less ripe? Might the meat be more or less tender? As a result of the variation, your family may have a different reaction to your second stew.
Most manufacturers cannot afford to tolerate much variation. When parts vary, they do not fit together the same way. For example, we bought a two-pack of spaghetti sauces, in which one lid was sealed properly, and the other was not. The improperly sealed container had a strong foul odor of plastic, and the contents were unsafe and discarded.
Products sometimes fail internal testing (at production facilities), but products also fail in the hands of consumers. In fact, data on product failures in the marketplace abounds. Several agencies collect such data (such as cpsc.gov and nhtsa.gov). Many websites provide outlets for consumers to review and complain about products (such as CNET.com, bizrate.com, amazon.com, consumerreports.org, JDPower.com among numerous others).
There are three primary reasons for product failures:
1. inadequate engineering (design shortcomings)
2. variation in production (so parts perform differently for consumers) and
3. customer abuse (misuse of a product)
The first two reasons are far more prevalent—as seen in the data. Part I of this series illustrated the tremendous costs of these failures. Part III of this series explains how manufacturers can prevent these product failures and their expensive consequences.
How to Regain American Manufacturing Jobs (Part I)
This blog is broken into three parts. Part I describes costly manufacturing sector problems. Part II explains the primary causes of the problems. Part III offers solutions.
The Truth about Manufacturing Profitability
The United States has been steadily losing manufacturing jobs over the past 30 years. In January, 1980, there were over 19 million manufacturing employees in the United States. Now, there are just 13 million manufacturing employees (source: Bureau of Labor Statistics). In other words, the United States has lost over 30% of its manufacturing positions in that time frame. Even more amazing is that according to the U.S. Bureau of Economic Analysis, U.S. demand for products grew by an average of 3.5% each year. That equates to U.S. demand nearly tripling during that 30 year period. The demand worldwide has grown even more—due to the economic growth in other parts of the world.
Many explanations for outsourcing manufacturing jobs have been given—including labor, healthcare, pension costs, oil prices, regulations, and the problems in the financial sector. However, these are not the primary reasons for our inability to compete. Quite simply, the United States has not been able to design and manufacture products profitably enough, and if we continue the same behaviors, we will continue to lose the relatively high-paying manufacturing jobs.
Internal Plant Waste
Over the past 20 years, we have heard estimates of internal waste within a plant ranging from $2 Million per year to over $50 Million per year. These costs ONLY include internal scrap (i.e. not making the product right the first time) in a single plant. There are currently over 350,000 such plants in the United States (source: U.S. Census Bureau).
News of internal plant waste does not usually reach the executive levels. Instead, the waste gets covered up—it is hidden. Since production personnel know that unacceptable parts will be made, they invest in expensive inspection equipment to detect the defective products—further reducing profits.
Warranty and Recalls
Warranty costs of large U.S. manufacturers typically average 2% of revenue. So, for every $1 Billion in revenue, a company spends a needless $20 Million in warranty expenses.
Recall costs (just for consumer products and excluding automotive recalls) are more than $700 Billion annually (according to the Consumer Product Safety Commission). It is difficult to estimate the total cost of vehicle recalls. There are over 76,000 vehicle recall records in NHTSA’s database covering the years of 1966 – 2008. The largest 10 recalls included 55.5 million vehicles. If a dealer is paid $50 per repaired vehicle, then the 10 largest recalls cost nearly $2.8 Billion dollars. Of course, there are thousands of automotive recalls.
Lawsuits
The costs of the lawsuits associated with any one recall are also shocking. Each lawsuit typically costs a manufacturer millions of dollars in legal fees and losses, and in some cases, tens or hundreds of millions.
Irate Customers
CNET.com offers a great venue for customers to review electronics before and after purchasing them. The customers’ remarks on this site are priceless. Nearly every complaint has to do with flaws in the design or manufacture of the product, and yet most manufacturing executives pay more attention to sales, marketing, accounting, and purchasing issues. Who’s listening to the customers?
Exporting Our Jobs
Millions of jobs have needlessly been sent to other countries. And, manufacturers in low-wage countries also experience high rates of internal scrap and waste. There have been many costly quality and reliability problems associated with products made overseas. The cost of shipping goods is higher, and it is expensive to train foreign workers and transfer technology (and foolish to relinquish our technology and intellectual property). Furthermore, the cost of labor is rising in developing countries, making these decisions myopic.
Sending jobs overseas has also weakened the United States considerably. Manufacturing jobs have traditionally been high-paying jobs, and with the loss of those jobs, Americans have less spending power, so many other businesses will continue to fail. To make matters worse, the government has fewer income tax dollars—at a time when people will need even more assistance.
Summary
In summary, the enormous and overlooked costs crippling manufacturers are:
1. Internal plant waste and inspection costs
2. Warranty costs
3. Recall costs
4. Lawsuit costs
5. Losses in market share due to the above (irate customers)
The next blog in this series explains why products and processes fail and create the excessive costs described in this segment.
The Truth about Manufacturing Profitability
The United States has been steadily losing manufacturing jobs over the past 30 years. In January, 1980, there were over 19 million manufacturing employees in the United States. Now, there are just 13 million manufacturing employees (source: Bureau of Labor Statistics). In other words, the United States has lost over 30% of its manufacturing positions in that time frame. Even more amazing is that according to the U.S. Bureau of Economic Analysis, U.S. demand for products grew by an average of 3.5% each year. That equates to U.S. demand nearly tripling during that 30 year period. The demand worldwide has grown even more—due to the economic growth in other parts of the world.
Many explanations for outsourcing manufacturing jobs have been given—including labor, healthcare, pension costs, oil prices, regulations, and the problems in the financial sector. However, these are not the primary reasons for our inability to compete. Quite simply, the United States has not been able to design and manufacture products profitably enough, and if we continue the same behaviors, we will continue to lose the relatively high-paying manufacturing jobs.
Internal Plant Waste
Over the past 20 years, we have heard estimates of internal waste within a plant ranging from $2 Million per year to over $50 Million per year. These costs ONLY include internal scrap (i.e. not making the product right the first time) in a single plant. There are currently over 350,000 such plants in the United States (source: U.S. Census Bureau).
News of internal plant waste does not usually reach the executive levels. Instead, the waste gets covered up—it is hidden. Since production personnel know that unacceptable parts will be made, they invest in expensive inspection equipment to detect the defective products—further reducing profits.
Warranty and Recalls
Warranty costs of large U.S. manufacturers typically average 2% of revenue. So, for every $1 Billion in revenue, a company spends a needless $20 Million in warranty expenses.
Recall costs (just for consumer products and excluding automotive recalls) are more than $700 Billion annually (according to the Consumer Product Safety Commission). It is difficult to estimate the total cost of vehicle recalls. There are over 76,000 vehicle recall records in NHTSA’s database covering the years of 1966 – 2008. The largest 10 recalls included 55.5 million vehicles. If a dealer is paid $50 per repaired vehicle, then the 10 largest recalls cost nearly $2.8 Billion dollars. Of course, there are thousands of automotive recalls.
Lawsuits
The costs of the lawsuits associated with any one recall are also shocking. Each lawsuit typically costs a manufacturer millions of dollars in legal fees and losses, and in some cases, tens or hundreds of millions.
Irate Customers
CNET.com offers a great venue for customers to review electronics before and after purchasing them. The customers’ remarks on this site are priceless. Nearly every complaint has to do with flaws in the design or manufacture of the product, and yet most manufacturing executives pay more attention to sales, marketing, accounting, and purchasing issues. Who’s listening to the customers?
Exporting Our Jobs
Millions of jobs have needlessly been sent to other countries. And, manufacturers in low-wage countries also experience high rates of internal scrap and waste. There have been many costly quality and reliability problems associated with products made overseas. The cost of shipping goods is higher, and it is expensive to train foreign workers and transfer technology (and foolish to relinquish our technology and intellectual property). Furthermore, the cost of labor is rising in developing countries, making these decisions myopic.
Sending jobs overseas has also weakened the United States considerably. Manufacturing jobs have traditionally been high-paying jobs, and with the loss of those jobs, Americans have less spending power, so many other businesses will continue to fail. To make matters worse, the government has fewer income tax dollars—at a time when people will need even more assistance.
Summary
In summary, the enormous and overlooked costs crippling manufacturers are:
1. Internal plant waste and inspection costs
2. Warranty costs
3. Recall costs
4. Lawsuit costs
5. Losses in market share due to the above (irate customers)
The next blog in this series explains why products and processes fail and create the excessive costs described in this segment.
Tuesday, November 18, 2008
Why Product Development Takes Too Long--and How To Accelerate It Effectively!
While new product development in some industries (such as computers and consumer electronics) occurs at dizzying speed, other industries seem to move at a snail’s pace. Developing products such as new drugs, new energy sources, engines, transmissions, aircraft, and military equipment takes an exceedingly long time.
Lengthy development processes not only reduce companies’ profitability, they deprive us access to life-saving cures and energy independence! Most R&D and Product Development processes do not utilize the many optimization methods that exist – methods that not only speed up the process drastically, but also result in optimal solutions (safe, reliable and robust products).
Typical Product Development efforts often include:
• Haphazard and wasteful “trial and error” approaches to evaluating design alternatives
• Inappropriate comparison methods (not statistically based) when evaluating alternatives—inadequate analysis
• A significant amount of guessing or “judgment” is used for decisions regarding materials, dimensions, processing steps, and other important product details
• Inadequate reliability testing is done to ensure products will perform over time in the conditions that products will encounter
• In some cases, products are over-designed (at a higher cost) due to the uncertainty from lack of knowledge
• Testing or experimentation which does NOT generate predictive models to identify optimal solutions
All of the above points contribute to lengthy and often unsuccessful development efforts. Additional outcomes often include products that are not reliable, competitive, desired, effective, or safe.
Optimal solutions (meeting all product requirements at the lowest overall cost) are rarely stumbled upon by chance—that is, trial and error. The fact that products must simultaneously satisfy multiple requirements (what good is a rechargeable battery that goes 500 miles between charges but occasionally catches fire?) makes finding optimal solutions even more difficult.
What Can Be Done
Fortunately, help is available. Academics have produced a plethora of mathematical and statistical methods to quickly evaluate an infinite set of combinations and find optimums. While you may be thinking of computer simulations that may or may not capture reality, we are not. Instead, we refer to acquiring actual data (physical data) from the judicious and strategic testing of products.
Most industry professionals are unaware of these methods. This is extremely disturbing—especially given challenges such as cancer, the quest for alternative energy sources, and the survival of some of our economically important industries.
Researchers may be highly trained in subjects such as chemistry, physics, biology, medicine, and engineering, but they are typically not armed with strong quantitative and optimization backgrounds. As a result, they do not apply advanced mathematical methods to aid them in their research. However, by pairing scientists and engineers with talented applied mathematicians, critical discoveries and solutions could be achieved quickly.
Design of Experiments (DOE)
Applied mathematicians with specialties in optimization can significantly reduce product development time via numerous quantitative techniques. One of the many useful techniques is Design of Experiments (often referred to as “DOE”).
Unfortunately, most DOE approaches (and efforts) are unsuccessful, and most do not produce effective predictive models. However, when applied correctly, DOE results in powerful predictive models that predict product performance as a function of relevant design choices. The predictive models also empower researchers to determine how changes in inputs (e.g. specifications) impact product performance.
A critical feature of these models is that they describe the effect of interactions between various inputs (where the effect of one factor depends on yet another factor). The functional forms can be non-linear—which is important since much physical behavior is not linear. Ultimately, mathematical models can be used to identify optimal solutions, and these may be validated with strategic follow-up testing.
Future blogs will delve into the use (and misuse) of DOE in practice as well as other techniques for accelerating product development to produce superior products and new technologies. Pharmaceutical R&D (and other formulation problems) have unique challenges—and while traditional DOE does not apply (due to many constraints on formulation problems), Mixture Experiments definitely apply—and offer tremendous benefits.
The question is whether scientists and engineers will welcome mathematicians in their quest for crucial innovations. Einstein did…but will today’s researchers?
By: Steven Wachs & Allise Wachs, http://www.integral-concepts.com/
Lengthy development processes not only reduce companies’ profitability, they deprive us access to life-saving cures and energy independence! Most R&D and Product Development processes do not utilize the many optimization methods that exist – methods that not only speed up the process drastically, but also result in optimal solutions (safe, reliable and robust products).
Typical Product Development efforts often include:
• Haphazard and wasteful “trial and error” approaches to evaluating design alternatives
• Inappropriate comparison methods (not statistically based) when evaluating alternatives—inadequate analysis
• A significant amount of guessing or “judgment” is used for decisions regarding materials, dimensions, processing steps, and other important product details
• Inadequate reliability testing is done to ensure products will perform over time in the conditions that products will encounter
• In some cases, products are over-designed (at a higher cost) due to the uncertainty from lack of knowledge
• Testing or experimentation which does NOT generate predictive models to identify optimal solutions
All of the above points contribute to lengthy and often unsuccessful development efforts. Additional outcomes often include products that are not reliable, competitive, desired, effective, or safe.
Optimal solutions (meeting all product requirements at the lowest overall cost) are rarely stumbled upon by chance—that is, trial and error. The fact that products must simultaneously satisfy multiple requirements (what good is a rechargeable battery that goes 500 miles between charges but occasionally catches fire?) makes finding optimal solutions even more difficult.
What Can Be Done
Fortunately, help is available. Academics have produced a plethora of mathematical and statistical methods to quickly evaluate an infinite set of combinations and find optimums. While you may be thinking of computer simulations that may or may not capture reality, we are not. Instead, we refer to acquiring actual data (physical data) from the judicious and strategic testing of products.
Most industry professionals are unaware of these methods. This is extremely disturbing—especially given challenges such as cancer, the quest for alternative energy sources, and the survival of some of our economically important industries.
Researchers may be highly trained in subjects such as chemistry, physics, biology, medicine, and engineering, but they are typically not armed with strong quantitative and optimization backgrounds. As a result, they do not apply advanced mathematical methods to aid them in their research. However, by pairing scientists and engineers with talented applied mathematicians, critical discoveries and solutions could be achieved quickly.
Design of Experiments (DOE)
Applied mathematicians with specialties in optimization can significantly reduce product development time via numerous quantitative techniques. One of the many useful techniques is Design of Experiments (often referred to as “DOE”).
Unfortunately, most DOE approaches (and efforts) are unsuccessful, and most do not produce effective predictive models. However, when applied correctly, DOE results in powerful predictive models that predict product performance as a function of relevant design choices. The predictive models also empower researchers to determine how changes in inputs (e.g. specifications) impact product performance.
A critical feature of these models is that they describe the effect of interactions between various inputs (where the effect of one factor depends on yet another factor). The functional forms can be non-linear—which is important since much physical behavior is not linear. Ultimately, mathematical models can be used to identify optimal solutions, and these may be validated with strategic follow-up testing.
Future blogs will delve into the use (and misuse) of DOE in practice as well as other techniques for accelerating product development to produce superior products and new technologies. Pharmaceutical R&D (and other formulation problems) have unique challenges—and while traditional DOE does not apply (due to many constraints on formulation problems), Mixture Experiments definitely apply—and offer tremendous benefits.
The question is whether scientists and engineers will welcome mathematicians in their quest for crucial innovations. Einstein did…but will today’s researchers?
By: Steven Wachs & Allise Wachs, http://www.integral-concepts.com/
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