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.
Showing posts with label manufacturing. Show all posts
Showing posts with label manufacturing. Show all posts
Monday, January 12, 2009
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.
Friday, October 31, 2008
American Manufacturing…Fight Back!
Most Americans seem uninformed and disinterested in the most fundamental reasons for our industrial collapse. Instead, we blame foreign competition, unions, healthcare costs, pensions, government regulations, oil, and now the financial sector. While all those reasons are valid and have contributed to the massive loss of jobs in the manufacturing sector, there are more fundamental reasons for our inability to compete successfully in the global marketplace. And, nobody is talking about these reasons.
Let’s begin with a question. “How did a small, bombed-out nation with few natural resources become one of the greatest industrial powerhouses?” In answering this question, fundamental reasons for our industrial demise will become clear—but so will solutions.
There was a time when the United States produced the most highly demanded automobiles, electronics, appliances, furniture, textiles, and consumer products. However, over the past decade, the United States has become notorious for exporting jobs and factories.
Consider the automotive industry. The steady downward trend began in the 1970s. In that era, the top 3 US automakers held 90% of the U.S. market share. Today, the top 3 US automakers have only 40% of the U.S. market for passenger cars. They have 50% of the entire U.S. market which includes trucks.
During the same time period, the Japanese automotive market (in the U.S.) share grew steadily from roughly 1% to 38%. So now, nearly 4 in 10 vehicles sold in the U.S. are Japanese.
Unemployment rates in Michigan (the state most affiliated with automotive jobs) have more than doubled from 3.3% in January 2000 to almost 9% now. Since 2000, the state has lost over 400,000 jobs—mostly from the auto industry.
Similar trends exist in electronics. Japanese brands like Sony, Sanyo, Sharp, Mitsubishi, Nintendo, Panasonic, Toshiba, Canon, Epson, and many more now dominate in electronics manufacturing.
Consider a statistic from last year’s import and export data collected by the U.S. Census Bureau. In 2007, the U.S. exported 136.4 Billion dollars worth of consumer goods category. However, that same year, the U.S. imported 465.7 Billion dollars worth of consumer goods. Despite the weak U.S. dollar, we are importing nearly 3.5 times more than we are exporting in the consumer goods category.
Once we acknowledge that we have lost our position as the greatest producer of highly demanded products in the marketplace, we can look for ways to regain our preeminence.
Let’s begin with a question. “How did a small, bombed-out nation with few natural resources become one of the greatest industrial powerhouses?” In answering this question, fundamental reasons for our industrial demise will become clear—but so will solutions.
There was a time when the United States produced the most highly demanded automobiles, electronics, appliances, furniture, textiles, and consumer products. However, over the past decade, the United States has become notorious for exporting jobs and factories.
Consider the automotive industry. The steady downward trend began in the 1970s. In that era, the top 3 US automakers held 90% of the U.S. market share. Today, the top 3 US automakers have only 40% of the U.S. market for passenger cars. They have 50% of the entire U.S. market which includes trucks.
During the same time period, the Japanese automotive market (in the U.S.) share grew steadily from roughly 1% to 38%. So now, nearly 4 in 10 vehicles sold in the U.S. are Japanese.
Unemployment rates in Michigan (the state most affiliated with automotive jobs) have more than doubled from 3.3% in January 2000 to almost 9% now. Since 2000, the state has lost over 400,000 jobs—mostly from the auto industry.
Similar trends exist in electronics. Japanese brands like Sony, Sanyo, Sharp, Mitsubishi, Nintendo, Panasonic, Toshiba, Canon, Epson, and many more now dominate in electronics manufacturing.
Consider a statistic from last year’s import and export data collected by the U.S. Census Bureau. In 2007, the U.S. exported 136.4 Billion dollars worth of consumer goods category. However, that same year, the U.S. imported 465.7 Billion dollars worth of consumer goods. Despite the weak U.S. dollar, we are importing nearly 3.5 times more than we are exporting in the consumer goods category.
Once we acknowledge that we have lost our position as the greatest producer of highly demanded products in the marketplace, we can look for ways to regain our preeminence.
Japan
After World War II, Japan’s major cities, industries, and transportation networks had been destroyed and it faced a serious food shortage.
Japan is approximately the size of California, and it has faced two significant problems. First, it has a large population, and second, it has almost no natural resources. Energy, food, and most raw materials such as iron ore, copper, gold, silver, and wood must be imported.
Despite Japan’s size and deficiencies, it became the world’s second largest economy when measured by gross domestic product. So, how did Japan rise from a state of devastation and poverty to become a respected manufacturer of automobiles, electronics and high-tech equipment?
The answer is deceptively simple. Some of the Japanese companies learned four of the most important keys to manufacturing excellence:
1. Manufacture products with as little variation as possible—that is, make components as similar to each other as possible (Minimize Variation)
2. Design/Engineer parts so that they will work in all realistic environments and for a long period of time (High Reliability)
3. Prevent and Predict issues—rather than Detect and React
4. Educate Employees in statistical methods to accomplish the first 3 items
One of the most famous consultants responsible for the success of many Japanese manufacturers was William Edwards Deming. He was an American with a Ph.D. in mathematical physics, but his passion was industrial statistical methods.
He helped manufacturers predict system performance and minimize variation and inefficiencies via the use of industrial statistical methods. From Deming, several Japanese companies quickly learned how to quantify variation, understand its grave ramifications, and to ultimately minimize it. Most American companies were uninterested in the topic of variation.
Variation
Let’s digress shortly to discuss variation—and why it destroys a manufacturer’s ability to compete. Suppose we are making caps that need to fit on a particular type of soft drink bottle. The first thing to understand is that no two items are identical. So, no two caps are identical. Because they are not identical, we say there is “variation.”
Suppose we have a bottle, and we try to fit one of our caps on it. It will fit a certain way….it will take a certain torque to remove the cap, and the contents may or may not leak. However, if we put a different cap on the bottle, it will not possess the exact same fit. It may take a slightly different amount of torque to remove it, and it may leak at a different rate. Whether or not the two caps behave differently in a practical way, we do not know, but they do fit differently.
The variation in product components (and how they fit together) largely explains why products fail at different times. For example, two of the same model washing machines may fail at drastically different times in service. The impact of variation is often noticed immediately in assembly operations, but when it’s not observed during assembly, it will be noticed by customers. Variation is measured by a statistic called a “standard deviation.” It should be one of the most important measures to manufacturers.
Products fail at different times. You’ll hear someone rave about their model X vehicle—while someone else curses it. The data that supports largely different failure times resides in warranty databases, customer satisfaction datasets, recall databases, and vehicle registration databases.
Despite Japan’s size and deficiencies, it became the world’s second largest economy when measured by gross domestic product. So, how did Japan rise from a state of devastation and poverty to become a respected manufacturer of automobiles, electronics and high-tech equipment?
The answer is deceptively simple. Some of the Japanese companies learned four of the most important keys to manufacturing excellence:
1. Manufacture products with as little variation as possible—that is, make components as similar to each other as possible (Minimize Variation)
2. Design/Engineer parts so that they will work in all realistic environments and for a long period of time (High Reliability)
3. Prevent and Predict issues—rather than Detect and React
4. Educate Employees in statistical methods to accomplish the first 3 items
One of the most famous consultants responsible for the success of many Japanese manufacturers was William Edwards Deming. He was an American with a Ph.D. in mathematical physics, but his passion was industrial statistical methods.
He helped manufacturers predict system performance and minimize variation and inefficiencies via the use of industrial statistical methods. From Deming, several Japanese companies quickly learned how to quantify variation, understand its grave ramifications, and to ultimately minimize it. Most American companies were uninterested in the topic of variation.
Variation
Let’s digress shortly to discuss variation—and why it destroys a manufacturer’s ability to compete. Suppose we are making caps that need to fit on a particular type of soft drink bottle. The first thing to understand is that no two items are identical. So, no two caps are identical. Because they are not identical, we say there is “variation.”
Suppose we have a bottle, and we try to fit one of our caps on it. It will fit a certain way….it will take a certain torque to remove the cap, and the contents may or may not leak. However, if we put a different cap on the bottle, it will not possess the exact same fit. It may take a slightly different amount of torque to remove it, and it may leak at a different rate. Whether or not the two caps behave differently in a practical way, we do not know, but they do fit differently.
The variation in product components (and how they fit together) largely explains why products fail at different times. For example, two of the same model washing machines may fail at drastically different times in service. The impact of variation is often noticed immediately in assembly operations, but when it’s not observed during assembly, it will be noticed by customers. Variation is measured by a statistic called a “standard deviation.” It should be one of the most important measures to manufacturers.
Products fail at different times. You’ll hear someone rave about their model X vehicle—while someone else curses it. The data that supports largely different failure times resides in warranty databases, customer satisfaction datasets, recall databases, and vehicle registration databases.
Product Variation
While some manufacturers have historically tried to reduce variation, others have not. Consider some automotive data. It is estimated that GM spent approximately $4.5 Billion in warranty costs last year, and Ford spent roughly $3.8 Billion in warranty costs last year. The reason for vehicle failing during warranty periods is product variation—since many vehicles do not fail during that time. If all products were identical, they would fail at the same time (approximately).
From 2003 to 2006, DaimlerChrysler spent an average of approximately 4.5% of revenue on warranty. GM spent roughly 3% of revenue on warranty. Toyota spent only 1.25% of its revenue on warranty.
Then, there is safety recall data (see www.nhtsa.gov). Although it’s not a completely fair comparison because production units and severity of the recall have not been adjusted, it is still interesting to note that from 2000 – 2006, GM had 1,014 safety-related recalls, Ford had 558, DaimlerChrysler had 374, Honda had 165, and Toyota had 131—despite the fact that Toyota surpasses both Ford and Chrysler in U.S. sales volumes.
Perhaps the biggest reason for the U.S.’s decline of the automotive and electronics industry is what happens after warranty periods. When consumers find themselves forced to pay for costly repairs after the warranty ends, many lose loyalty to the brand. It is difficult to quantify the actual lifetimes of automotive and electronics products, but if used sales are any indication of the long-term performance of products, an interesting picture emerges.
According to Edmunds & KBB, all 10 of the Top 10 Resale Value Vehicles are foreign. 8 of those 10 are Japanese.
Profit Losses from Product Variation
While some manufacturers have historically tried to reduce variation, others have not. Consider some automotive data. It is estimated that GM spent approximately $4.5 Billion in warranty costs last year, and Ford spent roughly $3.8 Billion in warranty costs last year. The reason for vehicle failing during warranty periods is product variation—since many vehicles do not fail during that time. If all products were identical, they would fail at the same time (approximately).
From 2003 to 2006, DaimlerChrysler spent an average of approximately 4.5% of revenue on warranty. GM spent roughly 3% of revenue on warranty. Toyota spent only 1.25% of its revenue on warranty.
Then, there is safety recall data (see www.nhtsa.gov). Although it’s not a completely fair comparison because production units and severity of the recall have not been adjusted, it is still interesting to note that from 2000 – 2006, GM had 1,014 safety-related recalls, Ford had 558, DaimlerChrysler had 374, Honda had 165, and Toyota had 131—despite the fact that Toyota surpasses both Ford and Chrysler in U.S. sales volumes.
Perhaps the biggest reason for the U.S.’s decline of the automotive and electronics industry is what happens after warranty periods. When consumers find themselves forced to pay for costly repairs after the warranty ends, many lose loyalty to the brand. It is difficult to quantify the actual lifetimes of automotive and electronics products, but if used sales are any indication of the long-term performance of products, an interesting picture emerges.
According to Edmunds & KBB, all 10 of the Top 10 Resale Value Vehicles are foreign. 8 of those 10 are Japanese.
Profit Losses from Product Variation
The consequences of product variability include huge losses within a manufacturing facility as well. When products are being made, and they vary significantly, some of the products will not be good enough to be sold. So, those products are thrown away or re-worked. The loss in profits can be considerable, and often, the rate of “bad parts” being made internally is disturbingly high.
Because of excessive variation, huge investments are made in inspection processes and equipment. Since most manufacturers neither predict nor prevent the production of bad product, they purchase expensive (and not always reliable) systems to detect bad product. Again, the impact on profitability can be staggering. Deming’s goal was to eliminate the need for inspection by understanding and predicting manufacturing process behavior.
Huge costs that we don’t hear about in the news are losses from launching new products. The problems and expenses associated with introducing new products into the market are difficult to quantify, because the information is not publicly available, but consumers hear of numerous examples daily. They include potentially unsafe drugs, unsafe consumer goods, unsafe vehicles, and so on. They also include delays in promised arrival dates of new products.
It’s not clear whether our industrial executives disregard the statistics or simply fail to comprehend them. It is disconcerting to continually hear U.S. executives (and the media) claim that quality and reliability of U.S. products has essentially caught up with the Japanese. They argue that there is only a “perception problem.” However, the data strongly suggests otherwise. These executives only need to look at statistics kept by organizations like Consumer Reports, the Consumer Product Safety Commission, the National Highway Traffic Safety Administration, the Federal Trade Commission, the National Transportation Safety Board, their own warranty data, and customer complaint data. They should also consider the lawsuits against their products, and the scrap and rework within their plants that obliterate their profits.
Quality & Productivity Improvement Programs
Because of excessive variation, huge investments are made in inspection processes and equipment. Since most manufacturers neither predict nor prevent the production of bad product, they purchase expensive (and not always reliable) systems to detect bad product. Again, the impact on profitability can be staggering. Deming’s goal was to eliminate the need for inspection by understanding and predicting manufacturing process behavior.
Huge costs that we don’t hear about in the news are losses from launching new products. The problems and expenses associated with introducing new products into the market are difficult to quantify, because the information is not publicly available, but consumers hear of numerous examples daily. They include potentially unsafe drugs, unsafe consumer goods, unsafe vehicles, and so on. They also include delays in promised arrival dates of new products.
It’s not clear whether our industrial executives disregard the statistics or simply fail to comprehend them. It is disconcerting to continually hear U.S. executives (and the media) claim that quality and reliability of U.S. products has essentially caught up with the Japanese. They argue that there is only a “perception problem.” However, the data strongly suggests otherwise. These executives only need to look at statistics kept by organizations like Consumer Reports, the Consumer Product Safety Commission, the National Highway Traffic Safety Administration, the Federal Trade Commission, the National Transportation Safety Board, their own warranty data, and customer complaint data. They should also consider the lawsuits against their products, and the scrap and rework within their plants that obliterate their profits.
Quality & Productivity Improvement Programs
Our industrial leaders do not appear to be looking at relevant data. Instead, they have instituted programs which they believe replicate what the Japanese have done. They bring in programs like Six Sigma, Red X, and Lean Manufacturing—hoping to turn things around. What most ignore is the application of proper statistical methods to understand, control, and minimize variation.
While some of these corporate programs have returned some improvements, they clearly have not made the vital difference we need. The economic statistics indicate a continuation in the decline of our manufacturing base.
The dire consequences of a diminished manufacturing base include dwindling consumer purchasing power, limited opportunities for our children, less tax dollars, higher poverty levels, and many other dismal results.
While some of these corporate programs have returned some improvements, they clearly have not made the vital difference we need. The economic statistics indicate a continuation in the decline of our manufacturing base.
The dire consequences of a diminished manufacturing base include dwindling consumer purchasing power, limited opportunities for our children, less tax dollars, higher poverty levels, and many other dismal results.
What Can We Do?
So, what can the United States do? We can:
1. Use proper industrial statistical methods to understand and minimize variation in components and products.
2. Develop products with high long-term reliability—not just acceptable initial quality; this can only happen with the use of PROPER statistical and reliability methods.
3. Prevent manufacturing problems through the use of PROPER statistical process control (SPC).
4. Educate employees in necessary quantitative methods for superior engineering and statistical prediction of process and product behavior.
This may be a difficult pill for our society to swallow. There are several reasons:
1. There are very few highly educated/credentialed statisticians working in manufacturing.
2. Most engineering programs require little or no training in statistical methods.
3. Most corporate trainers and consultants of statistical methods possess no formal education in statistics.
4. Many professionals believe that 4 weeks of statistical training makes one an “expert.” Yet, one might be hesitant to go to a physician, attorney, or any professional who has only received 4 weeks of training.
5. Methods like Designed Experimentation, Reliability Analysis, and Statistical Process Control are either misapplied or not used at all. Hence, manufacturers have gained nothing in terms of predictive ability and prevention of problems.
6. Our best-educated statisticians are working in areas that appreciate statistical reasoning: Insurance, Medical Research, Marketing, Academia, and “Think Tanks.” The manufacturing sector does not appreciate the value of industrial statistics.
Education
Finally, we have an education crisis—especially in mathematics, statistics, engineering, and the physical sciences. Consider the following data from http://www.nces.ed.gov/:
1. Our 15 year-olds rank 25th worst in standardized mathematics testing among 30 participating countries.
2. Our 15 year-olds ranked 21st worst in standardized science testing among 30 participating countries.
3. Of the masters degrees we issue in science and engineering, 40% go to foreign nationals.
4. Of the Ph.D. degrees we issue in science and engineering, over 60% go to foreign nationals.
5. 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.
Most U.S. companies do not teach proper industrial statistical methods to their employees. Nor do they attract highly competent industrial statisticians. Instead, they offer “easy-to-digest” quality programs taught by non-statisticians, like the currently popular “Six Sigma” programs and Taguchi programs.
If these programs were so effective, why hasn’t our manufacturing base been able to improve its warranty situation? Why haven’t they improved profitability, market share, and their ability to compete? Why do our manufacturing imports continue to rise in spite of a weak dollar? Why haven’t product safety-recalls declined? Why do we continue to send our jobs and plants overseas?
Early statistics seem to indicate that Japanese products and performance is now declining due to more variation in their products. The Chinese manufacturers are the new threat. The time is ripe for U.S. manufacturers to heed the advice of Dr. W. Edwards Deming. Our economic future depends on it.
By: Allise Wachs, Integral Concepts, http://www.integral-concepts.com/
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