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.

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.

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 and Many websites provide outlets for consumers to review and complain about products (such as,,,, 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.

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 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.

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.