Currently browsing the Data Category

posts relating to data, data based decisions, statistics, SPC, data quality, data analysis, data collection... It is critical to understand common and special cause variation.
Recommended posts: Data is Only a Proxy - Targets Distorting the System - Measurement and Data Collection - Evidence-based Management - Visible Data - Understanding Data
Related: Operational definition - Control chart


CEO’s Castles and Company Performance

Posted on August 8, 2009  Comments (2)

Where are the Shareholders’ Mansions? CEOs’ Home Purchases, Stock Sales, and Subsequent Company Performance by Crocker H. Liu, Arizona State University and David Yermack, New York University – Stern School of Business

We study real estate purchases by major company CEOs, compiling a database of the principal residences of nearly every top executive in the Standard & Poor’s 500 index. When a CEO buys real estate, future company performance is inversely related to the CEO’s liquidation of company shares and options for financing the transaction. We also find that, regardless of the source of finance, future company performance deteriorates when CEOs acquire extremely large or costly mansions and estates. We therefore interpret large home acquisitions as signals of CEO entrenchment. Our research also provides useful insights for calibrating utility based models of executive compensation and for understanding patterns of Veblenian conspicuous consumption.
…
To understand better the reasons behind the underperformance of companies whose CEOs acquire very large homesteads, we read news stories about major events affecting the firms in our sample in which a CEO acquires a property with at least 10 acres or a 10,000 square foot house. These news stories suggest parallels between the CEOs’ oversight of their personal assets and management of their companies. No less than nine of the 25 CEOs attempted major corporate acquisitions in the two years following their personal acquisitions of very large real estate,9 and seven of the 25 announced significant capital investment initiatives involving the construction or expansion of corporate facilities. An additional two firms became mired in accounting scandals shortly after their CEOs purchased mansions, and one firm saw a previously agreed merger collapse.
…
Using a database of principal residences of company CEOs, we study whether these executives’ decisions about home ownership contain information useful for predicting the future path of their companies’ stock prices. We find that CEOs who acquire extremely large properties exhibit inferior ex post stock performance, a result consistent with large mansions and estates being proxies for CEO entrenchment. We also find that the method of financing a home’s acquisition is informative about future stock returns. A general pattern of CEO sales of their firms’ shares and options exists over the twelve months leading up to the date of home acquisition. However, when the CEO does not sell any shares, his stock performs significantly better ex post than the stocks of firms whose CEOs do liquidate equity to finance their houses. The retention of company shares simultaneous with a new home purchase, despite the presence of an evident personal liquidity need, appears to send a signal of commitment by a CEO to his company.

That we put in power CEO’s that see themselves as nobility with the right to build castles (and many of these CEO castles dwarf all but the most conspicuous castle built by nobility) by taking the wealth produced by others from corporate coffers is a sign of our failure to select acceptable leaders for companies.

Related: Another Year of CEO’s Taking Hugely Excessive Pay – Excessive Executive Pay – Exposing CEO Pay Excesses – Narcissistic Cadre of Senior Executives – 9 Deadly Diseases

When Performance-related Pay Backfires

Posted on August 3, 2009  Comments (3)

When Economic Incentives Backfire by Samuel Bowles, Sante Fe Institute

Dozens of recent experiments show that rewarding self-interest with Economic incentives can backfire when they undermine what Adam Smith called “the moral sentiments.”

Punished by Rewards, by Alfie Kohn, is a great book on this topic. The area of “motivating” employees is one it is often hard for managers to learn. Even managers that have been studying Deming, Ackoff, Ohno… for years still have trouble with the idea that trying to find the right incentive scheme to motivate the right behavior is the wrong approach. Read the The Human Side Of Enterprise by Douglas Mcgregor (in 1960) to re-enforce the understanding of human motivation provided by Toyota’s respect for people principles.

Managers need to eliminate de-motivation in the work systems not try and find bonus schemes to motivate behavior. Eliminating de-motivation is often much more work. You can’t just get some money from the bonus pool and start giving it away. You have to manage. But if you are a manager you shouldn’t be afraid to actually manage the system and make it better.

Related: “Pay for Performance” is a Bad Idea – Reward and Incentive Programs are Ineffective — Even Harmful by Peter Scholtes – The Defect Black Market – What’s the Value of a Big Bonus? – Problems with Bonuses – Losses Covered Up to Protect Bonuses – Stop Demotivating Employees

When performance-related pay backfires:
Continue reading →

Google’s Innovative Use of Economics

Posted on May 24, 2009  Comments (0)

Secret of Googlenomics: Data-Fueled Recipe Brews Profitability

Google depends on economic principles to hone what has become the search engine of choice for more than 60 percent of all Internet surfers, and the company uses auction theory to grease the skids of its own operations. All these calculations require an army of math geeks, algorithms of Ramanujanian complexity, and a sales force more comfortable with whiteboard markers than fairway irons.
…
Varian tried to understand the process better by applying game theory. “I think I was the first person to do that,” he says. After just a few weeks at Google, he went back to Schmidt. “It’s amazing!” Varian said. “You’ve managed to design an auction perfectly.” To Schmidt, who had been at Google barely a year, this was an incredible relief. “Remember, this was when the company had 200 employees and no cash,” he says. “All of a sudden we realized we were in the auction business.”
…
Google even uses auctions for internal operations, like allocating servers among its various business units. Since moving a product’s storage and computation to a new data center is disruptive, engineers often put it off. “I suggested we run an auction similar to what the airlines do when they oversell a flight. They keep offering bigger vouchers until enough customers give up their seats,” Varian says. “In our case, we offer more machines in exchange for moving to new servers. One group might do it for 50 new ones, another for 100, and another won’t move unless we give them 300. So we give them to the lowest bidder—they get their extra capacity, and we get computation shifted to the new data center.”

Google continues to make bold moves putting faith in their ability to find innovative solutions that others reject as impossible. It is a challenging but interesting path to success, for them, at least.

Related: Google Should Stay True to Their Management Practices – Google’s Answer to Filling Jobs Is an Algorithm – The Google Way: Give Engineers Room – Google Website Optimizer – Google: Experiment Quickly and Often – posts on innovation in management

Revealed Preference

Posted on May 16, 2009  Comments (1)

Revealed Preference: the preference consumers display by their action, in contrast to what they may say they prefer. While surveys may be useful people often say they will do one thing and actually when given the choice to do so, don’t.

Normally what matters is not what people say they want but what they actually will choose. For that reason revealed preference is a better measure than stated preference. Stated preference is often used as a proxy for actual preference (which may be fine) but it is important to understand that it is just a proxy for actual preference.

See more explanations from the Curious Cat Management Dictionary.

Related: Packaging Improvement – All Models Are Wrong But Some Are Useful – Dangers of Forgetting the Proxy Nature of Data – Confirmation Bias – Be Careful What You Measure

Why Setting Goals can Backfire

Posted on April 27, 2009  Comments (2)

Dr. Deming long ago stated in his 14 obligations of management: “Eliminate numerical goals, numerical quotas and management by objectives.” I think he was right then, and is right now. A goal can help set the scope of the effort. If you are aiming for 2% improvement different strategies may make sense than if you are aiming at 50% improvement. But that type of use is rare. The problem with goals is what actually happens in organizations. They create serious systemic problems and should be avoided (other than in setting the scope). They are deeply ingrained in the way many people think, but we would be better if we could eliminate the use of goals, as they are used now (mainly as arbitrary numerical goals).

Ready, Aim … Fail, Why setting goals can backfire

In clawing toward its number, GM offered deep discounts and no-interest car loans. The energy and time that might have been applied to the longer-term problem of designing better cars went instead toward selling more of its generally unloved vehicles. As a result, GM was less prepared for the future, and made less money on the cars it did sell. In other words, the world’s largest car company – a title it lost to Toyota last year – fell victim to a goal.
…
Rather than reflexively relying on goals, argues Max Bazerman, a Harvard Business School professor and the fourth coauthor of “Goals Gone Wild,” we might also be better off creating workplaces and schools that foster our own inherent interest in the work. “There are lots of organizations where people want to do well, and they don’t need those goals,” he says. Bazerman and others hold up Google as an example of a company that manages to do this, in part by explicitly setting aside time for employees to pursue their own projects and interests.

Today, as the economic situation upends millions of lives, it is also forcing the reexamination of millions of goals – not only the revenue targets of battered firms, but the career aims of workers and students, and even the ambitions of the newly installed administration. And while it never feels good to give up on a goal, it may be a good time to ask which of the goals we had set for ourselves were things we really needed to achieve, and which were things we only thought we should – and what the difference has been costing us.

Related: Measuring and Managing Performance in Organizations – Arbitrary Rules Don’t Work – The Defect Black Market – Goals can Distract from Improvement – Be Careful What You Measure

Red Bead Experiment Webcast

Posted on April 22, 2009  Comments (3)

Dr. Deming used the red bead experiment to present a view into management practices and his management philosophy. The experiment provides insight into all four aspects of Dr. Deming’s management system: understanding variation, understanding psychology, systems thinking and the theory of knowledge.

Red Bead Experiment by Steve Prevette

Various techniques are used to ensure a quality (no red bead) product. There are quality control inspectors, feedback to the workers, merit pay for superior performance, performance appraisals, procedure compliance, posters and quality programs. The foreman, quality control, and the workers all put forth their best efforts to produce a quality product. The experiment allows the demonstration of the effectiveness (or ineffectiveness) of the various methods.

Related: Fooled by Randomness – Performance Measures and Statistics Course – Performance without Appraisal – Exploring Deming’s Management Ideas – Eliminate Slogans

How to Create a Control Chart for Seasonal or Trending Data

Posted on March 25, 2009  Comments (1)

Lynda Finn, President of Statistical Insight, has written an article on how to create a control chart for seasonal or trending data (where there is an underlying structural variation in the data). Essentially you need to account for the structural variation to create the control limits for the control chart. She also provides a Minitab project file. Both are available for download from the Curious Cat Management Improvement Library.

Related: Control Charts in Health Care – Common Cause Variation – Managing with Control Charts – Measurement and Data Collection – Fourth Generation Management

Friday Fun: Correlation

Posted on March 6, 2009  Comments (0)

Correlation doesn't imply causation

From the excellent xkcd comic.

Related: Correlation is Not Causation – Does the Data Deluge Make the Scientific Method Obsolete? – Understanding Data – Theory of Knowledge – What Makes Scientists Different :-) – Dangers of Forgetting the Proxy Nature of Data – Seeing Patterns Where None Exists

Statistics for Experimenters in Spanish

Posted on January 5, 2009  Comments (0)

book cover of Estadística para Investigadores

Statistics for Experimenters, second edition, by George E. P. Box, J. Stuart Hunter and William G. Hunter (my father) is now available in Spanish.

Read a bit more can find a bit more on the Spanish edition, in Spanish. Estadística para Investigadores Diseño, innovación y descubrimiento Segunda edición.

Statistics for Experimenters – Second Edition:

Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.
…
* Graphical Analysis of Variance
* Computer Analysis of Complex Designs
* Simplification by transformation
* Hands-on experimentation using Response Service Methods
* Further development of robust product and process design using split plot arrangements and minimization of error transmission
* Introduction to Process Control, Forecasting and Time Series

Book available via Editorial Reverte

Related: Statistics for Experimenters Review – Correlation is Not Causation – Statistics for Experimenters Data – posts on design of experiments

What’s the Value of a Big Bonus?

Posted on November 21, 2008  Comments (0)

What’s the Value of a Big Bonus? by Dan Ariely

To look at this question, three colleagues and I conducted an experiment. We presented 87 participants with an array of tasks that demanded attention, memory, concentration and creativity. We asked them, for instance, to fit pieces of metal puzzle into a plastic frame, to play a memory game that required them to reproduce a string of numbers and to throw tennis balls at a target. We promised them payment if they performed the tasks exceptionally well. About a third of the subjects were told they’d be given a small bonus, another third were promised a medium-level bonus, and the last third could earn a high bonus.
…
So it turns out that social pressure has the same effect that money has. It motivates people, especially when the tasks at hand require only effort and no skill. But it can provide stress, too, and at some point that stress overwhelms the motivating influence.

When I recently presented these results to a group of banking executives, they assured me that their own work and that of their employees would not follow this pattern. (I pointed out that with the right research budget, and their participation, we could examine this assertion. They weren’t that interested.)

This is an interesting look at an effect of bonuses. We all know monetary bonuses can influence behavior. The problem is the type of behaviors that result. Huge bonuses, for example, create huge incentives to risk the future of the company for the chance at a huge bonus for the executive. Extrinsic motivation leads to many problems.

Problems with bonuses: Losses Covered Up to Protect Bonuses – “Pay for Performance” is a Bad Idea – Problems with Bonuses – Book: Punished By Rewards: The Trouble With Gold Stars, Incentive Plans, A’s, Praise, and Other Bribes by Alfie Kohn – posts on executive pay

Tilting at Ludicrous CEO Pay 2008

Posted on November 10, 2008  Comments (5)

I continue to tilt at the robber barron CEO pay packages (2007 post on CEO pay abuses).

2007 pay
rank		 Company CEO Pay 5 Year Pay CEO % of 2007 Earnings
1 Apple Steve Jobs $646,600,000 $650,170,000
18.5%
2 Occidental Petroleum Ray Irani $321,640,000 $509,530,000
5.9%
3 IAC Barry Diller $295,140,000 $512,270,000
Company Lost Money
4 Fidelity National Financial William Folley $179,560,000 NA
138.4%
5 Yahoo! Terry Semel $174,200,000 $432,490,000
26.4%
7 Countrywide Financial Angelo Mozilo $141,980,000 $295,730,000
Company Lost Money
13 XTO Energy Bob Simpson $72,270,000 $215,280,000
4.2%

Data via: Forbes CEO Compensation (Total compensation for each chief executive includes the following: salary and bonuses; other compensation, such as vested restricted stock grants, LTIP payouts and perks; and stock gains, the value realized by exercising stock options.) and Google Finance (using 2007 earnings – Countrywide from SEC). I realize this chart could be improved by spending more time (the effect of stock options exercised in one year distorts things a bit but the excess are so massively huge that the clarity of the data does not need to be very precise).
Continue reading →

Global Manufacturing Data 2007

Posted on October 30, 2008  Comments (1)

The updated data from the United Nations on manufacturing output by country clearly shows the USA remains by far the largest manufacturer in the world. UN Data, in billions of current US dollars:

Country 1990 1995 2000 2005 2006 2007
USA 1,041 1,289 1,543 1,663 1,700 1,831
China 143 299 484 734 891 1,106
Japan 804 1,209 1.034 954 934 926
Germany 438 517 392 566 595 670
Russian Federation 211 104 73 222 281 362
Italy 240 226 206 289 299 345
United Kingdom 207 219 228 269 303 342
France 224 259 190 249 248 296
Korea 65 129 134 200 220 241
Canada 92 100 129 177 195 218

See manufacturing data for more countries.

The USA’s share of the manufacturing output of the countries that manufactured over $200 billion in 2007 (the 12 countries on the top of the chart above) in 1990 was 28%, 1995 28%, 2000 33%, 2005 30%, 2006 28%, 2007 27%. China’s share has grown from 4% in 1990, 1995 7%, 2000 11%, 2005 13%, 2006 15%, 2007 16%.

Total manufacturing output in the USA was up 76% in 2007 from the 1990 level. Japan, the second largest manufacturer in 1990, and third today, has increased output 15% (the lowest of the top 12, France is next lowest at 32%) while China is up an amazing 673% (Korea is next at an increase of 271%).
Continue reading →

Does the Data Deluge Make the Scientific Method Obsolete?

Posted on September 29, 2008  Comments (6)

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete by Chris Anderson

“All models are wrong, but some are useful.”

So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now. Today companies like Google, which have grown up in an era of massively abundant data, don’t have to settle for wrong models. Indeed, they don’t have to settle for models at all.
…
Speaking at the O’Reilly Emerging Technology Conference this past March, Peter Norvig, Google’s research director, offered an update to George Box’s maxim: “All models are wrong, and increasingly you can succeed without them.”
…
There is now a better way. Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

see update, below. Norvig was misquoted, he agrees with Box’s maxim

I must say I am not at all convinced that a new method without theory ready to supplant the existing scientific method. Now I can’t find peter Norvig’s exact words online (come on Google – organize all the world’s information for me please). If he said that using massive stores of data to make discoveries in new ways radically changing how we can learn and create useful systems, that I believe. I do enjoy the idea of trying radical new ways of viewing what is possible.

Practice Makes Perfect: How Billions of Examples Lead to Better Models (summary of his talk on the conference web site):

In this talk we will see that a computer might not learn in the same way that a person does, but it can use massive amounts of data to perform selected tasks very well. We will see that a computer can correct spelling mistakes, translate from Arabic to English, and recognize celebrity faces about as well as an average human—and can do it all by learning from examples rather than by relying on programming.

Related: Will the Data Deluge Makes the Scientific Method Obsolete? – Pragmatism and Management Knowledge – Data Based Decision Making at Google – Seeing Patterns Where None Exists – Manage what you can’t measure – Data Based Blathering – Understanding Data – Webcast on Google Innovation
Continue reading →

Hiring the Right Person

Posted on September 2, 2008  Comments (1)

Malcolm Gladwell presented at the New Yorker conference on the Challenge of Hiring in the Modern World. As usually, he provides some great thoughts. I wrote on Hiring the Right Workers

The job market is an inefficient market. There are many reasons for this including relying on specification (this job requires a BS in Computer Science – no Bill Gates you don’t meet the spec) instead of understanding the system. Insisting on managing by the numbers even when the most important figures are unknown and maybe unknowable. Using HR to find the right person to work in a process they don’t understand (which reinforces the desire to focus on specifications instead of a more nuanced approach). The inflexibility of companies: so if a great person wants to work 32 hours a week – too bad we can’t hire them. And on and on.

Malcolm Gladwell doesn’t use the same language but I think he says many of the same ideas: “Insisting on managing by the numbers even when the most important figures are unknown and maybe unknowable.” etc. This idea he frames as a mismatch problem.

Related: Hiring: Silicon Valley Style – People are Our Most Important Asset – Malcolm Gladwell Synchronicity – Hiring, Does College Matter? – Interviewing and Hiring Programmers – Gladwell (and Drucker) on Pensions

California Uses More Gas than China

Posted on July 17, 2008  Comments (0)

Amazing Stat: California Uses More Gas than China:

California alone uses more gasoline than any country in the world (except the US as a whole, of course). That means California’s 20 billion gallon gasoline and diesel habit is greater than China’s! (Or Russia’s. Or India’s. Or Brazil’s. Or Germany’s.)

That’s according to the California Energy Commission’s State Alternative Fuels Plan, which was posted online last Christmas Eve (pdf). The whole report makes for some fascinating reading because it’s a blueprint for a low-carbon and renewable transportation fuel future. The dominant takeaway: it ain’t going to be easy.

One more choice statistic: gasoline usage in California has increased 50 percent, that’s 10 6.7 billion gallons, since 1988.
…
But China’s oil thirst is growing — to almost 20 billion gallons in 2007 — and perhaps as early as this year, China’s 1.3 billion people will overtake California’s 37 million people in total gasoline and diesel usage.

Interesting data. The Curious Cat Economics Blog recently posted on the top oil consuming countries.

Related: Car Powered Using Compressed Air – Failure to Increase Gas Tax – Curious Cat Science and Engineering Blog – Energy posts

Outcome and In-Process Measures

Posted on July 14, 2008  Comments (0)

An outcome measure is used to measure the success of a system. For example, the outcome measure could be the percentage of people who do not get polio (the result). An output measure, for example, would be the number of people vaccinated with the polio vaccine (the output). Often we measure inputs (amount of money spent) or outputs (number of people vaccinated). They are usually easy to measure but obviously less valuable proxies for what the objective of the system (reducing the incidence of polio).

You should have all these types of measures but outcome measures are most likely to be missing so special care should be taken to make sure you are using them. It is important to define good outcome measures to use in determining the success of systems, and in determining the whether improvement projects actually result in improved outcomes.

In-process measures can be valuable in providing actionable information sooner than the outcome measure would allow action. In the polio example, an in process measure example could be % of vaccination by the time a babies is 18 months old. And looking across a country say it might well make sense to stratify the data to see if certain areas were doing poorly on this measure. If so that might be where to focus improvement. You don’t need to wait until people not vaccinated start contracting polio (which will likely be delayed for years after the system starts to have processes fail, in this example) to then notice the problem and then react.

Waiting for the outcome measure to point to a problem in this case (and in many cases) is far too late for process improvement. So process measures are needed to aid in managing the system and reacting to process results, before those processes create poor results (and can be seen as poor outcome measures). More on outcome measures.

Related: Operational Definition – tampering – management improvement web search – Measuring and Managing Performance in Organizations – Data is a Proxy – posts on managing using data

Fed Funds Rate Changes Don’t Presage Mortgage Rate Changes

Posted on May 13, 2008  Comments (0)

The recent drastic reductions again emphasize (once again) that changes in the federal funds rate are not correlated with changes in the 30 year fixed mortgage rate. In the last 4 months the discount rate has been reduced nearly 200 basis points, while 30 year fixed mortgage rates have fallen 18 basis points.

I have update my article showing the historical comparison of 30 year fixed mortgage rates and the federal funds rate. The chart shows the federal funds rate and the 30 year fixed rate mortgage rate from January 2000 through April 2008 (for more details see the article).

graph of fed funds rate and mortgage rate

It is surprising to me how incorrect beliefs about what predicted federal funds rate cuts will mean for mortgage rates are so common when the data makes it clear that such beliefs (I should wait because the fed is going to cut the fed funds rate and so if I wait my mortgage rate will be lower) are unwarranted. But I should not be this type of behavior goes on all the time, inside organization and outside them.
Continue reading →

The Defect Black Market

Posted on April 18, 2008  Comments (6)

The Defect Black Market

It all started a week before, when the CTO of Damon’s midsize warehousing and transportation company in Northern California announced an innovative program to motivate employees and boost the quality of their logistics software. For every bug found by a tester and fixed by a programmer, both would get $10.

Well, this doesn’t sound very well thought out. Bonuses often distort behavior. Dr. Deming was not against such targets and bonuses because he thought they would not result in bugs being fixed: Dr. Deming on the problems with targets or goals. It is a question of how that will happen. The system being distorted is the most likely result of any such system.

Everyone worked a bit harder the next day. Testers made sure to check and double-check every test case they ran, while developers worked through lunch to fix their assigned bugs. And it paid off. On that second day each had earned an average bonus of $50.

Everyone worked even harder on the third day. On the fourth day, however, the well had started to dry up. The testers ran, re-ran, and re-ran again the test cases, but they could only find a handful of issues. The developers strained the issue-tracking system, constantly reloading the “unassigned bugs” page and rushing to self-assign anything that appeared.

And then something strange happened at lunch. Instead of going out to eat with his usual teammates, one of the developers went out with a tester. Soon after, another developer went out with another tester. Within a few minutes, almost all of the developers had paired up with testers.

As the developers returned from lunch, they immediately got to work. Instead of scavenging for newly found bugs, they worked on “code refactoring” and new functionality. And as soon as they deployed their changes, testers found bugs — minor, obscure bugs that a developer could easily overlook. And just as quickly as testers found bugs, the developers were able to fix them and re-deploy. By the end of the day, developers and testers had earned an average of $120.

Continue reading →

Software Supporting Processes Not the Other Way Around

Posted on February 19, 2008  Comments (6)

Rental Car IT

What was funny about that exercise were the looks we got from the no nonsense King of IT: “Of course, we want things to be simple and flexible — why are you bothering to tell us this?” Yet, in the next sentence, they are talking about spending 3 million dollars on a packaged application to help them with one small part of their business, rather than building it themselves (which we all thought would be cheaper but take longer). That’s $3,000,000. But, of course, the packaged application talks directly to their databases, meaning that we can no longer freely make changes to the database without breaking the package, meaning that we can’t evolve the database, meaning that we’ve lost both simplicity and flexibility. Over and over, they complain when we talk about rethinking their priorities, then turn around and make the same decisions that got them where they are now. Frustrating!

This is a good post on the systemic drivers of complex processes, take the time to read the whole post. I have a bias is against off the shelf software as it often ends up forcing the process to be designed around the software. And with the amazing power and relative ease of web based applications creating solutions that are specifically designed to the organization are often relatively easy. And yet, as indicated in this article there is often a strong bias in the other direction for buying off the shelf software because it is cheaper and/or faster.

Of course, the decision in each case must be weighed to determine the benefits and cost of the various alternatives. Just remember, if you decide you want simple and flexible, to have your decisions reflect that. I enjoy a telling quote from a software vendor on Toyota’s IT expectations: “it demands that the software or technology be flexible and adapt, often by customizing the code, to its business processes, and not the other way around.” They are right.

Related: Agile Software Development – Complicating Simplicity – Joy in Software Development

Improvement Through Designed Experiments

Posted on February 4, 2008  Comments (0)

The Rationale of Scientific Experimentation by John Dowd explains the value of designed experiments.

Another difficulty in industrial experimentation is the existence of interactions. As has been stated, manufacturing processes are complex with many factors involved. In many processes these factors interact. This is particularly so for continuous processes such as plating or sputtering. Saying that the factors interact means more than that they are related to each other. It means that the effect of one (or more) factors on the response variable(s) changes when one (or more) other factor(s) changes its value.
…
In order to detect interactions and understand the nature of their effects it is necessary to combine the interacting factors into the same experimental runs. The problem is not necessarily knowing in advance if the interactions exist. Sometimes they are predictable with theory. Sometimes they are discovered when the process behaves ‘strangely’.

In addition to their efficiency, factorial designs also offer the only method of detecting interactions through experimentation. Because numerous factors can be combined in the same series of experimental runs, the interactions can be detected and the nature of their effects can be evaluated when they are present.

The paper also explains analytic and enumerative studies. Dr. Deming stressed the importance of understanding the distinction between the two.

Related: management improvement articles – Design of Experiments articles – Statistics for Experimenters – search statistical management improvement sites – Using Design of Experiments

  • Recent Trackbacks

  • Comments