Management

scrum-rugby

Nov 1 2011

Black belts, scrums, and other metaphors

To be useful, a metaphor must help understanding. For the promoters of Six Sigma to call their certification Black Belt was marketing genius. A more descriptive label might have been Staff Statistician, but what self-respecting manufacturing professional would want to be that? Borrowing a term from Japanese martial arts not only  appealed to their fighting spirit, but also gave the impression that an approach developed at Motorola in the US had a connection with Japan, ground-zero of manufacturing excellence. Even in Japan, Black Belt (“Kuroto”) and White Belt (“Shiroto”) have migrated from martial arts to everyday language, to designate respectively a real pro and an amateur.

As a metaphor,  Black Belt also made sense because there is a parallel between the Six Sigma and martial arts training programs. Traditional  masters in the martial arts of China trained one or two disciples at the Bruce Lee level in a lifetime, just as universities trained only a handful of experts in statistical design of experiments that could be effective in electronics manufacturing. One Karate instructor, on the other hand, can train hundreds of Black Belts, just as a Six Sigma program can teach a focused subset of statistical design of experiments to hundreds of engineers.

Scrum, in software development, is also a sports metaphor, a term borrowed from rugby, which few Americans know. The connection between a rugby scrum and what software people call by the same name, however, is not obvious. A rugby scrum involves the forward players of two teams locked in the pattern of Figure 1.

Figure 1. A rugby scrum

The ball is released in the middle of the scrum and both team try to take possession by kicking it backwards while pushing the other team forwards. It is exciting and bruising to participate in, as well as a great spectacle. For software developers, scrum is an approach to project management illustrated by the status panel in Figure 2.

Figure 2. A software development scrum

It leaves you wondering what plays the role of the opposing team, the ball, or the player positions. In other words, in what way is a rugby scrum a metaphor for this approach at all?

By • Management • 11 • Tags: ,

yardstick

Oct 28 2011

The staying power of bad metrics

A speaker I once heard on manufacturing metrics started with a quote from football coach Vince Lombardi: “If you’re not keeping score, you’re only practicing.” In a sport, your score or your rank is, by definition, the correct measure of success, and we assume too easily that this kind of thinking crosses over to every human endeavor, from national economies to plant performance or education. In this process, we begin using highly aggregated metrics as if they were physical measurements like mass or speed, and avert our eyes from how these sausages are made.

Following are a few of the egregious examples:

  • GDP. Gross Domestic Product (GDP), for example, is in the news every day. If you pollute and spend money to clean up your toxic waste, you contribute more to the GDP than if you produce cleanly. Because of this kind of absurdity, GDP as a metric has been criticized by many economists, including Joseph Stiglitz. In 2009, he even convinced French president Nicolas Sarkozy to seek alternatives. Yet, two years later, the same president is pushing to include in the country’s constitution a “Golden Rule” that caps budget deficits at a percentage of the same flawed GDP!
  • IQ. In the US, IQ  is still widely treated as a measure of intelligence. On its face, the notion that human intelligence is reducible to a number is an insult to its subject. In fact, all an IQ measures is the ability to take an IQ test. Psychologists recognize this, but many school teachers and the public at large don’t. (See Steven Jay Gould’s The Mismeasure of Man.)
  • Food calories. Calories are the most commonly used metric in nutrition. What this number actually represents is the heat generated by drying and burning a food item. But is digestion the same as combustion? Obviously not, for example, for fibers, which cross the human body unchanged. The absurdity of assigning calories to fibers has not escaped one dieter, who questioned it on a Calorie Count forum, and received, among other replies, the following:

Fiber calories are included in nutrition information, but only in come countries. In the US, it is legal to not put in fiber calories because they are not digestible. Therefore, they do not “count” as such. however, if you, like most people, tend to underestimate cals sightly, there is nothing wrong with including them to create a “buffer zone.”

In other words, it makes no sense but you should pretend it does.

Do we behave the same way in the manufacturing world? Yes. For example, many companies measure productivity in terms of Sales/Employee. There is an easy way to boost this metric: outsource all production, close all plants and become a trading company. It is not easy to find metrics for quality, cost, delivery, safety, and morale that are meaningful and cannot be gamed, but it can be done. For overall company productivity, for example, you can use Value added/Employee, where

Value-added = Sales – (Materials + Energy + Outsourced Services)

This is what Peter Drucker called Contributed Value. Value added/Employee is not a perfect metric, but at least it does not provide a perverse incentive to outsource, and the US census bureau publishes statistics on value-added and employment by industry, that are helpful for benchmarking.

Following are a few conditions that a good metric must meet:

  1. A good metric is immediately understandable. No training or even explanation is required to figure out what it means, and the number directly maps to reality, free of any manipulation. One type of common manipulation is to assume that one particular ratio cannot possibly be over 85%, and redefine 85% for this ratio as “100% performance.” While this makes performance look better, it also makes the number misleading and difficult to interpret.
  2. People see how they can affect the outcome. With a good metric, it is also easy to understand what kind of actions can affect the value of the measurement. A shop floor metric, for example, should not a be a function of the price of oil in the world market, because there is nothing the operators can do to affect it. Their actions, on the other hand, can affect the number of labor-hours required per unit, or the rework rate.
  3. A better value for the metric always means better business performance for the company. One of the most difficult characteristics to guarantee is that a better value of a metric always translates to better business performance for the company. Equipment efficiency measures are notorious for failing in this area because maximizing them often leads to overproduction and WIP accumulation.
  4. The input data of the metric should be easy to collect. Lead time statistics, for example, require entry and exit timestamps by unit of production. The difference between these times then only gives you the lead time is calendar time, not in work time. The get lead times in work time, you then have to match the timestamps against the plant’s work calendar. Lead time information, however, can be inferred from WIP and WIP age data, which can be collected by direct observation of WIP on the shop floor. Metrics of
    WIP, therefore, contain essentially the same information but are easier to  calculate. (See Little’s Law.)
  5. All metrics should have the appropriate sensitivity. If daily fluctuations are not what is of interest, then they need to be filtered out. A common method for doing this is to plot 5-day moving averages instead of individual values — that is, the point plotted today is the average of the values observed in the last five days. Daily fluctuations are smoothed away, but weekly trends stand out.

Peter Drucker sold corporate America on the idea that you can’t manage what you can’t measure, and this has led many managers to believe that employees would do whatever it takes to maximize their scores. Given flawed metrics, it if fortunate for the companies that these managers were wrong. If they had been right, all the companies that measure productivity in terms of  Sales/Employee would actually have outsourced all production. They didn’t, because metrics are only one of many factors influencing behavior. Most employees, at all level, will not maximize their metrics through actions they feel violate common sense or are inconsistent with their personal ethics.

By • Metrics • 12 • Tags: ,

Speaker competing for attention with laptops, reduced resolution

Oct 16 2011

Meetings, seminars, and laptops

Communicating face to face is supposed to be more effective than electronically. Yet it is common today to see professionals travel thousands of miles to be in the same room and then engage in parallel play on their laptops while one of them is giving a presentation. The speaker stands in front of tables arranged in a U, facing a wall of open laptops. The people behind are silent and focused, and seldom make comments or ask questions. They appear to be taking notes, but in fact, they are completing an unrelated budget spreadsheet, checking emails, or playing solitaire.

It takes the presentation skills of a Steve Jobs to successfully compete for attention with the rest of the world coming to each seat through the web, and most speakers do not have these skills, especially when in a foreign language, and it is also quite possible that the information they have to convey does not lend itself to an entertaining presentation, but today’s business audiences make no allowance for this.  The notion of giving each person a polite hearing, as taught in elementary school, is gone.

The intrinsic discourtesy of this behavior, however, is not the main problem. By providing escapes,web-connected laptops make badly-organized, boring meetings bearable, and allow them to continue unchallenged. Possibly the most tedious of all business rites, the regular staff meeting involves members of a department standing up one after the other and giving a status update on issues that are usually of interest only to themselves and the manager. Without laptops, some members would fall asleep and snore, and others would eventually rebel against this waste of time. And this would lead to a positive change. Wired in to their laptops, they don’t challenge the status quo.

A few years ago, cell phones routinely disrupted meetings,   with their owners leaving the room to take the calls, which were obviously more important than anything the people present in the flesh might have to say.  Today, cell phones disruptions are abating, as meeting participants usually comply with requests to turn them off, or, even better, to “turn them back on at the end of the meeting, so that they don’t miss their important calls.” The current situation with laptops, however, is not sustainable. It is a technology-induced problem, that technology may actually solve. Laptops are an endangered species, likely to be replaced with tablets that do not provide the same kind of visual shield as a raised laptop screen. Already today, people who bring iPads to meetings actually participate in the meetings, as doing otherwise would be too conspicuous.

By • Management • 0 • Tags: ,

Vilfredo_Pareto

Oct 14 2011

Revisiting Pareto

Revisiting Pareto is a paper about the application of the 80/20 law, also known as the law of the vital few and the trivial many and the Pareto principle. It presents new ways of applying it to manufacturing operations and is scheduled for publication in Industrial Engineer Magazine in January, 2012. As a preview, I am including here the following:

  1. The complete abstract of the paper as it will be published.
  2. A section on a simulation of a game leading to the emergence of a Pareto distribution of wealth among players, which was cut from the article because it is meant to stimulate thinking and does not contain actionable recommendations.

Abstract of the paper as scheduled for publication in 1/2012

The Pareto principle, so named by J.M. Juran in the 1940s, is also known as the 80/20 law, the law of the vital few and the trivial many, and ABC classification. It is a simple yet powerful idea, generally accepted but still underutilized in business. First recognized by Vilfredo Pareto in the distribution of farm land in Italy around 1900, it applies in various forms to city populations as counted in the 2010 census of the US, as well as to quality defects in manufacturing, the market demand for products, the consumption of components in production, and various metrics of item quantities in warehouses.

The key issues in making it useful are (1) to properly define quantities and categories, (2) to collect or retrieve the data needed, (3) to identify the actions to take as a result of its application and (4) to present the analysis and recommendations in a compelling way to decision makers.

In the classical Pareto analysis of quality problems, defect categories only partition the defectives if none has more than one type of defect. When multiple defects can be found on the same unit, the elimination of the tallest bar may  increase the height of its runner-up. With independent defect categories and an electronic spreadsheet, this is avoided by calculating the yield of every test in the sequence instead of simply counting occurrences, and multiplying these yields to obtain the yield of a sequence of tests. We can then plot a bar chart of the relative frequencies each defect in the population that is actually tested for it, and, as a cumulative chart, the probability of observing at least one of the preceding defects.

When analyzing warehouse content, there are usually too many different items to count physically, and we must rely on data in the plant’s databases.  The working capital tied up by each item is usually the most accessible. But physical characteristics like volume occupied, weight or part count are often scattered in multiple databases and sometimes not recorded. The challenge then is to extract, clean, and integrate the relevant data from these different sources.

In both supply chain and in-plant logistics, the design of the system should start with an analysis of part consumption by a production line in terms of frequency of use, another quantity that is not additive across items. In this application, the classical Pareto chart is replaced by the S-curve, which plots the number of shippable product units as a function of the frequency ranks of the components used, and thus provides a business basis for grouping them into A-, B- and C-items, also known as runners, repeaters and strangers.

The presentation of the results should be focused on communication, not decoration,  and use graphics only to the extent that they serve this purpose.

Emergence of the Pareto distribution in a simulation

While the Pareto principle can be easily validated in many contexts, we are short on explanations as to why that is.  We do not provide one, but we show how, in a simulation of a multiple-round roulette game in which each player’s winnings at each round determine his odds in the next one, the distribution of chips among the players eventually comes close to the 80/20 rule. This happens even though it is a pure game of chance: players start out with equal numbers of chips and have no way to influence the outcome.

Assume we use a roulette wheel to allocate chips among players in multiple rounds, with the following rules:

  1. On the first round, the players all have equal wedges of the roulette wheel, and the roulette is spun once for each chip.
  2. On each following round, each player’s wedge is proportional to the amount he won on the preceding round.

This is a pure game of chance. There is nothing players can do to influence the outcome, and the players who do best in one round are set to do even better on the next one. It is also a zero-sum game, in that whatever a player wins comes out of the other players’ hands. There is no safety net: a player with no chips after any round is out of the game.  If you know the present state of the game –how many chips the player in each rank has– then the results of future rounds is independent of the path by which you arrived at the present state. Technically, it is a random walk and a Markov process. The concept of the game is shown in Figure 1.

Figure 1. Simulation concept

It could be viewed as a representing a simplified Oklahoma Land Rush, where farmers of equal talent are initially given identical plots of land. Even in the first round, some plots will have higher crop yields than others. If the average crop is needed to survive, the farmers with a surplus sell it to the farmers with a shortfall in exchange for land and start the next season with land in proportion to the crop they had on the previous one.  The players could also be functionally equivalent, similarly priced consumable products sold in the same channels, like toothpaste or soft drinks, and the chips would be the buyers of these products.

If we run this game through enough rounds, do we tend towards a distribution where 20% of the players hold 80% of the chips, or do all the chips end up in the hands of only one player? We simulated this game in software with 1 million chips to be divided among 1,000 players in 100,000 rounds, and the simulation takes about 2 minutes to run on a desktop PC.

The results show that the “wealth distribution” is stable, beyond 10,000 rounds, with 23% of the players collectively holding 80% of the chips Figure 2 shows the distribution after 100,000 rounds, which roughly satisfies the 80/20 law, but not recursively, because it takes the top 49% of the players to account for 96% of the chips, instead of 36%.

Figure 2. Chip distribution after 100,000 rounds

Figure 3 shows other indicators of the simulation results every 10,000 rounds. The proportion of the top players holding 80% of all the chips oscillates between 21.5% and 25%, with a very slow downward trend. The number of chips held by the top 10 players also varies, but is essentially flat.Figure 3. Indicators every 10,000 rounds

This pure game of chance, which favors at every round the winners of the previous round, does not rapidly concentrate all the chips in a few hands. Instead, it produces a distribution that is as close to Pareto’s as anything we can observe in demographics, quality, or production control. This suggests the following two conclusions:

  1. The reason the 80/20 law is so often verified with actual data that it does not require some categories to be “better” than others. A long random walk is sufficient to produce it, even when you start out with equal quantities assigned to each category. The products that are Runners are not necessarily better in any way than the Repeaters or the Strangers. From the point of view of Manufacturing, it does not matter why or how they became Runners: as long as they dominate the volume, they need dedicated resources.
  2. If some categories are intrinsically better able to attract the quantity to be distributed than others, we can expect the distribution to be more unequal than 80/20. If the players are farmers, differences in skill may skew the distribution further. If they are products competing for customers, the better products may take over the market, but it does not always happen.

The simulation has shown the limitations of the conclusions drawn from a Pareto chart. When a product is a Runner, by definition it deserves a dedicated production line. That is a direct consequence of its being at the top of the chart. It does not, however, imply that it is “better” in any way than that the other products. The simulation arrived at a near 80/20 distribution of the chips without assuming any difference in the players’ ability to acquire chips: the roulette wheel has no favorites, and the players can do nothing to influence the outcome.

By • Technology • 4 • Tags: , , ,

Jun 14 2011

Management Whack-a-Mole and the Value of Lean

On 5/24/2011, NWLEAN moderator Bill Kluck launched the richest, most vigorous debate in the 13-year history of that forum by asking members whether they were cost cutters or capacity enhancers. Over the following three weeks, 29 authors posted 68 spirited, yet courteous messages accessible from NWLEAN to anyone with a Yahoo! ID.  The contributors included several well-known authors and some of my favorite sparring partners. In alphabetical order, they were  Dan Barch, William Bowman, Robert Byrd, Abhijit Deshpande, Mark Graban, Jim Harrington, Jonathan Harrison, Rob Herhold, Blair Hogg, Gangadhar Joshi, Bill Kluck, Joachim Knuf, Ed Larmore, Paul Layton, Ted Mayeshiba, Jim McKechnie, Larry Miller, Joe Murli, John Nelson, Stephen P. O’Brien, Anthony Reardon, Tom Robinson, Sunita Sangra, Dale Savage, Patrick D. Smith, Tom Stogsdill, Mike Thelen, Chuck Woods, plus a few others who did not use their real names.

The message that started the thread was as follows:

“It seems more and more (especially in this recession!) that lean professionals fall into 2 camps:
– Cost cutters
– Capacity enhancers
There are some out there that are saying that you HAVE to cut costs, or you’re not getting lean.
There are others that say lean is about improving capacity, so you can enhance the customer experience.
Which are you, and why? Is this driven by the top of your organization, people who may not understand what lean is? Do we risk becoming cost cutters, or smoke-and-mirror guys?
Just askin’!

Bill”

Although I didn’t participate initially, it was clear to me that the effect of a successful Lean implementation was limited neither to cost cutting nor to capacity enhancement. Instead, I see Lean as the pursuit of concurrent improvement in all dimensions of manufacturing performance, but I had already shared my thoughts on this matter in Lean as the End of Management Whack-a-Mole. Management Whack-a-Mole is the game illustrated in Figure 1, in which managers  boost performance in one dimension at the expense of the others, shifting focus every few months without ever achieving a genuine overall improvement.

Figure 1. Management Whack-a-Mole

Several of the early posts, however, prompted me to  jump into the fray and respond on a variety of topics, initially as follows:

  1. People trained in Finance and Management Accounting have had their shot at running US Manufacturing from the 1950s to the 1970s, with outcomes that should make them modest about the value of their approaches.
  2. Companies that have grown through Lean include Toyota since 1950, Wiremold from 1991 to 2000, and many others that are not publicized. A successful Lean effort helps Sales in specific ways, as, for example, when more flexibility enables Manufacturing to accept a sample order for a new component by a customer, who then designs it into his own products and becomes a major OEM.
  3. Cost reductions are a by-product of Lean but not its primary purpose. Manufacturing is a competitive sport, and Lean a strategy that makes you a stronger player, and not just right now but in the long run as well. When you hire a top coach for a sports team it’s not to cut costs but to win games.
  4. ROI is just one ratio, invented at DuPont 100 years ago to report in a common form the activities of multiple business units. There is nothing magic about it, and, by itself, it certainly does not give a complete picture of the health of a business, as even finance people recognize.

This caused further exchanges with Robert Byrd, who wrote: “…The most basic concept is Profit = Price – Cost. The market dictates price, so we have the best leverage for maximizing profit through controlling our cost structure…”  My response:

Is this always true? What about situations in which time to market is of the essence? I remember a client that was introducing a new frozen food product. On one of our visits they had us taste the R&D prototype, which was delicious. Three months later, they had a jury-rigged production line making 900 units/minute. The line left much to be desired, but we could not argue with their focus on getting the product to market. During all that time, their focus was not on getting it done cheaply but on getting it done at all. Then they could go back and improve the line.

He also wrote: “…Which is best accomplished through the elimination of waste and problem solving…”  My response:

This is the key point. We have yet to encounter a manufacturing or service organization in which the details of how work is being done contains no improvement opportunity, and this is what most managers simply do not believe. They are trained to think in terms of “big pictures,” and look for cost cutting opportunities like eliminating entire departments or reducing every department’s budget by 5%.

And finally: “If the aim is only to increase profit by reducing cost, without focus to grow the business, then the culture will never take root and only a short-term improvement will be realized. Or the company will never achieve it’s true potential.”

Your point does not fit in a 30-sec sound-bite or tag line. Good managers are able to monitor all aspects of performance at the same time, from growth to costs. But communication is tricky. For example, if you showcase ” Profit = Price – Cost,” what behaviors do you expect to stimulate, other than cost cutting?

In his second response, he added: “However, what was their business objective once they focused on improvement? I would venture to guess that it was focused on cost reduction.” My response:

I don’t recall them mentioning cost reduction explicitly. When the line first started, about 20% of the units were defective, and that was a focus of concern. Of course, quality improvements reduce costs as a side effect, but they also have other effects: quality problems have an impact on customer perceptions of the product because of variability among the units that are not defective.

In their other production lines, improvement was driven by marketing concerns. This was in Europe, and, in spite of the existence of the EU, frozen food packages had a different sizes by country, in addition to having labels in different languages. Based in Italy, they pursued quick changeovers in package sizes in order to be able to export to Germany, France, the UK, etc.

Their Lean program fit within their business strategy, but it is a stretch to say that this strategy was centered on cost reduction.

By • Management • 10 • Tags: , , ,

Apr 24 2011

Recruiting and Lean Human Resource Management

Some companies subject job applicants to hands-on tests of the skills required for a position. This says that they appear more interested in filling a capacity gap for a skill set than in recruiting people for careers. The most extreme cases are the “coding interviews”  given at Silicon Valley software companies, during which candidates are asked to solve programming problems. This  has spawned a whole sub-industry of coaches and books to help cram for such interviews. The problems are typically the kind of textbook exercises given in college that experienced programmers have long forgotten and are irrelevant to their actual work. College students, for example, learn various ways of sorting records, while professional application programmers just use built-in Sort functions. Software developers with, say, 20 years of experience with databases perceive these interviews as silly and demeaning, raising the question of whether they  are intended to bias the hiring process in favor of recent college graduates.

This is the opposite of the Lean approach. During a career at a company, a person would have to acquire many technical and managerial skills. With that in mind, the willingness and ability to learn are more important than what the person knows walking in. When Honda set up its Marysville facility, they deliberately hired people with no prior experience in car manufacturing, to train them from scratch in the Honda way. As an employee, the background knowledge you need is supposed to have been provided at school. Whether in the US or Japan, however, schools never work perfectly, and companies end up providing remedial training they feel they shouldn’t have to. However, if all you need today is a technical skill set, you are probably better off hiring a  contractor than an employee.

By • Management • 1 • Tags: , ,

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