Lean manufacturing

The pursuit of concurrent improvement in all dimensions of manufacturing performance through projects involving both the production floor and support services.

Continuous improvement from bolts to clamps

Oct 19 2011

The Role of Technology in Continuous Improvement

In an article on this topic in Industry Week today, Ralph Keller asserts that Continuous Improvement is focused on business processes rather than technology.

However, if you wrap tinfoil around the feet of a welding fixture to make it easier to clean, replace bolts with clamps on a machine to reduce setup time, or mount a hand tool on the machine on which it is used, it usually counts as Continuous Improvement but involves technical changes to work that I don’t think anyone would describe as business processes.

Yes, Continuous Improvement is done without expensive technology, but it does involve cheap technology.

Ralph Keller also reminds us that Continuous Improvement is not “rocket science,” which implies that it is easier. I agree that it is different, but not easier. I don’t know any rocket scientist with the skills to facilitate Continuous Improvement.

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: , , ,

Feb 11 2011

Comparative advantage in the allocation of work among machines

Another NWLEAN post in response to Mike Thelen’s query on Laws of Nature, posted on 2/11/2011

On several occasions, I ran into the problem of allocating work among machines of different generations with overlapping capabilities. There were several products that could be processed to the same levels of quality in both the new and the old machines. The machines worked differently. For example, the old machines would process parts in batches while the new ones supported one-piece flow. But the resulting time per part was shorter on the new machines for all products. In other words, the new machines had a higher capacity for everything.

Given that the products were components going into the same assemblies, they were to be made in matching quantities per the assembly bill of materials and the demand was such that the plant had to make as many matching sets as possible. The question then is: how do you allocate the work among the machines?

When I first saw this problem, I thought it was unique, but, in fact, many machine shops keep multiple generations of machines on their floors and make parts in matching sets for their customers, and it is in fact quite common. The solution that maximizes the total output is to apply the law of comparative advantage from classical economics. Adapted to this context, it says that the key is the ratio of performance between the old and the new machines on each product. For example, if the new machine can do product X 30% faster than the old machine and product Y ten times faster, then the old machine is said to have a comparative advantage on product X, and you should run as much as possible of product X on the old machine.

It is a bit surprising at first, but easy to apply. What is more surprising is that so few plants do. The logic that is actually most commonly used is to load up the new machine with as much work as possible, on the grounds that it has a high depreciation and needs to “earn its keep.” What many managers have a difficult time coming to terms with is that what you paid for a machine and when you paid it is irrelevant when allocating work, because it is in the past and nothing you do will change it. You produce today with the machines you have, and the only thing that matters is what they can do, now and in the future.

The law of comparative advantage is taught in economics, not manufacturing or industrial engineering, and pertains to the benefits of free trade between countries, not work allocation among machines. The similarity is not obvious. This law is attributed to David Ricardo who published in 1817, based on an analysis of the production of wine and cloth in England and Portugal. Trade was free because, at the time, Portugal was under British occupation. Both wine and cloth were cheaper to produce in Portugal, but wine was much cheaper and cloth only slightly cheaper. England had therefore a comparative advantage on cloth, and the total output of wine and cloth was maximized by specializing England on cloth and Portugal on wine. You transplant that reasoning to your machine shop by mapping the countries to machines and costs to process times.

This simple approach works in a specific context. It is not general, but is of value because that context occurs in reality. The literature on operations research is full of more complicated ways to arrive at solutions in different situations. There is an article from IE Magazine in July, 2006 that I wrote about this entitled “Not-so-basic equipment: the pitfalls to avoid when allocating work among machines.” It used to be available on line for free on the magazine’s web site. Now you have to buy it on Amazon to download it.

By • Laws of nature • 0 • Tags: , ,

Little's Law concept

Jan 13 2011

Lead times, work sampling, and Little’s Law

On 1/11/2011, Michael Thelen asked in the NWLEAN forum about “laws of nature” as they related to Lean. This is based on one of my answers. 

Lead time is a key performance indicator of manufacturing operations, but how do you measure it? It is not a quantity that you can directly observe by walking through a plant. To measure it directly you need to retrieve start and finish timestamps from historical data, assuming they are available and accurate. Or you could put tracers on a sample of parts, which means that it would take you at least six weeks to measure a six-week lead time. In most plants, however, a quick and rough estimate is more useful than a precise one that takes extensive time and effort to achieve.

That is where work sampling and Little’s Law come in handy. The key idea of work sampling, which the Wikipedia article fails to make clear, is that lets you infer a breakdown of each entity’s status over time from snapshots of the status of multiple identical entities. If, every time you go to the shop floor, you see 2 out  of 10 operators walking the aisle, you infer that, on the average, each operator spends 20% of the time walking the aisle.

There are obviously necessary conditions for such an inference to be valid. For example, you want to take snapshots during normal operations, not during startup orshutdown, and the group you are measuring must be homogeneous: if it is comprised of two materials handlers and eight production operators, the 20% average is not interesting, even if it is accurate. Work sampling is usually described as applied to people, but the same logic is applicable to machines and to work pieces, and that is what makes it possible to infer lead times from snapshots of inventory and throughput rates.

On the shop floor, you can count parts, bins or pallets, and observe the pace at which they are being consumed. Let us assume we are in the context shown in Figure 1, and want to estimate how long we take to turn a blank into a finished good.


Figure 1. Context of Little’s Law

Little’s Law, then, says that on the average,  in steady state, within one process or process segment,

Inventory = Lead time X Throughput

The reason this is true is best explained graphically, as in Figure 2, in the simple case of constant throughput and lead time. The cumulative count of blanks in is a straight line going up over time, and so is the count of finished goods out, offset by the lead time.  The vertical distance between the curves is the number of blanks that have come in but not yet made it out as products, and represents therefore the inventory. The slope of each curve is the throughput, and it is clearly the ratio of the inventory to the lead time.

Figure 2. Little’s Law with constant throughput and lead time

What is interesting about Little’s Law is that it remains valid about averages when both rates of arrivals of blanks and departures of finished goods are allowed to fluctuate randomly about an average. This is probably the best known and most useful general result of queueing theory.

Since we can count inventory and measure throughput, we can infer average lead times from just this data. One snapshot will not give you an accurate estimate, but it is still considerably easier to take a few snapshots of a production line to get a more accurate estimate than it is to research history. The point is to get close to an answer that would take much longer to get if you actually had to be accurate.

By • Laws of nature • 2 • Tags: , , , , ,

Apr 22 2010

Lean is about simultaneously improving all dimensions of performance, including quality

Originally posted on The Lean Edge on 4/22/2010

Quality not central to Lean? Says who? Lean is about simultaneously improving all dimensions of performance, including quality.

Quality professionals frequently miss this, because what they learned primarily addresses process capability issues that are central only in high technology, where, if your process is mature, your product is obsolete. This is the context where statistical approaches like Six Sigma make a difference.

Modern machine tools, on the other hand, can easily hold required tolerances, and most quality problems are not due to lack of process capability. They are instead due to discrete failure of the equipment or human error.

The main issue with discrete equipment failures is to detect them quickly so that they affect few parts and can be diagnosed before their trail is cold. With one-piece flow, defects are detected immediately instead of being buried in WIP, and this is why conversion from batch production to one-piece flow typically yields large improvements in quality.

The next step is having machines stop as soon as they start producing defectives, but this still leaves human error, and that is addressed by mistake-proofing.

Beyond these approaches, there is also management to prevent the deterioration over time, and planned responses to potential new problems.

This is a hierarchy of approaches. Actual numbers vary, but, in orders of magnitude, statistical tools will get you from 30% defectives to 3%, one-piece flow to 0.3%, mistake-proofing to 15ppm, and I know of one case of a Toyota supplier achieving <1ppm on some parts.

By • Management • 0 • Tags: ,

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