Some of the standard charts used in manufacturing for decades don’t meet today’s criteria for effective visualization. But using them is now a tradition; they are taught in school and their value is unchallenged, but it is time to challenge it. If we were to see these charts for the first time in 2015, would we consider the information they provide useful, and would we want to use the classical formats? This post suggests answers in the case of the venerable Pareto chart.
“Apparently the folks writing about stratifying inventory into A, B and C items and building calculations of such into ERP packages didn’t get the lean memo.
Wikipedia is typical of such thinkers when they describe the ABC thought process as:
- ‘A’ items – 20% of the items accounts for 70% of the annual consumption value of the items.
- ‘B’ items – 30% of the items accounts for 25% of the annual consumption value of the items.
- ‘C’ items – 50% of the items accounts for 5% of the annual consumption value of the items.
The idea of micromanaging some items and slacking off on others based on purchase price is the very same theory they taught me at the University of Cincinnati back in the days when … ”
I agree with Bill that, from the point of view of manufacturing operations, the purchase price of materials is not the most important parameter. because the lack of a nail can prevent the completion of a product as effectively as the lack of a pump costing 1,000 times more.
It doesn’t mean, however, that classifying items to treat them differently is wrong, but it must be done by frequency of use rather than price, and I prefer to call the categories “Runners,” “Repeaters,” and “Strangers” rather than A, B, and C.
As a function of rank, I then look for the percentage of units actually built that can be fully assembled with only the items of this rank and higher. It starts at 0%, and, as long as it stays at 0%, I consider the items to be Runners, essentially items you can’t build any product without. At the other end of the spectrum, I call Strangers all the items without which you can make 95% of the units. And everything in-between is a Repeater.
Then you may decide, for example, to dedicate an easily accessible storage location to each Runner, and make special arrangements with suppliers. For Repeaters, you may use the Kanban system, with smaller dedicated locations. And you don’t keep any stock of Strangers, but order them as needed and store them, if at all, in dynamically allocated slots.
See on www.idatix.com
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:
- The complete abstract of the paper as it will be published.
- 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:
- On the first round, the players all have equal wedges of the roulette wheel, and the roulette is spun once for each chip.
- 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.
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 on 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 on 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 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:
- The reason the 80/20 law is so often verified with actual data is that is 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 recame Runners: as long as they dominate the volume, they need dedicated resources.
- 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.