Visual management

Mar 5 2014

Avoid Inaccurate Signage!

The following is a sign I saw in a plane yesterday:

Unintended signage in airliner galley
Unintended signage in airliner galley

I thought it was amusing, and told a flight attendant that it was unlikely any passenger would mistake that location for a lavatory. She explained  that this sticker was all they could find to hold up the lid of the waste container. While it may not have conveyed the best image to passengers, functionally, it was harmless, but it reminded me of not-so-harmless cases of wrong, obsolete, or ignored signage on factory floors.

Many such signs are often posted hastily as part of a “5S event.” Three months later, you see shadow boards with tools permanently missing, full pallets in front of signs that reserve the space for empties, and junk encroaching on marked transportation aisles. While each instance is a minor issue, collectively, even a small number is sufficient to destroy the credibility of the signage plantwide.

Signage on factory floors must be posted with excruciating care for accuracy and clarity, and it must then be enforced rigorously and consistently. Otherwise, it is a waste of effort.

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5S example from Korry

Jul 9 2013

Visual management as a “tier 2” tool

In the TPS Principles and Practice discussion group on LinkedIn, Emmanuel Jallas asked whether visual management was a “lever to make other tools work, or  a tool by itself.”

Visual management as embedded into other tools

As I see it, visual management should be part of everything else we do, but not treated as a stand-alone topic. Visual management should be considered in the design of a plant, a production line, a supermarket, a shipping/receiving area, a crossdock, a cafeteria, a restroom, etc. It is part of setup time reduction, cell design, or kanban implementation. It make visual management a “tier 2” tool.

If, however, you try to discuss it or teach it as a stand-alone, generic subject, you quickly get dragged into what it involves in different, specific contexts. If you have a mixed group, whatever you say will only be of interest to a minority at a time. On the other hand, if you are teaching Lean Logistics, then the discussion of visual management in materials handling comes naturally.

If you write a “How to” guide, you really have to think who your intended readers are. If you write on how to design a machining cell, you know exactly who they are. And, if you do a good job of writing it, all of it will be of use to this audience.

But who is the audience for visual management? It’s everybody! But the general theory of visual management fits in a few pages. After that, you have to go to examples, and each example is for an application that is only of interest to a tiny sliver of a manufacturing organization. So maybe 1% of your How-to book is of interest to each reader, but you can’t cut any of it, because another reader’s 1% is somewhere in the  remaining 99%.

By the way, people like Gwendolyn Galsworth or Michel Greif, who have written several books on visual management obviously disagree with me on this. I use their books like dictionaries, not how-to guides.

Visual management and Potemkin villages

Since visual management is, … visible, it is commonly part of the Potemkin villages put up by companies that want to look lean to outsiders. But the fakery is easy to spot when, for example, you see bins under a sign that says the area should be clear, the operators don’t know what the colors on the andon lights mean, the color codes are inconsistent across the floor, or a production monitor shows overproduction and production continues,… It does not take many discrepancies to torpedo the credibility of visual management.

Complicated color codes are a tell-tale sign that a system is not used.  The andon lights I have seen in Japan have only three colors with one and only one solidly lit at a time. It’s Red, Yellow, and Green, with Red meaning that the machine is stopped, Yellow that it is available, and Green that it is working. Used consistently throughout a shop floor, it gives you an overall equipment status at a glance.

Of course, the light suppliers prefer to sell more elaborate models, but I have never met an operator who could tell me what White with blinking Blue was supposed to mean, especially when it was not consistent across machines. So, if you see that, you know that the lights are just there for decoration.

Visual versus verbal communication

Reliance on words is not recommended for an audience that does not have a common language. That is why traffic signs in Europe are mostly wordless and European car dashboards are covered with pictograms, that are sometimes but not always self-explanatory, which is why I have taken to calling them “euroglyphs.”

An American car dashboard with words is actually easier to understand, but only if you know English. Like European roads, a California production shop floor may have a work force with multiple nationalities and uneven English proficiency. As a consequence, using words for instructions or safety warnings is not much of an option.

Two resources I find helpful is thinking through these issues are usability engineering experts Don Norman, author of The Design of Everyday Things, and Asaf Degani, author of Taming Hal.

If you include hearing, touch and smell, I suppose it should be called “sensory management” rather than “visual management.” If we use “visual management” for all forms of sensory management, what term are we going to use for what is specifically visual?

Visual management as part of the information system

The term “information system” should encompass all the means used in a plant to exchange and process information. Visible management is part of it, along all the computer applications, from CNCs, PLCs and SCADA systems to corporate servers for technical and business data. They are all components of the same information system and both are needed to run a plant.

Japanese terms for visible management

The Japanese term I have heard for visual management is ”medemirukanri”(目で見る管理), literally “management you can see with your eyes.” Mieruka (見える化) is new to me; it means “transformation into something visible.” I see it as an improvement, as it is shorter and just as self-explanatory. I suppose you could say that medemirukanri is the result you achieve and mieruka the process by which you achieve it, but I don’t see that nuance in the usage.

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Jan 26 2013

5S in Sri Lanka: Passing fad or firm philosophy? | The Sundaytimes Sri Lanka

See on Scoop.it – lean manufacturing

Over the past few years, Sri Lanka has seen a tremendous increase in the application of the Japanese workplace organization method, the 5S system, particularly

Michel Baudin‘s insight:

A well-written, warts-and-all account of a development I was not aware of. The last paragraph says: “…those companies which have succeeded in embracing it as a philosophy have benefited in numerous ways, financially and non-financially…” And this is as specific as it gets.

See on www.sundaytimes.lk

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Jun 17 2012

Data, information, knowledge, and Lean

cuneiform

Terms like data and information are often used interchangeably. Value Stream Mapping (VSM), for example, is also called Materials and Information Flow Analysis (MIFA) and, in this context, there is no difference between information and data. Why then should we bother with two terms? Because  “retrieving information from data” is meaningless unless we distinguish the two.

The term knowledge is used to call a document a “body of knowledge” or an online resource a “knowledge base,” when their contents might be more aptly described as dogmas or beliefs with sometimes a tenuous relationship with reality. Computer scientists are fond of calling  knowledge anything that takes the form of rules or recommendations. Having an assertion in a “knowledge base,”  however, does not make it knowledge in any sense the rest of the world would recognize. If it did, astrology would qualify as knowledge.

In Lean, as well as for many other topics, clarity enriches communication, and, in this case, can be achieved easily, in a way that is useful both technically and in everyday language. In a nutshell:

  1. Data is what is read or written.
  2. Information is what you learn from reading data.
  3. Knowledge is information that agrees with reality.

Authors like Chaim Zins have written theories about data, information, and knowledge that are much more complicated and I don’t believe more enlightening than the simple points that follow. They also go one step further, and discuss how you extract wisdom from knowledge, but I won’t follow them there. The search for wisdom is usually called philosophy, and it is too theoretical a topic for this blog.

Data

In his landmark book on computer programming, Don Knuth defined data as “the stuff that’s input or output.” While appropriate in context, this definition needs refinement to include data that is not necessarily used in a computer, such as the Wright Brothers’ lift measurements (See Figure 1). If we just say data is the stuff that’s read or written, this small change does the trick. It can be read by a human or a machine. It can be written on paper by hand or by a printer, it can be displayed on a computer screen, it can be a colored lamp that turns on, or even a siren sound.

Figure 1. Data example: the Wright Brothers’ lift measurements

More generally, all our efforts we make a plant visible have the purpose of making it easier to read and, although it is not often presented this way, 5S is really about data acquisition. Conversely, team performance boards, kanbans andons, or real-time production monitors are all ways to write data for people, while any means used to pass instructions to machines can be viewed as writing data, whether it is done manually or by control systems.

What is noteworthy about reading and writing is that both involve replication rather than consumption. Flow diagrams for materials and data can look similar, but, once you used a screw to fasten two parts, you no longer have that screw, and you need to keep track of how many you have left. On the other hand the instructions you read on how to fasten these parts are still there once you have read them: they have been replicated in your memory. Writing data does not make you forget it. This fundamental difference between materials and data needs to be kept in mind when generating or reviewing, for example, Value Stream Maps.

Information

Information is a more subtle quantity. If you don’t know who won the 2010 Soccer World Cup and read a news headline that tells you Spain did, you would agree that reading it gave you some information. On the other hand, if you already knew it, it would not inform you, and, if you read it a second time, it won’t inform you either. In other words, information is not a quantity that you can attach to the data alone, but to a reading of the data by an agent.

If you think of it as a quantity, it has the following characteristics:

  1. It is positive. You can learn from reading data, but reading data cannot make you forget. As a quantity, information can therefore only be positive or zero, and is zero only  if the data tells you nothing you didn’t already know. In addition, data in a news story about an outcome that you know to be impossible add no information.
  2. It is maximum for equally likely outcomes.A product order gives you the most information when you had no idea what product it might be for. Conversely, if you know that 90% of all orders are for product X, the content of the next order is not much of a surprise: you will lose only 10% of the time if you bet on X. The amount of information you get from reading data is maximum when you know the least, and therefore all possible values are equally likely to you.
  3. It is subadditive. If you read two news stories, the information you acquire from both will be at most the sum of the information you acquire from each. If you read about independent topics like the flow of orders on the company’s e-commerce website and lubrication problems in the machine shop, the information from both stories will be the sum of the information in each. If, however, the second story is about, say, dealer orders, then the two stories are on related subjects, and the total information received will be less than the sum of the two.

The above discussion embodies our intuitive, everyday notion of what information is. For most of our purposes —  like designing a performance board for Lean daily management, an andon system, a dashboard on a manager’s screen, or a report — this qualitative discussion of information is sufficient. We need to make sure they provide content the reader does not already know, and make the world in which he or she operates less uncertain. In other words, reading the data we provide should allow readers to make decisions that are safer bets about the future.

In the mid 20th century, however, the mathematician Claude Shannon took it a step further, formalized this principle into a quantitative definition of information, and proved that there was one only one mathematical function that could be used to measure it. He then  introduced the bit as its unit of measure. Let us assume that you read a headline that says “Spain defeats the Netherlands in the Soccer World Cup Final.” If you already knew that the finalists were Spain and the Netherlands and thought they were evenly matched, then the headline gives you one bit of information. If you had no idea which of the 32 teams that entered the tournament would be finalists, and, to you, they had all equal chances, then, by telling you it was Spain and the Netherlands, the headline gives you an additional 8.9 bits.

Over the decades, his theory has had applications ranging from the design communication networks to counting cards in blackjack, more than to help manufacturers understand factory data. It has a use, however, in assigning an economic value to the acquisition of information, and thereby justify the needed investment.

Knowledge

On November 3, 1948, the readers of the Chicago Tribune received information in the “Dewey defeats Truman” headline (See Figure 2). None of them would, however, describe this information as knowledge, just because it was not true. It should not need to be said, and, outside the software industry, it doesn’t. Software marketers, however, have muddied the water be calling rules derived from these assertions “knowledge,” regardless of any connection with reality. By doing so, they have erased the vital distinction between belief, superstition or delusion on one side and knowledge on the other.

Figure 2. When information is not knowledge

As Mortimer Adler put it in Ten Philosophical Mistakes (pp. 83-84), “it is generally understood that those who have knowledge of anything are in possession of the truth about it.  […] The phrase ‘false knowledge’ is a contradiction in terms; ‘true knowledge’ is manifestly redundant.”

When “knowledge bases” were first heard from in the 1980’s, they contained rules to arrive at a decision, and only worked well with rules that were true by definition. For example, insurance companies have procedures to set premiums, which translate well to “if-then” rules. A software system applying these rules could then be faster and more accurate than a human underwriter retrieving them from a thick binder.

On the other hand, in machine failures diagnosis, rules are true only to the extent that they actually work with the machine; this is substantially more complex and error-prone that applying procedures, and the rule-based knowledge systems of the 1980’s were not successful in this area.  Nowadays, a “knowledge base” is more often a forum where users of a particular software product post solutions to problems. While these forums are useful, there is no guarantee that their content is, in any way, knowledge.

The books on data mining are all about algorithms, and assume the availability of accurate data. In real situations, and particularly in manufacturing, algorithms are much less of a problem than data quality. There is no algorithm sophisticated enough to make wrong data tell a true story. The key point here is that, if we want to acquire knowledge from the data we collect, the starting point is to make sure it is accurate. Then we can all be credible Hulks (See Figure 3).

Figure 3. The Credible Hulk (received from Arun Rao, source unknown)

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The-Most-Complicated-Formula

Feb 16 2012

Safety Stocks: Beware of Formulas

A formula you find in a book or learn in school is always tempting. It is a “standard.” If you follow it, others are less likely to challenge your results. These results, however, may be worthless unless you take a few precautions. Following are a few guidelines:

  1. Don’t use a formula you know nothing about. Its validity depends on assumptions that may or may not be satisfied. You don’t need to know how to prove the formula, but you need to know its range of applicability.
  2. Examine your data. Don’t just assume they meet the requirements. Examine their summary stats, check for the presence of outliers, generate histograms, scatter plots, time series, etc.
  3. Don’t make up missing data. If you are missing the data you need to estimate a parameter, find what you can infer about the situation from other parameters, by other methods. Do not plug in arbitrary values.
  4. Make your Excel formulas less prone to error by using named ranges rather than cell coordinates. If a formula is even slightly complicated, referring to variables by names like “mean” or “sigma” makes formulas easier to proof-read than with names like “AJ” or “AK.”

The safety stock formula for the reorder point method

Safety stock is a case in point. The literature gives you a formula that is supposed to allow you to set up reorder point loops with just the minimum amount of safety needed to prevent shortages under certain conditions of variability in both your consumption rate and your replenishment lead time. It is a beautiful application of 19th century mathematics but I have never seen it successfully used in manufacturing.

Let us look more closely at what it is so you can judge whether you would want to rely on it. Figure 1 shows you a model of the stock over time when you use the Reorder Point method and both consumption and replenishment lead time vary according to a normal distribution. The amount in stock when the reorder point is crossed should be just sufficient to cover your needs until the replenishment arrives. But since both replenishment lead time and demand vary, you need some safety stock to protect against shortages.

Figure 1. The reorder point inventory model

If your demand is the sum of small quantities from a large number of agents, such as sugar purchases by retail customers in a supermarket, then the demand model makes sense. In  a manufacturing context, there are many situations in which it doesn’t. If you produce in batches, then the demand for a component item will be lumpy: it will be either the quantity required for a batch or nothing. If you use heijunka, it will be so close to constant that you don’t need to worry about its variations.

What about replenishment lead times? If in-plant transportation is by forklifts dispatched like taxis, replenishment lead times cannot be  consistent. On the other hand, if it takes the form of periodic milk runs, then replenishment lead times are fixed at the milk run period or small multiples of it. With external suppliers, the replenishment lead times are much longer, and cannot be controlled as tightly as within the plant, and a safety stock is usually needed.

Let us assume that all the conditions shown in Figure 1 are met. Then there is a formula for calculating safety stock that you can find on Wikipedia or in David Simchi-Levy’s Designing and Managing the Supply Chain (pp. 53-54).  Remember that it is only valid for the Reorder Point method and that it is based on standard deviations of demand and lead time that are not accessible for future operations and rarely easy to estimate on past operations. The formula is as follows:

Where:

  • S is the safety stock you need.
  • C  is a coefficient set to guarantee that the probability of a stockout is small enough. You can think of it a number of standard deviations above the mean item demand needed to protect you against shortages. In terms of Excel built-in functions, C is given by:

C = NORMSINV(Service level)

Service levelC
90.0% 1.28
95.0% 1.64
99.0% 2.33
99.9% 3.09

  • The other factor, under the radical sign, is the corresponding standard deviation.
  • μL and σL are the mean and standard deviations of the lead time.
  • μD and σD are the mean and standard deviation of the demand per unit time, so that the demand for a period of length T has a mean of μD xT and a standard deviation of σDx √T

Case study: Misapplication of the safety stock formula

This formula is occasionally discussed in Manufacturing or Supply Chain Management discussion groups, but I have only ever seen one attempt to use it,  and it was a failure. It was for the supply of components to a factory, and 14 monthly values were available for demand, but only an average for lead times.

The first problem was the distribution of the demand, for which 14 monthly values were available. This is too few for a histogram, but you could plot their cumulative distribution and compare it with that of a normal distribution with the same mean and standard deviation, as in Figure 2. You can tell visually that the actual distribution is much more concentrated in the center than the normal model, which is anything but an obvious fit.  Ignoring such objections, the analyst proceeded to generate a spreadsheet.

Figure 2. Actual versus normal cumulative distribution

The second problem is that he entered the formula incorrectly, which was not easy to see, because of the way it was written in Excel.  The formula in the spreadsheet was as follows:

C*SQRT((AJ4*AL4^2)+(AI4^2*AM4^2))

then, looking at the spreadsheet columns, you found that they were used as follows:

  • AJ  for Standard Deviation of Daily Demand, and
  • AL for Average Replenishment time.

And therefore the first term under the square root sign was σDxμL2 instead of μLxσD2.

The third problem was that the formula requires estimates of standard deviations for both consumption and replenishment lead times, but no data was available on the latter. To make the formula produce numbers, the standard deviations of replenishment lead times was arbitrarily assumed to be 20% of the average.

For all of these reasons, the calculated safety stock values made no sense, but nobody noticed. They caused no shortage, and the “scientific” formula proved that they were the minimum prudent level to maintain.

Sizing safety stocks in practice

There is no universal formula to determine an optimal size of safety stocks. What can often be done is to simulate the operation of a particular replenishment loop under specified rules. For a simulation of a Kanban loop using Excel, see Lean Logistics, pp. 208-213.

No calculation or simulation, however, is a substitute for keeping an eye on what actually happens on the shop floor during production. One approach is to separate the safety stock physically from the regular, operational stock and monitor how often you have to dig into it. If, say, six months go by without you ever needing it, you are probably keeping too much and you cut it in half. With a Kanban loop, you tentatively remove a card from circulation. If no shortage results, then the card was unnecessary. If a shortage occurs, you return the card and look for an opportunity to improve the process so that the card can be removed.

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Feb 8 2012

Graphic representation of a Lean schedule

Via Scoop.it – lean manufacturing

A clever graphic tool. According to the author, Prasad Velaga, the schedule was actually generated by finite capacity scheduling logic from a real test dataset that was taken from a job shop.
Via optisol.biz

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