5S

Nov 18 2012

Why 5S fails

In the Lean CEO discussion group on LinkedIn, Paul Renoir started a discussion on why 5S implementations are not sustained. As one of the participants, Sammy Obara, pointed out, if it’s not sustained, by definition it’s not 5S. The discussion is really about why 5S fails, and failing it does, massively and systematically.  Among the 22 contributions to this discussion to date, there isn’t a single one contradicting its basic premise, and asserting what a great success 5S has been in specific facilities.

What I have written on 5S in this blog before may make me sound as if I thought of it as worthless. It’s not the case. 5S  is a valuable tool, and it is implemented with success in many factories in Japan. The failures that can be seen in the US and Europe are due to misunderstandings, translation errors, and wrong decisions as to when and why it should be implemented. My previous posts on the subjects are as follows:

Why consultants recommend starting with 5S

Consultants often recommend that a company start with 5S for the wrong reasons. One quick look at a plant and you know that it would be better with 5S, but that doesn’t mean that 5S would solve its problems or that the organization is capable of implementing it.

It’s like a kid with problems at school who has a messy room. It’s easy to tell the kid to tidy up the room, but it won’t solve the problems at school, and it won’t be sustained. Whether with a plant or a kid, figuring out what the problems are takes more time and effort, but it is necessary if you want to identify projects (1) that put the organization on track to a solution, (2) that it has the skills and the will to conduct successfully, and (3) that entail changes that will be sustained.

Initial projects that work

Art Byrne, among others, recommend giving stretch goals to projects. The point of stretch goals is that they cannot be reached just by putting in extra effort temporarily. Instead, stretch goals require you to make substantial, physical changes to the work, including modifications of machines or fixtures. Once you have made such changes, not only do you achieve your stretch goals, but you don’t easily revert to the old way. In the initial stages of Lean implementation, the only way you get any 5S to stick is by making it the “finishing touches” on other projects, like cells or SMED. If, instead, 5S is the project, it won’t be sustainable.

5S and involvement by everyone

One aspect of 5S that is lacking in just about every discussion of it that I have seen in English is that, when you make 5S a project on its own, it must involve everyone. Participation is not on a voluntary basis. Everyone from the CEO to the janitor must participate, and it fails unless this actually happens. Most employees consider this cleaning up to be beneath them, and top managers’ direct participation is essential to prevent them from feeling this way and acting accordingly.

This is why 5S is so difficult to implement, especially as your first step towards Lean. On the other hand, if you have taken the content of 5S and, as I suggested before, made it part of such other projects as cells or SMED, you may have, after a year or two, about 20% of your work force unknowingly practicing 5S. At that point, you may choose to make 5S your next project and leverage this 20% to achieve 100% involvement. Then you a have a chance to make it stick.

There are other features of Lean that require participation by everyone, particularly autonomous maintenance, which is the only aspect of TPM that you see widely implemented. Somewhere along your Lean journey, you have to learn how to implement practices that require participation by everyone, which is what, in Japan, is meant by “Total.”

5S is a good choice for your first “Total” program and, in particular, works as a stepping stone to TPM. Once you have your 5S daily routine in place, it is a natural transition to enhance it to include checks on the vital signs of your equipment.

Translation errors about 5S

If 5S efforts were broadly successful, there would be no point in raising an issue. Since, however, they are almost universal failures, it might help to communicate accurately on what 5S actually means.

I first learned about “4S” in Japan in the 1980s, from my mentor Kei Abe, and studied it in the Japanese literature. As the time, it was translated into English as R.I.C.K., for Remove, Identify, Clean, and Keep clean, and I thought it was a reasonable approximation. A few years later, my colleague Crispin Vincenti-Brown introduced me to a major American corporation with plants that bore the traces of a failed 5S implementations, from fading banners on the walls to obsolete markings and dirty work stations. Three years before, the top management had been on a tour of Japan, had seen 5S in action there, and had committed to implement it, going as far as putting a Vice President in charge of it. And this was the result. The operators’ version of the meaning of 5S was “Some Stupid Supervisor Said So.”

By then, it was no longer 4S but 5S, and someone had seen fit to translate the five Japanese words with English words that also started with S. While it was undoubtedly clever, the meaning of these five words just didn’t match the original, and these mistranslations, frequently repeated, now have  become some sort of standard.

Following are explanations of the original five S’s, to the best of my ability:

  • Seiri （整理)　does not mean Sort. In everyday Japanese, it means sort out, as in resolving administrative problems. In 5S, it means removing from the shop floor the items you don’t use routinely.
  • Seiton　（整頓） does not mean Set in order. In everyday Japanese, it means arranging neatly. In 5S, it refers to having assigned locations and labels for everything you retain on the shop floor.
  • Seiso (清掃) means Clean, not Shine. The idea is to have production operators clean their own workplaces at shift end, so that they notice details like spills, frayed cables, or broken lamps. It is not about making them pretty.
  • Seiketsu （清潔)　does not mean Standardize. In everyday Japanese, it is a noun meaning cleanliness. In 5S, it is the reduction of the first three S’s to daily practice by management enforcement, through things like checklists, assignment of responsibility for daily housekeeping activities, and routine audits.
  • Shitsuke （躾) does not mean Sustain. In everyday Japanese, it is a noun, meaning upbringing. It is not an action but the condition you reach when the performance of the first three S’s has become second-nature to the organization.  As long as you tell your kid to brush his teeth every day, you are practicing Seiketsu; once he does it without prompting, you have achieved Shitsuke.

By • Policies • 6 • Tags: , , , , ,

Oct 30 2012

Implementing ‘5S’ Programs in Manufacturing Facilities | Hydrotech Motion Control Solutions

See on Scoop.it – lean manufacturing

This is one more misleading article about 5S. Following are a few of the more questionable assertions in it:

  • “5S is a system to […] optimize productivity” If all you do is 5S, you will not see a massive productivity increase.
  • “…the concept is especially attractive to older manufacturing facilities looking to reduce their costs.” Don’t expect any measurable cost reduction from just 5S.
  • “The 5S methodology is a simple and universal approach that works for companies all over the world.” It may look simple, but it is not, and most companies fail when they try to implement it. It is not for Lean beginners. If you try it with beginners, the most likely outcome is a mutiny.
  • “It is typically the first lean method that organizations implement.” Yes, due to simplistic advice, and it is a well-traveled shortcut to failure at Lean. Successful Lean implementations almost never start with 5S. Don’t take my word for it. Check out, for example, Art Byrne’s Lean Turnaround.
  • “The term refers to five steps — sort, set in order, shine, standardize, and sustain.” This is a common mistranslation of five Japanese terms that also start with S:
    • Seiri does not mean Sort. It means removing from the shop floor the items you don’t use routinely.
    • Seiton is not too badly mangled by “set in order.” It refers to having assigned locations and labels for everything you retain on the shop floor.
    • Seiso means Clean, not Shine. The idea is to have production operators clean their workplace at shift end, so that they notice details like spills, frayed cables, or broken lamps. It is not about making it pretty.
    • Seiketsu does not mean Standardize. It is the reduction of the first three S’s to daily practice by management enforcement, through things like checklists and audits.
    • Shitsuke does not mean Sustain, and it is not an action but the condition you reach when the performance of the first three S’s has become second-nature to the organization. As long as you tell your kid to brush his teeth every day, you are practicing Seiketsu; once he does it without prompting, you have achieved Shitsuke.

See on www.hydrotech.com

By • Press clippings • 3 • Tags: , ,

Oct 17 2012

Fab manager tries Lean with no support from the top, by starting with 5S…

See on Scoop.it – lean manufacturing

Tim Heston reports a conversation with the manager of a low-volume/high-mix fabrication shop who wants to implement Lean, without top management support, and starting with 5S, and it’s not working.

Two thirds of the article are not just about 5S but about the tool hoarding behavior of operators. Yes, organizing workstations with commonly used tools makes sense, but, if the manager starts by addressing this head-on, he will have a mutiny on his hands and his bosses won’t back him up.

To be successful, changes in tool management policies should be part of more major changes, such as the implementation of SMED on a machine, or the development of a machining cell. Once you have a team of operators who move between stations and rotate positions, then  tools naturally become attached to stations rather than individuals.

What should the manager do? I am currently reading Art Byrne’s Lean Turnaround, and, maybe, getting his CEO to take a look at it might be a good idea to get him or her on board. Next, he should get better advice getting started than focusing on 5S. Much of the literature recommends it because it looks easy. It’s not, and it almost never works as a first step.

See on blog.thefabricator.com

By • Blog clippings • 1 • Tags: , , , ,

Jul 3 2012

A Video Showing Office 5S Gone Wrong

See on Scoop.it – lean manufacturing

Not fresh news, but my colleague Maria Samsonova just pointed it out to me.

See on www.leanblog.org

By • Blog clippings • 2 • Tags: ,

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)

By • Technology • 3 • Tags: , , , , ,

Jun 15 2012

“5S is simple and easy” “Yeah, right.”

The following exchange took place yesterday as part of a Q&A session with the Lean manager of a plant employing about 1,000 people, after a tour of the shop floor:

“Why did you start with 5S?”

“It’s simple and easy, it provides a lot of benefits, and then you can move on to other tools.”

“When did you get started?”

“We have been at it since 2008.”

“And how far do you think you have gone?”

“In some places on the floor, we are at level 2. We haven’t been able to measure benefits yet, but you have to have faith that, if you are applying the right process, results will follow. We still need to convince the top managers.”

“What are you doing now?”

“We are training Six Sigma black belts.”

In other words, in four years of implementing 5S, the plant had brought itself up from nothing to a low level, but it did not occur to the manager to challenge his perception that it is a simple and easy tool. Like so many of his peers at other companies, he has gone down this path while leaving on the table opportunities to double productivity, reduce throughput time and defect generation that could be uncovered by analyzing the process. In his case, such opportunities were apparent  in manual, fixed station assembly areas where one operator takes 4 to 12 hours to assemble one unit from start to finish.

In this plant, by the manager’s own admission, Lean has not had a measurable impact in four years. From his own words as well, we have the answer to the first why: a misconception on where to start. But we still need to ask why four more times… The Lean manager is smart, personable, and knowledgeable in the company’s technology, products and markets. But the company’s management may have underestimated the need for outside help to get their Lean program started in a more fruitful direction.

By • Management • 4 • Tags: , , , ,

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