Technology

spc1

Dec 27 2011

Is SPC obsolete?

In the broadest sense, Statistical Process Control (SPC) is the application of statistical tools to characteristics of materials in order to achieve and maintain process capability. In this broad sense, you couldn’t say that it is obsolete, but common usage is more restrictive.

The semiconductor process engineers who apply statistical design of experiments (DOE) to the same goals don’t describe what they do as SPC. When manufacturing professionals talk about SPC, they usually mean Control Charts, Histograms, Scatter Plots, and other techniques dating back from the 1920s to World War II, and this body of knowledge in the 21st century is definitely obsolete.

Tools like Control Charts or Binomial Probability Paper have impressive theoretical foundations and are designed to work around the information technology of the 1920s. Data was recorded on paper spreadsheets, you looked up statistical parameters in books of tables, and computed with slide rules, adding machines or, in some parts of Asia, abacuses (See Figure 1).

In Control Charts, for example, using ranges instead of standard deviations was a way to simplify calculations. These clever tricks addressed issues we no longer have.

Figure 1. Information technology in the 1920s

Another consideration is the manufacturing technology for which process capability needs to be achieved. Shewhart developed control charts at Western Electric, AT&T’s manufacturing arm and the high technology of the 1920s.

The number of critical parameters and the tolerance requirements of their products have no common measure with those of their descendants in 21st century electronics.

For integrated circuits in particular, the key parameters cannot be measured until testing at the end of a process that takes weeks and hundreds of operations, and the root causes of problems are often more complex interactions between features built at multiple operations than can be understood with the tools of SPC.

In addition, the quantity of data generated is much larger than anything the SPC techniques were meant to handle. If you capture 140 parameters per chip, on 400 chips/wafer and 500 wafers/day, that is 28,000,000 measurements per day. SPC dealt with a trickle of data; in current electronics manufacturing, it comes out of a fire hose, and this is still nothing compared to the daily terabytes generated in e-commerce or internet search  (See Figure 2).

Figure 2. Data, from trickle to flood, 1920 to 2011

What about mature industries? SPC is a form of supervisory control. It is not about telling machines what to do and making sure they do it, but about checking that the output is as expected, detecting deviations or drifts, and triggering human intervention before these anomalies have a chance to damage products.

Since the 1920s, however, lower-level controls embedded in the machines have improved enough to make control charts redundant. The SPC literature recommends measurements over go/no-go checking, because measurements provide richer information, but the tables are turned once process capability is no longer the issue.

The quality problems in machining or fabrication today are generated by discrete events like tool breakage or human error, including picking wrong parts, mistyping machine settings or selecting the wrong process program. The challenge is to detect these incidents and react promptly, and, for this purpose, go/no-go checking with special-purpose gauges is faster and better than taking measurements.

In a nutshell, SPC is yesterday’s statistical technology to solve the problems of yesterday’s manufacturing. It doesn’t have the power to address the problems of today’s high technlogy, and it is unnecessary in mature industries. The reason it is not completely dead is that it has found its way into standards that customers impose on their suppliers, even when they don’t comply themselves. This is why you still see Control Charts posted on hallway walls in so many plants.

But SPC has left a legacy. In many ways,  Six Sigma is SPC 2.0. It has the same goals, with more modern tools and a different implementation approach to address the challenge of bringing statistical thinking to the shop floor.

That TV journalists describe all changes as “significant” reveals how far the vocabulary of statistics has spread; that they use it without qualifiers shows that they don’t know what it means. They might argue that levels of significance would take too long to explain in a newscast, but, if that were the concern, they could save air time by just saying “change.” In fact, they are just using the word to add weight to make the change sound more, well, significant.

In factories, the promoters of SPC, over decades, have not succeeded in getting basic statistical concepts understood in factories. Even in plants that claimed to practice “standard SPC,” I have seen technicians arbitrarily picking parts here and there in a bin and describing it as “random sampling.”

When asking why Shewhart used averages rather than individual measurements on X-bar charts, I have yet to hear anyone answer that averages follow a Bell-shaped distribution even when individual measurements don’t. I have also seen software “solutions” that checked individual measurements against control limits set for averages…

I believe the Black Belt concept in Six Sigma was intended as a solution to this problem. The idea was to give solid statistical training to 1% of the work force and let them be a resource for the remaining 99%.

The Black Belts were not expected to be statisticians at the level of academic specialists, but process engineers with enough knowledge of modern statistics to be effective in achieving process capability where it is a challenge.

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I'm six sigma - I'm Lean

Dec 9 2011

Six Sigma R.I.P.

If you google Six Sigma, you get the impression that it is a going concern, with all sorts of organizations offering training and consulting on how to implement it. If you dig just a bit deeper, you run into a Business Week article from June 11, 2007 entitled Six Sigma: So Yesterday? It explained how the best known Six Sigma icons, like GE, 3M, Home Depot, or Motorola were “dialing it back.” Whatever this may mean, it is difficult to imagine ambitious employees in a company showing enthusiasm for a program that is being “dialed back.”

The same article attributes the following statement to GE’s former CEO Jack Welch about Six Sigma: “Even if the concept is applied in areas where perhaps it shouldn’t be, it’ll be worth it in the long run.” It makes you wonder how he would have liked to work in such an area, with management knowingly pressuring him to implement an irrelevant method.

Now that the Six Sigma craze is over, there is no much merit in criticizing it. Ever since I was first exposed to it in the 1990s, I have perceived it as a welcome update of the now 90-year-old tools of Statistical Process Control (SPC), useful in industries where, if your process is mature, your product is obsolete. This applies in semiconductors and other high-technology manufacturing sectors, but not in mature sectors like automotive.

It never struck me as having the potential to be a revolution in business or comparable in scope and impact to Lean. Saying so 10 years ago made many people angry but I did worse: I put in writing, in an article entitled Six Sigma and Lean Manufacturing that was published by the SME in a Six Sigma newsletter in July, 2002.

If you google Motorola +Six-Sigma, you learn that Motorola no longer teaches Six Sigma business improvement. Given that Motorola is where Six Sigma was invented, the equivalent would be for Toyota to dump Lean. Maybe it is time to dial down the Six Sigma training programs.

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kanbancardfromliker

Nov 29 2011

More about Kanbans, and What it Takes to Use Them.

Ever since the world outside of Toyota started noticing its production system in the late 1970s, the Kanban system has received a disproportionate amount of attention compared to other features. It does not mean, however, that it has been accurately implemented in many of the factories that claim to have done it.  To anyone who cared to study it, details have been available in English at least since Robert Hall’s Zero Inventories (1983), the JMA’s Kanban, Just-In-Time at Toyota (1985), Yasuhiro Monden’s Toyota Production System (1993), or an updated treatment in Lean Logistics (2005).

Pressured to implement Kanbans by executives to whom it was little more than a buzzword, many manufacturing professionals found it more expedient to take old, familiar approaches like the two-bin system or reorder-point and call them Kanban. One such system implementing reorder-point through cards placed on a board has become so popular in France that I suggested calling it “French Kanban.” As can be seen in Figure 1, each column on the board is a mirror of the inventory level for an item. Each pocket filled by a card corresponds to an empty slot in stores, so that the remaining amount is visually indicated by the empty pockets on top. The reorder point is crossed when the cards reach the red zone.

Figure 1. French Kanban: Reorder-Point with Cards

Meanwhile, a few academics like J.T. Black at Auburn University or Robert Hall at Indiana University took the trouble to thoroughly investigate the Toyota system as a whole and the Kanban system in particular, but most of their colleagues didn’t, preferring a simplistic rendition of the Kanban system that made their own ideas stand out by contrast. In this context, insisting on the genuine Kanban system is perceived as nitpicking, because the differences are not in the big idea but in the details. You can easily dismiss these details as insignificant until you consider their cumulative effect on thousands of shop floor transactions every day.

Here are two examples, found today in a blog post:

  1. A common misconception is that you pull a Kanban from a bin when it is empty. If this were true, you would just be using a card to implement the Two-Bin system. The Kanban is not pulled when the bin is empty but when you withdraw the first part from the bin, to allow the bin to cover consumption during the replenishment lead time.
  2. Another in the same post was that the eKanban system did not involve physical cards. It is conceivable that, in the future, goods in transit will only be identified by RFID tags, but it is not the state of the art. They still need some form of human-readable identification and routing, for which a purely electronic system would require some kind of screen on each container. In fact, the electronic signal is used only in the return part of the loop, to eliminate the labor-intensive, slow and error-prone handling of unattached cards. On the supplier side, you print single-use cards that are attached to bins for transfer to the customer. When you detach the car on the customer side, you scan its barcode, and this triggers the electronic replenishment signal.

When evaluating or learning a tool like the Kanban system, you have to consider the following:

  1. The objects. They may be cards carrying specific data, bins of particular sizes and configurations, electronic messages of a given structure, … This is what we have to play with. Their physical nature makes a difference, not in a philosophical way but in basic, practical ways. For example, cards can be shuffled and posted on boards but bins cannot. When you send a card, you no longer have it, but when you send an electronic message, you still do. With the former, you have to make sure it doesn’t lose its way; with the latter, that it isn’t accidentally sent multiple times.
  2. The rules. These are protocols for users to follow. They specify who is allowed or required to do what to which objects when. In the Kanban system, the rules say who can issue new Kanbans or remove them from circulation, who attaches Kanbans to bins and detaches them, and what events trigger these actions. The rules give the objects meaning, as the rules of poker do to a deck of cards.
  3. The mapping to reality. This is what happens to materials and goods  in production and logistics when people follow the rules. When applied rigorously in the right context, the Kanban system tells production operators and materials handlers exactly what they should work on. Unlike the traditional dispatch lists, instructions in the form of Kanbans leave no ambiguity and require no judgement call by the leader or supervisor.

Within its range of applicability, the Kanban system is both simple enough for people to apply and sophisticated enough to get the job done. This is a tall order, and we should not underestimate what it takes.

Even in Japanese, the word Kanban has many different meanings, the most common being a sign advertising a store on the street, as you can see by searching Google images for “看板” (Kanban). Figure 2 shows, on a sidewalk,  the Kanban of a beauty salon located on the 2nd floor of the building.

Figure 2. A Kanban in everyday Japanese

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Shape sorter

Nov 22 2011

Key details on Poka-Yoke/Mistake-Proofing

The following are  my inputs to a discussion on AME’s LinkedIn group initiated last August by Xola Qhogwana, which also  included contributions from Steve Bathe, Richard Foster, Karen Wilhelm, Steven Wade, Wesley Bushby, Ron Turkett, and Trevor Krawchyk.

When to use Poka-Yoke

Poka-Yokes prevent human error, and are therefore relevant when and only when human error is the main cause of your quality problems.

If you have process capability issues, focus on resolving them, not on preventing human error. What you need is deep understanding of your technology combined with statistical tools to enable your process to consistently hold tolerances.

If your process is capable but you are still producing in batches, focus on converting to flow to prevent your defectives being buried in WIP. Your problem is that it takes you too long to detect problems, not human error.

If your process is capable and you practice one-piece flow, then the defects you still produce are due to human error. At this point, and not before, Poka-Yoke is the relevant technique.

For details, see When to use statistics, one-piece flow, or mistake-proofing to improve quality.

Poka-Yokes do not require extensive a-priori analysis

Poka-Yokes are usually small devices, such as a permanent magnet to suck up a panel already containing a metal bracket, or a hole in a container to prevent overfill.

Doing an FMEA do decide whether to design and implement a Poka-Yoke is more expensive than just doing it. If you sort of think a process might need a Poka-Yoke and you have an idea of what it might be, just go ahead, try it, and document it in your company-specific Poka-Yoke library to inspire others. Don’t over-analyze it upfront. On the other hand, if you are building a spacecraft, you should definitely do an FMEA.

If it adds labor, it’s not a Poka-Yoke

By definition, also a Poka-Yoke device adds no labor. Manually scanned barcodes on parts to validate picks, for example, do not qualify as a Poka-Yokes because they add labor. A barcode that is automatically read or an RFID tag , on the other hand, would qualify. A Poka-Yoke has to become part of the process in the long run. If you look at the old big red book of Poka-Yoke from Productivity Press, you will notice that none of the examples adds labor, and there is a reason: any device that adds labor is likely to be bypassed under pressure.

This even happens with safety. Take, for example, the traditional approach of requiring the pressing of two buttons to start a press. How many times to you see plants where one button is taped down so that you can start the press with just the other one? By contrast, safety light curtains add no labor, and are not bypassed.

Using bar codes reading for data acquisition effectively eliminates the errors due to keyboarding because it is faster. If it weren’t, operators would revert to keyboarding and typos would creep back in. This is exactly what you see happening after two or three failed attempts at scanning a code. A barcode on a workpiece that is automatically read can be a Poka-Yoke. The workpiece passes under a reader in the proper orientation and under good lighting conditions and the barcode is reliably read. Under these conditions, it can even drive the lighting of the proper bins in a pick-to-light system. It does not work as a Poka-Yoke s if an operator has to wave a bar code gun in front of a part for pick validation.

Just because you use a device with the intent of preventing mistakes doesn’t mean it works. You have to make sure it does, and not just at the time you implement it. If you don’t pay attention, Poka-Yokes tend to deteriorate and to be set aside, for example when new operators are assigned to the station.

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Nov 21 2011

What is chaku chaku? definition and meaning

Via Scoop.it – Cellular manufacturing
The definition of chaku chaku in the online business dictionary is missing the concept of machines with automatic unloading and incorrectly states that the line must encompass the entire production process, which is not a requirement.

Via www.businessdictionary.com

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Nov 18 2011

What’s unique about the Kanban system? (Revisited)

10 years ago, I wrote an article by this title in Karen Wilhelm’s Lean Directions, and a detailed treatment of pull systems in Lean Logistics, pp. 199-270 (2005). While 6 to 10 years in an eternity in Information Technology, it is not in Manufacturing, and I have not seen evidence that technological advances since then have invalidated these discussions yet. Also in 2005, Arun Rao and I wrote a paper on RFID Applications in Manufacturing, which outlined ways this technology could be used, among other things, to implement the Kanban replenishment logic on the side of an assembly line. To the best of our knowledge, it still isn’t broadly used, and bar codes are still the state of the art on the shop floor.

For placing orders with suppliers, on the other hand, the recirculating  hardcopy Kanban has never really taken root in the US, and orders are usually placed electronically. When Kanbans are used with suppliers, they are usually single-use cards printed by the supplier to match electronic orders, that are attached to parts and scanned when the corresponding parts are consumed to trigger a reorder. This is the eKanban system, and more a horseless carriage than a car, in that it is an electronic rendition of a system whose logic was constrained by the use of cards.

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