Jul 9 2015
Christoph Roser has more impressive credentials than most Lean consultants, from a PhD in Engineering to a research job at Toyota labs, stints in operations at Bosch, and a professorship at Karslruhe University of Applied Sciences. So, if anyone is qualified to write a theory of Lean, he is, and he is trying his hand at it in production planning and scheduling.
Feb 1 2015
According to Chip Chapados, the concept of one-piece flow emerged from the need to rapidly detect defects in engine castings when Kiichiro Toyoda was reverse-engineering a Chevrolet engine in 1934, and it was originally called “one-by-one confirmation.”
Mar 5 2014
Philip Marris told me of the case of a machining cell in an auto parts plant where management was ready to buy more machines because it was “lacking capacity,” but he was able to find a cheaper way to increase capacity by 17% in 15 minutes.
Unlike manual assembly cells, in which work can be balanced among stations, cells that involve machines always have one that is slower than all others, and, reallocating work among machines with different capabilities is not an option. In particular, almost all machining cells have a bottleneck, and the situation Philip described involved this bottleneck and the machine feeding it. The cell practiced one-piece flow. Therefore, if the feeder machine had worked perfectly, the timelines of the Feeder and the Bottleneck would have been as follows:
The Feeder would have started one piece at the beginning of each takt interval, and, since it is faster than the Bottleneck, it would have finished the piece before the end of the interval. The Feeder then would have waited for the bottleneck to pick up the piece before starting the next one. The Bottleneck would have been working 100% of the time; the Feeder would not.
But what Philip discovered by observing operations was that the Feeder had microstoppages. When the Feeder was hit by a microstoppage, the delay it caused passed to the bottleneck, which was prevented from working 100% of the time, as shown below:
This reduced the capacity of the entire cell. In the actual case, even with its microstoppages, the Feeder had enough capacity to feed the Bottleneck, on the average, just not on a takt basis. The microstoppages caused the output of the Feeder to fluctuate and disrupt the operation of the Bottleneck.
To anyone trained in Lean, the only appropriate solution was to eliminate the microstoppages… But it was easier said than done. Sometimes, all it takes is slowing down the machine, or changing a maintenance policy from “clean for one minute” to “clean until it is clean.” But it is not always that simple.
Microstoppages are often unreported because they are fixed on the fly by production operators. To understand microstoppages, you need to monitor the machine to observe when they occur and trace their causes. Eliminating them may require you to modify chutes, fixtures, jigs or dies, or even the basic process, and it can take time, but you need to do it if you want one-piece flow to work.
In the meantime, what do you do? Buying more equipment is an expensive solution, especially when you don’t expect to need it once you are rid of the microstoppages. A cheaper countermeasure is to protect the supply of parts to the bottleneck against fluctuations by decoupling the two machines with a buffer of WIP. You can set the size of this buffer by trial and error, knowing that it is not a long-term solution.
Of course, manufacturing engineers understand that you cannot have one-piece flow with microstoppages. So why did they ignore their own wisdom? The most likely explanation is a demand from a corporate “Lean group” to implement one-piece flow everywhere and “damn the torpedoes!” These engineers had complied not because they thought it was a good idea, but because it was required to keep their jobs.
Technically, Philip sees this story as a case study in the addition of Theory of Constraints (TOC) thinking to Lean; I just see it as due consideration of equipment issues in cell design, as I was taught it more than 25 years ago. From a management standpoint, I see it as an example of the local consequences of half-baked corporate mandates.
Aug 13 2012
See on Scoop.it – lean manufacturing
Festo has implemented a manufacturing system that allows it to produce parts for linear handling systems economically in batch sizes of just one item. At its global production centre for electric and pneumatic drives at St. Ingbert-Rohrbach in Germany, the company has introduced a “one piece flow” method of assembly, based on Toyota’s production system.
See on www.drives.co.uk
Jun 17 2025
How One-Piece Flow Improves Quality
There is a phase in the maturation of a manufacturing process where one-piece flow is the key to improving quality. Once the defective rate is low enough, one-piece flow reduces it by up to a factor of 10. The magnitude of the improvement often surprises managers. The cause-and-effect relationship is not obvious, and the literature on manufacturing quality is mute on the subject. We explore it here.
Quality prerequisites for one-piece flow
Imagine a process that is a sequence of 5 operations you perform each one piece at a time. It would be a candidate for one-piece flow, except that it has a yield of 95.5\% at each operation. Then the yield of the whole process is .955^5 = 79.4\%.
In a leaky-pipe model of this process, you would have to start 100/0.794 = 126 pieces in order to obtain 100 good pieces out. It would happen, for example, if you lost exactly 4.5\% of the pieces at each operation.
This is the kind of simplistic calculations MRP systems do but it’s not real. Instead, at each operation, the production of defectives fluctuates around the rate, and each unit has a 4.5\% probability of being defective. As a consequence, you need inventory buffers between every two operations for production to proceed uninterrupted, which means that you cannot have one-piece flow.
If you have 400 minutes of net available work time to produce 100 units, with a yield of 100\% you would run at a takt time of 4 minutes. With a yield of 98.5\%, in order to almost always be able to produce 100 good units in a shift, you need to design your line for 111 units, for a takt time of 3 minutes and 36 seconds. This is buffering with time rather than WIP and, on most days, you will have slack time at the end of the shift.
Yield Tipping Points
There are tipping points for the yield of a manufacturing process beyond which the approach to improvement must change. We know they exist, even when we can’t tell exactly where they are. And not every process crosses these points.
The First Yield Tipping Point
Wherever it may be, there is a tipping point in the yield of your process, above which you can eliminate the WIP buffers and implement one-piece flow. It then enables you to use other tools and methods and make the next order-of-magnitude improvement in quality.
After that point, it’s no longer the identification of root causes within the process that matters most but instead the speedy detection of operational problems like tools breaking or a new operator on the line. Once you know that something has gone wrong, you usually know what to do about it, but it occurs randomly and batch-and-queue operations delay your discovery of the problems.
The next order-of-magnitude improvement in quality performance comes then from moving to one-piece flow with First-In-First-Out (FIFO) sequencing. It is well known that quality improves as a result of converting a sequence of operations to a U-shaped cell. The proportion of defectives traced to the operations in a cell decreases by at least 50%, and we have seen it drop as much as 90%.
The effect of one-piece flow and FIFO in turn levels off at percentages of defectives commonly on the order of 0.1% = 1,000 dppm. It is not by itself able to achieve single-digit dppm rates.
The second tipping point
Human error affecting individual parts is still possible, and this is where error-proofing comes in: it is the strategy that takes you from, say, 1,000 dppm to 1 dppm. The following log-log plot summarizes this discussion:
The log-log plot, consistent with learning curve thinking, assumes a fixed drop in defective rates every time the cumulative volume doubles. This is a commonly used inverse-power model for cost reductions. Its math never leads to a negative value, and shows a decreasing rate of improvement that is slow enough that you cannot make an infinite volume with only a finite set of defectives. And it gives you a straight line in a log-log plot.
This plot is not based on a dataset of processes having gone through these phases. It is merely an illustration of the concept that, while their exact locations may vary, there are tipping points beyond which you need different methods to continue improving quality. There is no one-size-fits-all approach.
The skills that are central in the first phase may still be occasionally needed in later phases, as problems with the physics and chemistry of the process never entirely go away, but they are no longer the main engine of improvement. Rapid product obsolescence in high-technology manufacturing companies may prevent them from ever experiencing the later stages.
Lack of process capability
If your main problem is lack of process capability, your percentage of failures will typically be in double digits, and this issue should be addressed ahead of anything else. You do it by applying knowledge of process physics/chemistry, data science, and control technology.
There is empirical evidence that the results of efforts centered on process capability level off around a few percentage points of internally detected defectives. The SPC literature, for example, does not consider what happens when an organization successfully uses its tools. While disruptions from assignable causes rarefy, false alarms from common causes maintain their frequency, and the ratio of true to false alarms drops to a level that destroys the credibility of the alarms.
One-piece flow and First-In-First-Out
Once you have established process capability, the plant faces a change in the nature of quality problems, from drifts in process parameters to infrequently occurring discrete events like tool breakage, skipped steps, or mislabeled components. The challenge at this point is to detect them quickly and react promptly. By definition, you have already solved most of the problems that require an elaborate root cause analysis, and now speed takes precedence over analytical sophistication.
One-piece Flow
The one-piece flow pattern is that, from the time it leaves the input buffer to the time it reaches the output buffer, each part moves individually from step to step without any accumulation of WIP allowed between machines or work stations.
Quality Impact
This has several consequences for quality:
You back up the effects of one-piece flow and FIFO with in-process quality checks, with go/no-go gauge checks or with what Shigeo Shingo called “successive inspection” in assembly, where each assembler touches the parts installed by the previous one. Here are a few examples of go/no-go gauges in current use:
For any characteristic, the options are:
A throwback to the 1870s?
Quality professionals with SPC training have a hard time accepting these concepts because they exclude both measurements and sampling. Based on their training, they think that you should never use a go/no-go gauge where you can take a measurement, because the measurement is much richer in information.
The measurement tells you more than whether a dimension falls between goal posts; it tells you where. They have also been told that measuring 100% of the parts is unaffordable, and that well-designed sampling plans can give you the same information at a lower cost.
No sampling
Sampling is not allowed inside cells, for several reasons:
part of this routine. Then you engineer go/no-go gauges to make these checks fast enough not to slow down production.
Also, given that the problems we are trying to detect are discrete state changes rather than drifts or fluctuations, the information loss due to using go/no-go checks instead of measurements is not as great as it would have been earlier, and is more than made up by application to every part rather than to a sample and by the increased speed of problem detection.
External mandates
Quality departments commonly collect measurements and compile them into control charts to satisfy external mandates. This can still be done without disrupting the flow of production, for example, when parts are finished or while they are between segments of the process executed in cells. Measurements, of course, also have a place in the solution of problems you have detected.
Contiguous Input and Output
One- piece flow and FIFO do not imply and are not always implemented in the form of a U-shaped cell. The U- shape, however. also has an impact on quality, simply because seeing incoming and outgoing parts side-by-side makes operators more conscious of quality issues.
Quality Management with One-Piece Flow
Kazuo Kawashima’s Quick Response Quality Control (QRQC) from Nissan, Sadao Nomura’s Dantotsu quality from Toyota Industrial Vehicles, and Shinichi Sasaki’s JKK from Toyota are all approaches targeted at this situation, and they share similar prescriptions.
Error-proofing
When your main remaining quality problem is operator errors, then clearly the last frontier for improvement is their prevention. But if your main problem is the inability to hold tolerances consistently, spending your resources on error-proofing would be lining up deck chairs while the ship is sinking. Conversely, data science is not much help in the prevention of errors that occur once a year.
Conclusion
We focused this discussion on technical tools. To improve quality, there are, of course, many other issues. Our goal was to position different approaches that are often perceived as being in conflict. We also wanted to draw attention to the middle layer where one-piece flow and FIFO play a central role. Companies that use flow lines and cells know that they improve quality. The quality literature, however, rarely mentions them among the tools to that end.
#onepieceflow, #flowline, #cell,#quality
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By Michel Baudin • Uncategorized • 2 • Tags: Flow, Flow line, One-piece flow, Quality