Tag Archives: Autonomation

Mar 17 2013

Toyota’s history rests on key textile invention | Long Island Newsday


Kiichiro and Eiji Toyoda

Kiichiro and Eiji Toyoda around a loom

See on Scoop.it – lean manufacturing

It was a single thread that gave a man a dream, created a little history and displayed the talents of a remarkable mind and a family with resourcefulness in its genes.

Sakichi Toyoda wasn’t all that interested in fast-moving machinery, just machines in motion. It’s how the Toyota Production System began. It’s how an inventor with a sharp eye and even sharper mind built an empire…

 

 

 

 

 

Michel Baudin‘s insight:

A summary of Toyota history with the usual omissions:

  1. Automatic shuttle change. The ability to stop when thread broke was not the only innovation of Toyoda looms. Automatic shuttle change was equally important, not just to looms but as a forerunner of autonomation, the Toyota approach to automation.
  2. The German connection. Toyota learned much about car technology from Germany through Kazuo Kumabe and his research team, in particular reverse-engineering a 1936 DKW. The concept of Takt also came from the German Junkers company via the Mitsubishi Aircraft plant in Nagoya.

See on www.newsday.com

Nov 17 2012

Lean’s High-Tech Makeover | Technology content from IndustryWeek


See on Scoop.it – lean manufacturing

This article from Industry Week suggests that for Toyota to use high technology in Manufacturing is something new or a departure from its traditional system. It presents the Assembly Line Control (ALC) system as something new, when it has been in existence since at least the early 1990s.

We should not forget that even Ohno described jidoka as one of the two pillars of the Toyota Production System, on a par with Just-in-Time, and that jidoka means “automation with a human touch,” or “autonomation.”

The English-language literature often reduces jidoka to making machines stop when they malfunction, but the actual jidoka includes a complete automation strategy, with sequences of steps to automate both fabrication and assembly operations, as well as an approach to managing the interactions between humans and machines on a manufacturing shop floor.

This is what I wrote about in Working with Machines.

See on www.industryweek.com

Greek temple diagram
Nov 17 2011

Jidoka versus automation


Toyota’s jidoka isn’t just about stopping production when something goes wrong. It is an automation strategy that works because it is incremental and centered on human-machine interactions. It is essential to the strength of manufacturing in high-wage economies and should command more attention than it has so far among Lean implementers.

The most striking characteristic of automation in manufacturing is that, while making progress, it has consistently fallen short of expectations. In Player Piano, Kurt Vonnegut articulated the 1950s vision of automated factories: integrated machines produce everything while their former operators are unemployed and the managers spend their time playing silly team-building games at offsite meetings. 60 years on, the most consistently implemented part of Vonnegut’s vision is the silly team-building games…

Nippon Steel’s Yawata Steel Works in Kitakyushu, Japan, produce as much today with 3,000 employees as they did with 40,000 in 1964, and this transition was accomplished without generating massive unemployment. There are other such limited areas of automation success, like the welding and painting of car bodies. When manufacturing jobs are lost today, it is almost never to automation and almost always to cheaper human competition elsewhere. In the words of an experienced operator in a plant making household goods in the US, “When I joined 25 years ago, I expected these jobs to be automated soon, but we’re still doing them the same way.”

What is holding up automation today is not technology  but the lack of consideration for people. There are entire books on automation without a paragraph on what their roles should be. Of course, a fully automatic, “lights-out” factory has nobody working inside, so why bother? There are at least two reasons. First, even an automatic plant needs people, to program its processes, tell it what work to do, maintain it, monitor its operations and respond to emergencies. Second, successful automation is incremental and cannot be developed without the help of the people working in the plants throughout the migration.

Enter autonomation, or jidoka, which is sometimes also called “automation with a human touch” but really should be called human-centered automation. Instead of systems of machines and controls, it is about human-machine interactions. In the classical House of Lean model, the two pillars holding up the roof at Just-In-Time and Autonomation, or Jidoka. Figure 1 is lifted from the introduction to Working with Machines, and shows what happens when the jidoka pillar is ignored.

Figure 1. Just-in-Time and Jidoka

More and more, the Lean literature in English uses the japanese word jidoka rather than autonomation, but with its scope reduced to the idea of stopping production whenever anything goes wrong, and the concept is tucked away under the umbrella of Quality Management.

Toyota’s jidoka is a tricky term, because it is an untranslatable pun. Originally,  the Japanese word for automation　is　jidoka (自動化) , literally meaning “transformation into something that moves by itself.” What Toyota did is add the  human radical 人　to the character　動　 for “move,” turning it into the character 働　for “work,” which is still pronounced “do” but changes the meaning to “transformation into something that works by itself.” It”s automation with the human radical added, but it is still automation, with all the technical issues the term implies.

The discussion of automation in the first draft of Working with Machines started with the following historical background, which was edited out like the chapter on locomotives and typewriters, on the ground that it contained no actionable recommendations. In this blog, I can let you be the judge of its value.

From tea-serving wind-up dolls to autonomation

The word automation was first used by Ford manufacturing Vice President Delmar Harder in 1947 for devices transferring materials between operations. He set as targets a payback period of at most one year in labor savings, which meant in practice that each device should not cost more than 15% above an operator’s average yearly wages and eliminate at least one operator. While this kind of economic analysis is still used, from the perspective of Toyota’s system, Ford’s focus on materials handling was putting the integration cart before the unit operation horse. Toyota’s approach focuses on individual operations first, and only then addresses movements of parts between them. In 1952, John Diebold broadened the meaning of automation to what has become the common usage, and painted a picture of the near future that was consistent with Kurt Vonnegut’s.

At that time, automatic feedback control was perceived to be the key enabling technology for automation, to be applied to ever larger and more complex systems. It was not a new concept, having been applied since 1788 in the centrifugal governor regulating pressure in a steam engine (See Figure 2)

Figure 2. James Watt’s 1788 centrifugal governor

Applying electronics to feedback control in World War II had made it possible, for example, to move a tank’s gun turret to a target angle just by turning a knob. Postwar progress in the theory and application of feedback control both caused many contemporary thinkers, like Norbert Wiener,  to see in the concept a philosophical depth that is truly not there, and to underestimate what else would need to be done in order to achieve automation. Of course, if you cannot tell a machine to take a simple step and expect it to be executed accurately and precisely, then not much else matters. Once you can, however, you are still faced with the problem of sequencing these steps to get a manufacturing job done.

While automatic feedback control was historically central to the development of automatic systems, it is not at center stage in manufacturing automation today. With sufficiently stable processes, open-loop systems work fine, or feedback control is buried deep inside such off-the-shelf components as mass flow controllers, thermostats, or humidity controllers. Manufacturing engineers are occasionally aware of it in the form of variable-speed drives or adaptive control for machine tools, but other issues dominate.

Fixed-sequence and even logic programming also have a history that is as long as that of feedback control and are by no means easier to achieve. Figure 2 shows two examples of 18th century automata moved by gears, levers and cams through sequences that are elaborate but fixed.

Figure 2. 18th century automata from France and Japan

These concepts found their way into practical applications in manufacturing as soon as 1784, with Oliver Evans’s continuous flour mill that integrated five water-powered machines through bucket elevators, conveyors and chutes (See Figure 3). The same kind of thinking later led to James Bonsack’s cigarette making machine in 1881, and to the kind of automatic systems that have dominated high-volume processing and bottling or cartonning plants for 100 years, and to the transfer lines that have been used in automotive machining since World War II.

Figure 3. Oliver Evans’ continuous flour mill (1784)

Fixed-sequence automation works, but only in dedicated lines for products with takt times under 1 second, where the investment is justifiable and flexibility unnecessary. Rube Goldberg machines parody this type of automation.

Figure 3. Winner of the 2008 Penn State Rube Goldberg machine contest

Automation with flexibility is of course a different goal, and one that has been pursued almost as long, through programmable machines. The earliest example used in production is the Jacquard loom from 1801, shown in Figure 4. It is also considered a precursor to the computer, but it was not possible to make a wide variety of machines programmable until the actual computer was not only invented but made sufficiently small, cheap and easy to use, which didn’t occur until decades after Vonnegut and Diebold were writing.

Figure 4. Jacquard loom from museum in Manchester, UK

By the mid 1980’s, the needed technology existed, but the vision of automation remained unfulfilled. In fact, more technology was available than the human beings on the shop floor, in engineering, and in management knew what to do with. As discussed in the post on Opinels and Swiss knives, the computer as a game changer. In manufacturing, this was not widely recognized when it became true, and it still is not today.

Writing in 1952, John Diebold saw nothing wrong with the way manufacturing was done in the best US plants, nor did he have any reason to, as the entire world was looking at the US as a model for management in general and manufacturing in particular. In the 1980’s, however, when GM invested $40B in factory automation, it was automating processes that were no longer competitive and, by automating them, making them more difficult to improve.

Whether the automation pioneers’ vision will ever come true is in question. So far, every time one obstacle has been overcome, another one has taken its place. Once feedback control issues were resolved came the challenge of machine programming. Next is the need to have a manufacturing concept that is worth automating, as opposed to an obsolete approach to flow and unit processes. And finally, the human interface issues discussed must be addressed.

21st century manufacturers do not make automation their overall strategy. Instead, automation is a tool. In a particular cell, for example, one operator is used only 20% of the time, and a targeted automation retrofit to one of the machines in the cell may be the key to eliminating this 20% and pulling the operator out of the cell.

Machining center
Nov 10 2011

Opinels, swiss knives, smart phones, and production machinery


Using Opinel knives while picnicking last summer got me thinking about their differences in design philosophy from Swiss knives, our traditional perception of multifunction tools, and how smart phones and machining centers contradict that perception.

Mostly known for snow-capped mountains, the Savoie region of France is also the birthplace of the Opinel, a pocket knife designed 120 years ago, and very popular there with anybody who hikes or just goes on a picnic. As you can see in Figure 1, it is a simple knife with a sharp, pointed blade, and a ring to lock it closed or open.

Figure 1. Opinel knife

As a concept, it is diametrically opposed to its cousin, the swiss knife, and its multiple functions:

Figure 2. Swiss knife

The Opinel only has one function, but performs it well; the swiss knife has many, but does not excel at any. It will cut, but not as well as the Opinel; it serves as a corkscrew, but provides no leverage to pull out the cork; it will open cans, but slowly and by pulling the sharp edge of the lid outwards towards you hand rather than into the can, etc. It is convenient because you only have to carry one tool around, but, for everything it does, there is a dedicated tool that does it better.

When we think of dedicated versus multifunction tools, we are accustomed to think that they are like Opinels and swiss knives and that, when we add more functions to a tool, we necessarily compromise on performance or quality for each function. But is that necessarily true?

Our smart phones let us talk to each other but also contain the contact data of everyone we have  met since elementary school. They tell you where we are on precise maps, wake us up in the morning, work as stopwatches and egg timers, play our music, receive our favorite radio station, identify a song from a snippet of a recording, etc.

Dedicated tools do not exist for everything a smart phone does and, when they do, rarely outperform the smart phone apps. For example, I have not seen an alarm clock do more than the clock app on my phone in terms of selecting whether it rings just once or every weekday at the same time, how loud, with what sound, etc.

What is it that makes a smart phone different from other multifunction devices? In what way is it not like a swiss knife?

The short answer is  that a smart phone is a computer. We often think of computers as machines like any other, or worse when we are frustrated with confusing interfaces or system crashes, but the reality is that they are qualitatively different, and that programmability allows them to outperform dedicated tools. Their hardware configurations make them smart phones, game systems, laptops, or industrial controllers but, within this context, the range of services they can render well  is limited only by the imagination and talent  of programmers.

In production, machining centers or computer-controlled fabrication facilities are not swiss knives, in that their flexibility is not bought by a compromise in performance, and this has far reaching consequences on production engineering and operations.

Mar 3 2011

A factory can always be improved


Based on an NWLEAN post entitled: Laws of Nature – Pareto efficiency and Pareto improvements, from 3/3/2011 

In manufacturing, Italian economist Vilfredo Pareto is mostly known for the Pareto diagrams and the 80/20 law, but  in economics, he is also known for the unrelated concept of Pareto efficiency, or Pareto optimality, which is also relevant to Lean. A basic tenet of Lean is that a factory can always be improved, and that, once you have achieved any level of performance, it is just the starting point for the next round of improvement. Perfection is something you never achieve but always pursue and, if you dig deep enough, you always find opportunities. This is the vocabulary you use when discussing the matter with fellow production people. If, however, you are taking college courses on the side, you might score more points with your instructor by saying, as an empirical law of nature, that a business system is never Pareto-efficient. It means the same thing, but our problem is that this way of thinking is taught neither in Engineering nor in Business school, and that few managers practice it.

A system is Pareto-efficient if you cannot improve any aspect of its performance without making something else worse. Managers who believe their factories to be Pareto-efficient think, for example, that you cannot improve quality without lengthening lead times and increasing costs, which is exactly what Lean does. In fact, eliminating waste is synonymous with making improvements in some dimensions of performance without degrading anything else, or taking advantage of the lack of Pareto-efficiency in the plant.

When we say that a factory can always be improved it is a postulate, an assumption you start from when you walk through the gates. The overwhelming empirical evidence is that, if you make that assumption, you find improvement opportunities. Obviously, if you don’t make that assumption, you won’t find any, because you won’t be trying.

This is not a minor issue. Writing in the Harvard Business Review back in 1991 about Activity-Based Costing, Robert Kaplan stated that all the possible shop floor improvements had already been made over the previous 50 years. He was teaching his MBA students that factories were Pareto-efficient and that it was therefore pointless to try and improve them. They would do better to focus on financial engineering and outsource production.

The idea that improving factories is futile and a distraction from more “strategic” pursuits dies hard. It is expressed repeatedly in a variety of ways. The diminishing returns argument is that, as you keep reaching for fruits that hang ever higher, the effort requires starts being excessive with respect to the benefits, but there are two things to consider:

  • As you make improvements, you enhance not only performance but your own skills as well, so that some of what was out of reach before no longer is.
  • Competition is constantly raising the bar. If your competitors keep improving and you don’t, you lose.

Another argument is that the focus on waste elimination discourages activities like R&D that do not have an immediate impact on sales. The improvement effort, however,  isn’t about what we do but how we do it. Nobody in his right mind would call R&D waste, even on projects that fail. Waste in R&D comes in the form of researchers waiting for test equipment, sitting through badly organized meetings, or filling out administrative paperwork.

In manufacturing itself, some see the pursuit of improvement as a deterrent to investment in new technology. While it is clear that the improvement mindset does not lead to solving every problem by buying new machines,  the  practitioners of continuous improvement are in fact better informed, savvier buyers of new technology. On one side of the shop floor, you see a cell with old machines on which incremental improvements over several years have reduced staffing requirements from 5 operators to 1. On the other side of the aisle, you see a brand new, fully automatic line with a design that incorporates the lessons learned on the old one.

Others have argued that a society that pursues improvement will be slower to develop and adopt new, disruptive technology. But does the machinist improving a fixture deter the founder of the next Facebook? There is no connection. If the machinist were not making improvements, his creativity would most likely be untapped. And his improvement work does not siphon off the venture capital needed for disruptive technology.