Are Part Numbers Too Smart for Their Own Good? |

[...] technology experts are warning that the use of such descriptive part numbers is not necessarily so “smart,” and that they could drag down productivity in today’s fast-changing manufacturing environments. A smarter tactic, they assert, is to employ auto-generated “insignificant” or “non-intelligent” part numbers and let information about the part reside in a database. [...]


See on - lean manufacturing

Michel Baudin's comments:
For details on the reasons to get rid of so-called "smart" part numbers, see  Why "Smart" part numbers should be replaced with keys and property lists.

Gauss with bell shape banknote

The bell curve: "Normal" or "Gaussian"?

Most discussions of statistical quality refer to the "Normal distribution," but "Normal" is a loaded word. If we talk about the "Normal distribution," it implies that all other distributions are, in some way, abnormal. The "Normal distribution" is also called "Gaussian," after the discoverer of many of its properties, and I prefer it as a more neutral term. Before Germany adopted the Euro, its last 10-Mark note featured the bell curve next to Gauss's face.

The Gaussian distribution is widely used, and abused, because its math is simple, well known, and wonderful. Here are a few of its remarkable properties:

  1. It applies to a broad class of measurement errors. John Herschel arrived at the Gaussian distribution for measurement errors in the position of bodies in the sky simply from the fact that the errors in x and y should be independent, that the probability of a given error should depend only on the distance from the true point.
  2. It is stable. If you add Gaussian variables, or take any linear combination of them, the result is also Gaussian.
  3. Many sums of variables converge to it.  The Central Limit Theorem (CLT) says that, if you add variables that are independent, identically distributed, with a distribution that has a mean and a standard deviation, they sum converges towards a Gaussian. It makes it an attractive model, for example, for order quantities for a product coming independently from a large number of customers.
  4. Diffusion  of syrup in waterIt solves the equation of diffusion. The concentration of, say, a dye introduced into clear water through a pinpoint is a Gaussian that spreads overt time. You can experience it in your kitchen: fill a white plate with about 1/8 in of water, and drop the smallest amount of grenadine syrup you can in the center. After a few seconds, the syrup in the water forms a cloud that looks very much like a two-dimensional Gaussian bell shape for concentration, as shown on the right. And it fact it is, because the Gaussian density function solves the diffusion equation, with a standard deviation that rises with time. It also happens in gases, but too quickly to observe in your kitchen, and in solids, but too slowly.
  5. It solves the equation of heat transfer by conduction. Likewise, when heat spreads by conduction from a point source in a solid, the temperature profile is Gaussian... The equation is the same as for diffusion.
  6. Unique filter. A time-series of raw data -- for temperatures, order quantities, stock prices,... -- usually has fluctuations that you want to smooth out in order to bring to light the trends or cycles your are looking for. A common way of doing this is replacing each point with a moving average of its neighbors, taken over windows of varying lengths, often with weights that decrease with distance, so that a point that is 30 minutes in the past counts for less than the point of 1 second ago. And you would like to set these weights so that, whenever you enlarge the window, the peaks in your signal are eroded and the valleys fill up. A surprising, and recent discovery (1986) is that the only weighting function that does this is the Gaussian bell curve, with its standard deviation as the scale parameter.
  7. Own transform. This is mostly of interest to mathematicians, but the Gaussian bell curve is its own Laplace transform, which drastically simplifies calculations.
  8. ...

For all these reasons, the Gaussian distribution deserves attention, but it doesn't mean that there aren't other models that do too. For example, when you pool the output of independent series of events, like failures of different types on a machine, you tend towards a Poisson process, characterized by independent numbers of events in disjoint time intervals, and a constant occurrence rate over time. It is also quite useful but it doesn't command the same level of attention as the gaussian.

The most egregious misuse of the gaussian distribution is in the rank-and-yank approach to human resources, which forces bosses to rate their subordinates "on a curve." Measuring several dimensions of people performance and examining their distributions might make sense, but mandating that grades be "normally distributed" is absurd.

Purpose and Etiquette of On-Line Discussions

In the Lean Six Sigma Worldwide discussion group on LinkedIn, Steven Borris asked about the purpose of on-line discussions, whether they should stick precisely to the topic they were started on, and how disagreements between participants should be expressed or handled. As a participant in a variety of professional forums for the past 16 years, I have come to think of an online discussion as a conference that is always in session, in which the posting etiquette should be the same as at conferences.

Contributors should think of readers first. LinkedIn members read discussions for enlightenment, not entertainment. This isn't Facebook. When readers browse a discussion, it is based on its subject, and that is what they expect to be covered. Like the title of a book, the name of a discussion announces what it is about. Readers are drawn to it by the need for information on that topic and have a legitimate expectation that the posts will be about it. If participants disappoint them, they go away upset at having been misled. For this reason,  discussions should stick to their subject, and group moderators or managers should make sure they do, with interesting digressions spawning new discussions.

Professional readers are also turned off by personal attacks and posts that question other posters' motives. The participants need to "play nice" with each other, but a discussion where they all express the exact same ideas would not be informative and would be dull. The contributors to the discussions I participate in often have decades of experience that have shaped their perspectives on the topics, differently based on the industries and companies they have worked for. They are not on the same wavelength.

Often, however, apparent disagreements disappear when the context is properly set. For example, in his 1999 book on Six Sigma,  Mikel Harry wrote that the future of all business depends on an understanding of statistics; Shigeo Shingo, on the other hand, had no use for this discipline and wrote in ZQC that it took him 26 years to become free of its spell.

That sounds like a clear-cut disagreement. Mikel Harry developed Six Sigma at Motorola in the 1980s; Shigeo Shingo was a consultant and trainer primarily in the Japanese auto industry from 1945 to the 1980s, too early for discussion groups. Harry and Shingo worked in different industries with different needs at different times.With proper context setting, they can be both right.  Posts that start with "In my experience..." and support topical conclusions with an account of what that experience go a long way towards setting that context.

Toyota Cutting the Fabled Andon Cord, Symbol of Toyota Way | Automotive News

Toyota is retiring the fabled “andon cord,” the emergency cable strung above assembly lines that came to symbolize the built-in quality of the Toyota Way and was widely copied through the auto industry and beyond.



Michel Baudin's comments:
The point of having a cord rather than buttons was that the cord could be pulled from anywhere along the line, whereas buttons require you to be where they are. It is the same reason many buses have cords for passengers to request stops rather than buttons.

Toyota's rationale for moving to buttons, according to the article, is the desire to clear the overhead space. Another advantage, not stated in the article, is that the alarm from a button is more location-specific than from a cord.

Another reason to use a cord was that you didn't have to change it when you rearranged the line, whereas relocating buttons required rewiring. But the wireless button technology has made this a moot point.

See on - lean manufacturing

The meaning(s) of "random"

Random and seq. access"That was random!" is my younger son's response to the many things I say that sound strange to him, and my computer has Random Access Memory (RAM), meaning that access to all memory locations is equally fast, as opposed to sequential access, as on a tape, where you have to go through a sequence of locations to reach the one you want.

In this sense, a side-loading truck provides random access to its load, while a back-loading truck provides sequential access.

While  these uses of random are common, they have nothing to do with probability or statistics, and it's no problem as long as the context is clear. In discussion of quality management or production control, on the other hand, randomness is connected with the application of models from probability and statistics, and misunderstanding it as a technical term leads to mistakes.

From the AMS blog (2012)

From the AMS blog (2012)

In factories, the only example I ever saw of Control Charts used as recommended in the literature was in a ceramics plant  that was firing thin rectangular plates for use as electronic substrates in batches of 5,000 in a tunnel kiln. They took dimensional measurements on plates prior to firing, as a control on the stamping machine used to cut them, and they made adjustments to the machine settings if control limits were crossed. They did not measure every one of the 5,000 plates on a wagon. The operator explained to us that he took measurements on a "random sample."

"And how do you take random samples?" I asked.

"Oh! I just pick here and there," the operator said, pointing to a kiln wagon.

That was the end of the conversation. One of the first things I remember learning when studying statistics was that picking "here and there" did not generate a random sample. A random sample is one in which every unit in the population has an equal probability of being selected, and it doesn't happen with humans acting arbitrarily.

A common human pattern, for example, is to refrain from picking two neighboring units in succession. A true random sampler does not know where the previous pick took place and selects the unit next to it with the same probability as any other. This is done by having a system select a location based on a random number generator, and direct the operator to it.

This meaning of the word "random" does not carry over to other uses even in probability theory. A mistake that is frequently encountered in discussions of quality is the idea that a random variable is one for which all values are equally likely.  What makes a variable random is that probabilities can be attached to values or sets of values in some fashion;  it does not have to be uniform. One value can have a 90% probability while all other values share the remaining 10%, and it is still a random variable.

When you say of a phenomenon that it is random, technically, it means that it is amenable to modeling using probability theory. Some real phenomena do not need it, because they are deterministic:  you insert the key into the lock and it opens, or you turn on a kettle and you have boiling water. Based on your input, you know what the outcome will be. There is no need to consider multiple outcomes and assign them probabilities.

There are other phenomena that vary so much, or on which you know so little, that you can't use probability theory. They are called by a variety of names; I use uncertain.  Earthquakes, financial crises, or wars can be generically expected to happen but cannot be specifically predicted. You apply earthquake engineering to construction in Japan or California, but you don't leave Fukushima or San Francisco based on a prediction that an earthquake will hit tomorrow, because no one knows how to make such a prediction.

Between the two extremes of deterministic and uncertain phenomena is the domain of randomness, where you can apply probabilistic models to estimate the most likely outcome, predict a range of outcomes, or detect when a system has shifted. It includes fluctuations in the critical dimensions of a product or in its daily demand.

The boundaries between the deterministic, random and uncertain domains are fuzzy. Which perspective you apply to a particular phenomenon is a judgement call, and depends on your needs. According to Nate Silver, over the past 20 years, daily weather has transitioned from uncertain to random, and forecasters could give you accurate probabilities that it will rain today. On the air, they overstate the probability of rain, because a wrong rain forecast elicits fewer viewer complaints than a wrong fair weather forecast. In manufacturing, the length of a rod is deterministic from the assembler's point of view but random from the perspective of an engineer trying to improve the capability of a cutting machine.

Rods for assemblers vs. engineers

Claude Shannon

Claude Shannon

This categorization suggests that that a phenomenon that is almost deterministic is, in some way, "less random" than one that is near uncertainty. But we need a metric of randomness to give a meaning to an expression like "less random."  Shannon's entropy does the job. It is not defined for every probabilistic model but, where you can calculate it, it works. It is zero for a deterministic phenomenon, and rises to a maximum where all outcomes are equally likely. This brings us back to random sampling.  We could more accurately  call it "maximum randomness sampling" or "maximum entropy sampling," but it would take too long.

How to Really See What is Going On in Your Workplace | IndustryWeek | Jamie Flinchbaugh

How managers can use the four levels of observation to really see what is going on in their workplace:

      1. Stories and anecdotes.
      2. Data and graphs.
      3. Pictures and diagrams.
      4. Direct observation."


Michel Baudin's comments:
Deep down, I believe I agree with Jamie Flinchbaugh on observation, but I am puzzled by the way he phrases it. He describes stories and anecdotes as "the most abstract level of observation." I see them as a means of persuasion, not observation, and concrete, not abstract.

I don't see data as necessarily dependent on assumptions. What assumptions are there behind, say, the number of boxes of Cereal Z you sold last month? It is just a fact. While photographs are a form of data, graphs and diagrams are ways of analyzing data and presenting results, which is also downstream from observation.

For the analysis of a plant, I see three main sources of input:

  1. Direct observation of the operations.
  2. Interviews with key members of the organization.
  3. The organization's data.

The Lean literature justifiably emphasizes direct observation. You go to where the work is being done, and then apply various mental techniques to help you notice relevant characteristics. You may even gather data in the form of photographs an videos for future analysis.

But it cannot be your only source. You also need to know what the manager's ambitions are for the organization, what they have tried to realize them, and what obstacles they feel they have encountered. Their perceptions may or may not agree with what you see with your own eyes, but you need to know what they are.

Finally, any business activity leaves a data trail that should not be ignored, including product and process definitions, current status, history, and plans for the near and distant future. All of this also needs to be reviewed and confronted with direct observation and human perceptions.

It's when you present your conclusions and recommendations that you use stories, graphs, diagrams, pictures, and videos to get your point across.

See on - lean manufacturing

"Studies show..." or do they?

Various organization put out studies that, for example, purport to "identify performances and practices in place among U.S. manufacturers."  The reports contain tables and charts, with narratives about "significant gaps" -- without stating any level of significance -- or "exponential growth" -- as if there were no other kind. They borrow the vocabulary of statistics or data science, but don't actually use the science; they just use the words to support sweeping statements about what manufacturers should do for the future.

At the bottom of the reports, there usually is a paragraph about the study methodology, explaining that the data was collected as answers to questionnaires mailed to manufacturers and made available on line, with the incentive for recipients to participate  being a free copy of the report. The participants are asked, for example, to rate "the importance of process improvement to their organization's success over the next five years" on a scale of 1 to 5.

The results are a compilation of subjective answers from a self-selected sample. In marketing, this kind of surveys makes sense. You throw out a questionnaire about a product or a service. The sheer proportion of respondents gives you information about the level of interest in what you are offering, and the responses may further tell you about popular features and shortcomings.

But it is not an effective approach to gauge the state of an industry. For this purpose, you need objective data, either on all companies involved or on a representative sample that you select. Government bodies like the Census Bureau or the Bureau of Labor Statistics collect useful global statistics like value-added per employee or the ratio indirect to direct labor by industry, but they are just a starting point.

Going beyond is so difficult that I don't know of any successful case. Any serious assessment of a company or factory requires visiting it, interviewing its leaders in person, and reviewing its data. It takes time, money, know-how, and a willing target. It means that the sample has to be small, but there is a clash between the objective of having a representative sample and the constraint of having a sample of the willing.

For these reasons, benchmarking is a more realistic approach, and I know of at least two successful benchmarking studies in manufacturing, both of which, I believe, were funded by the Sloan Foundation:

  • The first was the International Assembly Plant Study, conducted in the late 1980s about the car industry, whose findings were summarized in The Machine That Changed The World in 1990. The goal was not to identify the distribution of manufacturing practices worldwide but to compare the approaches followed in specific plants of specific companies, for the purpose of learning. Among other things, the use of the term "Lean" came out of this study.
  • The second is the Competitive Semiconductor Manufacturing Program, which started in the early 1990s with a benchmarking study of wafer fabrication facilities worldwide. It did not have the public impact of the car assembly plant study, but it did provide valuable information to industry participants.

The car study was conducted out of MIT; the semiconductor study, out of UC Berkeley. Leadership from prestigious academic organizations helped in convincing companies to participate and provided students to collect and analyze the data. Consulting firms might have had better expertise, but could not have been perceived as neutral with respect to the approaches used by the different participants.

The bottom line is that studies based on subjective answers from a self-selected sample are not worth the disk space you can download them onto.

The Toyota Production System (TPS), Philosophy, and DNA

According to Ranga Srinivas, "TPS is a 'Philosophy', not a system (System in TPS is given by Western world). That philosophy is in their DNA."

We tend to get carried away with metaphors, and I think we need to get back to earth.

Louie de Palma

In Japanese, TPS is not only NOT a philosophy, it is not even a system, but just a method! The term is Toyota Seisan Hoshiki (トヨタ生産方式), and Hoshiki means "method," not "system." It reminds me of Louie de Palma, the Danny de Vito character in the series Taxi, saying about his girlfriend,  "She sees something in me that no one ever saw, something that isn't there."

Let us study TPS for what it really is: the best known way to make cars. And, if Mark Graban can learn from it and improve hospitals, it's wonderful. But let us not go to a car maker for philosophy. It's the wrong shop.

Saying it's the best known way to make cars is not talking it down; it's what drew me to it. Philosophy is also a wonderful thing, but corporate philosophy is to philosophy as advertising is to poetry. If you parse it, it should be to understand the image management wants to project, not what the company does.

There is a Japanese word for philosophy (tetsugaku, 哲学). Googling "toyota tetsugaku" yields a single occurrence on the Toyota website, in one paragraph about "Business strategy" (hoshin), which translates as follows:

"Toyota aims to be a good corporate citizen through the provision of clean and safe products, to contribute to the prosperity of society, and earn the trust of the international community. I will introduce the vision for the future and Toyota's philosophy, which is alive in the Toyota Production System and the corporate concept."

For comparison purposes, this is what GM says about itself on its website:

"In order to achieve our goals, GM has remained committed to the following formula for success:

  1. Move faster and take risks to achieve sustained success, not just short-term results
  2. Lead in advanced technologies and quality in creating the world's best vehicles
  3. Give employees more responsibility and authority and then hold them accountable
  4. Create positive, lasting relationships with customers, dealers, communities, union partners and suppliers, to drive our operating success."

I have the greatest respect for TPS, and have experienced its adaptability to industries ranging from making frozen foods to computers and aerospace.  And I understand that you can't go to a hospital and tell administrators, doctors, and nurses that you are going to help them with a method for making cars. You not only have to adapt it, you must also present it in such a way that they will listen. For 25 years, the word "Lean" has been used for this purpose. It has also been abused, to leverage the respect inspired by TPS in order to promote unrelated ideas.

We also need to be careful about references to DNA in this context. I believe it started with Spear and Bowen Harvard Business Review Article Decoding the DNA of the Toyota Production System. Culture is nurture; DNA, nature. Your culture is the way your family, school, and society molded you; your DNA is the genetic program that made you.

Generally, we should treat national culture as irrelevant to manufacturing. If Japanese business leaders in the late 19th and early 20th century had considered it relevant, they would have decided that manufacturing was a product of European and American culture that could not be transplanted to Japan.

About housekeeping habits specifically, I remember being impressed, while walking the streets of Rotterdam at night, by houses with the drapes pulled and the lights on to let passers-by admire spotless living rooms. What we saw in factories in the same country, however, told us that the cultural obsession with neatness in daily life did not carry over to the production shop floor.

DNA is even less relevant. In every society, there are misguided individuals who believe that having been born into a particular group makes them better at some activities; the rest of society calls them bigots. If DNA had anything to do with manufacturing excellence, it could not be achieved by learning. You can learn a method, master a system, and even assimilate a culture, but you can't change your DNA.


What is "Operational Excellence"?

Who would not want something called "Operational Excellence"? "Excellence" is superlative goodness, and "Operational" suggests a scope that includes not only production, logistics, and maintenance in Manufacturing, but also administrative transaction processing like issuing car rental contracts or marriage licenses. The boundaries are fuzzy, but Marketing and R&D are not usually considered part of Operations.

Hearing "Operational Excellence" for the first time, everybody takes it to mean whatever they think is the best way to run operations, which makes it unlikely that any two people will have the same perception. If marketers of consulting services can prevail upon a profession to accept such a vague and generic term as a brand, they can sell pretty much anything under this label. By contrast, the Toyota Production System (TPS) specifically refers to the principles, approaches, methods, and tools that Toyota uses to make cars. When you first hear it, you may not know what those are, but you know that you don't know. Another difference between "Operational Excellence" -- also known as "OpEx' or "OE" -- and TPS, is that the first is a goal, while the second one is a means to achieve the unmentioned but obvious goal of thriving in the car industry.

Chevron OE

OE at Chevron

It is an increasingly popular term, perhaps because of its very lack of precision. Google it, and you find, for example, that, Chevron "has spent more than 20 years expanding systems that support a culture of safety and environmental stewardship that strives to achieve world-class performance and prevent all incidents. We call this Operational Excellence (OE),..."  So, at Chevron, OE is about avoiding accidents that directly hurt people and oil spills that ruin the environment.

It is certainly not what it means to the  Institute for Operational Excellence. Its website has a glossary that contains exclusively terms from TPS or Lean, like Andon, Cell, Chaku-Chaku, 5S, Kanban,..., which strongly suggests that Operational Excellence is just the latest avatar of TPS when applied outside of Toyota. For 25 years, "Lean" has reigned supreme in this role but may finally be getting stale after so many botched implementations.

Shingo Prize for OpExThe Utah State University website, on its Jon M. Huntsman School of Business page, has a directory entry for The Shingo Prize for Operational Excellence. The Shingo Prize site itself, however, while using "excellence" in almost every sentence, does not refer to operational excellence. The theme of this year's Shingo Prize conference, in Sandusky, OH in May, was "Enterprise Excellence," which sounds like a further generalization. But, digging deeper, you find that the Shingo Model Handbook contains "operational excellence" 31 times, "Lean" 7 times,  "Toyota"  twice, and "TPS" never.


Shigeo Shingo

Shigeo Shingo


Stuck gears on the Shingo Prize page

The Shingo Prize page uses as a banner a picture of three gears with the teeth enmeshed in such a way that they can't move, a picture that would have seemed odd to an engineer like  Shigeo Shingo. His legacy is primarily contributions to production engineering like SMED, Poka-Yoke, and line/work station design. On these subjects, you cannot see daylight between Shingo's work and the Toyota Production System (TPS). Therefore, when you see a document called "Shingo Model Handbook" that refers repeatedly to Operational Excellence and never to TPS, you can't help but conclude that Operational Excellence is just another name for TPS.

UC Berkeley OE Program Office Team

UC Berkeley OE Program Office Team

UC Berkeley has an Operational Excellence (OE) Program Office. Based on the family picture in its Spring 2014 Progress Report, it has 12 members. UC Berkeley has a total workforce of 29,000, of which 2,000 are full and part-time faculty members, and about 36,000 students. It works out to 1 member of the OE Program Office for every 2,417 members of the work force and 3,000 students. They present themselves as  internal consultants, with access to funding and expertise in "project management, change management, strategic planning, campus engagement, financial analysis and planning, business and data analysis, and communications." The director of the office has been on the administrative staff for 13 years and reports to the university's chief administrative officer. This is yet another take on it.

Do the proponents of Operational Excellence do a better job of capturing the essence of TPS than their predecessors in Lean, World-Class Manufacturing,  Synchronous Manufacturing, or Agile Manufacturing? The above-mentioned institute has a page defining Operational Excellence as "the point at which 'Each and every employee can see the flow of value to the customer, and fix that flow before it breaks down.'” 

At first, it sounds like another version of True North, as explained by Art Smalley. Taking a closer look, as a general statement, it does not make much sense. It implies that every employee of every organization is involved in something that can, at least metaphorically, by described as a "flow of value" to customers. It is no stretch to see how this applies to a hot dog street vendor, but how does it work for, say, a firefighter? A firefighter serves the public by putting out fires, but the value of a firefighter resides in the ability to put out fires when they occur, not in the number of fires put out. A firefighter "seeing a flow of value to customers" is a head scratcher. As for "fixing the flow before it breaks down," it conjures up the image of a plumber repairing a pipe that doesn't leak.

Even Wikipedia editors are uncomfortable with their article on Operational Excellence. They denounce it as "promoting the subject in a subjective manner without imparting real information." The definition is indeed short and confused:

Operational Excellence is an element of organizational leadership that stresses the application of a variety of principles, systems, and tools toward the sustainable improvement of key performance metrics.

Much of this management philosophy is based on earlier continuous improvement methodologies, such as Lean Manufacturing, Six Sigma, and Scientific Management. The focus of Operational Excellence goes beyond the traditional event-based model of improvement toward a long-term change in organizational culture.

It says what Operational Excellence is an element of, what it is based on, and what it goes beyond, but not what it is. And much of what these few words say raises eyebrows:

  1. The emphasis on metrics is a throwback to Management-By-Objectives, an approach that has historically not led to excellence at anything but gaming metrics.
  2. Lean Manufacturing, Six Sigma, and Scientific Management are emphatically not continuous improvement methodologies. Continuous improvement is a component of Lean but by no means all of it. Six Sigma is not continuous improvement at all, and Taylor's "scientific" management was about preventing operators from colluding to curtail output, not improving processes.
  3. Continuous improvement is not event-based.  Contrary to what the name suggests, "Kaizen events" don't do continuous improvement. This format was actually developed in the AME in the 1990s based on the realization that just continuous improvement could not accomplish changes of the scope that was needed.
  4. TPS/Lean, when correctly implemented, has always been about a long-term change in organizational culture.

40 Years on, the Barcode Has Turned Everything Into Information | Wired

"Without the barcode, FedEx couldn’t guarantee overnight delivery. The just-in-time supply chain logistics that allow Walmart to keep prices low would not exist, and neither would big-box stores. Toyota’s revolutionary kanban manufacturing system depends on barcodes. From boarding passes to hospital patients, rental cars to nuclear waste, barcodes have reduced friction like few other technologies in the world’s slide toward globalization."



Michel Baudin's comments:

While this article exaggerates a bit, the fact is that the bar code is the first successful auto-ID technology, so successful in fact that more advanced technologies, like RFID or even QR-codes, have yet to displace it. There are barcodes on Kanbans, but you really cannot say that the system depends on them, because Kanbans were used early on without barcodes more than two decades.

The 40th anniversary of the barcode is an opportunity to remember, or learn, who invented it and why. This article does not credit the actual inventors, Norman Woodland and Bernard Silver, who patented it in 1952, but only supermarket executive Alan Haberman, who made its use practical to improve inventory tracking and speed up checkout. In 1974 he led an industry committee to adopt the barcode as a vehicle to implement a Universal Product Code or UPC. It is as much the story of the emergence of a standard as a story of technology.

In Manufacturing, barcodes are used almost everywhere to identify warehouse locations and stock keeping units, to validate picks, and to track component serial numbers. While celebrating the success and the usefulness of this technology, we should, however, remain aware of its limitations. Even in supermarkets, barcode reading remains a largely manual process. A human still has to wave around small items in front of a reader, or a reading gun in front of large items, and it often takes multiple attempts before you hear the beep confirming a successful read.

Fully automatic barcode reading is occasionally found in manufacturing operations where the environment is clean, with good lighting, high contrast, and a controlled orientation. QR codes are less demanding. They can be, for example, etched on the surface of a metal workpiece, and read inside the work enclosure of a machine-tool.

RFID tags hold the promise of full automatic reading at a distance. It has made them successful in public transportation passes like the Octopus card in Hong Kong or the Navigo card in Paris, as well as in electronic toll collection in the Fastrak system in California.

Barcodes are also limited to IDs and cannot be updated. As a consequence, they have to be used in the context of an information system that contains all the data keyed on the ID, which can be "the cloud" in a supply chain, or a local manufacturing execution system in a factory. By contrast, a high-end RFID tag can locally contain the entire bill of materials of a product moving down an assembly line, its current location in the process, and any measurements that may have been made on it at earlier operations. For a finished product, it can contain the entire maintenance history of a unit.