Increasing Subassembly Productivity

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In my time spent onsite with the customer implementing PFEP (Plan-For-Every-Part) and advanced material flow techniques, I often was pulled into other projects. One of these projects was an effort …

Michel Baudin‘s insight:

This is a rare post on assembly engineering, dealing with the layout of subasembly cells for a mixed-flow line. This is the red meat of Lean, ignored in most of the English-language literature on the subject. Kudos to Kelcy Monday for getting involved.

Reading this, I can’t help but thinking of many issues I would have handled differently, but I have not seen the product of the shop floor. In any case, this is the right opportunity to work on, with order-of-magnitude performance improvements at stake, as opposed to the 5% others might have nibbled by  applying 5S on the old layout.

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Does US manufacturing need more universities?

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Needed: ‘manufacturing universities’ to keep U.S. competitive (blog)
The erosion in manufacturing capability weakened the U.S. economy over the past two decades, Atkinson and Ezell add.

Michel Baudin‘s insight:

Universities have essentially contributed nothing to the art of manufacturing in the past, and I have a hard time understanding how they could be essential to US manufacturing competitiveness in the future.

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Takt time: can it be universally applied to all types of production?

This is the question Casey Ng asked in the TPS Only discussion group on LinkedIn. He elaborated as follow:

What are the essential conditions to implement takt time successfully? What are the cases when it fails and if you refer to the fundamental principle of takt and yet couldn’t find the solution? Then what are the exception areas and what alternate solution can be used?

To date, it has generated 43 comments, many with the high level of depth and the implementation examples that are characteristic to this group. It is a new LinkedIn group, with only 118 members — compared with 151,503 for Lean Six Sigma — but passionate and providing meaty technical content. I recommend it in general, and this discussion in particular. Here, I will be including only my own contributions on takt time, and its relevance to the following:


With monuments — the very large machines used for heat treatment, electroplating, painting, cutting of sheet metal, etc. — you usually have to load multiple parts simultaneously in order to meet demand, but these parts often do not have to be identical. I don’t recall anyone mentioning this in the earlier comments, but the parts that you load together can be a matching set rather than a batch. If your takt times are long enough, as, for example, in aircraft production, you can actually process one plane-set at a time, to takt time.

Gigantic products with long takt times

Gigantic products, like oil tankers, with takt times of six months or more, are built out of vertical hull slices made at much shorter takt times in a shop, and then welded together in the dry dock. Pioneered in the US with Liberty Ships during World War II, this is now standard shipyard practice, and enhances of the repetitiveness of the process, applying the concepts of takt time and one-piece flow.

Refueling outages in nuclear power plants

I am puzzled by Todd McCann‘s use of the takt time concept in the context of nuclear plant refueling outages, a problem I had the opportunity to work on 20 years ago in France, not in the US. I don’t know who the top performers are in this area today. At the time, it was a tie between the French and the Japanese, at about three weeks from shutdown to restart for one reactor, which is substantially longer than the 193 hours you were quoting for 2006, assuming that the work continues 24 hours/day, 7 days/week.

The French performance was accomplished by standardization. They had 55 reactors with only two designs, producing respectively 900MW and 1300MW, run by a single company. The procedures were the same everywhere, with any improvement quickly shared. The Japanese performance was based on using techniques from TPM. They had nine different reactor designs, run by different utility companies.

Even though they had the best performance in the world, I saw many opportunities for improvement, based on borrowing techniques from SMED, improved planning and scheduling for materials, tooling, and the 1000+ contractors involved, and operational details. Such a detail, for example, was security. Their procedures were effective at controlling access, but inefficient, with utility employees at all levels spending too much time getting contractors into and out of the facility.

The concept of takt, on the other hand, did not strike me as particularly relevant, given that a refueling outage is a yearly burst of intense activity for any given reactor, as opposed to a repetitive process.

Rate work versus response work

More generally about takt time, most businesses have both what my colleague Crispin Vincenti-Brown called Rate Work and Response Work. In manufacturing, if you do a Runner-Repeater-Stranger analysis of your products, Your Runners and Repeaters are rate work; your Strangers, response work.

Runners are products with enough volume to warrant a dedicated line. Repeaters are products that you group into families that, in aggregate, have enough volume for a line. Strangers are all the other products, including R&D prototypes, sample quantities of new products, spare parts for obsolete products, and any other special request. Even in aggregate, they account neither for a high volume nor for high revenue, but you still must produce them promptly. They require a small job-shop set up with your most flexible equipment, staffed with your most versatile operators, and its own operating policies.

While takt time is fundamental to line design for Runners and Repeaters, it isn’t much use for Strangers.

Non-repetitive operations

I have a hard time seeing the relevance of takt time in the absence of repetitiveness. In an assembly process, the takt time gives you an upper bound for the process time at each operation. As you broaden the mix of products you assemble on the same line, it becomes more difficult to balance the work among stations. Past a certain point, you are better off using approaches like bucket brigades, a.k.a. bump-back system, or even a yatai, which are not based on takt time.

Takt time and its calculation

Outside of mathematics, concepts are not reducible to formulas. Time/Demand is the way you calculate takt time, but it tells you neither the rules by which you are supposed to use that number nor how it maps to shop floor activity.

In mass production plants, managers use the inverse of this ratio: Demand/Time, which gives you the same information. Mathematically, working at a takt time of 1 minute and making 60 units/hour (uph) is equivalent. Yet, you and I know that, depending on whether the manager thinks the plant is producing at a takt time of 1 minute or making 60 uph, the shop floor will be radically different.

If you think in terms of uph, it doesn’t matter if nothing comes out for the first 59 minutes of each hour as long as all 60 come out in the end. If you think in terms of takt, 1 unit will come out like clockwork every minute.

What this says is that there is more to takt time than the formula. This is discussed extensively in Lean Assembly, with the following definition of takt time:

“Assuming we complete the product one unit at a time at a constant rate during the net available work time, the takt time is the amount of time that must elapse between two consecutive unit completions in order to meet the demand.”

As I recall, this is,more formally, the way Ohno described the concept in Toyota Production System.

Takt-driven production as the ideal state

The takt time allows you to define an ideal state, that John Shook and Pascal Dennis call True North, but that I prefer to call takt-driven production. In this state, you perform all operations one-piece at a time with process times that exactly match the takt time, and with instant transfer to the next operation at every beat. Of course, it is never perfectly realized, even on an assembly line. Real lines can only be approximations of it. The point is that it gives us a direction.

All deviations from takt-driven production translate to Ohno’s waste categories, overproduction, waiting, excess inventory, etc. Since any local project that move production in this direction eliminates waste, it can be undertaken with the confidence that it contributes to global improvement and is not sub-optimization.

Chronos, kairos, and takt time

Joachim Knuf : “… The ancient Greeks differentiated between two types of time: chronos (chronological, sequence of intervals, typically of equal extension) and kairos, best thought of as ‘the opportune moment.’ In this case, intervals begin and end under sets of conditions. This concept applies, for example, to healthcare, when next process steps are initiated by a preset configuration of values (patient’s blood pressure, glycemic index, bowel movement), not by elapsed time…”

I had never heard of Kairos, but, if the ancient Greeks made the distinction between Kronos and Kairos, why shouldn’t we? There is a rich toolbox associated with the pursuit of takt-driven production. Where the concept of takt does not apply, we can’t use these tools. As you said, extending usage of the word to Kairos-driven activities just adds confusion. These activities need different tools, and Casey pointed out some of them in his comments on Strangers. Let us keep different words to hang them on.

Lean in a press shop

Andrew Turner, Managing Director at Ramsay Engineering, in Pietermaritzburg, South Africa posted the following question on The Lean Edge on 9/22/2012:

“Our company is split in 2 sections, the one a JIT assembly plant, the other a mass production Press Shop. Implementation of Lean in the JIT plant has been relatively simple (not that Lean is ever really simple), however, we are struggling with the implementation in our Press Shop. I know the importance of items like SMED and Heijunka in driving this journey, yet we are battling to get the ball rolling forward. Where do you think we should start the process in the Press Shop?”

To date, he has received responses from the following authors:

  1. Art Byrne: First link the logical value streams through product families, then get change over times under 10 mins
  2. Mike Rother: Depends on Your Goals
  3. Peter Handlinger: Establish a daily pattern production schedule to sequence your presses
  4. Tracey Richardson: Start with Production Control and Empower People through Standards

Of these, Art Byrne’s is the most specific and actionable. None of them, however, address the issue of skills development. At the outset, neither the press operators nor their managers can be expected to have a working knowledge of Lean or how to implement it. Therefore, feasibility by the organization you have is key in your initial choice of projects.

This is why, in shops where people work with machines, the first pilot projects are so often about SMED. This includes not only press shops but machine shops, injection molding shops and diecasting shops, with different technical specifics. On press operations, see Chapter 7 of Sekine & Arai’s Kaizen for Quick Changeover. Keeping in mind the skills development goal, you don’t start with your largest machine that currently takes 8 hours to set up, but with a small one that takes 30 minutes and that you can take down to 4 minutes in 2 months. This kind of success fires up your teams to take on tougher challenges.

After a successful pilot, when you want to apply SMED as appropriate in the entire shop, you need to analyze your activity to establish where it is most useful and easily achievable, and you need to realize the engineering implications. You usually cannot achieve SMED on presses with organization and standard work only; you also need to modify the machines, standardize the dies, and improve the flow of dies to and from the machines, including die maintenance, storage and retrieval. You have to know what and how long it takes to do it. This is not the sort of goals you reach with a few Kaizen events.

Peter Handlinger is the only responder to mention the issues of high- versus low-volume items, but only in the context of setting up daily schedules. I prefer to refer to this as demand analysis, and do it upfront, far upstream from production scheduling, with the objective of breaking down the output of your press shop into the following:

  • Runners, to each of which you dedicate on or more press or press lines.
  • Repeaters,  that you group in families by feature similarity to make in flexible press lines.
  • Strangers, that are items with low and sporadic demand that you make in small job-shop inside your press shop.

You use this breakdown to drive changes in the layout of your shop. In particular, it tells you which presses or press lines you can integrate with an assembly line, as Art Byrne recommends. Then you apply different approaches to production control for the different types of lines.

A Lean Journey: Meet-up: Michel Baudin

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Interview on Tim McMahon’s A Lean Journey.
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Brillopak wins lean manufacturing innovation award

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“The company was chosen as an inaugural winner of the Medway Innovation Voucher, towards the development of its new lean production control package. This is being developed to support small and medium manufacturers, using Brillopak’s new COMPACT C Series and robot cell packing and palletising solutions.”

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Yet another (wrong) definition of takt time

This is from a blog post published today that claims to clarify what a takt time is:

Takt Time:  This is the rate of time at which a product or service is being purchased.  For example, a Nissan commercial mentioned that every minute, someone in the world buys a new Nissan.  Selling a car every minute is an excellent example of takt time!

Writing a definition for a thing or an idea is tricky. Following Aristotle, I would say that you have done a good job if you have described what kind of a thing it is and how it differs from other things of the same kind, using terms your reader already understands.

In this definition, takt time is described as a “rate of time.” If there were such a thing as a rate of time, in what units would it be expressed? In production, a rate is expressed, for example, in pieces per hour; a time, in minutes or seconds. Takt Time, as its name suggests is a time, not a rate, and certainly not a rate of time, whatever that may be.

This definition then relates takt time exclusively to “a product or service [..] being purchased,” and gives the example of a Nissan being bought every minute in the world, suggesting that 1 minute is the takt time of a Nissan. Incidentally, if this figure were true, Nissan would sell about 500,000 cars/year, versus the 4 million it actually sells.

Takt time, as we use it in manufacturing and industrial engineering, is in fact not a parameter associated with just a product but with a production line making this product. Given the demand that is given to it and the amount of time that it actually works, the takt time of this production line for this product is the time that must elapse between two consecutive unit completions.

If a line is expected to produce 400 units of a product in a 400-minute shift, then, if you stand by the last station of the line, you will see one unit come out every minute, meaning that its takt time is 1 minute. If you switch from working 1 shift/day to 2 to meet the same demand, you double the takt time to 2 minutes.

This is why it is calculated as follows:

Takt\, time(Product, Production\, line)=\frac{Net\, available\, production\, time}{Demand}

It has a numerator and a denominator, and both matter. They are obviously calculated for the same time period.

Why “Smart” part numbers should be replaced with keys and property lists

Nomenclature matters. What you call parts, products, and resources, and how you attach technical, commercial, and administrative data to their names determines how easy they are to retrieve in daily operations and to analyze for planning or control purposes. Manufacturing, worldwide, is overwhelmingly dominated by part numbering systems  that are called “smart” because they embed data about materials, dimensions, number of holes, or other characteristics into part numbers, as in CEP-134M10FB-TT1234\R4, where each group of characters has an encoded meaning, for example  CEP for Capacitor and R4 for Revision 4.

In concept, these systems are a 100-year old legacies of the paper-and-pencil age, and obsolete in the age of databases. The alternative is to have part numbers that are just unique IDs, or primary keys in database terms, with all the data attached in the form of property lists in plain text. I propose to call it the key approach. The case for the key approach is overwhelming, and the technology to use it available. But nearly all manufacturing professionals active in 2012 have been educated in the old method, and taught that it was smart. They are unaware that it should be replaced.

In addition, people who have spent years mastering a complicated, antiquated system are always strongly attached to it and oppose its abandonment, as it would make their hard-won expertise irrelevant. This is why, for example, command-driven information systems were successfully marketed long after they had been supplanted by graphic user interfaces in home computers. It is also the reason spelling reforms almost never succeed. Part numbers with encoded information are a deeply rooted legacy.

These are great hurdles to overcome, requiring missionary work. Of course, even if this post is persuasive, if leaves unanswered the question of how you migrate from “smart” part numbers to the key approach, in an existing organization with legacy information systems. It is an essential question, and needs to be addressed separately, once the desirability of this transition is established, which is our point here.

Let us begin with detailed explanations of the value of the key approach.  First, it does not in any way limit functions. In addition, it has the following advantages:

  1. It reduces training costs.
  2. It is not affected when product information changes.
  3. Part numbers are short, and therefore easier for humans to work with.
  4. Part numbers are distinctive, even for similar parts, thus reducing picking errors.
  5. Nomenclature errors are more easily detected and corrected.
  6. Property lists in plaint text on labels are easier to read.
  7. Property lists support data mining.
  8. Product catalogs from different companies can be merged without loss of information.
  9. Company-private information is not unintentionally disclosed through part numbers.

Much of the material below is based on a recent discussion in the APICS discussion group on LinkedIn, initiated by Elvi M., and including contributions from Patrick DoyleJennifer Verellen, Joseph E. Harrington, Ph.D., Felipe Sanchez Ryckewaert, Martha M. Munson, PMP, Greg Pope, and T R Volpel CPSM.

Origins of “smart” numbering systems

Such systems have been introduced in libraries in the 19th century. In his latest movie, J. Edgar, Clint Eastwood highlights the role future FBI director J. Edgar Hoover played in the development of the Library of Congress cataloging system, in which “BX 378.5 .M38 1968”  breaks down as follows:

  • BX = Books on Religion
  • 378.5 .M38 = Shelf Address
  • 1968 = Year of publication

In the 1910s, it made sense because it allowed trained users to locate books quickly. You could search the card catalog by author, title or subject, and then locate the book by its call number, as shown in Figure 1:

Figure 1. Using a library card catalog


Most manufacturing professionals see nothing wrong with “smart” part numbers, to the point that they extend the approach to every aspect of operations, resulting, for example, in expense reports with lines like “HK0010 DB-ENG-122 M-3 RK 23.50,” which translates to  ” Helmut Katz spent €23.50 for dinner at the Ratskeller while working on the DaimlerBenz Engineering project.”

“Smart” part numbers have grown such deep roots in manufacturing operations that few managers or engineers are even aware of the unnecessary costs they generate in training and routine decoding, and of the obstacles they place in the way of manufacturing data mining. Some academics do realize that there is something wrong: in his 2005 textbook on Product Lifecycle Management (PLM), Purdue University’s Micheal Grieves reports that “With the implementation of PLM, smart part numbers are generally replaced by sequential part numbers.” In their LinkedIn profiles, however, several recent Purdue graduates report implementing “smart numbering systems” during internships in manufacturing companies as late as 2009!

Awareness of the issues is perceptible in blogs on PLM and CAD , as, for example, in the following:

Advocating the replacement of a method that its promoters got away with calling “smart” is an uphill battle. Who would want “dumb” systems instead? The first step may be to come up with an attractive generic name for alternatives, which is why I am suggesting we call it the key approach.

The key approach

Figure 2 shows an example of the key approach to an item and the characteristics that are usually embedded in a “smart” part number.

Figure 2. The key approach to part numbers

The item is uniquely identified by the key “13AB5,” and the nomenclature database has a record for each property of each item, in the form of a name-value pair. There are different views of what an item is. In some cases, the same key is used for all revisions, and you tell the revisions apart by their number. In this example, each revision has its own key, and contains the key of the prior revision in its property list, so that you can trace back the history of the part through multiple revisions.

Neither the property names nor the values contain any abbreviations or codes. They are all in plain text. In particular, you may notice that, where applicable, property values include units, while “smart” part numbers usually don’t. This is an important detail in multinational companies where metric and imperial measurements coexist. There is no need to store these characteristics in short fields. You can use as many characters as you need. 30 years ago, data storage space was expensive; now it is not.

Different items can have different property lists. Metal tubes have a length and a diameter; potato chips, expiration dates. The property values can also change over time without the item itself undergoing any change that would justify a new part number. For eample, if the item is a product that undergoes a drop in demand, its volume category may be downgraded from A to B, and it may then no longer have a dedicated warehouse location.

Reducing training costs

Unless the users of part numbers, in materials handling, production, engineering, production control, purchasing, or sales, are able to retrieve the information embedded in a “smart” part number, embedding it was a waste of time. But, in order for this to happen, hundreds or thousands of people would have to be trained on issues like the meaning of the field in characters 5 to 8 of a part number. In reality, it doesn’t happen. In pursuit of data mining, I often have to parse “smart” part numbers and find that, outside the department in charge of master data, very few people have a clue. When asked what a part number means, they have to painstakingly decode it field by field from a spec.

With the key approach and characteristics listed in name-value pairs in plain text, the information is accessible with no training, which is why it is used on e-commerce sites like Amazon. Each product’s unique ID is Amazon’s ASIN, which may designate anything from a book to a waffle iron. The Product Details are given in a list of name-value pairs, and this information is immediately obvious to a first-time visitor. You don’t have to take a class.

Historical continuity and traceability

Patrick Doyle used 1-015-113-029-2 as an example of a “smart” part number, meaning that it is a single part (1), used on a phone (015), is a diode (113), supplied by Diodes R Us (029), and is revision two (2).

Such a part number contains data that is subject to change as you may, for example, decide to switch suppliers. This puts you in a dilemma. If you change the part number so that it contains accurate supplier data, you break the continuity of your history with that part, or you keep the same part number and violate your own convention. If you do change the part number, you can still retrieve the complete history by maintaining a table of name changes, but it adds complexity.

With the key approach, instead of 1-015-113-029-2 and a dictionary to translate all properties into plain text, you have something like the following:

  • Part ID: EA5D4, a unique key unique, with no embedded data.
  • Property list in database, keyed on Part ID = “EA5D4”:

Where-used: iPhone
Family: Diode
Supplier: Diodes R Us
Revision: 2

All the properties can be changed without affecting the key, and the complete history of production volumes, deliveries, and quality problems can be retrieved with the key.

Short names, easy to work with

“Smart” part numbers tend to be preposterously long, and therefore practically impossible for people to remember. No matter how large a field an ERP system or MES provides for part numbers, some company “smart” numbering system will exceed it. In the key approach, the part number’s one and only job is unique identification within the scope of the system. There may be another object by the same name at the other end of the world, but not within the plant or the company.

A sequence of just 5 uppercase letters and digits is mercifully short and easy to remember. It provides (10+26)5 = 60.5 million possible IDs, and that is enough to meet the needs of even a high mix production organization for a long time. Some prefer to use digits only, but it reduces the name space to only 100,000 IDs.  Allowing lowercase letters would increase it to 916 million, but few people would reliably make the difference between “13AG5” and “13aG5.”

Distinctive part numbers

“Smart” part numbers give similar names to similar parts, which makes them more difficult to tell apart during picking and increases the risk of picking errors. Hergé’s  Tintin stories feature the two dim-witted detectives named Thompson and Thomson shown in Figure 3:

Figure 3. Thompson and Thomson

The only way to tell them apart is from the shape of their mustaches, and their nearly identical names don’t help. Now, in Figure 4, consider the following two, nearly-identical screws and their nearly identical part numbers:

Figure 4. Nearly identical parts with nearly identical part numbers

One of the screws is 1/8 in shorter than the other, which, with “smart” part numbers, results in only the last digit being different. How much more likely are they to be confused during picking than if their names were A23GT and 92WRT?

Detection and correction of nomenclature errors

The only way to be sure a part number is read and written accurately is respectively by reading it automatically from a bar code or an RFID tag, and by printing from the master database. Typing, handwriting, copying and pasting, and even selecting from a long pull-down menu with similar entries is error-prone, particularly when performed with data that has been extracted from an ERP, MES or PDM software system into an electronic spreadsheet. Besides typos, part numbers may have interchanged characters, be truncated, or be misinterpreted by the destination software, as when, for example, Excel treats a part number like a number and lops off leading zeros. And the slightest error in a part number is enough to make the consolidation of data about it from multiple sources impossible.

The above is true no matter what part numbering system you use. The risk of error, however, is much higher with “smart” part numbers than with keys, because they are two to three times longer and more similar, so that a part number with a typo may point to a different, existing part rather than nothing.

Labels with plain text characteristics

Several participants in the APICS discussion, and particularly Martha M. Munson, and Greg Pope, expressed concern about the human-readability of labels. For example, if no information is embedded in a part number, how can material handlers locate it in a warehouse? In fact, the key approach is not to hide this information but to separate it from the identification itself. As shown in Figure 5, all the data in Figure 2, or a relevant selection from it, can be printed on a label.

Figure 5. Box label with characteristics in plain text

Note also that all the characteristics on this label are printed in plain text. The best, cheapest and least error prone standard is using whole words from the native language of the work force. If the bolt category is called “Bolt,” no training is needed to recognize it. If you call it “256,” you need a dictionary. Human readability is, of course, not the only concern in daily operations. You also need to use bar codes or RFID tags for machine readability.

Support for data mining

The most convenient input to manufacturing data mining is a table in the dimensional model, meaning that some columns contain reference data — used for filtering and aggregating — and facts — the measured quantities or observed attributes for which you want to identify patterns. All the characteristics embedded in a “smart” part number are part of the reference data. You want to be able to analyze quality for parts made from a given raw material, or within a dimension class, etc.

Retrieving this reference data by parsing “smart” part numbers is a major effort, that has to be repeated with each naming convention.With the key approach, on the other hand, turning the property list of Figure 1 into the table of Figure 6 is just a matter of using a crosstab query in Access or a pivot table in Excel.

Figure 6. Item properties crosstab for data mining

Merger support

The possibility that the company might merge or be acquired is usually not a consideration in the design of its nomenclature. However, considering how often such events occur and how far reaching their consequences, it should be. When companies merge, their catalogs of products and parts should too. Merging information models, and nomenclature in particular, is always difficult and time-consuming. Mergers are mostly of unequal companies, and the larger company’s system becomes the merged company’s standard, even when the smaller company has a better one. In true mergers of equals, this situation often goes unresolved for years, with both divisions’ legacy systems remaining in operation, and possibly, their real-time content meeting through integration middleware and their histories in a data warehouse. To focus on what happens we part numbers, we consider two hypothetical mergers. In the first, both companies have “smart” part numbers; in the second, both use the key approach. Real situations, of course, have many other patterns.

I have never seen two companies with identical “smart” part numbers. For them to use different naming conventions is good for uniqueness after the mergers, because it makes it highly unlikely that they will have assigned the same name to different products or parts. As the software systems themselves usually do not constrain the naming conventions — except for the total length of part numbers — you can upload another company’s part numbers, and end up with a catalog that commingles part numbers based on both conventions.  But this adds further complexity for data mining, because, to retrieve the information embedded in part numbers, you now need to parse names generated with two different conventions, and to identify which convention each name is based on. The management of the acquiring company intends to standardize the part numbers on its own system, but implementation has a way of being postponed indefinitely.

Two companies using the key approach have a higher risk of key conflict, especially if the keys are sequential auto-numbers, because both companies will have products with keys 1, 2, 3,…, 4384, needing disambiguation. This is a problem that is usually not anticipated when systems are first implemented, but can be avoided by instead selecting names at random from a large name space. For example, if 20,000 keys are chosen with equal probabilities among the 78.3 billion  possible combinations of 7 digits and uppercase letters, then 99.7% of the time, no two will be identical. You would still need to search for duplicates just in case, but you only have a 0.3% probability of finding one.

The European Union had this problem with ZIP codes, and resolved it with country code prefixes. Another option, if you have two products both named 4384 by different companies is to rename one of them 4384.1. Any change to keys is undesirable, because all references to that key then need to be updated. This can usually be managed within one system, but not within all the ad-hoc extracts and spreadsheets that users have generated.

Merging the property lists from two companies systems is work in any case but is easier to do with property lists than with “smart” part numbers, where it requires you to develop a sophisticated parser. The properties in both systems are likely to have both different names and sets of values for the same data. The name value pair (Material, Steel) in one system may by (Mat’l, STL) in the other, and the engineers need to build a dictionary to translate them. At first, you may translate the data only in the data warehouse, which is sufficient to jointly mine the data from both systems, but, eventually, you would want to standardize the property lists across the divisions.  When you do this with the key approach, you deal with issues of  meaning that have to be addressed anyway as part of a merger. With “smart” part numbers, you also have to resolve format issues that are unrelated to meaning.

Data security

The protection of proprietary information is the only legitimate reason to encode and encrypt data. During development, for example, the marketing names of products are not used for fear that they might leak to competitors. Once a product is made and sold, all units often bear serial numbers that must be encrypted lest they provide competitors with information about production volumes. As mentioned in Data Mining in Manufacturing versus the Web, the use of plain text in serial numbers on World War II German tanks allowed British analysts to estimate the numbers produced from the serial numbers of the units that were captured or destroyed. The serial number of my iPad today is DKVG805HDFHY, from which Apple competitors can deduce nothing unless they know how to translate it back to a production sequence number.

“Smart” part numbers do not provide much security, as anyone who cares to can reverse-engineer them from actual products and labels. For example, on the Lands’ End catalog, once you find out that Men’s Regular Short Sleeve Jacquard Polo shirts are item#40600-5A63 in Regular sizes, and item# 40600-6A69 in large sizes, it doesn’t take Sherlock Holmes to guess that the numbers preceding the dash identify the polo, and the alphanumerics following it, a size class. A “smart” part number is like a house key that would carry information about which door is opens; a thief cannot infer your address just from your house key simply because this information is not on it. Likewise, in the key approach, a product ID by itself will not reveal anything to your competitors.