Cobots: more cooperation than collaboration

Cobot is the contraction of “collaborative” and “robot”, name and concept of a new kind of robots able to work literally hand-in-hand with humans without a safety fence between them.

fraunhoferCobots are hype and the word tends to become generic for any kind of robot working in close proximity of humans. A study from the German FRAUNHOFER – INSTITUT für Arbeitswirtschaft und Organisation IAO (2016) about first experiences with lightweight robots in manual assembly* distinguishes cooperation from collaboration.

*“Leichtbauroboter in der manuellen Montage – einfach einfach anfangen. Erste Erfahrungen von Anwenderunternehmen

This post is in great part my translation of the original study, with my personal comments.

>Lisez-moi en français

The study summarized different combinaisons in the use of robots near and with human operators, leading the authors to propose 5 classes:

  1. Robotic cell in which a robot operates on its own, fenced-off from humans by a safety fence. In such a case there is no human-robot collaboration.
  2. Coexistence of robot and human, a case in which both are close to each other but without a safety fence, yet have no common workspace. The robot has its own dedicated space distinct from the human one.
  3. Synchronized work: an organization in which human and robot share a common workspace but only one being active at a time. The work sequence is like a choreography between human and robot.
  4. Cooperation: the two “partners” work on their own tasks and can share a common space but not on the same product nor same part.
  5. Collaboration: an organization with common and simultaneous work on the same product or part. Typically the robot handles, presents and holds a part while the operator works on it.

Based on this classification, the studies reveals that collaboration is still seldom. Workers and robots work side by side on their own dedicated tasks, letting me conclude that for the time being, “cobots” are more cooperative than collaborative.

Motivation for investing in this kind of more expensive robots is mainly productivity improvement and secondary objectives are improvement of ergonomics (avoid heavy lifting for example) and testing innovative technologies.

The choice of this kind of solutions requires also new planning and management tools as well as consulting. New standards and regulations are in preparation that must be managed by companies themselves, not the system provider. All this carries additional costs.

Companies with no or only limited experience with these kinds of robots remain hesitant, therefore the authors of the study recommend to implement step wise, starting simple and going from human-robot coexistence to collaboration.

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What is Industry 4.0 – Juergen Kanz

“Industry 4.0” was coined in Germany and is becoming the European name of what is also known as Smart Factory, Smart Manufacturing, Industrial Internet among others.

Note: German and French write “industrie” instead of “industry”

In this slightly more than one hour video, Juergen Kanz, Systemic Thinker and Theory of Constraints expert, introduces to the concept of Industry 4.0

You may jump to 4:55 to the explanations of 4th industrial revolution and how the German federal government came to encourage this initiative, and 12:40 for the presentation of the structure of the “platform industrie 4.0”.

Juergen takes the viewers deeper into the details and implications before linking the opportunities of Industry 4.0 to the Theory of Constraints (ToC) Body of Knowledge (around 49:00).  ToC provides several mindsets, principles, methods and tools that may help to install and get the benefits of industrie 4.0 based solutions.

Re-SWOT your business with 3D printing in mind – Opportunities

In a prior post of this series, I explained why it is wise to (re)SWOT your business with 3D printing* in mind and in another one I suggested assessing the potential Threats your organization could be facing. In this post, it’s about Opportunities offered by the new manufacturing ways.

*I use “3D printing” and “additive manufacturing” interchangeably

Reminder: with the recent progresses in 3D printing (and 3D scanning) with regards to materials that can be 3D printed, every business is potentially at risk to discover a 3D printed substitute offered by an unsuspected and probably unknown competitor.

Yet what is a threat to some is an opportunity to others. The ability to offer a faster, cheaper, highly customized or whatever new product or alternative offer incredible new opportunities.

3D printing may break many barriers to entry, opening wide the gateway to previously protected markets.

Any competitor should evaluate the emerging opportunities to redefine the rules in his/her business with additive manufacturing and the opportunities to diversify or expand into new markets.

Some questions to assess the potential Opportunities your organization could be considering

The intent of the following questions is to make you think about the potential opportunities of a 3D printed product. The list of questions may evolve and readers are welcome to suggest additional or alternate ones (please use the comments).

  • Can you imagine any way 3D printing being applied in your business?
  • If 3D printing would be used in your business, what would it be for?
  • Can you think about a (more) disruptive way 3D printing could be used in your business?
  • What 3D printable product or substitute, if it (would) exist, may give you a cutting edge competitive advantage?
  • Could you offer a 3D printed substitute to existing products? What would its advantages be? What new or additional value would it bring? Would your customers want it?
  • Can you imagine expanding your business entering a new market (or segment) with a 3D printed product?
  • Are there any barriers to entry to a protected market you’ve considered that could be taken down with 3D printing / additive manufacturing?

You may have noticed that these questions are very similar to those about Threats. It is no surprise as opportunities for some are threats for others.


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Re-SWOT your business with 3D printing in mind – Threats

In a prior post of this series, I explained why it is wise to (re)SWOT your business with 3D printing* in mind. In this post, I propose some questions to assess the potential Threats your organization could be facing.

*I use “3D printing” and “additive manufacturing” interchangeably

With the recent progresses in 3D printing (and 3D scanning) with regards to materials that can be 3D printed, every business is potentially at risk to discover a 3D printed substitute offered by an unsuspected and probably unknown competitor.

Some questions to assess the potential Threats your organization could be facing
The intent of the following questions is to make you think about the potential threats of a 3D printed product. The list of questions may evolve and readers are welcome to suggest additional or alternate ones (please use the comments).

  • Can you imagine any way 3D printing being applied in your business?
  • If 3D printing would be used in your business, what would it be for?
  • Can you think about a more disruptive way 3D printing could be used in your business?
  • What 3D printable product or substitute, if it (would) exist, may disrupt your industry / your market / your business?
  • If a 3D printed substitute suddenly appeared, could you offer the same?
  • Does your organisation have any knowledge about 3D printing? Any know-how? If not, how and where would you quickly get the capacity to propose the 3D printed product? (me-too offering)
  • Did you evaluate how much savings a 3D printed substitute could earn?
  • Compared to 3D printed part or product, what are the advantages of your traditional way of manufacturing?
  • Would your customers continue to pay for if they had the choice with a new 3D printed substitute?

It is possible that going through the questions above, you sense opportunities more or as well as threats. Fine! Opportunities are the next topic to explore.


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Re-SWOT your business with 3D printing in mind – Introduction

In a previous post* I assumed that 3D printing, general naming for additive manufacturing techniques, will revitalize strategic business analysis.

*Will 3D printing revitalize strategic analysis?

In this series of posts, I’d like to invite people in charge or with influence on strategy to reconsider their business with the new possibilities offered by 3D printing.

The incredible pace of innovations among which many real disruptions with 3D printing may surprise unaware business owners. On the positive side, they are many new possibilities to rethink and expand business. On the negative side, one may be put out of business by unexpected new competitors or shift in technologies or a combination of all.

What is it all about?

Additive manufacturing is totally different to traditional manufacturing. Material is “printed” thin layer after thin layer in simple or complex shapes and various materials until the finished 3 Dimensional part is finished on the printer’s bed. Those parts can even embed printed full functionally moving parts, like gears or ball bearings.

Traditional manufacturing cuts away material from a bigger rawling and usually requires many steps and different resources to get the finished part.

The advantages of additive upon traditional manufacturing are already tremendous and everyday new breakthroughs are reported.

Additive manufacturing needs no costly moulds nor toolings, fixtures or jigs, it does not even need skilled workers to operate the machines.

3D printing does not require minimum batch sizes, a tremendous advantage for short lead time and low inventories.

3D printing parts allows changes on the fly, which is top for highly customized production, inventory reduction, quick response to quality issues, and so on.

From this short list, by far not complete, everyone can sense the competitive advantages offered by these new technologies.

Now, it’s time for everyone to assess the current business with additive manufacturing in mind.

In the next post I’ll introduce SWOT analysis: Strength Weaknesses Opportunities and Threats.


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Why you should (re)SWOT your business

There is barely a day without a new announcement about a 3D printing* breakthrough or new 3D printable material or finding 3D printing new applications.

*I use “3D printing” and “additive manufacturing” interchangeably

In most cases, 3D printing allows a totally new approach and reinvented value proposition, e.g. printing teeth, prosthetics, glasses frames, mechanical parts or fully functional systems, etc.

3D printing frees from many constraints, especially molds, complicated assemblies, other expensive tooling or multiple steps machining necessary to cut away material in traditional manufacturing.

It does not require to have huge series to ensure low unit cost and is “infinitely” flexible for design  changes and ready for almost any customization.

Additive manufacturing challenges former accumulated experience in traditional manufacturing and does not require expensive machines, thus breaks down many barriers and opens markets to new entrants.

As many applications of additive manufacturing already proved, there is virtually no business safe from 3D printing/additive manufacturing applications and the threat of new entrants.

Chances are that an unexpected competitor arises overnight with a revolutionary new approach to satisfy your customers, ruining years of investments, efforts and accumulation of experience.

The (potential) irony is that this new competitor can not only steal your business, but lock your company out of the market if it does not have the competences, know-how, agility nor resources to switch to the new manufacturing way.

Therefore it is wise for every – emphasize EVERY- business to SWOT-analyse itself from the perspective of the new manufacturing way.

Reminder: SWOT stand for Strengths, Weaknesses, Opportunities and Threats, a strategic analysis tool to assess the company’s position on the market and compare it to existing and potential competition.

Should Weaknesses and/or Threats prevail, the company is possibly in danger.

Yet 3D printing can also help reinvent the way your company does business and exploit the Strengths and/or Opportunities going the 3D printing way.

One more reason for swoting your business.


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So many wasted data

In many organizations people capture a lot of data and… just ignore them, wasting their potential value.
The latest case, at the moment I write this post, is with an aircraft MRO company.

This post echoes a previous one: Trouble with manual data capture

Every aircraft undergoing MRO requires a lot of mandatory paperwork for the sake of traceability. The required information is either directly captured in an IT system, either written on paper and later input into the IT systems.

As this company wants to drastically reduce the duration of the aircrafts’ grounding for MRO and improve the reliability of its planning, the primary source of information to understand the causes of the problems is the data logbook.

I could easily figure out what kind of analysis to do and the correlation to look for, as adherence to planning for example.
Alas, as I was presented the database my enthusiasm quickly faded.

Some of the data supposed to be entered into the system simply wasn’t. Of course it happen to be the most interesting data for my analysis.

Work Breakdown is not always consistent across the portfolio, which makes comparisons challenging.
Mechanics would not always report their work on the appropriate work order. Thus work order lead time to workload correlation would be flawed.

It didn’t seem to worry management as much as it worried me, not because it could compromise my analysis but because the clients would not be charged the right amount (hours spent on an aircraft are billed).

According to data some aircrafts departed the MRO facility before they flew in. An indication of the lack of rigorous tracking as well as a lack in the software’s input trustworthiness checking.

And the list of flaws goes on.

A bit troublesome in a business boasting about safety by the way.

The pity is, as so often, that companies allocate resources to capture data and just ignore them. It would just require a little extra energy and rigor to exploit the data and use them to monitor, drive and improve their business.

Instead of that, just accumulating the data without exploitation is nothing more than wasting its value.

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Play big on small data

Chris HOHMANN

Chris HOHMANN – Author

This weird title “Play big on small data” suggests the utilization of big data principles on small data sets. “Small” is to be considered relatively to huge amount of data big data can manage, which is not necessarily only a handful.


I came across big data with former colleagues who were IT experts and got a kind of epiphany about big data with the eponym book.

Since that reading I do not collect, structure and analyze data the same way anymore. I tend to be more tolerant about inaccuracies, mess and lack of data because what I am looking for is insight and big picture rather than certitude and accuracy.

As poorly tended datasets are the norm rather than exception, starting an analysis with this mindset saves some stress. The challenge is not to filter out valid data for a statistical significant analysis, but a way to depict a truthful “good enough” picture, suitable for decision-making.

Playing big on small data does not mean apply the technical solutions for handling huge amount of data or fast calculation on them, simply get inspired by an approach favoring the understanding of the “what” rather than the “why”, in other words, favor correlation instead of causation.

In many cases, a good enough understanding of the situation is just… good enough. Going down to the very details or make sure of the accuracy would not change much but would take time and divert resources for the sake of unnecessary precision.

When planning a 500km journey, you don’t need to know each meter’s details, some milestones are just good enough to depict the way.

Accepting to trade, when it’s meaningful, correlation for causation helps to get around the few and messy data usually available. Even so data may be plenty, for a given analysis they are too often few fitting the purpose and in the right format. It is then smart to look at other data sets, even if they are in the same state, and search for patterns and correlations that can validate or invalidate the initial assumption.

The conclusion is most of the time trustworthy enough to make a decision.

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Trouble with manual data capture

Asking people to fill out forms in order to monitor performance, track a phenomenon or try to gather data for problem solving, too often leads to trouble when data is ultimately collected and analysed.

The case is about manual data capture into paper forms and logbooks on production lines. A precious source of information for a consultant like me. Potentially.

Alas, as I started to capture the precious bits of information from the paper forms into a spreadsheet, I soon realized how poorly the initial data were written:

Most of the forms were not thoroughly filled out, some boxes not filled, fields left blank, totals not calculated or wrong, dates not specified and a lot of bad handwriting leading to possible misinterpretation, among other liberties taken.

It seems obvious that the production operators do not understand the importance of the data they are supposed to capture nor the reasons for desired accuracy and completeness.

To them it’s probably a mere chore and not understanding the future use of the stuff they are supposed to write, they pay minimum attention to it.

It is also obvious that management is complacent about the situation and does not use the data, otherwise somebody else would have pointed out the mess before me, and hopefully acted upon.

Well, we can’t change the past and all data lost are definitely lost. The poorly input ones is all I could get, so I’ll had to make with what I had.

Thanks to a relatively important (I dare not write big) amount of data, flaws do not have too much impact, the big picture remains truthful. For me the importance is the big picture, not the accuracy of each single data point. (A takeaway from my exposure to big data!)

I noticed that most of the worse filled forms related to “special events”, when production suffered a breakdown, shortages and the like. These dots on the performance curve would anyhow been regarded as outliers and discarded for the sake of a more significant trend.

So it was not a big deal to disregard them from the beginning.

However, the pity was that no robust and deeper analysis could be conducted on these “special events”, not that unusual over a six-month period.

Some incomplete data could be restored indirectly, for example calculating durations from start and end time or conversely a missing timestamp could be restored from another date and duration for example. Sometimes, these kind of fixes introduced some uncertainty on the values, but again I was not after accuracy but trying to depict and understanding the big picture.

In order to be fair with personnel on the lines, I have to agree that some of the forms had poor design. A better one could have led to less misunderstanding or confusion. This acknowledged, the data reporting was not left to everybody’s choice, as it is mandatory by regulation.

Because to my great surprise and disappointment, this happened in pharma industry.


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Why Big data may supersede Six Sigma

Chris HOHMANN

Chris HOHMANN – Author

In this post, I assume in near future correlation will be more important than causation* for decision-making, decisions will have to be made according to “incomplete, good enough” information rather than solid analyses, thus big data superseding Six Sigma.

*See my post “my takeaways from Big data” on this subject

In a world with increasing uncertainty, fast changing businesses and fiercer competition, I assume speed will make the difference between competitors. The winners will be those having:

  • fast development of new offers
  • short time-to-market
  • quick reaction to unpredictable changes and orders
  • fast response to customers requirements and complaints
  • etc.

Frenzy will be the new normal.

I also assume that for most industries, products will be increasingly customized, fashionable (changing rapidly from one generation to the next, or constantly changing in shapes, colors, materials, etc.) and with shorter life cycles.

That means that production batches are smaller and the repeating of an identical production run unlikely.

In such an environment, decisions must be made swiftly, most often based on partial, incomplete information, with “messy” data flowing in great numbers from various sources (customer service, social media, real-time sales data, sales reps reports, automated surveys, benchmarking…).

Furthermore, decisions have to be made the closest to customers or where decision matters, by empowered people. There is no more time to report to a higher authority and wait for the answer, decisions must be made almost at once.

There will be fewer opportunities to step back, collect relevant data, analyze them and find out the root cause of a problem, not even speaking about designing experiments and testing several possible solutions.

Decision making is going to be more and more stochastic: with the number and urgency of decisions to make what matters is making significantly more good decisions than bad ones, the latter being inevitable.

What is coming is what Big data is good at: fast handling a lots of messy bits of information and revealing existing correlations and/or patterns to help making decisions. Hence, decision-making will rely more on correlation than causation.

Six Sigma aficionados will probably argue that no problem can be sustainably solved if the root cause is not addressed.

Agreed, but who will care about trying to eradicate a problem that may be a one-shot and which solving time will probably exceed the problem duration?

In a world of growing interactions, transactions and in constant acceleration, time to get to the root cause may not be granted often. Furthermore, even knowing what the root cause is, this one may lay outside of the decision maker or company’s span of control.

Let’s take an example:

The final assembly of a widget requires several subsystems supplied by different suppliers.The production batches are small as the widgets are highly customized and with short life cycle (about a year).

The data survey – using big data techniques – foretells the high likelihood to have some trouble with the next production because of correlations between former experienced issues in combination of some of the supplies.

Given the short notice, relatively to the lengthy lead time to get alternate supplies, and the short production run, it is more efficient to prepare to overcome or bypass the possible problems than trying to solve them. Especially if the likelihood to assemble again these very same widgets is (extremely) low.

Issues are not certain, they are likely.

The sound decision is then to mitigate the risk by adding more tests, quality gates, screening procedures and the like, supply the market with flawless widgets, make the profit and head for the next production.

Decision is then based on probability, not on profound knowledge.

But even so the causes of issues are well-known, the decision must sometimes be the same: avoidance rather than solving.

This is already the case with quieter businesses, when parts, supplies or subsystems are supplied by remote unreliable suppliers and with no grip to control them.

I remember a major pump maker facing this kind of trouble with pig iron casted parts from India. No Six Sigma techniques could help make a decision or solve the problem: the problem laid beyond the span of control.


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