Making use of data in an industrial context – Factory Simulation


Just to add some fuel my recent post let me pick a topic to elaborate on (hoping I’ll get even more responses on your expectations on the upcoming webinar series). 

Today let me focus on “Factory Simulation” (which naturally relates to other topics like “Digital Twin” or “Machine Learning”)

The term “Factory Simulation” is widely used with lots of room for interpretation; and probably most of the interpretations have their own right of existence. 

So, a comment I made quite often over the years still stands true: Identify your use case, your business scenario, to put the endeavor into a business context – anything else is useless unless you want to apply technology just for the sake of applying technology.

Most production line managers I talked to really know what they’d like to get out of a factory simulation (vs. a lot of pure IT managers still tend to focus more on the fancy IT stuff), so let’s lean on the production line managers’ interpretations here:

  • I need to maintain my level of quality throughout all potential impacts
  • I need to maintain or improve my productivity rate
  • I need to increase OEE
  • I need to deliver on all contracts even if important ad hoc orders come in

Of course the list above is just a small starter, but I guess you get the picture – it’s (as in basically all Industrial IoT scenarios) coming down to making the optimal decision in real time.

Ideally these decisions would be fully automated, but a) a fully automated system will need some time to implement and b) we, as humans, will need some time to gain trust in these automated decisions. So the natural step before full automation is to implement a solution that will simulate scenarios, taking into account all impacting parameters (equipment, material, supply chain, order management….potentially external parameters like weather information), so an experienced line manager can see results for different possible decisions using real world and real time information before making the final decision that will be entered back into MES/ERP/SFM etc for execution.

The number and nature of impacting parameters can range from low and small complexity to high and ultra-complex. In a recent discussion with a customer I was told they’d only need to consider order management, current capacity and throughput rates over pre-production and assembly lines to cover ad hoc orders – not super-easy, but also not too complex. A shop floor worker for another customer told me a few months ago he exactly knows (after operating the equipment for 20+ years) how to tune the equipment in his responsibility to maintain quality with upcoming thunder storms – this example is not so easy to replicate in a simulation, as a lot of additional information and evaluation of this information is required to achieve reasonable results.

I hope this leads to additional thinking and requests – let me have your thoughts, please.



Making use of data in an industrial context – what’s on your wish list?

Twitter background 2016


Again it’s been way to long since I posted here….in the meantime I had a really large number of great customer conversations on how to make use of data in an industrial context. Conversations reach from easy things like creating transparency in shop floor, over to leveraging existing, sophisticated solutions for e.g. automated rescheduling based on advanced analytics, very often reaching more disruptive, challenging topics like Digital Twin or Factory Simulation. 

In that regard I am working with great colleagues including @JerryAOverton and @KaiUHess to set up a series of webinars on such topics. 

A few suggestions right here – let me know what you’d like to see covered in September/October 2016, out of these or others. I will use your comments to get the right experts into those discussions.

  • Digital Twin
  • Factory Simulation
  • Machine Learning in industrial context
  • Smart Analytics to automated manufacturing processes end2end
  • Predictive Supply Chain
  • Integrating IT/OT Islands via MachineLearning based REST APIs
  • CyberSecurity in IoX context
  • Leveraging hybrid cloud in manufacturing
I’d really appreciate your comments and wishes here, we definitely want to tell the right stories, including how we solved such situations for customer and/or what we see as emerging technologies fit to  provide value in the (near) future.
With your feedback we’ll select & schedule the topics and will publish dates & times here and on



Could we please start securing airports using consistent processes & modern technology?

While watching the horrible news on Bruxelles, I wanted to reflect on what happened to me a few months ago.

My wife & I were checking in in Frankfurt Airport for our vacation in China. For that trip I was bringing my brand new camera backpack, the thing was about 48 hours old, so first use. At security screening the folks decided to have another check specifically for the backpack – and I was (and still am) absolutely fine with that, as I rather have one check too many than one not enough.

So this was the flow:

Security officer wiped my backpack, let the machine do its thing and was really astonished when an alert come from the machine stating “TNT detected”. The security guy was absolutely clueless on how to proceed and so asked a colleague to came over. The colleague gave the advice to run a second test. Same response “TNT detected”. Now we had two helpless security folks standing with us, calling for the third. The third one was at least more impressing from looks – she was wearing uniform & weapon and made a decisive impression…for a few seconds until she also started to stare at the evaluation results with astonishment. We exchanged a few words, trying to understand how this could happen until my wife said “By the way, this backpack is brand new”. All three officers now found back to a happy smile and we were told “Oh, that’s OK then, it’s probably the chemicals from the production process. Have a nice trip!”

I guess everyone understands this is really not what I would expect from airport security – I would have expected something like the following (and I do know airports like e.g. Heathrow have systems & processes like this in place)

  • System detects (or assumes) TNT (well, that was done)
  • System is to notify a) the guy in front of the machine on next steps and b) call an educated authority in the background to get help (fast) to the helpless guy
  • Process is to make sure nobody gets close to my backpack until issue cleared

As none of this happened, I was not really feeling secure when entering the flight – I need to say that (and I’ve been flying a real lot in my life) the airports in China in general give you a more secure feeling than the ones in Europe (with a few exceptions)

Point is, there will never be 100% security, there will never be 100% process coverage and there will never be 100% adequately trained people doing the job. But with accepting those facts, at least please put in place the right processes and technologies (like in the example above just connecting the inspection machine to a workflow / business process management layer to automatically deduct next steps). it’s like in the good old SAP workflow days I had 20+ years ago – there will be unknown return codes, so make sure an unknown return code is routed to the best team in charge to understand if the process is to be change, a new action to be defined etc. Don’t let users without the respective know-how try to deal with it.

Just booked my next flights – thoughts with the victims in Bruxelles


Quick Company Health Status Check

As indicated in an earlier tweet, I think one of the easiest ways to find out about the cultural/health status of your company is to ask yourself, your colleagues & your employees the same simple question: “Would you bring your best friend in?”

This of course assumes you want to keep your best friend.

So in order to get some valid results I thought I’d make this a permanent poll – will publish snapshots as soon as there’s some data worth making up a statistic.

Thanks in advance for your participation!


P.S.: If you like the idea and want to see the results, please spread the link – I won’t publish before having a substantial amount of data per company.


Big Data Analytics just helped me

Just a quick update on how Realtime Analytics do provide value on a day2day basis:

Someone stole my credit card data – probably on one of the recent trips. @AmericanExpress found out via their #RTA, blocked the payment, called me, sent email & text message in parallel while blocking the payment waiting for my response. 

As this was fraud, my card got blocked immediately after I pressed the “not me” button (I was in a conference when this happened, so couldn’t get on the phone immediately) but the tech in use saved me a real lot of money + time.

So: Yes, I have to wait a few business days to get my new card, but no financial loss – Thanks AmEx – thanks #BigData #RTA

How did Amex find out? Of course using tailored analytics, taking into account (amongst other parameters) my usual spending behaviour (the fraud was done on a web shop I don’t use and had a significant amount)

In a nutshell: I am glad the guys do use modern technology to protect themselves (and sub sequentially my wallet) – Of course I could have reclaimed later when receiving the monthly statement, but when considering the time this would have cost me – this solution is way better!