I guess by now most people have fully understood the value of data driven decisions and are looking to find the right entry point, i.e. the right initial use cases to dip their toes into the data lake before really getting feet wet. One of the questions coming up quite often in customer discussions is how to start. Maybe the following can help some of you to find those initial use cases.
Please keep in mind: IoT / Smart Manufacturing / Industry 4.0 etc. is about making use of information for fast and accurate decisions for incidents, risks, need or opportunities, making use of modern technology to retrieve and visualise the information, simulate decisions and feed the decisions back into an (hopefully highly automated) execution system.
So let me walk you through a few steps (these steps apply to existing plants – a greenfield approach would have substantial differences):
Step 1 needs to be: Find the use case – for an incident, a risk, need or an opportunity – or a combination of those. An opportunity for a manufacturer could obviously be the increase of productivity – which in return could be based on the need to satisfy customers (which of course could also bear the risk of not satisfying so a wee bit more than a need – but that might lead a bit too deep here). So the first thing to do: Understand why the productivity is not sufficient, understand if there are unknown drivers and make these known drivers, map out the known drivers, select the ones that would make the largest impact with the least effort => That’s the first toe one wants to dip into the data lake! Let’s assume one of the known drivers is lack of transparency in the supply chain (which can be external or on campus / in plant like “where is my material, where does it have to be next?”
Step 2 now follows: Define the KPI(s) – don’t do more than 2 initially! What do you want to achieve and how do you want to measure? Using the final assumption in step 1 we could focus on an increase hit rate of “right material at the right time at the right location” – this is pretty easy to measure, but does require some real thinking on how to achieve; even if it sounds easy to achieve on campus (it actually is not – depending on complexity and size of plant and/or product) it’s getting tougher with inclusion of external supply chain (you might have the perfect system and routing in place on site, but what if suppliers can’t deliver for whatever reason?).
Step 3: Measure the KPI without improvement – and find out which improvements you could do without any investment; some might just be common sense, most won’t lead too far, but to later be able to justify the investment, you want to make sure you’ve done the business case throughly – this exercise might even lead you to select a different use case as the potential with investment might not be to great compared to the one without.
Step 4: Select your pilot. Don’t make it a proof of concept – that’s too theoretical – make it a real pilot with careful selection of scope. Scope should be able to deliver tangible results (like a small production line or a subset) and make sure you can honestly expect positive impact in a short time frame like 2-4 months after going live with the pilot. Another parameter of the pilot is easy ability to replicate – with success you want to roll out fast; so if you’d have a few similar lines you probably want to pick a small to medium sized ones of that group for the pilot.
Step 5: Define the pilot. This is also the latest point where you want to engage with external service & software partners (but of course – the earlier you include them, the better – most of us are happy to support in the early phases). This definition will also include architecture, technical & functional requirements, how KPIs will be implemented in the final solution. All specs for sensors, potential track & trace devices (especially for on campus / in plant tracking) interfaces to ERP, SFM, MES, PLM, PDM and to external supply chain (if applicable for the pilot) need to be agreed and contracted. The definition should be built as a template so with success (see step 4) you can roll to similar lines as fast as possible. If implementation time for the pilot is longer than 2-4 months: Go back to step 1. If no mobile, (near) real-time, always-on information or alerting: Change your partner. Also make sure you can get majority of services in an aaS model.
Step 6: Implement the pilot. Start measuring. Do constant review – this is after all the journey to a continuous digital improvement. Concentrate on evaluating the KPIs and start planning beyond step 7.
Step 7: Assuming success: Roll out pilot functionality to other lines (horizontal expansion) while in parallel work on extending functionality / KPIs (vertical expansion). Topics you want to think about in this and following expansions should also include how to integrate more closely with your external partners (e.g. API vs. interfaces) and/or move to co-creation.
These few steps will get you started – other companies have executed and e.g. achieved 20+% increase in OEE in short time frames (6-12 months), increase in productivity (200+% in 2-3 years) – so what’s next?
The minute you’ve started the Digital Journey, you’ll most probably want to continue the digital improvement. So let me give you some outlook on possible scenarios:
- Supply Chain Prediction: With high visibility / transparency along your supply chain you will be able to predict incidents (using data science scenarios similar to those used in predictive / preventive maintenance). This will of course enable you to plan / schedule accordingly, thus further improving productivity, planning accuracy etc.
- Also you might want to look into Demand Chain Prediction to cover end2end
- Decision simulation: With full transparency you can simulate your decision / reaction to any incident (or need, opportunity, risk) before executing. Eventually this could result in a full simulation of a full blown production environment before taking any physical measures (this of course requires all production data to be basically kept forever in a Big Data environment to rebuild the full plant with real data in a simulation environment – but still cheaper than rebuilding a plant)
- Potentially start looking into the intelligence of your products to move your now digitised company from product sales to service provisioning.
Let me have your thoughts please. Thank You!
2 thoughts on “Making use of data in Manufacturing”
Combining data science with the disciplines of Lean Six Sigma can get you a long way. Thank you for giving a practical view on this.
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I agree, Soren – just need to also add the respective business domain expertise to achieve targets (fast)