Digitisation, transformation and man/machine partnership: scaling innovation


I recently spoke to Thomas Gartzen, managing director of the European 4.0 Transformation Center. As well as talking about innovation labs in general, we also spoke about managing change and introducing an analytical approach to organisations.

Thomas, do you think we can say that all companies are now software companies?

In a way, yes. Production companies are thinking beyond making products, and into providing services. But there is another challenge when software companies meet manufacturing companies that are open-minded and willing to try something new, and want an app to deliver something. In practice, I think the manufacturing company has to define the application case very clearly. Typically software companies lack domain knowledge. So in the end, it comes down to what tools are needed by the producer and what are the relevant questions he is dealing with?

Can you give us an example of that?

Say I am a customer of Amazon. It's relatively easy, because we can all understand how I behave on the internet when I look for and buy things. It is possible to develop a very simple, value-added service based on analytics. But when I think of the work planner, buyer or production planner, then it is no longer so easy to understand because these jobs have very specific domain knowledge. I think it is important to work this out first and foremost, and then identify the tools that these people need for their day-to-day operations. And then there is a second step: How do I actually have to process this data and get to insights?

What would you say are the main challenges of implementing analytics or digitisation in an organisation?

I believe that the essential challenge is to get the right data and then process it. Some companies already have a huge data pool in their systems, but they don’t really know what to do with it. They may have movement data in ERP systems, construction data with histories in their PLM systems, data from the MES systems on the shop floor, but basically everything is still a bit unclear. It’s not exactly unstructured, but they just don’t know quite what they want to do with it.

How can that challenge be overcome?

In our experience, it is always best to introduce digitisation on two different levels. We call this the countercurrent method. The first level is strategic, which is basically top-down. It makes no sense to initiate any kind of digital transformation if it is not wanted and supported by the top management. They have to start thinking about digitisation at a strategic level, discuss it and work out a strategic programme to set out what it will mean for the company and where they would like to go in the future. But after that, you also need what I call a groundswell, or bottom-up push. In practical terms, that means small-scale pilots and use cases, building a viable minimum product. I think it is very important to get some experience and approach the whole topic in an agile way. That means starting with the first pilots and picking out areas where the technology could make sense, where you can define the use cases.

Does digitisation need a huge budget?

No, it’s not really about the budget at all. It is possible to develop smaller and more manageable pilots that give results very quickly, and show rapid benefits for the company and the department. Those that work can be validated quickly, and if it doesn’t work, you haven’t spent too much time or energy on it. But if it does work, you can implement it more widely, changing the culture of the organisation bit by bit, as you go. That does have its challenges, of course, but it can be done.

Digitisation is not really about the budget at all. You need the countercurrent method of strategic direction and groundswell together. #AI #transformation #Road2AI Click To Tweet

How can you embed this type of innovation from pilots into the organisation?

Here at E4TC, we have a big advantage because we have a real production environment, so we can develop things very close to the real processes of the companies. That means we don’t have the situation where we’re in a laboratory environment, and then have to move into production. I think this approach is an important one to try to use: Everything should be tested as close to real life as possible. Just start small, rather than separate, and then you know it will work in practice.

Thomas, thank you very much.


About Author

Andreas Gödde

Director Business Analytics

Andreas Gödde is specialist for strategies around Big Data Analytics, Digitalization and Internet of Things, helping organizations to get insights from data for business decisions. He leads the presales organization for Business Analytics of SAS in Germany, Austria and Switzerland. Andreas has a 25 years background in advising companies around Business Intelligence, Data Warehouse and Big Data concepts and projects. Andreas graduated in business informatics in Mannheim. He joined SAS in 1994 helping developing and growing the professional services organization in different management roles. In 2006 he moved to the presales organization building up teams for technical and strategic advisory for customers and for emerging technologies and trends like Big Data and the Internet of Things. Before joining SAS he worked for BASF in Ludwigshafen.

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