Enabling the 4th industrial revolution - "industrie 4.0" or the "internet of things"?

I've been struck recently by the range of people talking about new digital and data developments in manufacturing. Of particular interest has been the apparent explosion of discussion about industrie 4.0 (which is extremely popular in Germany), internet plus (which is being pushed by China) and the industrial internet (being promoted by GE among others).

Managers, consultants, policy makers and academics are all getting very excited about the potential of connected devices. The basic idea is that increasingly things (of all types) will be stuffed with sensors and connected to the internet. They will stream data back to the original equipment manufacturers who in turn will use sophisticated analytics to analyse and interpret the data. There are loads of examples. Caterpillar streams data back from mining and construction equipment, using this both to monitor the health of individual machines and also to identify ways in which productivity and efficiency might be increased. Rolls Royce monitors aero engines in flight, using sensors to track vibrations in fan blades, which allows them to predict whether or not maintenance is required. In the consumer world - wearable devices (e.g. Nike's fitbit or Garmin's forerunner) track and record exercise levels with the data being uploaded to the internet for benchmarking and comparison purposes.

One thing that I find interesting is the rate at which some of these ideas are developing and the level of interest there is in them. A good way of looking at this is to explore Google Trends, which basically tracks the popularity of search terms and plots these over time. Figure 1 shows a comparison of "industrie 4.0" and the "industrial internet". It neatly shows how effective the German Government and large industrial firms (including Bosch and Siemens) have been at promoting their vision of the future - industrie 4.0 - with a rapid rise of interest in industrie 4.0 since 2012.

 

Figure 1: Google Trends - Popularity of Search Terms "Industrie 4.0" and "Industrial Internet".

One could argue that industrie 4.0 is not a new vision. As Figure 1 also shows there has been interest in the industrial internet for at least a decade and indeed my colleagues at Cambridge IfM, most notably in DIAL (the Distributed Information and Automation Laboratory led by Professor Duncan McFarlane) have been getting our students to build demonstrators and simulations of intelligent factories for years. However, the recent excitement is a testament to the growing maturity of the technology and underlying data infrastructures that will enable a wider adoption of industrie 4.0 and this excitement has driven significant Government and policy interest, as well as research and development investment.

So is industrie 4.0 the answer? Are smart factories where materials and machines seamlessly collaborate to drive productivity and efficiency the future? I think the answer is "yes" and "no".  Much of the discussion about industrie 4.0 is still very internally focused - its a factory view of the world. A recent YouTube video illustrates the point. The video talks about a vision of tomorrow - the factory of the future - where machines and materials will use wireless data infrastructures to communicate and coordinate their activities. Yet the examples I started with are ones where the product has left the factory - manufacturers are worrying about how they can track their products once they go out into the field and are used in mines and quarries, on the wings of plans, or in our houses and cars. Here I would argue there is scope for a bigger and more impactful industrial revolution. The fourth industrial revolution will not just be about what happens inside factories, but it will encompass the entire value chain. It will involve remotely monitoring products as they are used in the field. Data will be collected and streamed back to original equipment manufacturers who will use these data to assess the health of assets, to determine whether any maintenance is required, to predict potential product breakdowns and failures. They'll use the data to improve the next generation of design, learning from experience. They'll use the data to look at how the customer's operation might be optimised. By gathering data from multiple machines in a quarry its possible to build a system model of the quarry and identify where bottlenecks lie and hence how productivity can be improved.

This extended view of the fourth industrial revolution won't just be enabled by industrie 4.0, but by the "internet of things" and that's why when you add "internet of things" to the Google Trends data a rather different picture emerges. Its clear that industrie 4.0 and the industrial internet are important component parts, but the real key to driving future success in manufacturing lies beyond the factory walls and this will be enabled by the internet of things.

 

Figure 2: Google Trends - Popularity of Search Terms Including "Internet of Things".

Watch Out for the Industrial App Economy as the Battle for the Industrial Internet Heats Up

About six months ago I wrote a blog entitled "GE, The Industrial Internet and the Battle to Come" - in which I asked the question "will GE be the equivalent of Apple, Facebook and Google for the industrial internet or will someone else seize this market?". Its clear the battle for the industrial interne is heating up.

Last week (on 5th March) Caterpillar announced it was extending its partnership with Uptake, a Chicago based predictive analytics company. Uptake have been developing predictive diagnostic and fleet optimisation solutions for Caterpillar's the locomotive business. Under the new agreement Caterpillar and Uptake will "develop an end-to-end platform for predictive diagnostics to help Caterpillar customers monitor and optimise their fleets more effectively". Notably the new technology will be available for both Cat and non-Cat products.

Today (12th March) Siemens announced it was creating an open cloud platform for industrial customers based on the SAP HANA cloud platform. Siemens will offer Apps for predictive maintenance, asset and data energy management. They are also opening their platform so other Original Equipment Manufacturers (OEMs) or indeed Apps developers can create their own applications to exploit the open infrastructure for data analytics.

Separately I've had conversations with half a dozen different firms, from a variety of sectors, in the last couple of weeks all of which have centered around the idea of an Industrial App Economy. It seems that there's a groundswell of opinion that the future for industrial services lies in open, cloud based platforms, where developers can offer Apps to make the end users service and support experience as seamless as possible.

There's an interesting question with all of these developments - namely how will the investments be monitisied? Is it through sale of the Apps? Provision of the insights that can be derived from the data? Or sales of new products and support services - as customers are tied in to particular OEMs? It'll be interesting to see how this battle evolves as other potential competitors for the industrial internet declare their hands.

The Big Data Revolution: What Happened to Data Quality?

There's a wonderful irony in the world of Big Data Analytics. At a time when interest in Big Data appears to be growing exponentially, it appears that some are forgetting the fundamental challenges of Data Quality. A quick Google Trends analysis highlights the point. The chart below shows two trend lines extracted from Google Trends. The line in blue reflects the popularity of searches for Big Data, while the line in red shows the popularity of searches for Data Quality. It is important to note that the lines show relative popularity, not absolute volumes of search terms. In fact, Google Keywords suggests that in absolute terms searches for Big Data are about 20 times as popular as searches for Data Quality.

This raises an interesting question - what's happened to Data Quality? At a time when organisations are becoming ever more interested in using their data to create performance insights and predictions, interest the Data Quality appears to be declining. Is this because Data Quality is no longer an issue?

I don't think so. On three separate occasions in the last week alone I have been involved in discussions with senior managers from some of the world's leading manufacturing and service businesses. Each time, the issue of Data Quality has come up loud and clear. These firms recognise the potential of Big Data and Analytics, but are realistic enough to know that unless they sort out their data fundamentals - unless the track the right things and make sure the raw data if accessible and of high quality, all of the Big Data Analytics in the world is not going to help them. That's why - in the Cambridge Service Alliance - one of our projects this year is focusing on creating a data diagnostic - a methodology that can be used to check whether the data you have access to is appropriate and can be better used to optimise the delivery of your services and solutions. We're in the process of testing this data diagnostic at the moment and would love to hear from you if you'd be interested in being one of the pilot test sites.

How Companies Learn Your Secrets: The Power and Pitfalls of Analytics

One of the most popular stories in last week’s New York Times was provocatively entitled – how companies learn your secrets. Drawing on material for a new book by author and journalist, Charles Duhigg, the article explores behavioural science and analytics in retailing. The highlight of the article is the story of a father who comes to a Target store complaining that Target is sending his high-school daughter vouchers for discounts on baby products. “Why are you sending my daughter these vouchers”, he screams. “Are you trying to encourage her to get pregnant”? A few days later the father calls the store manager to apologise – it turns out his teenage daughter is pregnant after all, she just hadn’t got round to telling her father yet!

How did Target get to know that the girl was pregnant before she’d even told her dad? Simple, through customer analytics – by looking at people’s shopping habits Target and many other retailers can make intimate predictions about people’s lives. Start buying lots of meals for one and the retailers will assume a relationship breakup. Stop buying eggs and the store might assume you’ve got your own chickens! Pregnancy is particularly important because it is such a major life change that it brings many other opportunities for the store. Most of us are creatures of habit. We buy the same toothpaste, soap and deodorant year after year – simply through habit. Research suggests that pregnancy is one of the best times to break old habits and form new ones. Target’s research suggests that an increase in sales of unscented lotions and vitamins is linked to pregnancy. Couple these two facts and the implications are profound. Target can predict who is and who is not pregnant, send those who are likely to be pregnant coupons and vouchers to use and try - in the process - to create new shopping habits for individual customers.

This brave new world, where big brother is watching, offers opportunities, but there are also significant risks for the organisations involved. Privacy concerns and reputational damage can be significant. Just look at the comments on the New York Times article – there are a lot people who are worried about the power of analytics and the potential for abuse of the data. Clearly organisations can see the benefits of analytics, but they also have to weight up the risks and put in place some very carefully considered governance mechanisms to avoid headlines like “how companies learn your secrets”.

Andy Neely and Ed Barrows