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Data Strategy for IoT and Industry 4.0

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By Ram NarasimhanGlobal Executive Director – AI and Bigdata at Xebia on Data Strategy for IoT and Industry 4.0

When people talk about industry and manufacturing, their heads often turn to large scale production with huge machines and a massive pool of people doing a multitude of jobs. Although many industrial companies and factory settings have been using forms of digital technology for some time now, only in recent years has this started to become more mainstream. There has been a realization that using artificial intelligence (AI) and internet of things (IoT) devices through the supplier chain, can have amazing effects on efficiency and cost savings. This article looks at how data, AI and IoT can superpower industry, the type of infrastructure needed to make it work and the steps required for it to happen. 

When you hear about AI in the media, it often sounds very futuristic, but in reality AI is not at the level that movies or television might lead us to believe. The most common application of AI is known as machine learning. This technique trains computer algorithms to think like humans do using existing data from the world around it. To put this into a real-world context, an IoT device is anything that connects to the internet. When we speak to Alexa (the IoT device), it takes what we say and converts that into data using a method called natural language processing (NLP). That data is then matched against a huge database of previous conversations to find the closest match. Every time you have a conversation, Alexa learns and becomes more accurate. So, for every IoT device we have, the more it gets used, the more useful it becomes as they learn from data. This is one reason that we are seeing a movement towards Industry 4.0. 

What is Industry 4.0?

When we talk about anything “4.0” right now, it generally refers to the next evolution of technology. In this case it refers to industry and manufacturing. The advances in computer power, data, AI and IoT will orchestrate rapid advancement over the coming years and by the end of the decade, it is fully expected that the world will look quite different. We are at a stage now where there is enough computing power and data available in the world to make fast progress within industrial settings, hence the buzz around Industry 4.0.

In this forward thinking era, having connected machines that communicate with each other is key without the need for human involvement. This will be a combination of physical systems, IoT and AI to create a data driven smart factory that is more efficient, productive and cost effective than we have ever seen before. The network of all these machines is what we are calling Industry 4.0. 

Key applications of Industry 4.0

Supply chain – data can be connected throughout the entire supply chain, from factory to the shelf for example. One common use case is in shipping when products are delayed and everything else connected to the system can be proactively adjusted. 

Autonomous equipment – Most of us will be aware that autonomous vehicles are on the horizon with the likes of Tesla suggesting that driver-less cars could be on the roads as early as 2020 (there are already some being used in trials). In the Industry 4.0 context, the same technology can be used with cranes or trucks to make operations more efficient. 

Robotics – this is the key to the future of Industry 4.0 as the machinery becomes more cost effective in factories. 

Architectural Framework

To make all these things happen, factories or industries need to invest in the right kind of architecture. Industry 4.0 architecture will vary depending on your business but in the main, the features below will be the key considerations for IoT and Data.

  • Gateway – sensors need to connect seamlessly throughout the framework. Having equipment that talks to each other is imperative for communicating data at the right time. This will usually require ethernet cables or wireless mechanisms, both of which have reducing costs making it feasible for factories to install
  • Edge computing – these are router services that can make fast, low latency decisions allowing for real-time data analytics. Edge services tend to sit nearer to sensors and machines to do faster communications; they do not connect to a wider Data Lake.
  • Ingesting data – multiple data sources need to be transformed into standard formats so they can be used in decision making processes. Data professionals with experience in such transformations will be able to apply the appropriate architecture for the best speed and consistency
  • Data Lake – if you have all of this data, it needs to be stored somewhere. A Data Lake is a cloud-based platform like Amazon Web Services (AWS) or Microsoft Azure or Google Cloud Platform (GCP) that is entirely scalable to the business needs and can be accessed from anywhere. Various scripting languages and libraries can be added to Data Lakes depending on the data ingestion strategy.
  • Analytics and ML – understanding patterns and developing accurate models will require good quality data at scale and will lead to significant gains in overall productivity.
  • Data Visualization – data from the Data Lake needs to be presented in such a way that the business can use it. Many businesses will do this using API’s that connect to commercial platforms like Tableau, Qlik, PowerBI or MS Dynamics to name a few. 
  • Data Security – appropriate software must be added to devices to ensure they are secure with the vast amounts of data they are producing and transforming.
  • Automation of Data Flows – Robotics process automation (RPA) platforms such as Uipath or Automation Anywhere or Blueprism would provide BOTS framework and API’s to delegate system workflow autonomously without any human intervention.

Achieving the Goals

Having the technology and data alone is not enough to succeed. Businesses need a well thought out strategy that clearly defines the objectives for Industry 4.0. Evaluate exactly how mature you are at the start and what it will take to close the gap. Work backwards from where you want to be. 

Industry 4.0, data and AI is not about a team of Data Scientists coming up with solutions. It needs support from the top down. As soon as a business begins to automate processes, job roles changes and people need to find a way of accepting the transforming business culture. 

Industry 4.0 relies on having massive amounts of data. For example, how do you know the optimal temperature for production if the sensor has only just been installed and not created data yet? Small experiments will start gathering data and learn over time. 

If standards and infrastructure don’t exist yet you are not going to be able to set those up overnight. Be pragmatic and aim for the “low hanging fruit” before starting to climb Everest. The chances are that you need a lot of support to achieve your objective. It is important to fully review the market, finding the right people to work with. 

Industry 4.0 depends on data and analyzing it in creative ways to spot opportunities or potential efficiencies. Businesses need to learn how to get the most out of the mass data coming from their devices and use it to make decisions. The whole business will need to adapt to a digital culture. There must be a continuous cycle of improvement as new data and opportunities are discovered. Working with third parties can be a useful exercise, especially in smaller businesses who do not have access to large volumes of data. The success of Industry 4.0 relies on working with other digital leaders. 


If you have an interesting article / experience / case study to share, please get in touch with us at [email protected]

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