What’s Ahead for Industry 4.0 in 2019

Below are thoughts from CEO Sridhar Iyengar regarding what's to come for Industry 4.0 in 2019.

The Elemental Machines team is excited for the new year and is hard at work building new tech for Industry 4.0. Our team wrapped up 2018 with a several major accomplishments to give us a springboard for knocking 2019 out of the park!  A quick recap of what we achieved: evolved our tech stack to be ready to scale rapidly, updated our system for compatibility with 21 CFR Part 11 workflows, closed a global commercial partnership with PerkinElmer - the leader in our space, and last but not least closed our $9M Series A funding with Digitalis Ventures as the lead.  

As we build upon our 2018 successes to deliver next gen Industry 4.0 solutions, I’d like to share some noteworthy Industry 4.0 trends to look out for in 2019.  These will help automate and accelerate operations across many industries, including life sciences, agriculture, food, energy, to name a few. Here are my top five picks:

Industrial Internet of Things (IIoT)

IIoT technology is expanding its reach into many functions and industries. It’s estimated that there will be 8 billion connected things in the world by 2020 for business applications. That means we can automate data and metadata collection from process equipment, facilities, robots, cars, etc. The immediate benefits of implementing IoT are real-time access to data 24/7 and faster response times when issues arise.  

IIoT is the foundation on which many other Industry 4.0 technologies rely. Other technologies such as machine learning (ML) and artificial intelligence (AI) are dependent on the input of reliable data: garbage in, garbage out.  If you listen to all the AI/ML pundits, very few of them say “we need better algorithms”. ALL of them say that AI performance is entirely dependent on the data that is used for training. The more granular the data you have, the better your AI models. IoT enables that granularity at ever falling cost and complexity. Feeding IoT data to ML/AI algorithms can rapidly reduce R&D and production costs, increase product throughput/yield, and make field service easier and more cost effective - it’s that simple.

Big Data and Cloud Storage

The whole point of collecting large volumes of data through IoT is to be able to do something useful with it. “Big Data” generally refers to information from may different sources and formats, generally unstructured, and of course very large. The irony of the terminology is that we use big data to detect SMALL trends as they occur.  That means we can identify the root cause of failures faster and correct it sooner. After all, we all know the longer it takes to pinpoint the problem, the more costly and cumbersome it is to fix.

Large amounts of data introduces a new problem - the need for a better data storage and retrieval solutions, hence the evolution of new cloud technologies like kubernetes, docker, and the like. While the most obvious benefit of the cloud is what is glaring us in the face - virtually unlimited storage and global access - the biggest advantages are actually what’s DISAPPEARING - we now have serverless computers. No, servers do actually exist, but to the outside world, the cloud provider virtualizes their internal servers and manages compute demand behind closed doors to allow theoretically infinite computing power. This segues well into the next benefit of the Cloud, which is the elimination of data silos and the creation of data lakes. Data from ANY IoT-equipped asset can be sent to the Cloud, allowing you to follow a process from start to finish, and you can scale and replicate your compute models with far simpler execution. Remember - for AI to work better, you need more data, and when you’re collecting large amounts of data (and processing them), the last thing you need to worry about is how to manage your computing stack. This infrastructure is making  IIoT and AI more accessible to more industries.

Machine Learning and Artificial Intelligence (ML/AI)

Machine Learning and Artificial Intelligence are the core of Industry 4.0 automation. We all recognize that there is day-to-day variability or even trending in equipment, environments, and processes that can impact outcomes.  Using data collected through IoT devices as input, ML/AI can detect which parameters have the most impact on your outcomes and self-correct for the variability. For example, imagine how hyper-local humidity changes could affect vaccine lyophilization or cannabis drying and curing processes, or how expanding a commissary kitchen from NYC to Denver may not produce the same quality baked goods (hint: baking times and temperature will need to be modified to compensate for higher altitude). But as you dig deeper into these processes, you can see that there are myriad variables that can affect outcomes. The only way to zero in on optimizing your process (or translating from one environment to another) is to quite literally start measuring “everything” and finding the hidden correlations and patterns. ML/AI approaches have the tools and systems to build the appropriate models with high-granularity data gathered from IoT devices.

Predictive Analytics

Predictive Analytics is an application of ML/AI to learn equipment and process behavior in order to predict events and trends before they occur. This is particularly useful for manufacturing where throughput is a key metric.  Whether you are manufacturing personal care products, pharmaceuticals, cars, or something else, maintaining equipment in good working order and avoiding unplanned downtime is key to meeting your production goals. One of the most useful applications is to forecast ideal times for equipment maintenance to maximize uptime and reduce the chances of surprise failures. This predictability ensures overall smoother operations.

Augmented and Virtual Reality (AR/VR)

Last but not least, Augmented and Virtual Reality are making us more productive at work by giving us access to the right information at the right time. Mobile apps and wearables provide vivid context to help us gain better insight through up close and/or superimposed views. Imagine focusing your attention on problematic equipment (eg. mass spectrometer) and viewing associated data (eg. laser settings) superimposed on your smart glasses to highlight the issue - your hands are free to do your work, and you have effectively gained x-ray vision!


Industry 4.0 is the next evolution of human productivity - offering industries reliable and efficient tools to automate and accelerate their workflows. Many of the technologies listed here are already in use and will continue to mature in 2019.  We are excited to work on building new technologies to enable these solutions and help our customers overcome their challenges in R&D troubleshooting, operations management, manufacturing yield optimization, and more.  Tell us how we can help your team!