When thinking about how to invest in your digitalization, Industry 4.0, Smart Factory, or other Big Data type budget, oftentimes, companies start with a research phase. They hope to uncover the best IIoT platform, AI, cloud offer, or connected edge that will completely revolutionize, transform, and uproot the habits of their factories into data-driven powerhouses.
While many Industrial IoT vendors promise these results, there are some things you should consider prior to researching IIoT solutions—starting with the ‘why’.
Getting Started with IIoT: Determine your baseline
Before implementing an IIoT platform or moving to the next stage of your digital transformation, it is important to revisit your organization’s ‘why.’
- Why are you searching for a solution?
- What problem(s) are you trying to solve?
- What is your end goal?
- What is your current connectivity?
- How will you know you’re successful?
Creating a SWOT analysis or conducting an assessment of your digital maturity will help to determine your current connectivity and opportunities for improvement. With these tools, you can begin to identify what you might actually be looking for—whether it is an IIoT platform to centralize and cleanse your data for democratization, an edge solution to complement your existing Big Data analytics, or a more robust Digital Thread of your end-to-end production. Successful digital transformations always start with a clear understanding and buy-in for the objectives and measures of success.
Choosing the right Cloud or Edge IIoT solution
When you’ve done an honest review of what you have and what you need, establish a vendor audit (see what to look for from a security standpoint here) to narrow your needs to a short list of providers.
Frequently, companies instantly rush to consider a cloud or edge solution. Should we start with a cloud computing and analytics solution to get a better understanding of our processes and give teams easier access to centralized data? Or do we leverage edge computing and analytics for hyper-focused optimizations in real-time, such as alerting when a machine breaks down?
While you will want to consider these questions—it can make sense to think of your needs:
- Do you need historical data that you can leverage to learn from your past to drive progress forward? Think, root cause analysis.
- Or is it more about a faster reaction time to data hot off the line? Think, alerting and live monitoring?
- Or is what we need a little bit of both?
In a perfect world, you would probably want a solution that helps you uncover the reasons why variation is happening and help you to optimize production in real-time. Cloud analytics enable you to learn from past performances while Edge analytics help you react in real-time. And while having both is attainable, you want to build out an IIoT implementation that will not only achieve quick wins to drive momentum, but also lay the foundation for solid OT/IT alignment that spans departments and a solution that can scale to all locations. Most importantly, you want a solution that fits your needs today and is able to grow with you into the future.
Implementing Edge & Cloud Analytics
Developing a digital transformation road map is critical—you want to crawl before you walk and walk before you run. For many manufacturers, this could be as straightforward as getting connected.
Do you have end-to-end visibility of your process from raw materials to finished goods? If not, it might make sense to look at edge computing devices and software to help bridge the gap in your visibility.
However, what if your main concern is the amount of time that your analysts and process engineers spend cleansing data instead of solving problems? If this is the primary need, a Cloud solution makes sense. Empower teams with an advanced analytics platform and cloud solution that helps you drive continuous improvement efforts.
When thinking about scaling, regardless of whether you start with cloud, edge, or a hybrid model to close the loop, you should think big picture. Ask yourself:
- What does your company need right now and what is part of the 1, 3, or 5-year plan?
- What do your best plants do well and is there an opportunity to start pilot projects and identify use cases?
- How will we grow the adoption of our IIoT solution?
With so many options on the market, it’s important to first understand your current state, your future goals, and your timeline. Digital transformation is a journey, so be cognizant of the fact that what you need today needs to be able to fit what you need in the future as well. Find an IIoT platform that allows you to grow within the constraints and goals of your organization so that you can find a robust, all-inclusive solution when the time is right.
Close the skills gap with self-service analytics
Learn how manufacturers are tapping into production data as a competitive edge.
Manufacturers need to easily access data from their production processes to take advantage of the new inroads available with Industry 4.0. Driven by the need to identify, analyze, and optimize industrial data, many manufacturers are considering (or have already adopted!) an Industrial Internet of Things (IIoT) platform to leverage the power of Machine Learning, AI, and Big Data discoveries.
While there are plenty of examples of best practices for implementing an IIoT platform, these five less-obvious approaches may be the key to helping drive a successful implementation. From choosing the right platform to multi-facility rollouts, these considerations can help you transition from a successful implementation to a successful Industry 4.0.
As organizations move more of their data to the cloud, you’ll need an IIoT platform that protects you against cyber security threats. From the way your platform processes your data to how your employees access that data, cyber security cannot afford to be an afterthought in your journey to Industry 4.0. Learn some of the best practices in protecting your organizational data against imminent cyber security threats.