A 6-Step Guide to Implementing IIoT in Manufacturing
The goal of this guide is to assist manufacturers with the implementation of IIoT (Industrial Internet of Things) on the plant floor to solve business challenges. With the right data and the right tools to visualize your data, IIoT projects can connect your machines and empower you to help solve many of the manufacturing challenges affecting productivity.
What Will I Learn?
The benefits of IIoT, specifically manufacturing analytics, are immense. The focus is to not only fix any issues or areas of concerns but to implement IIoT to create a connected factory floor that will lead to continuous improvement and growth.
- Why Implement IIoT?
- Our Approach: Start Small, Think Big, Move Fast
- Step 1 – Where Do You Begin with IIoT?
- Step 2 – Evaluate Your Machines
- Step 3 – What Data Do You Need?
- Step 4 – How Do You Gather That Data?
- Step 5 – You’ve Got the Data, Now Solve a Problem.
- Step 6 – Apply What You’ve Learned and Run with It. Achieve ROI
With the help of this 6-step guide, you’ll define your challenges and areas for growth, gather data, and apply the data to gain insights into how to solve your problems by understanding the IIoT implementation approach we take for our customers.
Start Small, Think Big, Move Fast
In order to begin implementing IIoT, it is important to first consider how your factory floor can improve to a point of increased productivity and efficiency. “What are your challenges?”
We believe in a “start small, think big, move fast” approach when implementing IIoT on the plant floor. It’s okay to start small but start ASAP. Then, once you’ve solved one problem, move to continuously improve the rest of your plant floor.
See How IIoT Can Solve Common Problems
IIoT offers a number of solutions that help you gain visibility. One of those solutions is manufacturing analytics.
Bringing manufacturing analytics to the factory floor tackles four common issues your company may be faced with regularly:
- Lack of visibility
- Persistent downtime issues
- Low quality
- No data regarding changeovers