Q: What are “Internet of Things (IoT)”, “Industrial IoT (IIoT)” and “Industry 4.0”?
A: The Internet of Things (IoT) is a system where “things” in the physical world are connected to the internet through sensors and wireless technology for data collection. The insights gained from the data will, in turn, provide actionable outcomes.
The term “Internet of Things” was coined by Kevin Ashton who was a brand manager at Procter & Gamble in 1999. He was trying to resolve the mystery of why a high demand product he had been pushing was constantly out of stock. He discovered the root cause was due to lack of data continuity in the supply chain inventory system. He spent a year preparing a proposal for his executive management team, titling the presentation “IOT”. What he proposed was to deploy RFID in physical products to keep track of inventory through the internet1. Since then, computational processing and sensor costs have dropped significantly. Major advancements in wireless communication have also pushed the IOT technology to new heights (Figure 1.)
Applying IoT to industries such as automotive, agriculture, energy and manufacturing, it becomes the “Industry Internet of Things (IIoT)”. IIoT has different levels of requirements in comparison to consumer IoT products such as smart home systems and health fitness wearables (Figure 2).
With the advent of IoT, society evolved from the 3rd industrial revolution, which is machine automation, to the 4th industrial revolution, where Cyber Physical Systems (CPS) dominate the manufacturing floor, linking real objects with information processing, and virtual objects via the internet. The goal is to converge Operational Technology (OT) and Information Technology (IT). In 2011, the German government launched a project under the name “Industrie 4.0”, providing a vision for advanced manufacturing with technological evolution from human controlled automation to machine controlled automation. Since then, the term “Industry 4.0” has been widely adopted to describe the 4th industrial revolution (Figure 3).
Q: What are some example scenarios of Industry 4.0 that affect ESD control?
A: As we enter the infancy stage of Industry 4.0, human-to-machine interactions will gradually replace by machine-to-machine interaction and communication, resulting in a paradigm shift from “centralized” to “decentralized” production. The traditional way of defining technical requirement of an ESD control program for Industry 3.0 factory will soon need to accommodate CPS in the industry 4.0 factory. ESD risk response plans will need to be built and coded into the CPS AI platform to minimize ESD risk during the CPS autonomous interactions and handle different ESD scenarios.
Furthermore, taking an IoT approach in ESD control will also be a game changer. Deploying sensors that collect ESD relevant information such as field voltage, EMI, temperature and humidity on the manufacturing floor can detect potential ESD threats in real time. Attribute data such as ESD failure rate can also be fed into an analytics AI engine along with the collected IoT sensor variable data, streaming in real time to provide continuous monitoring of ESD key performance indicators (KPI). Intelligence can be taught to the IoT platform to carry out big data analytics, predictive analytics and provide early warning signal regarding KPI downtrends (Figure 4.)
The Industry 4.0 IoT platform automatically becomes a reliable and dependable venue for compliance verification, eliminating the traditional way of tedious predefined period manual checks. Eventually, industry standards such as ANSI/ESD S20.20 will need to be updated to reflect the industry 4.0 landscape.
- Kevin Ashton, “That ‘Internet of Things’ Thing,” RFID Journal, 22 June 2009.
Michelle Lam has ESD control responsibilities at IBM worldwide Tape storage manufacturing. She is a certified ESD program manager professional. She has been a member of ESDA since 2005, actively involved in ESDA Symposium Technical Program Committee, presented papers, delivered presentations and invited talk. In the past few years, Michelle has been leading big data projects and initiatives to drive business transformation in supply chain engineering at IBM.