While the IT world has been growing since the mid-1900s, the traditional operational technology (OT) world has been a late bloomer. A lag of 10 years between the IT and OT worlds from a technology perspective was not uncommon. For example, the Windows operating system and Ethernet capabilities made it to the shop floor a decade after their introductions.

However, with the emergence of the IIoT, industrial organizations can now leverage cutting-edge digital technologies in manufacturing operations, and take advantage of better speed, flexibility, and precision in decision-making.

How IIoT Technologies are Disrupting Traditional Continuous Improvement (CI) Processes

Big Data Analytics:

Big Data analytics uses statistical and optimization tools to cleanse, monitor, and analyze structured and unstructured data to enable accelerated insights. CI processes like DMAIC and Five Whys are filled with the right opportunities to utilize Big Data analytics.

• DMAIC (Define, Measure, Analyze, Improve, and Control) is a fundamental methodology within Lean / Six Sigma that prescribes measuring everything, and adequately defining and recording improvement targets. In the design phase of DMAIC, an IIoT platform with Big Data analytics across IT and OT can be used to provide quick, easy-to-digest, detailed information. In the Analyze phase, predictive analytics on Big Data from across the enterprise can open entirely new ways to solve DMAIC challenges.
• Five Whys is a simple and effective way to dig down to the root cause of manufacturing issues. By applying Big Data analytics to this process, manufacturers can leverage machine learning algorithms like neural networks to perform root cause analysis on a more sophisticated scale. The benefits are enormous; manufacturers can use these advanced analytics on Big Data to answer questions they didn’t even know to ask in the first place.

Edge and Cloud Computing:

• One of the significant roadblocks companies face in digitalizing CI processes is scalability. Let’s take overall equipment effectiveness (OEE) for example. A single organization might have a different formula to calculate OEE in each of their plants based on the on-premise systems and data available at each site. By performing industrial analytics in the Cloud, companies can have access to consistent data and have an apples-to-apples view of operational performance across multiple plants.
• Edge analytics, on the other hand, is commonly used when shop floor data doesn’t need to be transported to a centralized data repository like a server or the Cloud. Performing analytics on Edge devices enables users to get instant access to real-time data and insights. From a CI perspective, Edge devices can find applications with Andon systems. A digital Andon system could provide critical metrics and analytics at the Edge, so the operator has instantaneous access to data and complete control to shut down the line, if needed.

Digital Twin:

• A Digital Twin is an executable set of software models of a physical product or system. Digital Twin finds applications in simulating products or processes where measurements and tests might not be possible. Continuous Flow and Gemba are two CI processes where a Digital Twin can be leveraged.

• Digital Twins can be used in Continuous Flow by simulating the production line to identify bottlenecks well ahead of time. Previously, Lean practitioners used manual diagrams on whiteboards; then MS Visio digitized the process flows, but it was limited to theory due to siloed data and systems. Digital Twins can solve that problem using real-time analytics on better, faster, and more data, providing new perspectives on visualizing the production line.

• Gemba is a CI initiative where managers walk the shop floor to discuss production issues with the operators. A Digital Twin offers a view of every detail of the production system, enabling the Gemba walker to engage shop floor operators with a fully up-to-date view of production.

Technologies such as Augmented and Virtual Reality (AR/VR) can be used to complement and extend the possibilities of next-gen factory floor user experiences. AR-enabled devices can enhance the Gemba walk with an overlay of performance data or instructions on top of a real-time view of production.

Manufacturers can enhance and change the way they approach Operational Excellence and Lean by digitalizing their CI with IIoT technologies. Most of these technologies can be used in combination with one another. The IIoT combined with supporting data from Edge and Cloud, and Big Data analytics is a perfect platform to implement Lean processes. A Digital Twin can be much more valuable when implemented with AR/VR technology on mobile devices.

Companies also must recognize that digitalizing CI is not just about adding new technology to existing processes. They should start by building business cases and data models for a digital Operational Excellence initiative, then design a suitable Operational Architecture framework and empower their workforce to get the most out of these technologies.

About the Author:

Vivek Murugesan, Research Associate LNS Research

Vivek Murugesan is a Research Associate with LNS Research; where he conducts market data analysis and creates data models that span the breadth of LNS coverage areas including Digital Transformation and the Industrial Internet of Things (IIoT), along with Manufacturing Operations Management, Asset Performance Management, Quality Management, and Environment Health and Safety. Vivek holds a Masters of Science degree in industrial engineering from Northeastern University, and earned a Bachelor of Engineering (BE) in mechanical engineering from Anna University in Chennai, India. Prior to LNS, Vivek worked with ARRIS where he focused on supply chain analysis, and co-founded Namma Café, a social and collaborative learning environment for young professionals to develop their skills in the areas of creating and building data models, interactive dashboards, and business intelligence reports.