Lean manufacturing is a series of principles and techniques to help improve performance in factories, production lines and machines by eliminating as much waste as possible. And more specifically, minimizing activities and products that add zero value.

The rise of Artificial Intelligence (AI) has introduced an innovative means to achieve such continuous improvements in production.

While the reward of implementing lean manufacturing techniques is great, and includes increased productivity, improved quality and minimized rework – until recently, the price has been high, and required much effort and the need for human experts.

Thankfully, as AI continues to play a huge role in leading industries, it has also entered the world of manufacturing with numerous use cases, such as predictive quality, predictive waste, and predictive maintenance.

Traditional AI vs. Process-Based Industrial AI

Before discussing how Industrial AI is helping manufacturers adopt a lean approach to manufacturing, it’s important to understand what Industrial AI is.

Both traditional AI and Process-Based Industrial AI analyze raw data generated from production lines (OT data). However, traditional AI applies unsupervised machine learning algorithms to the raw OT data, which eventually leads to a flood of alerts and many false-positives.

On the other hand, Process-Based AI, contextualizes the OT data by adding business data from IT systems into the dataset, together with the specific production process flow context, and by doing so,  builds a process-based data model.

It then applies process-based supervised machine learning algorithms, which are able to clear the noise of false-positives and pinpoint actionable insights for production teams.

The Role of Industrial AI in Lean Manufacturing

Machine learning can provide predictive insights to users – process and quality engineers – freeing up their time to focus on solving issues, rather than investigating them.

Users provide feedback to the machine learning algorithms in the form of an accuracy and relevance score, enabling the algorithms’ accuracy to improve over time. This is called “Human in the loop” and leverages the engineer’s production knowhow to fine tune the results of industrial AI.

 

Lean Manufacturing Techniques

One of the leading use cases of  Industrial AI in leading the industry to a leaner approach is by predicting waste, and by doing so – reducing it. This is sometimes referred to as Quality 4.0.

With industrial AI, process engineers can predict and prevent production waste by identifying areas of loss and prescribing focused actions that reduce product defects and inefficiencies.

This is done by employing predictive analytics and Automated Root Cause Analysis to anticipate process failures that result in wastage.

Beyond anticipating when waste will exceed thresholds, by implementing predictive simulation, process engineers test production parameters until optimal values are determined for minimizing waste and rework.

To summarize, waste is a strategic operational loss in manufacturing, but factories that harness industrial  AI technologies as an integral part of their lean methodologies can continually improve their production processes to minimize waste.

 

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