More and more manufacturers today are jumping on the data-driven manufacturing bandwagon. At a high level, the journey of data-driven manufacturing can be categorized into the following stages:

• Data collection
• Give meaning to your data
• Generate insights
• Build a picture
• Execute using insights

This article will explore how AI can add value to each stage of the data-driven manufacturing journey.

Data collection

Sensors and edge devices drive data collection in the smart manufacturing journey. Data collected can be refined at the edge itself using Edge AI. Many companies have collected and stored manufacturing data for decades but have never used it to generate meaningful insights. Often, the reason is an insane amount of unformatted data that is difficult to start with. Edge AI can help add initial insights to the data collected and help collect data concisely and meaningfully.

Add meaning to the data

As mentioned, having some context to the data collected is tremendously helpful. One way to add context, as discussed, is to add context and relevance at the edge itself during data collection. Secondary contextualization is another area where AI can help. Algorithms designed to contextualize collected data can help associate additional context to the data, like information about products and processes and, in some cases, business context (like if the batch run data was due to a run for an urgent order). Specifically designed algorithms can extract the information needed to contextualize collected data.

Generate insights

This is where AI is already being used extensively. From predictive maintenance to anomaly detection, AI algorithms are used across industries to help manufacturers. From the condition of assets to ensuring that the process is running within parameters, AI algorithms play a critical role in extracting meaningful insights from the contextualized data. The contextualization added in the previous stage helps generate more meaningful insights, like which products leverage assets the most, cycle time by product family, etc.

Build a picture

Siloed insights, though useful, are like opportunities wasted. Often, it is not possible manually to bring the siloed data together to create the “big picture.” One aspect is to build a factory-level picture. This is a capability that is already available. There are many smart manufacturing platforms that weave disparate insights into one entity-level insights dashboard. This helps you get siloed views, like asset-level performance and the overall performance of your processes and plant. However, AI algorithms can be leveraged to build a network-wide picture with critical business insights. If the previous stages of the journey have been mastered, building and training such an algorithm should not be complicated.

Execute using insights

All the previous stages of the data-driven journey are useless unless the insights generated are used. In the context of the smart factory, these insights can be used in real-time to control and flux your manufacturing process and asset parameters. At the “big-picture” level, AI can play a significant role. If you have developed and designed the algorithm correctly, you can generate critical insights from your manufacturing network that may have been difficult to unearth manually. Also, if you are leveraging AI algorithms in other sub-functions, like logistics, you can connect these algorithms to create a centralized “strategic AI” planning capability.