Using Manufacturing Data and AI to Stay Agile‍

In today's fast-paced business environment, manufacturing companies need to be agile and efficient to stay ahead of the competition and avoid production delays. Leveraging continuous intelligence solutions provides a bird’s eye view of your operations in real time, serving as a valuable resource when strategizing improvements.

Find out how using data and artificial intelligence (AI) can optimize your manufacturing processes by streamlining operations, reducing supply chain risks, and optimizing equipment effectiveness. 

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Identify Risks in the Manufacturing Supply Chain‍

One of the main benefits of using data and AI in manufacturing is the ability to identify risks in the supply chain. By analyzing data, manufacturers can identify which suppliers are susceptible to disruptions or delays, allowing them to take proactive measures to mitigate these risks. This is especially important in today's globalized economy where supply chains are often complex and involve many different suppliers.

Skylytics utilizes a graph database by Neo4j Graph Data Science (GDS) to identify patterns that may impact operations. Algorithms within the graph database can expose information about:

• Concentrations where parts are manufactured
• BOM’s that rely on the same part(s)
• Parts that come from a limited number or single suppliers
• Reorder points based on historical lead times and historical unit sales
• And more

A supernode in the graph reveals concentrations in specific areas of the supply chain. These concentrations indicate potential risks within a manufacturing setting.

For example, if your company has an important part in a car that you’re only sourcing from one supplier, that poses a systemic risk. You may not be able to ship the car at all if a supply chain issue arises.

Skylytics can look at your products/BOM’s, the parts they’re made of, and who the suppliers are, then alert your business to the potential risks.

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Data and AI Can Predict Equipment Maintenance Needs‍

Another critical area where data and AI can help streamline manufacturing processes is in predicting maintenance needs. By monitoring equipment performance and analyzing data, your company can predict when equipment is likely to fail and schedule maintenance accordingly. This helps prevent unscheduled production downtime, which can be costly and disrupt production schedules.

Data and AI insights also enable manufacturers to move from reactive maintenance to proactive maintenance, which is more efficient and cost-effective in the long run. By replacing parts before they are likely to fail, your business can avoid facing serious issues that may require hours of downtime,  expensive repairs, and wasted labor expense.

Additionally, machine learning models are capable of predicting when a failure is likely to occur based on historical and current data trends. If your company knows when a malfunction is likely to occur, you can schedule equipment maintenance during non-peak production times.

This predictive approach to maintenance carries numerous benefits: 

• Significantly reduces unplanned downtime
• The ability to schedule maintenance before it becomes an unplanned event
• Helps extend equipment lifespan
• Improved long-term cost efficiency
• Provides a safer working environment for employees
• Can increase Overall Equipment Effectiveness (OEE) score

If your business is striving for more efficient, reliable, and profitable manufacturing operations, data and AI technologies are an ideal solution.

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Common Misconceptions About Data and AI

Not everyone fully understands how data and AI work, which can lead to some common misconceptions. When it comes to data, many businesses research trends in their industry or even purchase data from third-party sources. While understanding trends within relevant industries is vital for benchmarking, it’s a mistake to place your own company’s data as a lower priority. 

Manufacturing industry data can provide insights into changing consumer demands, supply chain concerns, the latest technologies, and labor trends. While this information is useful, it pales in comparison to the value of understanding what your customers want, the cost of parts your business uses, and the efficiency of your equipment. It will be extremely challenging to make effective changes without knowing your own analytics.

When it comes to artificial intelligence, there is an abundance of misconceptions as a result of generative AI’s recent popularity. Large language models are often in the news as a result of being sued for plagiarism. This has led to feelings of distrust toward AI from the general public. However, in its current form, generative AI is more akin to machine learning (ML). 

The simplest way to understand how AI and ML relate to each other is:  

• AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human
• ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously over time

While there are certainly organizations that use AI, ML, and data in questionable ways, leveraging them correctly can help your business make more informed and even automate decisions.

Enhance Operations with Manufacturing Technologies

In conclusion, data and AI are powerful tools that can help manufacturers streamline their processes and operate as efficiently as possible. By using data analytics and machine learning algorithms, manufacturers can identify risks in the supply chain, optimize inventory management, predict maintenance needs, and improve product quality. This leads to increased efficiency, reduced costs, and improved customer satisfaction. 

At Skylytics, we have decades of experience leveraging continuous intelligence to help manufacturers make better business decisions. Contact us today to learn more and get started.