March 18, 2024 Using AI to Integrate, Optimize and Forecast

With the right tools in place, manufacturers can harness AI to boost efficiency, safety and performance at every stage of production.

Manufacturers have been using artificial intelligence (AI) for decades. However, recent advancements, including those in machine learning and automation, have paved the way for widespread adoption.

AI has the potential to improve the efficiency, safety and productivity of the plant. But, as with most technology, the best solutions augment, rather than replace, existing workflows.

In the case of AI adoption, that means designing a strategy that aligns the applications of the technology with the needs of a business, the strengths of its workforce and the challenges its employees confront in day-to-day operations.

Breaking Down Barriers

One of the most impactful ways manufacturers can use AI is to connect their systems, reducing barriers to visibility. For instance, employees working within a plant often struggle to get a holistic view of operations. As a result, they lack the relevant context when called on to make data-informed decisions.

A prime example of this is the siloing of information technology (IT) and operational technology (OT) systems. These systems typically separate data by layers that correspond to access levels and job functions. While these controls are created with good intentions — to safeguard information and simplify workflows — they frequently have the inverse effect. In addition, IT and OT systems usually run on different software platforms, making it hard to combine datasets, even for those with full access.

From the sensors that measure performance to the machines powering production, modern plants run on technology. AI can bring the information generated by these components into one seamless interface. This streamlines data analysis and provides enhanced visibility, allowing employees to understand the implications of a shift in production and enabling smarter, faster decision making.

Optimizing and Automating

Because AI can rapidly analyze large and complex datasets, manufacturers can harness it to optimize systems, reduce equipment downtime, cut waste and conserve resources. In many cases, AI can even automate some aspects of operations. This, in turn, can free up labor for tasks that rely on human creativity and ingenuity.

Stefanini recently worked with a leading logistics company to streamline the movement of cargo from ship to storage. A wide variety of variables — including weather conditions, local regulations and port and vessel layout — inform the process of unloading the company’s bulk cargo carriers. Because of this, each vessel and port requires a custom disembarkation plan. These are time-consuming and complicated to produce.

Using applied AI, Stefanini developed a platform to automate this procedure. Leveraging data from the vessel and port, the company could deploy this tool to generate strategic disembarkation plans. This improved the efficiency of the process, cutting disembarkation time and reducing overheads.

Fostering Resiliency and Grasping Opportunity 

Along with real-time optimization, AI can give unprecedented insight into the challenges and opportunities a company may encounter.

One example of this is predictive maintenance. AI can utilize performance data to forecast when equipment is likely to break down or require upkeep. This helps facilities prevent unnecessary downtime — enhancing safety and productivity on the factory floor.

Another use of this technology is in market analysis. AI modeling software can enable manufacturers to study trends and predict demand for a particular product. This allows businesses to align their production schedule and inventory to consumer preferences — right-sizing their processes to avoid shortages or overproduction.

Similarly, businesses can employ these tools to project the impact of a hypothetical scenario, such as a supply chain backup or material shortage. In doing so, they can prepare for potential issues before they arise.

Visibility across systems is critical here, too. To effectively model the patterns and conditions that could influence a company’s future, an AI platform will need to combine inputs from a broad range of sources, both external and internal.

With an artificial assistant, manufacturers can take these capabilities a step further. AI-powered assistants can respond to human language prompts and quickly gather, group and analyze information to create of-the-moment forecasts, cutting down on time spent manually collating data and inputting prompts. 

Taking a Tailored Approach

Unsurprisingly, an increasing number of companies in the manufacturing space are making strategic investments in AI. The technology has the capacity to boost efficiency, elevate productivity and provide unprecedented insight into the trends of tomorrow.

For those weighing the benefits of adoption, an experienced partner can help to identify the operational areas best suited to AI support. The key to implementation is to start with the data, mapping the challenges a business is grappling with against the solutions that AI can provide.

By taking a tailored and data-driven approach, companies can leverage emerging technology to their advantage — optimizing their work in the present while innovating for the future.

About the Author:
Leonardo Vieira is the smart manufacturing director at Stefanini, a global provider of AI and IT solutions. A thought leader in the future of Industry 4.0, he partners with clients to leverage intelligent, high-end technology to improve industrial processes and boost operational efficiency. For more information, visit stefanini.com.

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