Using AI in Manufacturing: The Weirdest Use Case You’ll Ever Read

Note: The Koosh ball part of this image is AI generated.

Process automation is great, but the technology that manufacturers are dreaming about today is something entirely different.

This technology goes beyond task automation to provide strategic insights and predictive analytics.

We’re talking about artificial intelligence (AI) technology.

Instead of writing a run-of-the-mill article about “the staggering potential of using AI in manufacturing,” we’re sharing a use case that really brings this concept to life.

Let’s talk about AI in Koosh ball manufacturing.

The Most Rad Manufacturing AI Use Case You’ll Ever Read

One of the most unique-looking toys from the 80s actually has a pretty straightforward manufacturing process involving injection molding.

Hypothetically, AI could help streamline this process as well as the entire Koosh ball design process, manufacturing process, and distribution process.

Design

AI-powered systems can analyze vast amounts of data in real-time, identify patterns, and make predictions.

The design of the Koosh ball involves several factors, including shape, size, material, and color.

Improving decision-making about these factors might entail the use of AI. The technology would analyze large amounts of data to identify trends in ball design. This data could then be used to develop new and innovative ball designs.

In addition, artificial intelligence could be used to simulate the performance of different ball designs, which could help designers identify the best design for a particular application.

A More Practical Example: In the automotive industry, AI-driven generative design software can propose optimal shapes and structures for car components that human engineers might not conceive, dramatically accelerating the innovation cycle.

Manufacturing

Some of the processes involved in Koosh ball manufacturing include injection molding, assembly, and packaging.

In terms of injection molding, AI might be used to analyze data from this process to identify the factors that are most likely to cause defects. Companies could then adjust the process parameters to reduce the number of defects.

Similarly, AI could be used to determine more efficient packaging solutions, which would reduce the amount of material used and the cost of shipping.

A More Practical Example: In semiconductor manufacturing, AI systems can analyze data from various sensors to predict equipment failures before they happen, significantly reducing downtime.

Distribution

AI technology can test different scenarios and predict the potential impact on your operations.

While Hasbro is the company that manufactures Koosh balls, the product is sold to consumers by a variety of online and brick-and-mortar retailers.

When distributing products to these retailers, AI might be used to optimize warehouse layouts by identifying areas that are most congested in terms of the pick, pack, and ship process.

For the retailers, AI could personalize the customer experience using predictive analytics and recommending additional products of potential interest – “Could I interest you in some Play-Doh?”

A More Practical Example: In industries like pharmaceuticals and food and beverage, AI is revolutionizing supply chain logistics. It’s helping companies forecast demand more accurately, optimize delivery routes, and more.

Reality Check: How Widespread is AI in Manufacturing?

Artificial intelligence hasn’t taken over the manufacturing sector just yet. Obstacles abound on the road to adopting AI in this industry in particular.

Despite the prevalence of ERP systems in manufacturing, many of these companies lack the infrastructure, data, expertise, and strategic alignment necessary for AI implementation.

Some of the challenges manufacturers face include:

1. Complexity of Implementation

One of the primary factors hindering widespread AI adoption in industrial manufacturing is the complexity of implementation.

Applying artificial intelligence to manufacturing processes requires the seamless integration of various technologies. However, many manufacturers lack the necessary technical expertise. In addition, their legacy systems aren’t designed to work with AI applications.

The emergence of more user-friendly AI platforms and as-a-service models is lowering the barriers to AI adoption, allowing even smaller manufacturers to tap into artificial intelligence capabilities.

2. Data Challenges

AI algorithms rely on vast amounts of reliable data to learn, adapt, and make informed decisions. Yet, many manufacturers lack the necessary infrastructure to collect, store, and process large volumes of real-time data.

Advancements such as IoT sensors and edge computing are helping some manufacturers address data challenges. These technologies facilitate data collection and processing at the source, enhancing the quality and speed of data analysis.

3. Resistance to Change

As their employees whisper to each other at the water cooler about their fear of losing their job, manufacturers are taking note. Either forget AI or incur employees’ wrath.

The reality is that humans outpace machines when it comes to identifying anomalies, making judgment calls, and performing complex tasks that require contextual understanding.

Manufacturers must use organizational change management strategies to communicate this concept and reduce fear. With the right communication strategy, companies can solidify their vision for human-machine collaboration.

Could AI Be Applied to Your Manufacturing Processes?

AI is not a one-size-fits-all solution. You must first define your business goals and specific areas where AI could deliver the most value.

Whether you manufacture Koosh balls or deep-sea drilling equipment, AI technology has the potential to optimize your operations, enhance product quality, and drive innovation.

Contact our enterprise software consultants below to learn how to maximize the potential of AI tools by preparing your systems, people, and data.