Adopting A New Manufacturing Mindset: Serving Customers In The Individualization Era

Robert Merlo

Part 3 in the series “Digital Manufacturing

The first time a colleague mentioned mass customization to me, I thought they were nuts.

You mean to tell me that a single production line can produce multiple, different items?

No way!

Looking back, I realize I was mired in traditional thinking, which prevented me from seeing the full possibilities of customization. I needed to adopt a new manufacturing mindset.

Groundbreaking technologies – such as the Internet of Things (IoT), digital twins, and machine learning – have helped usher in the age of product personalization. For today’s companies to serve customers with individualized goods, they must embrace these innovations and transform how they think about manufacturing.

Tear down the wall between engineering and manufacturing

At some point, a wall wedged itself between most engineering and manufacturing organizations. But to create a culture of greater customer-centricity and product personalization, that wall must be torn down.

After all, manufacturing is where you’ll need to institute changes to make variable configuration possible. So it’s crucial that your engineering and manufacturing processes are tightly integrated.

Technology can help.

By outfitting your products and assets with IoT sensors and creating digital twins that represent the physical objects, your engineering and manufacturing departments can see how customers interact with your goods. More importantly, they can gain a better understanding of how to meet your buyers’ unique specifications.

By leveraging digital twin 3D CAD models, your engineers can alter dimensions, tolerances, and materials more quickly and efficiently to speed time to market, even in the most highly variable product designs. And your shop-floor technicians can access this insight in a digital twin to better understand the assembly process, streamline production, and improve product quality.

Say you’re an aircraft engine manufacturer. With a digital twin, you can monitor the condition of your customers’ IoT-enabled engines in real time. The moment an engine demonstrates signs of wear, you can predict product failure and dispatch a maintenance crew to fix the engine. The problem could also indicate that you need to modify your designs to create a longer-lasting product.

Machine learning is another innovation that allows manufacturers to evolve from mass production to mass customization. It enables your company’s machinery to take on some of the work itself – to identify when products need to be configured differently and automatically adapt as they travel down production lines.

If only a fraction of your aircraft engines require customization, for instance, your machines will recognize that. They’ll be able to produce thousands of large, fuel-powered stock engines, but they’ll also possess the intelligence and agility to build a dozen small electric engines using the very same production line.

By automating your manufacturing processes with machine learning, you can ensure your operations remain efficient, your time to market remains short, and your costs remain managed.

Deliver on the promise of personalization

Market dynamics have shifted. Buyers are increasingly choosing personalized products that fit their individual needs over mass-produced goods.

The sooner you accept this and adopt a new manufacturing mindset that emphasizes customization, the better off your business will be.

And by embracing today’s most innovative technologies, you’ll gain the insight you need to accurately understand what your customers want and deliver on the promise of personalization.

Download “The Digital Road to Industry 4.0 and Beyond” to learn how your company can progress through the five stages of digital maturity.


Robert Merlo

About Robert Merlo

Robert Merlo is Vice President SAP Solution Marketing, responsible for Strategic Positioning of the Digital Supply Chain and Manufacturing Portfolio of SAP Solutions. Specifically focused on R&D and Manufacturing.