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Predictions: 4 Technology Gaps That Manufacturers Will Close in 2022

Jan. 31, 2022
The lessons learned from 2021 will make the new year better.

This year, manufacturers faced more disruption than any year in recent memory. The twin challenges of the COVID-19 pandemic and the breakdown of supply chains around the globe led to extreme volatility in the industry. Fortunately, however, the lessons learned from this year will make 2022 a much better one for manufacturers.

In fact, I predict 2022 will be about closing gaps to create new levels of understanding and control across processes and business relations. As a result, manufacturers will have unprecedented systems-level views, on micro and macro levels, that will enable more efficiency, innovation, and customer satisfaction.

Here are the most important gaps that I expect will close, or at least narrow significantly next year:

1. The most difficult gap will be the current disruptions in global supply chains, which I expect to persist through 2022.

Overcoming those disruptions requires closing the knowledge gaps in demand, supply, logistics, and external factors—and understanding the dependencies and risks as these areas interrelate. 

Better understanding, prediction, and collaboration are needed to bridge these gaps. Initiatives that provide better data gathering and sharing—plus dashboards, analytics, and seamless connections to artificial intelligence services—are already helping better connect systems and build greater understanding. This will ultimately help manufacturers better adapt.

2. The manufacturing world will make significant progress in closing gaps between people and objects.

For example, the rise of "empathetic machines”—designed to interact with and learn from consumers and then serve them better by using personalization—is closing the gap between an object delivered from a factory and the customer's experience of that product. 

Over the last year, I've seen manufacturers designing products and customer experiences with this gap-closing cycle of learning built-in. For example, Toyota created an intelligent user manual that allows drivers to search for car information that is personalized to the car's identification number, so the car can better explain itself to its owner.

3. With little warning, enterprises at every point in the manufacturing process will soon identify and act on the size of their carbon footprint.

Manufacturers will close the gap between the need for sustainability and knowledge of their operations’ direct impact on the environment. Using powerful analytics and AI, manufacturers are increasingly able to model and act on large-scale systems. For example, in quality control, we're seeing dramatic gains in accuracy—sometimes a tenfold improvement—as we standardize and shrink the size of training sets needed to build models and apply computer vision not just to individual parts, but to groups of parts—and eventually to the overall system. 

This system-view approach will be critical in addressing sustainability. Some years ago, Google employed deep learning to cut its data centers’ energy use by 40%. Companies like Renault are now closing gaps among manufacturing processes in even greater ways, creating more impressive system models. By accelerating its infrastructure changes and bringing more technological solutions onto the factory floor, Renault has succeeded in gathering, merging, and harmonizing industrial data from plants in a scalable, reliable, and cost-effective platform and allowing it to be used in a controlled and secure way by data scientists, business teams or applications. 

Taken to sourcing, this approach can also help close supply chain gaps. For example, Unilever is combining satellite imagery and AI for holistic views of forests, water cycles, and biodiversity within the supply chain to help ensure materials sourcing is sustainable. 

4. Manufacturers will narrow the "pilot gap" in AI—which happens when they pilot a project, but can't profitably generate results at scale.

The greater ease of use we're seeing computer vision and AI tools—along with the rich training courses now available—is being replicated in many more products and services. For instance, Ford currently has numerous AI projects and is integrating thousands of engineers into AI-based engineering in order to address issues including supply-chain challenges. 

Of course, AI can only deliver at scale when it solves for the human component—closing the training gap so that new tech can work in large markets. Some years ago, ordinary people began using AI in say, conducting a Google search, thanks to the AI available to them through Google in the cloud. Soon, consumers may use AI to help them cook turkeys–for example, GE Appliances’ Turkey Mode, which was pushed via an over-the-air software upgrade, takes the prep and guesswork out of roasting that big bird. Or enjoy preventive maintenance by identifying patterns in sensor data that can indicate wear and tear. 

But innovations such as this one—and GE Appliances’ recent over-the-air update to add an air fryer mode to its ovens—help provide reasons to consider the upgrade next time you’re appliance shopping. An oven that gets new, helpful features like Turkey Mode after you’ve bought it is pretty smart.

In all of these cases, the gaps we can close in 2022 will help manufacturers see and optimize larger-scale systems in product creation, delivery, and use. There is no question that new challenges will arise, which is good news because that is the basis of progress. The manufacturers that close the gaps this year will be best positioned to solve and grow in the future.