Digital technology is submitting new ways to transform organizations and manufacturing operations. Despite that manufacturers have questions about it. Here is answers;


Digital technology has brought on new ways to transform businesses and manufacturing operations, but it still raises questions from manufacturers. I hear, “Digital transformation? Isn’t that what we’ve been doing for the past three decades? We’ve already connected our business, manufacturing, and supply chain systems with ERP and MOM/MES.”

In many ways, they’re right. It could be argued that manufacturers have been in a near constant state of transformation since the first Industrial Revolution, with change happening at a more rapid pace with each new era. What’s different now is that Industry 4.0 technology, such as Artificial Intelligence (AI) and Machine Learning (ML), are amplifying human capabilities. They’re delivering insights, predictions, and recommendations to solve problems that couldn’t be solved with earlier technologies. That’s why the current era is being called as the Fourth Industrial Revolution; it’s a fundamental break from the past.

When we talked about transformation—even digital transformation—during the Third Industrial Revolution, we talked about how to use new tools and systems to make specific processes or products faster and better than they could be made before. Leveraging the alphabet soup of applications—ERP, MES, MRP, etc.—manufacturers incrementally streamlined, connected and accelerated nearly every part of their business.

The Fourth Industrial Revolution builds on these advances and leverages Internet of Things (IoT) technology to integrate and streamline entire systems, from design to delivery. Augmented with AI and ML, these systems are always on and self-learning, improving their capabilities over time from observation of the processes, user actions, and their corresponding outcomes. The result is an architecture that enables humans to answer new types of questions.

Let me explain by using the example of problem-solving. In many facilities, when there’s a problem on the factory floor, the high-value, highly salaried engineers spend their time collecting data from different machines and systems, exporting it to excel spreadsheets, and wrangling with it to find a solution. This data collection and analysis process can take anywhere from days to weeks—and sometimes longer. There are two ways to transform this process: the traditional way and the intelligent way.

The Traditional Way to Transform

The first way, the traditional way, to transform this time-consuming approach to problem-solving is to implement a system that allows an engineer to solve problems that they otherwise could have addressed, but much faster. For example, a typical digital system can automate data collection into one database—a single source of truth, where engineers have instant access to it. With all the data readily available, the engineers can perform root cause analysis on the factory floor in a matter of minutes, which means they can solve more problems each day.

The importance of improving the quality and speed of your company’s problem-solving capability can’t be understated. As noted by Martin Lorentzon, co-founder of Spotify: “The value of a company is the sum of the problems you solve together.” Solving more problems quickly creates immediate value for your organization. By automating the data collection process, your company enables its engineers to deliver greater value on higher value tasks.

The Intelligent Way To Transform

However, while merely solving more problems more quickly is valuable, it isn’t revolutionary. To become part of the Fourth Industrial Revolution, you need to implement an intelligent system that helps manufacturers solve not only ongoing problems faster but also tackles new issues they wouldn’t otherwise be able to address.

Take for example, a manufacturer using technology to analyze environmental variables in their factories. By approaching digital transformation intelligently, they can make new recommendations to operators about how to make specific products on particular days that will dramatically improve quality and output. Because of the vast amount of data that needs to be analyzed, no person—not even a highly trained and experienced engineer—would be able to make such recommendations.

Another example is found in the electronics industry where fault detection is critical. Rather than having highly trained people examine each part, companies are using cameras powered with computer vision and machine learning algorithms. These technologies not only detect flaws more efficiently than humanly possible, but they can also make hypotheses and analyses about flaws that no human could make. With intelligent systems, companies can discern the ideal predictive maintenance protocol or identify the optimal way of processing materials.

Intelligent systems leverage the best of two worlds: People can do more of what they do best—that is, using their intuition, domain knowledge, and understanding of data visualization to solve problems. And intelligent systems do what they do best—quickly scan through billions of data points to find solutions that a person could never elicit.

As you think about how you will transform your business, it’s critical to distinguish between the two ways, or steps, of doing so. The traditional way is to build a system that helps people solve problems faster. The revolutionary way is to implement an intelligent system that helps people solve problems they otherwise couldn’t have solved.

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