Technical Article

The Relationship Between Predictive Maintenance and Digital Transformation

November 22, 2021 by Jeff Kerns

This article discusses some trends in digital transformation, specifically matching real-world data with resources and gaining control in an ever-changing world.

How to Adapt to Digital Transformation

There are many digital transformation trends in manufacturing. Additive manufacturing, connected devices, cloud services, and more help businesses gain control in an ever-changing world. Many companies are talking about digital transformations, but recent events have shown that technology is still driven by traditional factors, such as natural resources, population, and disease.

We already covered how 3D printing has been driving digital transformation. The following will quickly review predictive technology advancing manufacturing and the drivers currently accelerating industries' push toward a digital future. Later, we will review how data, dashboards, and cloud computing impact digital transformation. 


Predictive Data: Being Flexible and Fast

Predictive maintenance can help show when a machine needs servicing. This is done through data and has proven to be valuable. The term predictive maintenance has been around for years and is often a relatively easy way to show a return on investment (ROI) for adopting new technology. However, as enough data sets are collected, much more can be done with predictive abilities. Companies want to be as flexible and fast as they can to adjust to market needs.

Implementing predictive maintenance has its own set of challenges. In a future article, we will touch on those challenges, along with some best mitigating practices.


digital transformation

Figure 1. A cobot working alongside a human using an HMI. Image used courtesy of SYSPRO


While digital transformations can improve flexibility, companies need technology to help predict future market demands to stay ahead of the change. Demand planning is another predictive tool companies are using to stay ahead of market needs. It could be argued that demand planning was how early societies created calendars or tracked seasons.

One example could include using similar technology with weather data to manage supply chains of allergy medications to pharmacies. Weather data with predictive software might determine when, where, and what type of allergies could hit an area.

This technology gives companies the chance to act to market needs rather than react after the demand happens. This example is just one simple solution to matching real-world data with resources and population to create an efficient business model. 


Digital Accelerators

Digital transformations help industry, but companies still need to justify the cost and ROI. As many entrepreneurs and investors will ask, what problem is this solving?


digital transformation

Figure 2. Lots of technologies go into digital transformation. 


To determine where or how fast technology will be adopted shouldn't focus on devices and solutions. Companies and investors should focus on problems. Often the larger the problem, the faster industries adopt solutions. 

For example, recently, COVID-19 has become one of the largest digital transformation drivers in the world. This response not only demonstrates the power of large problems but the importance of traditional drivers such as resources, population, disease, etc.

Industry often focuses on solutions that are cost-effective or easy to integrate. However, the best solutions are not necessarily the smartest or strongest but ones that adjust accurately and quickly to the current environment.

These paradigm-shifting moments help us remember that it is often best to focus on the problem and not get distracted by shiny new technology.