How Industrial Companies Can Get the Most Value Out of Digitization

DigitalizationAutomation, cloud computing, the Industrial Internet of Things (IIoT), digital twins — there are many directions your digital transformation efforts might take you. The question is: Which will bring you the most value? Should you focus on boosting throughput or eliminating downtime? Improving energy efficiency or optimizing maintenance schedules?

Of course these things aren’t mutually exclusive, and many digitization efforts can help you achieve multiple goals. But, especially if you’re just getting started, it’s important to choose your initiatives and technologies wisely so you can achieve your desired business objectives.

Here are five ways to ensure you’re getting the most value out of digitization.

Identify the problem you want to solve

This seems obvious, but planning often overlooked in favor of rushing to adopt an exciting new technology just for the sake of adopting an exciting new technology. McKinsey & Co.’s Richard Kelly warned against this at a recent Association of Equipment Manufacturer’s summit, urging manufacturers to start by identifying the problem they want to solve:

“There’s a theme that we think is very important here, which is making sure we look at this through the lens of business transformation and business value rather than cold technology. When the latest iPhones come out, people will just rush to the store. They don’t calculate the ROI of going and buying the latest iPhone. But sadly, in the world of B2B, that doesn’t really exist. We need a business case, we need a roadmap, and we need ROI.”

He recommends starting with your most pressing business need and then figuring out how technology can help.

Focus on solutions that will make you more agile

Change is happening at a dizzying speed. The companies best positioned to succeed are those that are agile enough to respond to change quickly.

Digital twins are an excellent example of a technological solution that can make you more agile. By creating a virtual version of a product, process, or plant, you can find and fix problems before committing any resources to the physical buildout. After the project is built, the digital twin provides real-time data about the state of the physical version, so you can test different configurations, plan maintenance schedules, and make changes on the fly if necessary.

Watch the video to learn from McKinsey & Co.’s Chris Gagnon why agility is so important.

Analyze your data to find opportunities

In many industries (such as oil and gas, where drilling new wells may not be an option, and food manufacturing, which operates with very low profit margins), the most valuable thing you can do is boost the productivity of existing processes and equipment.

This is typically the focus of the most common type of IIoT implementation: putting sensors on equipment to collect performance data. This data is a goldmine for finding inefficiencies and other opportunities for operational improvements.

Here’s an example from PwC’s 2017 Industrial Manufacturing Trends report:

“One drill manufacturer has been helping its customers — chemical plants, oil refineries, and other process manufacturers — operate their plants more effectively by leveraging IoT in the form of wireless drill sensors, which can detect potential failures in valves before they lead to a spill or shutdown….This new technology makes it easier to safely capture, monitor, and analyze this constantly changing data in real time, enabling oil and gas companies to save millions by reducing the number of unforeseen outages by half, and increasing crude output by as much as 10%.”

Combine data from different sources into a single database

Analyzing your data can help you identify areas for improvement. You can magnify that effect by combining data from different.

Sight Machine CEO John Sobel gave an example in a recent Knowledge@Wharton interview:

“Today, we can put up a screen that shows the actual performance and the reasons for variation. In some cases, operators might be doing things differently. In other cases, aspects of the process itself are different. Another example is quality. We often find that in manufacturing there is a large percentage of scrapper rework. If you’re making drugs, for example, sometimes a batch is bad. This can cost a million dollars. What happened in the process that explains why that went wrong? Many times we don’t know.

Amazingly, manufacturers have huge amounts of data on production. And then, sitting right next door, there is a big pile of data about quality. But putting it all together so that you can know immediately — this batch was bad because of parameter A — is a very challenging problem for them to solve in any sort of systematic way. So those are some very basic examples. You can get much more sophisticated quickly. Bottleneck analysis — looking at a number of lines and seeing where in each line the process is being held back. It varies from line to line.

The data to answer all of these questions is there.”

Our Engineering Base software platform does the same thing for engineering data. It brings data for wiring, instrumentation, PI&D, and more together into a common data model that provides a single source of truth for an entire project. This improves efficiency, facilitates collaboration, and eliminates mistakes caused by a lack of standardization and communication across project teams.

Train your workforce to use the technology effectively

On the Schneider Electric blog, Keith Chambers points out that “while the majority of conversation around IIoT has focused on the effects of machine-machine communication, perhaps the greatest benefit is its ability to empower people to make more informed decisions.”

According to GE Digital’s Industrial Evolution Index, companies still have a ways to go. The survey of IT and operations decision-makers found that the industrial workforce needs to improve in the following areas:

  • Understanding how to use digital interfaces/processes (59%)
  • Understanding of AI and machine learning (48%)
  • Ability to read technical data (48%)
  • Ability to critically think about computer-generated output (46%)
  • Ability to work in teams (39%)
  • Sensitivity to cultural differences (24%)

Even with technological advances, your people are still your greatest asset. Give them the tools they need to succeed.

What to learn more about the digital transformation of industry? Check out these 13 statistics.

Leave a Reply

Your email address will not be published. Required fields are marked *