Leveraging Digital Transformation in Manufacturing

6 minute read

Digital transformation (DX) is ever proliferating and advancing, demanding that the world around us become more and more responsive and adaptable at every step. From the machines and networks we use to do business, to the applications and smart technologies we use to manage our everyday lives, everything seems to be talking to everything else. Smart devices monitor insulin levels of diabetes patients wherever they go, notifying healthcare professionals if those levels get too high. We can tell smart devices to turn on the lights for us and ask them what the temperature is outside. Smart devices also surveil crops and monitor soil to keep farmers abreast of changes in climate and temperature, enabling water efficiency and better crop yields. All of this requires connection and communication between various devices, making up a network known as the Internet of Things (IoT).

Despite operating in an age where smart technologies have become integral to our lives, manufacturing and production facilities have been historically slow in taking advantage of technological advances. The cost and task of wide implementation and the ways in which it would change production processes seem daunting, but the benefits of smart production far outweigh the challenges.

Many manufacturers see the most potential for digital transformation ROI in cost savings associated with improved efficiencies. However, comprehensive integration of a plant’s technologies into a single industrial IoT (IIoT) platform empowered by machine learning has great potential to impact business advancements, to propel business innovation, and to create market disruption.

Where we are and where we’re going

Gartner reports that only 10 percent of IIoT analytics is currently derived from on-premises IoT platforms coupled with edge computing, and less than one percent of manufacturers have developed or acquired IoT platforms at all. However, the rate of adoption is increasing, and these percentages are predicted to grow exponentially within just five years.

ESRI notes that businesses must drive efficiencies of five to 10 percent in the next three years to stay competitive, and the only way to accomplish this is to replace manual internal processes with digital workflows.


The slow adoption and talent gap in industrial digital transformation point to the inherent complexity of DX, and lack of experience and support to make the strong business case for the cost and implementation of DX. To make the case for implementation and gain top-to-bottom organization buy-in, it helps to simplify what digital transformation encompasses. Let’s break it down. 

Digital transformation breakdown

When something is “digitalized,” physical bits of information are converted into data that can then be acted upon, accessed through networks (like the Internet), and used to enable the automation of manual processes. The proliferation of this process was at the center of the Digital Age. DX is the next evolutionary step, ushering in the age of Industry 4.0.

Gartner defines digital transformation as “the use of digital technologies [and their data] to change a business model and provide new revenue and value-producing opportunities.” In other words, it’s the adoption of digital technologies to automate and transform business processes, enable informed decisions on actionable insights, and create new revenue streams and business models. And it begins with IT/OT integration and IIoT.

The difference between IT and OT

Information technology (IT) is made up of software and business technology infrastructure with wide computing and networking capabilities. Manufacturers will also be familiar with the term OT or operational technology, which consists of production machines, monitoring equipment and other production technologies. OT is generally very task-oriented, having specific, narrowly defined capabilities. Where OT was previously limited by these narrow parameters, integrating OT systems with IT systems opens the door for IT infrastructure and software, such as IoT platforms and machine learning, to extract information and data from OT to enable improvements and innovation.

“IIoT” is not a hybrid acronym that stands for IT/OT integrations, but it may as well be. Integrating business technologies and software with operations technology enables manufacturers to make connections, calculations and deductions they weren’t able to make before. IIoT platforms are the vehicle for this integration.


IT vs OT vs IIOT 

What really drives value behind IIoT are, again, the analytics and insights that can be gained. Getting to this value faster than the competition is the aim, and this is made easier by platforms with AI components. AI and machine learning drive advanced analytics and detect patterns and nuances to solve business problems and identify opportunities.

Speed enhancements to technologies such as CPUs, SSDs, storage-class memory (SCM) and GPUs are all occurring in an effort to provide faster performance and consolidate compute functions with storage data in the same place. Graphical processing units (GPUs) are quickly becoming the standard for computational workloads. Originally developed for gaming, GPUs are now being used to accelerate computing workloads, which is enabling faster machine learning and deployment of DX.

IT/OT integration via IIoT platforms, empowered by machine learning and analytics tools, speeds DX and facilitates:

  • Federation of siloed data
  • Lowered production costs
  • Shared, streamlined resources
  • Faster and better insights for improving efficiency, decision-making and identification of potential new revenue streams


Industry 4.0 – Great expectations

As DX technologies are implemented, the digital revolution in manufacturing will continue to advance. This revolution, termed Industry 4.0, will re-engineer the way products and goods are manufactured. Convergence is key; IIoT platforms are paving the way for that to happen, but it can only happen as we begin to acknowledge that IIoT and DX, when applied holistically, have the potential to create new revenue streams, not just cost savings.


5 steps to digital transformation

The sooner you implement a DX strategy, the faster you can get to business transformation. Follow these five steps to get on the right path.

  5 steps to digital transformation

Getting started with IIoT in pursuit of DX

IIoT implementation will play a major role in your digital transformation strategy. Here are some tips for successful implementation:

  1. Plan. Having a digital transformation plan with clear objectives is how you are going to get to value fastest. Work with an IIoT solution provider with deep domain experience to identify the insights and outcomes you are looking for.
  2. Implement comprehensively. Implementing only one or two components of DX undermines and minimizes the impact of these advancements. For instance, why integrate IT and OT to collect big data if you don’t have the tools and strategy in place to do something with it? IIoT platforms help with this.
  3. Keep things moving forward. Implementation of IIoT is ongoing. Avoid stall-outs in advancement by executing integration and analysis continuously and consulting with IIoT experts.

It’s clear that IIoT solution providers will be an important part of helping manufacturers actualize digital transformation in their plants. Teaming with these experts is essential to industry business transformation, because they bring with them the knowledge, experience and skills needed to keep pace with technological evolution in the industry. But of course, all experts are not created equal. Here are some traits that indicate quality in a solution expert:

what to look for in a quality solution provider 


what to look for in a quality digital transformation solution 

Where are you in the digital transformation process?

Establishing convergence between machines, devices and systems in your factories and plants through an IIoT platform and strategy paves the road for digital transformation. Not only will the shift to smart manufacturing enable automation and improvement of manufacturing processes and performance, identify and prevent errors, and optimize time and efficiency; the data and insights harvested have the potential to bring about new revenue streams and create market disruption—especially when implemented with machine learning. Follow the steps and tips we’ve discussed, and you’ll be ahead of the curve.

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