Digital transformation is at the core of business today, yet many companies still struggle to execute with this imperative. The Covid-19 pandemic has made this imperative even more pressing: companies that don’t act with urgency to get through the crisis and recovery manner risk falling behind at a time when they can least afford to.

Among the slow adopters are those companies trying hard to execute the industrial internet of items (IIoT), one of a handful of new technologies that the World Economic Forum and McKinsey consider pivotal to the future of manufacturing-and an essential improvement lever to assist operations emerge from the crisis.

A common mistake leaders make is to focus on new technologies in isolation, without thinking about the business value the technologies can generate. But implementing IIoT systems and tools before imagining all the possibilities they can create puts the cart before the horse.

It can lead companies to squander their investment of money, management attention, and–perhaps the most valuable resource of all in this rapidly evolving landscape‬.

Effective digital transformations are business-backed and business-led. Digital transformation is about envisioning how technologies such as IIoT can redefine value creations by accelerating and scaling existing operations, rethinking how customers are served, and even by reinventing business models. It is a complete reimagining of the way work is done. Above all, it is about a new way of competing.

So what’s holding some companies back? From surveys and our job with manufacturers around the world, we have identified six myths about IIoT that are needlessly troubling business leaders.

Here, we debunk them drawing from our experiences in dealing with a broad range of companies, and from the nearly four dozen manufacturers globally that are leading the way in applying Fourth Industrial Revolution technology to help advance their companies financially and operationally (see sidebar,”The myth busters: Lighthouses guiding new-technology adoption at scale”).

Myth #1: IloT is only a high-tech dashboard

Folks fixate about the data-collection and predictive-maintenance aspects of IIoT, which is similar to viewing the airplane solely as a conveyance or the net as a collection of electronically delivered information. Instead, IIoT–and digital transformation more broadly– represent a wholesale rethinking of value creation: a way to increase it, improve it, and accelerate it. IIoT helps companies acquire and analyze data, turn it into actionable insights to solve issues, and make decisions faster.

Think about, for example, the way the speed of real-time data collection and analysis has vastly accelerated insight gathering for epidemiologists and medical researchers pursuing Covid-19 treatments, testing methods, and vaccine candidates.

Many companies are already amassing data but don’t understand how to use it. In combination with data lakes and processing platforms, IIoT generates insights automatically and alerts users, which enables a more efficient way to identify a issue and take action. 

Solutions and advice can then be disseminated across other teams, locations, and applications, thus establishing their value throughout the enterprise. When properly integrated, the data gathered in real time can translate into important innovations and strategy.

Deploying IIoT wisely requires a full understanding of the business and the way it creates value. It also means knowing the business’s pain points, so that the company invests in IIoT where it matters most and where it can realize finishing value. Where and how to deploy IIoT is therefore an important strategic decision that needs to be directed by the business strategy set by senior management.

At Microsoft’s plant in Suzhou, China, IIoT has awarded the company the ability to set up new use cases with a minimal investment. Within hours of launching machine learning, the company was able to identify stock that was on the verge of obsolescence. According to Darren Coil, director of business strategy,

The data was always there, however, we weren’t seeing it IoT highlighted it for us” A five-person team discovered and addressed this finding, saving the company nearly $5 million in 1 year and cutting inventory costs by $200 million.

BMW Group’s IoT platform functions as the backbone for all digital apps, allowing plug-and-play with minimal installation costs and effort. A connected digital arsenal promotes productivity and allows employees to share best practices quickly through the Group. 

At Texas-based Texmark, sensored devices and advanced analytics software automate the petrochemical company’s plant, generating insights and diminishing the risk of human error. Planned maintenance costs have been cut by 50 percent.

Myth #2: IIoT will displace employees

People fear that automation will eliminate jobs. But the new technologies used in digital transformation are also generators. These new jobs, moreover, free a substantial portion of the workforce from repetitive and often unhealthy tasks and allow them to gain new capabilities. The big issue for companies adopting IIoT is rather the best way to reskill their workforce to take advantage of the new technologies.

Companies need people to manage the machines and operate the control towers and digital twins. They want employees that are able to carry out digital performance management, translate the data extracted from the thousands of sensors on the shop floor, and find ways to boost returns and derive actionable insights. Companies need more people in IT who can develop apps for accessing the volume of data being generated.

Beyond data scientists, data engineers, and technology leaders, companies need domain experts, digital and analytics translators, and merchandise owners–the latter, to confer with a team of business and functional-area leaders and data and technology experts to identify issues and make sure that the ideal digital solutions are manufactured (exhibition ).

Reskilling must happen across the value chain as well: In procurement, for example, companies will need more price engineers who can use cost-modeling tools to assess equipment and procurement costs.

The ability to identify opportunities for improvement demands training and education. Companies can teach employees to think and act differently, to be more involved with problem solving and devising local solutions that can later be disseminated enterprise-wide through technology platforms for maximum impact.

Given the breadth of new skills that are required –not just in training for the specific technologies, but also in new ways of working–some companies are developing programs in partnership with postsecondary educational institutions.

Tata Steel Europe established its internal Advanced Analytics Academy to train workers for the new functions triggered by the new technologies. Instead of hiring pure data scientists, Tata trains its domain experts in data science.

At its plant in IJmuiden, Netherlands, the company spent in building the digital skills of its website team, an approach that enabled it to realize significant productivity, cost, and quality gains.

Leaders at Petrosea, an Indonesia mining company, trained supervisors and thousands of front-line workers in new digital tools. At digital boot camps, the company educates select team members on agile methodology, large data, IT security, and analytics.

Petrosea developed a mobile training app that uses gamification to make continuous learning accessible and fun. For example, it illustrates standard operating procedures primarily via visuals.

The app offers the added benefit of enabling leaders to track how well employees understand the new procedures.

Myth #3: IIoT requires greenfield sites

Some business leaders believe that elderly facilities are an impediment to digital transformation and that legacy gear must be replaced. Certainly, new gear will be necessary. But equating IIoT with brand-new, “greenfield” sites capable of fully automated “lights out” manufacturing is a major overstatement.

Most of IIoT’s value comes from advancing brownfield sites: in linking and optimizing existing infrastructure and augmenting it by select new machinery on an ongoing basis. By adding sensors, apps, and connectivity to existing gear, companies can collect data and transform it into business insights that are placed right at employees’ fingertips.

From the shop floor during the value chain, IIoT and the new technology can help employees manage results.

Formula One race cars offer a useful analogy of the immediate value IIoT can deliver. These race cars have always been high-performance cars, but until the advent of sensors, drivers and crews had little understanding of what was happening in real time under the hood.

Today, dozens of sensors in the engine control unit collect data from the engine, transmission, suspension, and elsewhere and feed it into the trackside support teams in mid-race. Teams now have the ability to anticipate and fix issues on the spot, to maximize performance in a competitive environment with razor-thin of margins of error.

The 4IR factory utilizes essentially two types of new equipment. First, an army of sensors, which are embedded across the shop floor to collect data on productivity, equipment utilization, machine breakdowns, maintenance, and so forth. 

Second is a new apparatus that standardizes and automates a process or task. This might be a production-line apparatus that captures data about the amount of merchandise passing by each minute, or that tracks gear vibration levels to help predict maintenance requirements.

More crucial than new facilities and new machines is having a robust technology ecosystem, along with IIoT architecture that has scaling potential. In a recent McKinsey survey of managers at more than 700 industrial manufacturers globally, more than 40 percent pointed to IT deficiencies as the main challenge in successfully implementing digital initiatives–even though digital manufacturing, by definition, is technology driven.

Lighthouse companies are proof that new technology doesn’t require brand new facilities or costly makeovers. Petkim, a Turkish petrochemical company, deployed IIoT and other digital solutions in its own 35-year-old facility, successfully enhancing yields and quality, optimizing energy usage, and instituting digital maintenance.

Shanghai-based Baoshan Iron & Steel adopted advanced IIoT for process optimization, artificial intelligence for visual inspection, and other advanced technologies in its 40-year-old factory.

Procter & Gamble’s Rakona plant in the Czech Republic, which generates some 4 million cases of dishwashing soaps and fabric enhancers each and every day. After weathering the shift to liquid products, the plant had to ramp up capacity, which required digitization and automation.

To help address the shortcomings of manual sampling and subsequent delays in product releases, the company eventually rolled out an in-process quality-control program in its legacy systems. 

Sensors now monitor product characteristics, allowing operators to obtain data that can help determine batch quality for release. It also enables them to stop the line if a deviation occurs. The outcome: a 50 percent reduction in reworking and complaints, less scrap, fewer quality inspections, and a throughput time reduction of 24 hours.