Rolling out a pilot program for a new Industry 4.0 technology is an exciting part of the digitization journey. Harnessing that technology to create tangible, scalable value in production often turns into a much rockier road as the pressure for delivering results ramps up quickly. Without a clear gameplan in place, it can be easy to get stuck in dreaded pilot purgatory.
Pilot purgatory is unfortunately extremely common. McKinsey’s 2020 Industry 4.0 survey of 400 global manufacturing companies showed that 74% of surveyed manufacturers said that they have not yet successfully scaled some or many Industry 4.0 use cases. That number is 18% higher than the level recorded in the same survey in 2019.
To add even more pressure, COVID-19 has raised the bar for what “successful scaling” means among manufacturers. Many companies realized they have further to go than they previously thought before their implementations are truly, fully scaled. At the same time, the pandemic also brought attention to the importance of Industry 4.0 technology for manufacturers. Those with mature Industry 4.0 use cases that were past their pilot phase were better able to respond to the crisis.
With Industry 4.0 demand high and the importance clear to the market, the digital transformation of manufacturing is accelerating. Below are tips for making sure that your next Industry 4.0 initiative moves swiftly through its pilot phase and becomes a successful fully scaled initiative.
Define What Successful Scaling Means
McKinsey reported that during the pandemic, agility in operations became manufacturers’ top strategic priority (18.4%), above raising productivity and minimizing cost (17.2%). Companies may now be factoring in whether agility has increased when measuring whether they have successfully scaled an Industry 4.0 use case. Eliminating unplanned downtime, for example, increases agility, since production capacity becomes more predictable.
Successful scaling means more than just deriving value from an Industry 4.0 use case at multiple sites. It also means deriving higher-level value at more mature sites, accelerating the rollout of other Industry 4.0 use cases and improving agility and flexibility across the entire manufacturing operation.
Choose the Right Use Case(s)
When selecting an Industry 4.0 initiative, focus on the problem you want to solve first. Focus on the use cases and then figure out what technology would work best for achieving your goals, whether it’s AI, sensors, IoT or something else. Choose a handful of digital use cases that target the top one or two strategic objectives for your organization, and pursue a rapid, agile process to refine, roll out, and aggressively scale these technologies.
For instance, a common goal for manufacturers is to avoid costly machine downtime. In this case, focusing on machine health technology would be a valuable Industry 4.0 use case. Machine health improves maintenance programs by providing insights into whether a machine needs to be repaired or a part replaced before the machine breaks down and stalls production on the factory floor.
The fact that Machine Health combines artificial intelligence and the Industrial Internet of Things (IIoT) to achieve its objective should be a secondary factor in the decision to pursue an initiative like this. These are the tools for accomplishing what you want to build. Unfortunately, it’s common for manufacturers to start with the tools they want to use, which later leads to trouble in figuring out what to build.
Assemble the Value Team
The organization around the technology, and not just the technology itself, helps define whether a pilot becomes successful and provides value.
Value is the organization’s perception of the worth of a particular Industry 4.0 use case and may consist of a mix of quantitative and qualitative benefits. A value team should be assembled to define that value and how to get it fast and at scale.
Going back to the machine health example, organizations implementing this technology often want to reduce unplanned downtime and maintenance costs or improve OEE. Qualitative value include improving agility, safety, reducing reactive maintenance and upskilling maintenance and reliability teams.
The ideal value team includes diverse perspectives, levels and skill sets along with a shared commitment to acting according to data and turning promising technologies into practical solutions. The value team should contain the following members:
● Corporate champion: Contributes a C-suite perspective and takes responsibility for delivering enterprise-level results
● Corporate enabler: Provides subject-matter expertise on whatever business unit the Industry 4.0 use case affects, such as production or quality assurance;
● Site leader: Leads the site where the technology will be deployed and serves as both champion and supervisor of the use case
● Problem solver: A technician who directly works with the Industry 4.0 technology.
Onboarding: Generating Value Fast
The goal of onboarding is to help a site develop a basic understanding of the new technology, why it is important and how to derive value from it. This involves planning a rollout and doing installation and training, but also achieving the first small wins and starting to integrate the new use case into every day workflows.
Set concrete targets which must be reached to exit this phase at a site. Maintenance teams often need to go through three to five machine improvement cycles in order to create new habits, so that is an onboarding target that should be used for a new site.
Adoption: Capture More Value
The quick wins achieved during onboarding help to build momentum behind the new use case at a site. During adoption, here are ways you can harness that momentum to capture more value:
● Quantify every win: If possible translate it directly into dollars. For machine health, that means calculating downtime costs saved by each machine improvement.
● Communicate wins: Wins should not only be quantified; they should be communicated inside and outside the site.
● Set adoption targets: For machine health, during onboarding, maintenance teams should pay attention to machines that are not in imminent danger of failure but have problems like early indications of bearing wear. By the end of the adoption stage, sites should be addressing 85% of machine health alerts and acting on them to make repairs in less than 10 days.
Scaling: Expand Value
Once adoption is high at one or two sites, you can expand value by rolling out the use case at new sites, as well as capturing new types of value at existing sites.
Document and package lessons learned at the first sites, including challenges and roadblocks, in order to get value even faster at new sites. Next, send a successful site champion to new sites and use corporate champions to reach multiple facilities and stakeholders quickly. Eventually, you can establish a community of champions who mentor others across the organization.
After a successful pilot, focus should shift to getting higher-level value from the Industry 4.0 use case. The exact nature of this higher-level value will vary according to the use case. For machine health, during the onboarding and early adoption stages, focus on getting quick wins by repairing machines in imminent danger of failure. Once machines stop failing unexpectedly, the focus can shift to planning shutdowns more efficiently or identifying systemic reliability problems like misalignment issues.
Future Outlook
COVID exposed the need for agility within all aspects of our supply chains. While many manufacturers have likely had bad experiences with technology rollouts, the next project doesn’t have to hit the same pitfalls. Identifying the problem that needs to be solved and what success means to your organization; assembling the right team; and organizing how value is recorded, communicated and shared are all key steps for ensuring that Industry 4.0 initiatives don’t get stuck in purgatory and are able to scale quickly. It’s this ability to successfully manage pilot programs that will separate manufacturing leaders from the rest of the pack.
Saar Yoskovitz is co-Founder and chief executive officer at Augury, a digital machine health company. Since co-founding Augury in 2011, he has been working with customers and partners to transform how they work to make products, deliver services and improve lives through real-time insight into the health and performance of industrial equipment and systems. Saar holds a dual bachelor's degree in electrical engineering and physics from the Technion, the Israel Institute of Technology.
