Key Considerations for Scaling Your IIoT Platform

Industrial organizations are beginning to recognize and seek the advantages of an IIoT platform. However, the path to effectively and efficiently leveraging this solution is often a challenge. Taking on an IIoT implementation comes with its own challenges—including speed to implement in a cost-effective way—but many digital transformation leaders don’t consider how to scale this approach until it’s too late. 

Whether you’re growing in use and adoption of your IIoT platform or beginning your digital transformation journey, the hurdles you’ll face along the way don’t need to come as a surprise. Here’s some key considerations for scaling an IIoT platform from managing an initial implementation to a global roll-out.

Setting the Wrong Benchmarks

When building an IIoT strategy, it’s natural to read case studies of companies that successfully implemented digital transformation initiatives. Reading about the success of other companies is a great way to learn how your company can set benchmarks and goals. 

However, you should steer clear of simply replicating what other companies set out to achieve with their IIoT initiatives. Using generic industry benchmarks or mimicking what others in your market have done can mean you aren’t setting benchmarks that are right for your company’s digital journey.

Rather than rely on standardized benchmarks, you must create your own benchmarks for ROI. This requires you to fully understand your company’s problems and the most important variables for your organization to improve. (Not sure how to define your company’s goals? Check out our “5 Pillars for Digital Transformation” white paper to build a solid foundation for your IIoT strategy.) 

Remember: your initial IIoT projects should be small in scope. This makes it possible for you to understand where improvements are made and why. Answering these questions and pointing towards specific wins makes it easier to convey successes to management when asking for more resources to scale IIoT initiatives. Working with a strategic consultant or partner who can help guide you in defining your goals could be a good option for your company, too. They can help you understand how to extract or cleanse data for your needs.

Another benefit of having a firm grasp on your organization’s pain points is your ability to see beyond isolated problems to wider, overarching issues that impact multiple aspects of your organization. Informed leaders are capable of looking beyond today’s challenges. These leaders set larger, all-encompassing goals and have a wider vision. When the opportunity arises to scale your IIoT initiatives, you’ll be armed with the necessary knowledge to make a case for where the next big wins are most likely to be found.

Engaging Plant Stakeholders

Many IIoT initiatives start at the corporate level. Executives vet third-party IIoT vendors or try to orchestrate in-house solutions. They also set company-wide objectives that fall on operational teams to implement. 

In the midst of planning and bringing on new tools, many executives neglect engaging key stakeholders at their facilities. Not surprisingly, this often leads to resistance from operational teams. Employees won’t be compelled to carry out objectives that they didn’t help set or use tools that they didn’t get to try before purchasing. 

As you start building your IIoT implementation plan, actively engage with key stakeholders at your facilities. Securing early buy-in from these stakeholders will not only improve their willingness to take on new company objectives, but it is very likely to improve support from their teams, too. Reassure them that their opinions, insights, and feedback are valued by bringing them into the conversation and taking their input seriously. 

The benefits will transcend simply gaining buy-in during the implementation stage. Working closely with plant managers can help you build robust business cases for why the company needs to continue investing in Industry 4.0 tools. Sharing success stories from your facilities with the C-Suite (and proving that previous investments are utilized daily by operations teams) can help you obtain further resources for scaling IIoT initiatives. These wins also make it easier for executives to bolster positive messaging regarding IIoT to the rest of the company.

Balancing Team Dynamics

It’s a common situation in today’s manufacturing world: incoming young talent goes head-to-head with your experienced workforce. 

Today’s incoming manufacturing workforce understands the IIoT tools, techniques, and strategies that power digital transformation. However, these younger employees aren’t as familiar with your company’s specific processes or operational nuances. 

On the other hand, your existing employees have years of implicit knowledge. These operators possess skills that aren’t easily conveyed verbally to new hires because they have gathered and honed these skills over countless years of experience. The problem arises when these existing operations teams rely on their “intuition” over new technologies designed to help them perform their job better. 

As an executive or manager, you must find a way to bridge the gap between your new employees and existing employees. Strained relationships between these two groups will make it nearly impossible to scale your IIoT initiatives. 

Artificial Intelligence (AI) can act as a bridge between these two different workforces. Utilizing new IIoT technologies—such as AI—will draw in young talent that is excited to use these modern tools. Implementing this kind of technology means you are better positioned to bring in young talent as your aging workforce retires.  

Your existing workforce plays a crucial role in properly training your AI software. Their process-specific knowledge informs the AI platform and helps the AI learn faster. In other words, without these employees, your AI may take longer to fully grasp all the nuances of your existing processes. 

It’s vital that your existing employees understand that their unique skillsets are the driving force behind the AI’s success. Without their expertise to properly train the AI, the company may run up against false starts or dead ends. Remind them that AI is not there to replace their job. On the contrary, the AI will only be as successful as the valuable knowledge it receives from these operators.

As you continue to hone your AI technology, your incoming workforce can fully leverage its power to quickly get up to speed on correct operational procedures. The AI tools make it easier for these employees to understand where problems arise during production. While they may not have the implicit knowledge of your existing workforce in their minds, your existing operators trained the AI platform informing the decisions of new operators. They can see problems arise on the screen and send in crews to make the proper repairs.

Stagnant Digital Twin

The word “Digital Twin” is thrown around a lot when IIoT vendors pitch their products. Not all Digital Twins are created equally, though. 

In fact, most Digital Twins are a “snapshot” of your production process from a given point in time. It is built using historical process data from previous operational runs but doesn’t take into account any new information once built. These models are useful for understanding a plant’s dynamics and showing you areas that need to be updated. 

This is not a responsive operational model. It does not take new process data into consideration or update in real-time. As your teams start making operational improvements or process adjustments, this kind of model doesn’t account for new changes. In short, it is a stagnant representation that doesn’t give you a true representation of a given machine or process in real-time.

While useful for planning, a “snapshot Digital Twin” is not a viable solution for scaling your IIoT strategy. It won’t take long for this model to become dead weight. It is impossible for your organization to move forward and make new discoveries without building another Digital Twin of your new process.

If your goal is long-term autonomy and continuous improvement, it is crucial to have a responsive Digital Twin that continuously brings in live operating data. As your teams constantly discover new opportunities for improving your operations, your organization can continuously level-up from wherever you are now. 

Merging IT and OT

According to Gartner, Operational Technology (OT) is “hardware and software that detects or causes a change, through the direct monitoring and/or control of industrial equipment, assets, processes, and events.” At most factories, though, OT isn’t traditionally set up to work within an Information Technology (IT) infrastructure (defined by Gartner as the “entire spectrum of technologies for information processing, including software, hardware, communications technologies and related services.”)

Most IT solutions are geared towards satisfying a large number of end-users. IT teams manage massive systems that require broad implementation strategies in order to make things work for every level of the organization. Simply put, IT solutions are designed to work well on a global scale. 

OT teams, on the other hand, tend to solve problems using custom approaches. Every plant or facility comes with its own unique set of challenges and pain points that often require a variety of tailor-made solutions. As a result, OT teams are usually focused on finding solutions that are specific to their own facility. These solutions could range from updating physical assets, bringing in new systems, organization restructuring, or a range of other options. Unlike global IT solutions, your OT teams are typically solving problems on a localized scale.

In addition to these different problem-solving approaches, you likely have technological gaps between IT and OT. Many plants use legacy machines or equipment that lag behind the rapid pace of IT advancements. Outdated hardware makes it difficult for even the most advanced IT solutions to accurately obtain and track process data. As a result of this mismatch, long-standing companies struggle to fully utilize their data and understand its value. 

How do you bring new IT capabilities into existing OT spaces in order to scale your digitalization objectives? You’ll be able to develop a plan once you can find a way to get your IT and OT teams working towards the same overarching goals, though the answers will almost certainly depend on the specifics of your organization.

Try to find areas where IT and OT teams can have a shared common vision. For example, employee safety is at the forefront of every manufacturing company, regardless of an employee’s team or role. If it’s possible to bring in robots or drones to perform operational tasks that are high-risk for humans, this is advantageous to both IT and OT alike. 

OT teams will be motivated to implement new technology that ensures the safety of their team members. This also gives IT teams the opportunity to implement updated technologies that make it easier to gather important data for Industry 4.0 goals. 

Focusing on the same common goal—in this case, improving employee safety—facilitates cross-departmental conversations that will get IT and OT teams out of their silos. This encourages them to work towards wider IIoT objectives.

Conclusion

Scaling your IIoT solution requires foresight into future needs as well as effective planning for today’s challenges and opportunities. Create alignment across departments and buy-in amongst stakeholders early on and continue to reinforce the value of an IIoT platform throughout the process.