According to LNS Research, Industrial Transformation (IX) is defined as a proactive and coordinated approach to leverage digital technologies to create step-change improvements in industrial operations. Today, around 45 percent of companies are currently executing an IX program. But just because an organization is executing an IX program does not mean it is experiencing the benefits of IX. In fact, it is estimated that 15 percent of companies are stuck in the pilot phase.
Usually, when companies are stuck or have failed, it is because new technology was pursued, because it seemed like a quick-fix or was the next “shiny new tech toy.” Unfortunately, in most cases, the project/implementation would stop, never making it into widespread adoption or achieving the intended ROI. Organizations who find themselves in an extended pilot phase are likely experiencing one of four major failure modes of Industrial Transformation:
- Failure to Converge
Leaders in Industrial Transformation (IX) also tend to be leaders in IT-OT convergence, meaning they have entirely transformed their organizational structures and embraced data-centric or services-centric cultures. Conversely, organizations that are neglecting IT-OT convergence and instead focusing too heavily on physical technology are falling behind. This mode of failure reminds us that, to unlock the benefits of IX, organizational structures must undergo a transformation of their own.
- Persistent Data Silos
Whether it be due to privacy disputes or stringent requirements surrounding mission-critical systems, many enterprises have taken a siloed approach to data management. While it is important for sensitive data to remain secure, these data silos have become insurmountable roadblocks to system evolution. By keeping data sources separate, businesses cannot gain a comprehensive view of their projects, resulting in isolated OT and IT data, limited results, and ultimately, long-term failure. This mode of failure reinforces that standardized, usable information, not just data, is foundational to IX success. Combining financial data, supply chain data, customer data, and OT or IIoT data can lead to dramatic improvements in your business.
- Internal-Only Mindset
Other businesses take on an internal-only mindset, focusing on internal data to resolve challenges without considering information from suppliers, customers, or third-party systems. By limiting data collection to internal systems only—even though external systems have significant impacts on internal operations—an organization inherently limits its visibility into the problem and its ability to pursue a solution. Companies need to look outside of their four walls when building their enterprise data model and think more broadly about the challenges they’re trying to address. This third mode of failure highlights the importance of taking a holistic view that extends beyond a company’s four walls.
- Treating Factory Operations as Off-Limit
Often, businesses are busy anticipating the results of new digital implementations, such as AI or analytics, and neglect the importance of core industrial operations. Sometimes, the decision to treat factory and plant operations as “off-limits” in IX efforts comes from an avoidance of these areas because of their inherent complexity. However, industrial companies that do not treat the plant or factory as a core pillar of their IX efforts will not make the step-change improvements required to elevate business performance. This final, but perhaps most significant, mode of failure demonstrates that the factory floor is inseparable from real IX.
Starting with the Right Use Cases
We discussed what leads to IX failure, but what can companies do to ensure their IX journeys are positioned for success? To answer this question, LNS Research examined 35 business use cases for IX. It divided them into six critical pillars of success: Connected Experience, Connected Supply Chain, Connected Operations, Connected Worker, Connected Product, and Connected Assets. Of these six categories, only two included use cases that were both high-impact and required a low amount of effort on the part of the organization.
The first pillar of success is Connected Assets, which included three top-performing use cases. First, predictive maintenance has proven itself immensely valuable to high-cost assets, using sensor and machine data, rather than usage or schedule tracking, to improve uptime and lower the cost of maintenance.
The second pillar, predictive quality, which also leverages sensor and machine data, has the power to deliver high returns for manufacturers. While quality has been drastically improved over the years, many continuous improvement programs were producing diminishing returns in the recent years. But by leveraging sensor and machine data, this new model for predictive quality is proving to be very beneficial for many companies.
The third pillar of success is Connected Workers, which included one top-performing use case. In order to scale rare or expert skills and augment training from anywhere in the world, industrial enterprises have been increasingly equipping employees with mobile apps or AR headset systems to guide remote technicians through complex processes. This use case, like the first three, also affects change within the plant.
All these scenarios underscore the fact that the most successful IX journeys begin with industrial operations, taking common business challenges with financial parameters and turning them into tangible ROI.
What This Means for You
Ultimately, IX failure modes and top-performing use cases reveal four key recommendations for IX success. The first is to start with data, which is integral—no matter where a company’s efforts are focused. The second is to look beyond your organization, incorporating data, expertise, and decision-makers from beyond your four walls. The third is to pursue IT-OT convergence, an indispensable aspect of achieving IX success. Finally, the fourth is to start with industrial operations. Most of the top-performing use cases are focused on the factory. There is no IX success without industrial operations.
BUT Buyer Beware
Data is integral, but there are many vendors out there that will tell you that they can connect to machines and get you the data. Without contextualizing that data, you have huge data lakes that deliver ZERO value. Contextualization does not just happen overnight, and it is not something you can “append” onto a system, and it simply works. Contextualization takes years of both connectivity and MES experience as well as a platform that is architected from the beginning to operate upon a contextualized data model for any of that data to have meaning to drive tangible benefits. If you are evaluating technologies to leverage the mountains of data, you need to dig deep into their offering. Many companies will throw out the word “contextualization,” but you need to challenge them. Are they inserting that data into an ontological schema that means something? If they answer yes, then they should be able to prove it to you and not just in a “canned” demo environment but demonstrate their contextualization claims by using your data. Guarantee, they are not contextualizing the data in a standardized way, making it excruciatingly hard, if not impossible, to use.
If you’re interested to find out more:
Watch our full webinar here: IIoT & Industry 4.0: Separating Fact from Fiction
Access the latest LNS Research whitepaper: Avoiding Pilot Purgatory
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