Industry 4.0 project stuck in pilot? Address these 5 challenges

Maybe you’re tired of hearing about Industry 4.0, but we’re just now getting to the good part. Attitudes about Industry 4.0 use cases—and digital transformation as a whole—are largely optimistic among industrial companies. State of the Industry research conducted by IndustryWeek in the last two years tells us that:

• 86% of manufacturers indicate their company is moving forward with digital transformation.
• 45% of manufacturers indicate a positive attitude with larger-scale teams engaged and significant utilization.

With a front row seat at HighByte for the last 7 years, it’s hard for me to overstate how impactful Industry 4.0 success can be for manufacturers. We’ve seen meaningful outcomes when a digital foundation is in place to collect data from the factory floor and orchestrate it in a way that prevents downtime, ensures reliable reporting, improves product quality, enables innovation, and increases revenue.

But despite the potential, many organizations face significant challenges with Industry 4.0 use cases—especially when it comes to managing and scaling their data.

According to a study from the World Economic Forum, 70% of Industry 4.0 projects still fail to move beyond the pilot phase of development. While there are compounding reasons for “pilot purgatory,” the lack of an industrial data strategy is at its core.

An industrial data strategy adds resilience to your architecture—but it cannot simply be outsourced, and it does not originate from the deployment of new technology. Instead, it is a discipline that requires OT/IT collaboration and a commitment to solving data problems, from the edge to the cloud and back again. To understand why an industrial data strategy is necessary, let’s review the top 5 digitalization challenges faced by industrial companies that prevent them from achieving their Industry 4.0 projects at scale and pace.

1. Complex Architectures

First, the architecture of systems that cross the OT and IT domains are exceptionally complex. Ask any of the world’s largest industrial enterprises who manage a digital environment spanning various devices, software, protocols, geographies, systems, and processes. Even the system architecture within just one plant can vary widely. One cell or line might look completely different from another at the same site. The differences are amplified across unique plant locations, especially internationally. 

The complexity of these architectures inherently creates diversity in data sources and targets while exposing point-to-point integrations as fragile connections. This approach is not easily set up, maintained, or possible to extract value from.

To address complex architectures, it is important to consider abstraction. Ripping and replacing legacy systems and those inherited in brownfield acquisitions is not practical. Instead, implement processes, teams, and technology that can help create a digital abstraction layer between these diverse data sources and targets in your enterprise architecture. Ask yourself:

• Do we have a space where cross-functional teams have an opportunity to align on their project goals and standards of practice?
• What is our method for governing and documenting system integrations and the roles that impact data movement from the OT to the IT domain? 
  
  

2. Data Variety 

The variety of industrial data is vast and not consistently structured. You’re likely dealing with machine and vendor variability. As my colleague John Harrington explained, it’s typical for vendors to implement the same standard slightly differently. Historically, vendors have refined their systems and changed data models over time to suit their needs. As a result, even minor variations in data sets require human interaction to link these machines to other systems in the network and automate dashboards or analytics.

Furthermore, standardizing device-level data into structures is important, but only the beginning. Industrial data goes far beyond telemetry data. Your transactional systems, time series databases, and files, are producing data as well—and you can’t make strategic decisions if you’re not linking your machine data to other systems across your organization. 

Having the agility to work with various types of data can be the difference between an Industry 4.0 project that achieves sustainable success or one that is too nearsighted and adds technical debt. Ask yourself:

• Are we able to curate and merge various data types at the edge?
• What’s stopping us from addressing industrial data problems in real time? Is it our technology, processes, or fear of change/failure?
• How do we onboard brownfield facilities in terms of data access and analytics? 
  
  

3. Missing Context 

Data coming from machines and industrial systems is raw and not prepared for specific applications beyond process control. Edge devices like machines, sensors, cameras and industrial tools do not provide data that is ready to be sent northbound to storage and analysis layers in the Cloud.  

To make industrial data usable, we need to add context to it. This can be achieved by merging edge data with information from other systems, like a MES, ERP, or even imagery or inspection reports, and adding additional context for line of business users who will leverage the dataset downstream. Critically, the data contextualization process should happen at the Edge, prior to datasets being sent to any storage or consuming applications in the Cloud. Ask yourself:

• Can we contextualize data close to where it is created?
• Who “owns” data contextualization in our organization?
• Do operators have a no-code/low-code means of preparing industrial data on-premises for downstream applications? 
  
  

4. Lack of Resources 

You probably have a backlog of projects and not enough resources to tackle them. This is common. Industry 4.0 projects often stall due to a lack of resources. Budgets are tight, human resources are scarce, knowledge silos limit progress, and OT-IT alignment remains a challenge. Accomplishing a limited POC is achievable, but scaling that POC to other lines, areas, and sites usually requires more resources involved.

While Industry 4.0 success does not hinge solely on access to resources, it can be a decisive factor for the pace at which the project moves and the completeness of the solution. Fortunately, we have observed the power that an industrial data strategy provides for our customers at HighByte. A strategy—which includes a strong digital foundation and prioritized technical projects tied to business outcomes—can help offset resource constraints. Ask yourself:

• Does our organization have an industrial data strategy?
• Who owns the data strategy, how often is it reviewed and amended, and who can contribute to it?
• What tools could enable our teams to do more with less?
  
  

5. Outside Demands

All these challenges are compounded by outside demands from other teams, your company’s executive leadership, and the market. 

These demands can be technology driven, like the push to adopt a Unified Namespace (UNS) architecture, or to deploy AI services on the factory floor. However, they can also go a level higher, like the demands to meet sustainability goals, navigate mergers and acquisitions, work around tariff and labor uncertainty, and coordinate onshoring/reshoring.

The market is moving fast. Technology is changing rapidly, and global economic uncertainty has put some manufacturers into decision paralysis. Ask yourself:

• How can we establish an industrial data strategy that makes our organization more connected and agile in the face of opaque demands?
• Do we have an enterprise architecture in place that will allow us to pivot or scale without interrupting operations? 
  
  

Wrapping Up: Solving Industry 4.0 Challenges with DataOps

Addressing these 5 challenges is hard work, but making continuous improvement on them is a must for companies who are serious about driving ROI from Industry 4.0 projects. 

At HighByte, we believe Industrial DataOps is a critical enabling technology of a mature industrial data strategy, allowing Industry 4.0 projects to go from POC to mass-deployment with relative ease. The research shows that awareness and application of DataOps is growing, and manufacturers are using it to not only solve increasingly diverse and strategic operational and business challenges, but to also position their organizations for a more competitive future.