How the Combination of Analytics and Automation Drives Manufacturing Success

• Data-driven decision-making replaces guesswork across all organizational levels, giving plant operators immediate feedback, supervisors shift-based insights, and managers strategic visibility into production trends and business alignment. 
• Cultural transformation is essential for successful analytics adoption, requiring employee training, cross-departmental collaboration between IT and OT teams, leadership support and data champions who model effective analytics application. 
• Strategic implementation follows proven best practices, including starting with clearly defined use cases for quick wins, engaging stakeholders early, prioritizing system integration, ensuring intuitive usability and regularly reviewing progress to maintain momentum. 

Industry 4.0 and the digital transformation of industry have accelerated our expectations around data-driven decision-making. Now, manufacturers must compete not only on product but also on their operational efficiency, agility and resilience. 

Integrating analytics into plant automation systems supports these goals by:

• Improving efficiency: Analytics highlights inefficiencies in equipment use, material flow and workforce deployment and processes. 
• Reducing downtime: Real-time alerts and predictive capabilities help identify issues before they become costly failures or safety hazards. 
• Supporting sustainability: Energy, water and raw material use can be optimized, reducing costs and lessening harmful environmental impacts. 

In short, analytics can shift facilities from reactive to proactive, giving them a competitive edge in both day-to-day performance and long-term strategy.

Adopting use of data analytics software also lays the groundwork for AI as machine learning and artificial intelligence depend on clean and reliable data. Analytics builds the foundation for adopting these advanced technologies.

Solving operational challenges

The main plant floor challenges facing manufacturers today should sound familiar: bottlenecks, unplanned downtime and inconsistent product quality. Analytics-enabled automation addresses these problems in the following ways:

• Bottlenecks and throughput: By monitoring production in real time, analytics reveals where processes slow down and why. Teams can then adjust scheduling or equipment parameters to improve flow. 
• Downtime and maintenance: Predictive insights help to identify early signs of equipment wear, allowing maintenance to be scheduled well before failures occur. 
• Quality issues: Analytics detects anomalies in production parameters, which helps prevent defective products from reaching customers. 
• Energy and resource use: Plant data measures consumption against output, highlighting where utilities and raw materials can be conserved. 

Instead of diagnosing problems after the fact, teams gain the ability to address them as they happen or even prevent them entirely.

Improving operations with better data

One of the most significant benefits of integrating analytics into automation is its impact on decision-making across all roles. Plant operators receive real-time feedback, enabling quicker responses to abnormal conditions or alerts. Supervisors can track performance across multiple shifts, adjusting their staffing or scheduling as needed. Plant managers can gain transparency into production trends, aligning an organization’s resources with its broader business objectives.

Daily meetings also become more effective. Instead of debating assumptions, teams can review clear data stories and learn what worked, what didn’t and what to prioritize next. This builds a culture where decisions are grounded in evidence, not guesswork.

Confronting the cultural concerns

Many companies hesitate to adopt analytics, often because of misconceptions. Some may believe analytics requires replacing their existing systems (in reality, it can often be layered onto current infrastructure). Others assume analytics is only for large enterprises, but small and mid-sized facilities can benefit just as much. 

You shouldn’t expect instant ROI because analytics delivers value over time as data quality and adoption improve. However, addressing challenges and misconceptions through ongoing training, implementing strong data governance and securing OT networks and establishing robust virtualization environments sets the stage for quantifiable success.

It's important to remember that while technology enables change, it’s people who drive it. Facilities adopting analytics typically experience a cultural shift in that teams move from relying on intuition to trusting data-backed insights. This shift requires:

• Training and reskilling to help your employees engage with new tools. 
• Collaboration across departments, especially between IT and OT. 
• Leadership support to encourage experimentation and adoption. 
• Data champions or individuals within operations who model how to apply analytics effectively. 

The goal is not to replace experience but to enhance it, combining operator knowledge with data-driven visibility for stronger outcomes.

Best practices for analytics integration

Facilities that successfully integrate analytics tend to follow a common set of practices:

• Begin with a clearly defined use case that can deliver quick wins, building credibility and confidence early in the process. 
• Stakeholders from across IT, OT, maintenance and operations are engaged from the outset to ensure alignment and buy-in. 
• Careful attention is given to integration with existing control systems and processes, which helps avoid unnecessary disruption. 
• Usability is prioritized so that dashboards and visualizations are intuitive, actionable and support decision-making at all levels. 
• Finally, teams regularly review progress, adjusting goals and strategies to stay aligned with evolving business objectives. 

Taken together, these practices provide a foundation for scaling analytics incrementally while maintaining momentum and trust.

David King is client delivery manager and Jeremy Van Den Berg is product manager at Interstates, a certified member of the Control System Integrators Association (CSIA). For more information about Interstates, visit its profile on the Industrial Automation Exchange.