Chances are you are planning for or implementing some type of digitization project at your plant? But do you understand what it all means in terms of where to start, how to deal with the common challenges and where you should be in the future if you manage it correctly?
To truly comprehend this industry shift, it helps to first define what it means. Digital transformation integrates digital technologies into plant operations to improve product quality, eliminate data silos, enhance asset productivity and significantly reduce unplanned downtime.
Most importantly, you should recognize that this is not a one-size-fits-all process—it’s a unique journey for each organization.
As organizations embark on this journey, it’s essential to understand that a solid foundation is crucial. That’s why each phase of digitization must happen before advancing to the next stage.
Stage 1. Modernizing—laying the foundation
At this stage, organizations with legacy machinery and basic digital control systems—including outdated field devices and human machine interface (HMI) panels—recognize the necessity for upgrades. Data collection at this point is relatively simple, involving basic operational data from machines, system health metrics and the initial digital conversion of analog signals.
Important aspects of this stage include reducing manual interventions through automation, extending the machinery lifespan and setting the stage for future digital expansion. Modernizing machinery brings remarkable benefits, such as reduced downtime due to fewer manual interventions and human errors, more energy-efficient systems and potentially lower maintenance costs. Additionally, employees shift from manual monitoring to supervisory and problem-solving tasks, adding more value to the organization.
Stage 2. Connections—exploring your network infrastructure
After the journey begins, connectivity starts. This stage requires proper planning and sizing of your network infrastructure. As you add more data collection, your network must handle the additional traffic. If the busiest highway networks in the world were poorly designed with only two or four lanes, there would be major traffic problems. Your plant is no different. Advanced data collection capabilities such as real-time machine data and sensor readings need a more robust network to operate well.
Key challenges in this stage include rectifying data silos, keeping data secure and improving real-time data accessibility. Moreover, this connectivity stage emphasizes security and standardization, incorporating data validation checks, regular security audits and redundancy mechanisms.
Stage 3. Visibility—progressing with data insights
Visibility is like the scenic overlook on a long road trip. It’s an exciting stage where organizations begin to see the tangible benefits of digital transformation. Technologies at this stage include advanced data visualization tools, HMI panels and supervisory control and data acquisition systems (SCADA). More elaborate forms of data, such as production metrics, quality metrics, performance KPIs and environmental and safety metrics are collected and viewed. This data provides a deeper understanding of production processes, helping to identify inefficiencies and bottlenecks. Process engineers and stakeholders see how real-time data availability helps make smarter decisions. Employees also have actionable metrics for quick problem-solving and a goal-oriented approach.
Stage 4. Transparency—improving compliance and risk
The transparency stage involves complex systems like cloud-based data storage, manufacturing execution systems (MES) and integrated enterprise resource planning (ERP) solutions. This stage marks a shift to process flow data, material traceability and employee performance metrics. The challenges for this stage are ensuring consistent product quality, providing an end-to-end view of operations and centralizing data storage to share insight across the organization. This transparency allows leaders to be precise with compliance audits with data readily available.
Stage 5. Predictability—starting a proactive approach
Predictability involves using predictive analytics tools, machine learning models and advanced sensors. Data collection focuses on condition monitoring metrics like vibration, temperature, power use and quality, and even cybersecurity log data. The benefits here range from maximizing asset longevity, reducing unplanned downtime, minimizing waste and bolstering cybersecurity. However, implementing these technologies requires a significant budget and the involvement of experts on your team. Collaborating with a reliable partner will be essential to successfully navigating the last two phases. This stage also features a marked shift in employee skill sets from reactive to proactive thinking that includes being able to interpret data and understand predictive analytics.
Stage 6. Prescriptive—responding to market changes
This stage is characterized by advanced AI algorithms, decision-support systems and automated feedback loops. Data collected for use at this stage optimizes production pathways and machine settings, while root cause analysis identifies precisely what went wrong and where. Challenges like resource optimization and process refinement are continuously addressed, enabling agile responses to external factors such as market shifts and competitive threats. Upon reaching this stage, be sure to balance automation with human oversight to prevent unforeseen consequences.
