Feature article - The Visual Factory: From data to operational awareness

Andreas Eschbach, founder and CEO of Eschbach.com explains how visualisation data can be used to collaborate and improve production in pharmaceutical and chemical process manufacturing

Pharmaceutical manufacturing operations produce vast quantities of information daily. Batch records, environmental monitoring data, sensor logs, operator notes, deviation reports and shift handover logs are all components of the operational landscape. These data streams are essential for maintaining regulatory compliance, product quality and operational efficiency. However, they are often fragmented across multiple digital systems and informal communication channels.

Typical sources include manufacturing execution systems (MES), laboratory information management systems (LIMS), quality management systems (QMS), supervisory control and data acquisition (SCADA) platforms and enterprise resource planning (ERP) systems. All of these may have their own interface, structure, and reporting frequency. As a result, accessing and contextualising information can be time-consuming and inconsistent.

Poor visibility increases the risk of communication gaps during shift handovers, impedes deviation resolution and can lead to duplicated effort across departments. Operators may spend valuable time hunting for data, managers may make decisions based on incomplete information, and maintenance teams may act without full awareness of process context. Simply increasing data collection does not solve this; in fact, it can make matters worse by overwhelming teams with raw information.

Role of visualisation

To address these challenges, more and more sites are turning to visualisation techniques. The human brain is far more effective at recognising patterns and anomalies from visual representations than from raw text or tables. Dashboards and graphical interfaces can distil complex, multi-source data into formats that are easier to interpret.

The Visual Factory idea takes this further by integrating data from disparate sources into a single operational view. Well-designed visual tools use techniques such as colour coding, spatial layout and concise metrics to make essential information immediately clear to different user groups. For example, temperature or pressure deviations might be flagged in red, while throughput and quality metrics are displayed as trend lines.

Crucially, visualisation is most effective when it is role-specific. An operator might need a line-level dashboard showing equipment status and batch progress in real time. A quality engineer might want to prioritise deviations and investigations, while a site leader may require an aggregated, high-level overview to identify systemic trends. By serving each role with the appropriate visual context—derived from the same underlying data—the Visual Factory provides a shared yet customised view of operations.

Applications in daily operations

This approach has been applied at several large biopharmaceutical sites to support day-to-day coordination. One example comes from a German facility operating around the clock with four shifts. Historically, shift handovers relied on handwritten notes and siloed system data. Key information was often delayed, lost or misunderstood between teams.

By introducing shared dashboards that consolidate real-time process data with human inputs such as observations and event logs, the site created a consistent handover framework. Operators, technicians and supervisors now begin each shift with the same up-to-date operational picture. Open issues are clearly listed, ongoing investigations are visible, and critical process deviations are highlighted automatically. This has reduced communication gaps and improved task accountability across shifts.

For supervisors, visual tools also support rapid response to issues. Consider a fill-finish line where a temperature deviation occurs during a production run. Instead of searching through multiple logs, a shift supervisor can view the dashboard, see exactly when the deviation started, identify which batches are affected, and check if similar events have happened before. This enables immediate, informed decision-making, such as initiating a targeted investigation or contacting maintenance, without delay.

At management level, consolidated dashboards provide a broader operational view. Site leaders can compare throughput and quality trends across lines and shifts, monitor the status of investigations and escalate persistent bottlenecks. This helps identify recurring issues that may not be obvious within a single shift or department, enabling more strategic interventions.

Collaboration across shifts

Visual dashboards also play an important role in improving collaboration across functions. Pharmaceutical operations typically involve many specialised groups—production, quality, maintenance, engineering—each with its own responsibilities and priorities. Traditional reporting structures and manual communication can reinforce functional silos.

When these teams work from the same set of live, visualised data, collaboration becomes more straightforward. During daily meetings, for example, production and quality personnel can view the same dashboard, discuss deviations in real time and align on priorities without relying on separate reports. Maintenance teams can track open work orders alongside process deviations, helping them address root causes more effectively.

Standardised visualisation also improves escalation procedures. Issues raised on one shift remain visible to subsequent shifts, with clear ownership and status tracking. This transparency helps ensure that problems are resolved rather than forgotten, while also reducing duplicated effort. Over time, this fosters a culture of accountability and shared situational awareness across the organisation.

Adding AI for deeper insight

While visualisation enhances operational awareness, artificial intelligence (AI) is increasingly being used to add an additional layer of analytical capability. Modern pharmaceutical facilities generate both structured data (e.g. sensor readings) and unstructured data (e.g. free-text shift logs). Analysing these large and varied datasets manually can be slow and error-prone. AI can assist by identifying patterns, correlations and anomalies that might otherwise go unnoticed.

For example, when a recurring deviation appears on a dashboard, AI can automatically link it to similar past events, highlight contributing factors such as maintenance interventions or upstream process changes, and suggest corrective actions that were previously effective. This gives teams a head start in their investigations.

During daily operations, supervisors may use AI-enabled tools to query historical data conversationally, for example, asking which equipment units have experienced similar deviations in the past six months and how they were resolved. This ability to rapidly surface relevant context supports faster and more consistent decision-making.

At a strategic level, AI can be applied to long-term trend analysis. By examining large volumes of data across lines or sites, it can help continuous improvement teams prioritise issues by frequency, severity or potential quality impact. This allows organisations to focus resources on the most significant operational challenges.

Importantly, embedding AI capabilities within the same visualisation environment ensures that insight is delivered in context. Teams can move seamlessly from awareness (noticing an issue) to understanding (identifying cause) to action (implementing solutions).

Outlook

Pharmaceutical manufacturing faces increasing pressure to operate efficiently while maintaining high standards of safety and regulatory compliance. Data volumes continue to grow, and operations are becoming more complex. The Visual Factory approach, combining role-specific visualisation with shared data sources and AI-driven insight, offers a practical way to keep this complexity manageable.

Rather than replacing existing systems, it connects them into a more coherent operational environment. Operators, engineers, quality personnel and site leaders each gain the information they need in a clear, timely format, while the organisation benefits from improved coordination and transparency.

As the industry continues to embrace digitalisation, visualisation and AI will play a growing role in ensuring that data leads to actionable knowledge. Sites adopting these techniques are finding that they can improve responsiveness to deviations, strengthen cross-functional collaboration and support continuous improvement, all without adding additional layers of complexity.

Contact

Andreas Eschbach

CEO & Founder

Eschbach 

[email protected]

www.eschbach.com