Deep Dive

Pre-Automated RCA

Without an automated system for manufacturing root cause analysis, companies typically rely on manual, time-consuming investigations into quality excursions or production issues.

This can lead to significant delays in identifying the true cause of a problem, prolonging downtime, and slowing down quality control or batch release processes. Human-led investigations, while critical for complex issues, can be prone to bias, miss subtle correlations in vast datasets, or misidentify symptoms as root causes, leading to ineffective or incomplete CAPAs (corrective and preventative actions).

The ripple effects include recurring quality defects, increased scrap rates, higher manufacturing costs, potential product recalls, and even regulatory non-compliance, which can result in hefty fines, legal action and severe damage to a company's reputation and ability to supply life-saving medicines.

Ultimately, this compromises patient safety if defective or substandard products reach the market.

Automated Manufacturing RCA

Automated Manufacturing RCA is a sophisticated approach that leverages technology to swiftly identify the fundamental causes of production deviations, quality issues, or equipment failures.

This system continuously monitors vast amounts of data generated during the manufacturing process, from raw material inspection to final product packaging. Its primary function is to go beyond merely detecting a problem to precisely pinpointing why it occurred, enabling pharmaceutical companies to implement targeted and effective CAPAs to ensure product quality, regulatory compliance and operational efficiency.

Comprehensive Data Integration

To effectively implement automated manufacturing root cause analysis, extensive data integration is paramount. The system requires a consolidated view of real-time and historical data from various sources across the manufacturing floor.

This includes data from:

• Internet of Things (IoT) sensors on machinery (e.g., temperature, pressure, vibration, flow rates)
• Manufacturing Execution (MES) systems capturing batch records and operational data
• Laboratory Information Management Systems (LIMS) for quality control test results
• Process Analytical Technology (PAT) tools providing in-line and at-line measurements of critical process parameters (CPPs) and critical quality attributes (CQAs)
• Enterprise Resource Planning (ERP) systems for material and inventory traceability

Integrating these disparate data streams into a unified data lake or platform is essential for the system to perform comprehensive correlation and causation analysis across all relevant variables.

Platforms like HASH which support the synthesis of both structured and unstructured information can additionally incorporate information contained within natural language communications between workers, or documents and reports in non-standardized formats, to further assist in RCA investigation and prescriptive response.

Benefits

The integration of Artificial Intelligence (AI) elevates automated manufacturing root cause analysis from a reactive problem-solver to a proactive predictive intelligence tool. AI algorithms, particularly machine learning and causal AI, can analyze complex, multivariate datasets to uncover hidden patterns and relationships that human analysis might miss.

This enables faster and more accurate identification of root causes, often in real-time, significantly reducing investigation cycles from weeks to minutes. AI can also predict potential failures or deviations before they occur by identifying subtle anomalies, allowing for pre-emptive adjustments and minimizing downtime.

AI-assisted RCA systems can recommend optimal CAPAs based on a comprehensive range of historical data and outcomes (considering more information than human analysts could feasibly review and incorporate). They can also be deployed so that they continuously learn from new incidents, and even simulate the impact of proposed changes, supporting continuous process improvement, enhanced product quality, reduced waste and stronger regulatory compliance.

Implementation & Enhancement

Selecting A Solution

When exploring automated manufacturing RCA solutions, it is important to consider:

• Data quality & latency: real-time integration, master-data governance, streaming ETL – requirements to ensure data is up-to-date.
• Change management: upskilling schedulers in their usage – start by rolling out Automated Manufacturing RCA as copilots to humans before relying on them in any autonomous capacity.
• Cybersecurity: ensure the system supports granular permissions and access controls consistent with your business requirements, and any partner access (e.g., via portals) is similarly secure.
• Ongoing maintenance costs: does the solution run in the cloud, or must it be hosted internally/on-premises and maintained manually?
• Open-source: is the Automated Manufacturing RCA solution open-source, or is there risk of platform/vendor lock-in?
• Incremental adoption: can the solution be used to target specific use-cases initially to prove its value and deliver quick wins (e.g., selected production assets or sub-processes) before broader roll-out?

Roadmap To Value

A typical automated manufacturing RCA solution built on HASH looks like:

1. Baseline & cleanse data: connect key ERPs, IoT feeds, LiMS and QMS systems and specify any unstructured data sources of interest.
2. Launch visibility layer: real-time event dashboards, basic ML alerts.
3. Add predictive & prescriptive apps: automated deviation RCA suggestions and reporting, proactive deviation predictions etc.
4. Continuous learning: reinforce models with new data; driving a culture of decision-intelligence through an "OODA loop".