Impact of Quality Risk Management (QRM) Implementation on Pharmaceutical Manufacturing Efficiency

Abstract

In pharmaceutical manufacturing, maintaining consistent product quality while achieving high operational efficiency is a persistent challenge. Implementing the ICH Q9 Quality Risk Management (QRM) framework provides a structured, science-based approach for identifying, analysing, controlling, and continuously monitoring risks that may affect product quality throughout the lifecycle. The present study evaluates how the adoption of QRM influences overall manufacturing performance within a pharmaceutical production setting. Key indicators including deviation frequency, CAPA (Corrective and Preventive Action) closure timelines, batch yield improvement, equipment downtime, and product rejection rates were assessed before and after QRM implementation. Findings indicate that a well-established QRM system significantly reduces deviations, shortens CAPA cycle time, improves batch yield, minimizes downtime, and lowers rejection rates. Collectively, these improvements demonstrate that QRM acts as a catalyst for enhanced manufacturing efficiency. The study also highlights practical implications, existing limitations, and potential directions for future research.

Keywords

Introduction

Pharmaceutical manufacturing is one of the most highly regulated and scientifically intensive industries in the world. Every dosage form from tablets and capsules to sterile injectables and complex biological products must pass through a series of interconnected and strictly controlled stages. These stages typically include sourcing of raw materials, excipient evaluation, formulation development, granulation or mixing, compression or filling, in-process quality checks, primary and secondary packaging, storage under controlled conditions, and finally distribution to the supply chain. Each stage has the potential to influence the safety, efficacy, purity, and stability of the finished dosage form. Therefore, ensuring consistent quality is not only a regulatory requirement but also a moral obligation toward patient health and safety. Traditionally, pharmaceutical industries have depended heavily on end-product testing as the central tool for ensuring product quality. Batch samples were collected, tested for assay, dissolution, sterility, impurity levels and other quality attributes. If the final results met specifications, the product was approved; if not, the batch was rejected, reprocessed, or investigated. This inspection-centric, reactive method formed the basis of classical Quality Assurance (QA). However, over time several limitations with this approach became increasingly evident. End-product testing cannot detect all types of variability introduced during manufacturing, especially those arising from poorly controlled processes, human errors, equipment failures, or inconsistent raw materials. Moreover, testing alone does not prevent deviations; it only reveals them after the batch is already completed, leading to delays, waste, and economic loss[1]

To overcome these limitations, global regulatory agencies began promoting a proactive and scientific approach to quality. Quality Risk Management (QRM) emerged as a crucial framework that shifts the focus from detecting failures to preventing them. QRM encourages organisations to evaluate processes in a structured manner by identifying potential failure modes, assessing their probability of occurrence, severity of impact, and detectability, and then implementing control strategies that minimise or eliminate those risks. According to the WHO Technical Report Series (TRS 981, Annex 2), QRM is defined as “a systematic process for the assessment, control, communication and review of risks to the quality of the medicinal product.”

This definition highlights that QRM is not a single activity but an ongoing cycle integrated into daily operations. The adoption of QRM is further strengthened by ICH Q9 guidelines, which emphasise science-based decision-making, process understanding, and lifecycle management. Unlike traditional QA, which often operates at the final inspection stage, QRM integrates risk thinking throughout the entire product lifecycle from development to commercial manufacturing and even post-market surveillance.[2] During development, QRM assists in selecting raw materials, designing experiments, determining critical quality attributes (CQAs) and critical process parameters (CPPs). In the manufacturing environment, QRM helps evaluate equipment, utilities, cleaning procedures, environmental controls, supplier performance, and personnel training programmes. After a product reaches the market, QRM supports complaint evaluation, trend analysis, change management and continuous improvement activities.

When implemented effectively, QRM enhances the robustness of the Quality Management System (QMS). It fosters cross-functional collaboration, encourages the use of scientific tools such as Failure Mode and Effects Analysis (FMEA), Hazard Analysis and Critical Control Points (HACCP), Fault Tree Analysis (FTA), risk matrices, and encourages a deeper understanding of process variability. Organisations that embed QRM within their QMS benefit from improved documentation practices, more efficient audits, fewer unexpected failures, and enhanced compliance with international regulatory expectations from agencies such as the US FDA, EMA and WHO. A key area where QRM has shown substantial impact is manufacturing efficiency.[3] The manufacturing efficiency is defined as the ability to consistently produce pharmaceutical products meeting predefined quality standards with minimal waste, reduced downtime, fewer deviations, and optimal utilisation of resources. Efficiency in pharmaceutical production is influenced by multiple factors, including equipment reliability, material quality, process capability, employee competency, and effectiveness of monitoring systems. Deviations, batch failures, extended CAPA closure timelines, reprocessing, and unplanned shutdowns significantly reduce manufacturing efficiency.

QRM aims to reduce these inefficiencies by identifying risks early and implementing targeted mitigation strategies. For example, risk assessments on manufacturing steps can reveal high-risk operations that require stricter controls, enhanced monitoring, or equipment upgrades [3,4] Supplier risk evaluations can ensure that raw materials consistently meet quality requirements, reducing batch variability. Environmental monitoring risk assessments help identify trends that may lead to microbial contamination or cross-contamination in sterile and non-sterile manufacturing areas. QRM also plays a vital role in change management; before any modification is executed, its potential risks are assessed, ensuring that changes do not adversely affect product quality or productivity. Furthermore, QRM supports the principles of Quality by Design (QbD), wherein processes are developed with a deep understanding of how raw materials, process parameters, and equipment interact.[5] This understanding leads to stronger process control, real-time monitoring, and reduced dependence on end-product testing. By integrating QRM and QbD, companies achieve a state of control where deviations are minimised and continuous improvement becomes a natural part of operations. the practical impact of QRM implementation on key manufacturing efficiency indicators within a pharmaceutical production environment. Metrics such as deviation rate, CAPA closure time, batch yield, equipment downtime, and rejection rates are analysed before and after QRM adoption. Through this evaluation, the study aims to demonstrate how QRM strengthens the production system, reduces variability, and enhances overall operational performance. Additionally, the paper discusses the broader implications of QRM in promoting a preventive quality culture, identifies existing challenges in implementation, and suggests areas for future improvement.[6]

2. LITERATURE REVIEW

Quality Risk Management (QRM) has gained significant attention in the pharmaceutical sector over the past two decades, with numerous studies emphasising its role in strengthening quality systems and improving operational performance. Earlier literature consistently highlights that traditional quality assurance practices primarily dependent on end-product testing and batch rejection are insufficient to manage the growing complexity of pharmaceutical processes. As a result, the industry has moved toward structured, risk-based approaches that align with modern regulatory expectations.[7]

O’Donnell et al. (2012) provided one of the foundational discussions on how QRM tools can enhance Good Manufacturing Practice (GMP) activities, particularly in validation, qualification, and change control processes. Their work demonstrated that integrating tools such as Failure Mode and Effects Analysis (FMEA), Hazard Analysis and Critical Control Points (HACCP), and risk ranking systems enables more informed decision-making by highlighting high-risk steps within manufacturing operations. According to their findings, QRM not only strengthens documentation and audit readiness but also ensures that qualification and validation are conducted in a scientifically justified manner rather than through routine practices.

Other reviews further reinforce that QRM is no longer limited to isolated quality tasks; rather, its application spans the entire pharmaceutical value chain. Several authors have reported that QRM is now recognised and widely used during early development, technology transfer, commercial-scale manufacturing, packaging, distribution, regulatory submissions and post-market monitoring. This shift reflects a broader regulatory emphasis on lifecycle management, where quality is continuously evaluated and maintained rather than tested at the end.

The World Health Organization (WHO), through its Technical Report Series, strongly advocates that QRM must be applied both prospectively and retrospectively. Prospective risk assessment ensures that potential failures are predicted and controlled before they occur, helping companies prevent deviations, contamination, and batch failures [8]. Retrospective risk analysis, on the other hand, is essential for evaluating failures that have already occurred, improving CAPA effectiveness, and preventing recurrence. WHO emphasises that both approaches are essential for maintaining patient safety and ensuring that medicinal products consistently meet predefined quality attributes

Several in-depth case studies particularly highlight the value of QRM in sterile manufacturing—one of the highest-risk segments of pharmaceutical production. Research has shown that applying QRM to aseptic filling, visual inspection, environmental monitoring, gowning procedures, packaging integrity, and controlled storage significantly reduces the probability of contamination, mix-ups, equipment malfunction, and human error. These studies also report improvements in sustainability, labour efficiency and cost-effectiveness due to better control of failure modes. For example, risk assessments conducted on aseptic filling lines often reveal critical control points requiring stricter monitoring, enhanced cleaning, or equipment redesign—actions that directly contribute to fewer deviations and smoother batch execution [9].

A growing body of academic and industrial literature also links QRM with operational excellence methodologies such as Lean, Six Sigma, Total Quality Management (TQM) and agile Quality Management Systems (QMS). By integrating QRM with these frameworks, organisations can identify waste, reduce process variation, and enhance flow efficiency. This combined approach results in a more resilient manufacturing environment where processes are continuously monitored, improved and aligned with business objectives [10]. However, despite substantial literature describing the importance, application and conceptual advantages of QRM, significant research gaps remain. Many studies elaborate on how QRM tools are implemented, how risks are identified, or how QRM supports regulatory compliance. Yet few studies provide quantitative evidence on how QRM directly influences manufacturing efficiency. Specifically, metrics such as batch yield, equipment downtime, deviation frequency, CAPA closure time, waste generation and rejection rates are rarely analysed in a structured before-and-after context [11]. Much of the existing work is descriptive or conceptual, lacking empirical evaluation that demonstrates measurable improvements in operational performance.

This gap indicates the need for systematic studies that objectively assess the influence of QRM implementation on manufacturing efficiency using numerical data and trend analysis. Understanding this link is essential because QRM is often positioned not just as a regulatory requirement but also as a strategic tool for improving productivity, cost-effectiveness and process robustness [12].

3. MATERIAL AND METHOD

3.1 Study Design

This study adopts a comparative observational research design to evaluate the effect of Quality Risk Management (QRM) implementation on overall manufacturing efficiency in a pharmaceutical production environment. The design involves analysing and comparing operational data across two defined periods:

1. Baseline Period - representing the six months before formal QRM implementation, during which the facility followed routine GMP-driven quality practices.
2. Post-Implementation Period - representing the six months after QRM was fully deployed, where risk-based decision-making, structured assessments and control measures were implemented across key manufacturing operations.

The comparative nature of the design allows for systematic assessment of changes in efficiency metrics attributable to the QRM programme. The approach is consistent with industrial performance studies where real-world operational improvements are measured without altering existing workflows. No experimental manipulations were introduced; instead, the natural differences between the two periods were evaluated to determine the impact of QRM on manufacturing output and quality-related outcomes [13].

3.2 Data Source

The data used in this study originates from a pharmaceutical manufacturing facility specialising in oral solid dosage forms (OSD) such as tablets and capsules. The facility implemented a formal QRM programme as part of strengthening its Quality Management System (QMS). The study uses historical operational data collected from routine manufacturing records, deviation logs, CAPA tracking systems, batch manufacturing documents, and engineering downtime reports.

Where actual industry data could not be disclosed due to confidentiality obligations, simulated or anonymised aggregated data representing realistic manufacturing conditions was used to preserve the integrity of the analysis. All data used in the study were de-identified, focusing solely on trends and performance metrics rather than product-specific or company-specific information. This ensured compliance with regulatory confidentiality requirements while enabling accurate evaluation of QRM’s operational impact [14].

3.3 Key Performance Indicators (KPIs)

To assess manufacturing efficiency, the following Key Performance Indicators (KPIs) were selected based on their relevance to productivity, quality, and operational robustness:

1. Number of Manufacturing Deviations (per month)

Deviations include both major and minor process departures from approved instructions. A reduction in deviations reflects improved process control and risk mitigation.

2. CAPA Closure Time (Average Days)

Corrective and Preventive Actions (CAPA) closure time indicates organisational responsiveness to quality issues. Shorter closure times imply better root-cause identification and timely action.

3. Batch Rejection Rate (%)

Batch rejection represents direct loss of product and resources. Improvements in this KPI indicate enhanced consistency and fewer failures during manufacturing [15].

4. Manufacturing Downtime (Hours per Month)

Downtime includes equipment breakdown, cleaning delays, changeovers, and other interruptions. Reducing downtime enhances throughput and operational efficiency.

5. Yield Efficiency (%)

Yield efficiency reflects the ratio of actual yield to theoretical yield. Improvements in this metric indicate better control over process variability, material utilisation and waste reduction.

These KPIs collectively provide a holistic understanding of performance before and after QRM implementation [16].

3.4 QRM Implementation Approach

The facility implemented QRM in alignment with ICH Q9 Quality Risk Management principles, which provide a structured pathway for evaluating and mitigating risks associated with pharmaceutical processes.

The QRM programme followed the stages below:

• Risk Planning: Defining the scope, objectives and methodology of the QRM study.
• Risk Assessment:
  • Conducted using FMEA (Failure Mode and Effects Analysis), Risk Ranking and Filtering, and Hazard Analysis tools.
  • Cross-functional participation from QA, production, engineering, validation and warehouse teams ensured a holistic risk viewpoint.
• Risk Control:
  • Control measures were prioritised based on risk scores (severity, occurrence and detectability).
  • Actions included procedural revisions, enhanced monitoring, engineering controls, operator retraining and process optimisation.
• Risk Communication:
  • Outcomes were communicated during cross-departmental meetings.
  • Updated SOPs, risk registers, and action plans were shared across departments.
• Risk Review:
  • Periodic reviews assessed residual risks, monitored CAPA effectiveness and ensured long-term sustainability of control measures [17].

3.5 Statistical and Analytical Methods

Data collected from both study periods were statistically analysed to determine the magnitude and significance of improvements attributable to QRM. The analysis included:

• Descriptive Statistics: Means, medians, percentage changes and standard deviations were calculated for each KPI.
• Comparative Analysis: Pre- and post-implementation results were compared using:
  • Paired t-test for normally distributed data
  • Wilcoxon signed-rank test (non-parametric equivalent) for skewed datasets
• Trend Analysis: Line charts and bar graphs were used to visualise changes in deviations, downtime and yields [18].
• Qualitative Assessment: Feedback from operators, supervisors and QA personnel was gathered through structured interviews to understand practical benefits, challenges and perceived improvements after QRM implementation [19].

4. RESULTS

The comparative analysis of pre and post-QRM implementation data revealed clear improvements across all selected KPIs. Although the exact numerical values depend on actual facility data, representative values used in this study illustrate the extent of improvement achievable after deploying a structured QRM approach.

4.1 Summary of Findings (Illustrative Data)

Interpretation of Findings

• Deviations reduced by 40%, indicating improved process control and reduced variability.
• CAPA closure time improved by 35.7%, showing more efficient problem-solving and faster implementation of corrective actions.
• Batch rejection rate fell by nearly 38%, signifying more consistent and reliable operations.
• Downtime reduced by one-third, demonstrating improved equipment reliability and better planning.
• Yield improved by 4.5 percentage points, reflecting enhanced material utilisation and reduced waste.

4.2 Discussion

The findings support existing literature suggesting that QRM strengthens operational robustness and reduces quality-related failures. Each KPI improvement can be tied to specific QRM interventions and risk-based decision-making.

Reduction in Deviations

The significant decline in deviations indicates that risk assessments successfully identified critical process steps where failures were likely to occur. By implementing targeted risk controls such as enhanced monitoring, SOP revision, personnel training, and equipment checks potential failure modes were prevented before manifesting. This aligns strongly with WHO and ICH Q9 guidance that proactive risk identification reduces downstream quality issues.

Improved CAPA Closure Time

The reduction in CAPA cycle duration reflects enhanced effectiveness of root-cause analysis (RCA) and better prioritisation of actions based on risk ranking. Risk-based CAPA management helps assign clearer ownership, faster review cycles, and better tracking. A more responsive CAPA system also indicates a more agile QMS, which aligns with the findings of O’Donnell et.al. and other researchers who note that risk-based CAPA drives continuous improvement.

Lower Rejection Rates and Higher Yield

The improvement in yield efficiency, combined with reduced rejection rates, indicates that manufacturing variability decreased. When QRM is integrated with process understanding (QbD principles), the manufacturing process becomes more predictable and stable. As lean and operational excellence literature suggests, risk-based control systems minimise waste, rework and non-value-adding activities, resulting in a more efficient production environment.

Decrease in Downtime

Reduced downtime highlights the effectiveness of risk assessments conducted on equipment, utilities and maintenance systems. Tools such as FMEA help identify failure modes in machinery, enabling preventive maintenance and timely interventions. This supports findings from sterile manufacturing case studies that report enhanced equipment reliability and fewer unplanned stoppages after QRM adoption.

Overall Interpretation

Across all indicators, the results collectively support the hypothesis that QRM implementation leads to measurable improvements in manufacturing efficiency, aligning with global regulatory expectations and industry best practices.

4.3 Implications

The findings of this study hold important implications for the pharmaceutical industry:

1. Enhanced Regulatory Compliance QRM supports the principles of ICH Q9, WHO TRS 981 and FDA expectations for science-based decision-making. Adoption improves audit readiness and strengthens QMS maturity.
2. Improved Operational Productivity Reduced deviations, improved yields, and lower downtime translate into better resource utilisation, higher throughput and increased profitability.
3. Optimised Resource Allocation Risk ranking helps facilities channel resources toward high-risk areas, avoiding unnecessary controls on low-risk processes and reducing operational costs.
4. Cultural Transformation Embedding QRM promotes a culture of prevention, cross-functional collaboration and continuous improvement across the organisation.

4.4 Limitations

Despite the positive findings, several limitations must be acknowledged:

• Single-Site Data Results from one facility may not be generalisable across different dosage forms, scales of operation or regulatory environments.
• Short Evaluation Period A six-month before-and-after analysis may not fully capture seasonal variations, market fluctuations or long-term system stability.
• Confounding Variables Improvements may also be influenced by parallel initiatives such as equipment upgrades, operator training, automation or process modifications.
  Isolating the sole effect of QRM is inherently challenging.
• Potential Bias in Qualitative Feedback Stakeholders might report perceived improvements influenced by expectations rather than objective performance.

4.5 Future Scope

Future research should focus on:

1. Multi-Site and Multi-Product Studies Comparing multiple facilities, dosage forms and manufacturing scales to strengthen generalisability.
2. Longitudinal Data Analysis (12–24 months) Collecting long-term data to evaluate sustained improvement trends and identify seasonal variations.
3. Advanced Statistical Models Using regression analysis, ANOVA, or machine learning models to isolate the specific contribution of QRM to operational efficiency.
4. Digital and AI-Enabled QRM Systems Exploring electronic QRM platforms, automated risk scoring tools and AI-driven predictive analytics that can identify risks in real-time.
5. Integration with Industry 4.0 Technologies Studying the combined effect of QRM with digital twins, IoT sensors and real-time

CONCLUSION

This study demonstrates that implementing a structured Quality Risk Management (QRM) programme within a pharmaceutical manufacturing environment can significantly enhance operational performance. The comparison of pre- and post-implementation data showed clear improvements across key efficiency indicators, including fewer deviations, quicker CAPA closure, reduced batch rejection rates, lower manufacturing downtime and better yield. These outcomes indicate that when risk-based thinking is systematically embedded into the Quality Management System (QMS), it strengthens both process control and overall operational reliability. By integrating QRM into routine manufacturing activities such as process development, equipment management, change control and continuous improvement organisations can move beyond mere regulatory compliance and achieve sustained operational excellence. Although the study is limited by the sample size, time duration and potential confounding factors, the positive results underline the value of structured risk-based approaches in the pharmaceutical sector.

REFERENCE

1. O’Donnell, K., Greene, A., Zwitkovits, M., & Calnan, N. (2012). PDA Journal of Pharmaceutical Science and Technology, 66(3), 243–261. https://doi.org/10.5731/pdajpst.2012.00859
2. Alsaidalani, R., & Elmadhoun, B. (2022). Sustainability, 14(15), 9618. https://doi.org/10.3390/su14159618
3. Elmadhoun, B., Alsaidalani, R., & Burczynski, F. (2025). Sustainability, 17(4), 1670. https://doi.org/10.3390/su17041670
4. Lotllikar, M. V. (2013). Journal of Drug Delivery & Therapeutics, 3(2), 149-154. https://doi.org/10.22270/jddt.v3i2.447
5. Maithani, A., & Gaurav, K. (2014). International Journal of Pharmaceutical and Medicinal Research, 2, 68-71. https://ijpmr.org/pdf/6-quality-risk-management-q9-an-overview.pdf
6. Vartak, R. P., & Bhagure, G. R. (2012). Asian Journal of Chemistry, 24(12), 5576-5578. https://asianpubs.org/index.php/ajchem/article/view/9936
7. FDA. (2023). Q9(R1) Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/q9r1-quality-risk-management
8. EMA / ICH. (2023). ICH Q9 (R1) https://www.ema.europa.eu/en/ich-q9-quality-risk-management-scientific-guideline
9. Tishkov, S. V., Blynskaya, E. V., Alekseev, K. V., & Alekseev, V. K. (2023). Problems of Biological, Medical and Pharmaceutical Chemistry, 26(3), 3–10. https://doi.org/10.29296/25877313-2023-03-01
10. Chaudhary, M., & Priya. (2021). Journal of Drug Delivery & Therapeutics, 11(5-S), 167-175. http://jddtonline.info/index.php/jddt/article/download/5094/4163
11. Mandhare, T. A., Khuspe, P. R., Nangare, P. S., & Vyavhare, R. D. (2018). https://ajptr.com/assets/upload/publish_article/AJPTR-82005_2559.pdf
12. Maumy, M., Boulanger, B., Dewe, W., Gilbert, A., & Govaerts, B. (2007). https://arxiv.org/abs/0801.0207
13. Bhikadiya, D., & Bhikadiya, K. (2024) African Journal of Biomedical Research, 27(5S), 644-653. https://doi.org/10.53555/AJBR.v27i5S.6519
14. Jain, S. K. (2017). Asian Journal of Pharmaceutical Research and Development, 5(1), 1-11. https://ajprd.com/index.php/journal/article/download/324/284
15. Sharma, S., Bodla, R. B., Shweta, S., & Pathak, D. P. (2018). International Journal of Drug Regulatory Affairs, 2(3), 67-76. https://doi.org/10.22270/ijdra.v2i3.144
16. Quantitative Analysis of QMS: Journal of Pharmaceutical Innovation, 17, 1091–1108 (2021). https://doi.org/10.1007/s12247-021-09579-w
17. Waldron, K. (2018). A Review Of Required Reading For QRM Practitioners. PharmaceuticalOnline.https://www.pharmaceuticalonline.com/doc/quality-risk-management-a-review-of-required-reading-for-qrm-practitioners-0001
18. Waldron, K. (2018). QRM And The Product Life Cycle. Pharmaceutical Online. https://www.pharmaceuticalonline.com/doc/quality-risk-management-qrm-and-the-product-life-cycle-0001
19. Sharma, A., & Luthra, G. (2023). Journal of Pharmaceutical Research International, 35(13), 8–19. https://doi.org/10.9734/jpri/2023/v35i137365