Guillaume Gigon, VP of technology and AI, and Robert Taylor, associate director, clinical operations – process improvement, at Indero, explore how Lean Six Sigma (LSS) principles – minimising waste, standardising workflows, and driving improvement through data – are being adapted from manufacturing industries to deliver clinical trials faster, more efficiently, and with greater consistency.
The past few years have been turbulent times for the pharmaceutical industry, especially in the clinical trials sector. Factors such as reduced investment and a lack of public trust have made it particularly challenging for small and medium-sized biotechs to get efficient trials off the ground. At the same time, the industry has seen a shift in demand, with innovative therapies such as cell and gene treatments requiring a move away from traditional randomised controlled trial (RCT) models. Instead, companies have found themselves having to learn to run adaptive trial designs and manage small patient populations, yet still provide regulators with robust data.
These pressures add to the mounting expectation for sponsors and CROs to deliver studies faster, more efficiently, and with greater consistency. Although these challenges are somewhat unique to clinical research, they mirror those faced by other industries for decades, which have been solved by adopting structured approaches such as LSS. Originally developed for manufacturing, LSS uses statistical tools and data to minimise waste, standardise workflows and improve processes. However, to be effective in a service-driven industry like clinical research, these principles must be carefully modified, because processes in this sector are less linear, data is less standardised, and multiple stakeholders must be aligned.
From factory floors to clinical operations
In manufacturing, LSS is straightforward to implement as inputs and outputs are clearly defined, and data is abundant. In contrast, clinical trials rely on interdependent workflows including site activation, data management and monitoring, where human interaction and expertise play major roles. Despite these underlying differences, the core principles of LSS – eliminating inefficiencies, improving quality and ensuring repeatability – remain highly relevant. With the right approach, these same principles can be adapted to a service-based environment, allowing research organisations to create clearer, faster and more predictable trial processes. Indeed, they have already been applied in similar healthcare and the life sciences environments, where LSS has proven extremely effective.
Where process improvement adds value
In the same way, several areas of clinical trial operations can benefit from structured improvement initiatives. From the very early stages, streamlined workflows and automation can speed up site activation, allowing studies to start on schedule. Throughout a trial, mapping workflows and analysing data can help to identify bottlenecks in monitoring, report drafting and approval, reducing delays and accelerating follow-up when necessary. Tasks such as standardising reviews and resolving protocol deviations are streamlined, preventing inconsistencies, minimising rework and ensuring regulatory compliance. Better cross-functional coordination and data visibility can also shorten the time between the last patient visit and database lock. In addition, proactive document tracking helps to maintain trial master file (TMF) completeness, avoiding the last-minute rush to collect missing materials at study closeout and reducing frustrations for both sites and sponsors. Together, these improvements demonstrate how applying structured methodologies can deliver measurable benefits, from faster timelines and lower costs to improved quality and compliance.
The human factor in process improvement
While process optimisation is often viewed as a technical challenge, cultural change is equally important. Teams must understand why new ways of working are necessary and feel engaged in the process. Change management frameworks, such as the ADKAR (Awareness, Desire, Knowledge, Ability and Reinforcement) model, are increasingly being applied alongside LSS. These approaches focus on creating awareness, building desire for change, and reinforcing success, and are proving very effective at ensuring that improvements are adopted and sustained, not abandoned after initial enthusiasm fades. Organisations that combine data-driven improvements with well-planned change management are better positioned to create a lasting culture of continuous improvement, where employees actively look for ways to enhance quality and efficiency rather than seeing it as a top-down directive.
The role of technology and AI
Centralised systems also help, making it easier to track performance data and measure the impact of process changes. For example, electronic workflows enable real-time visibility of key metrics such as site activation or report turnaround times, providing the evidence needed to target and validate improvements. AI promises to take this a step further by analysing data, identifying inefficiencies, and even recommending optimisations. However, while AI may accelerate analysis, it will still rely on the principles of LSS, and human oversight will remain essential to interpret results and implement the changes required.
Putting process improvement into practice
Specialist CRO Indero has already put these principles into practice across its clinical operations to great effect, using LSS tools to reduce monitoring report timelines by 40%, cut site activation timelines by 10-20%, streamline protocol deviation workflows for faster resolution, and shorten database lock timelines through improved visibility and coordination. Improvements in eTMF completeness have also reduced site frustrations and accelerated study closeout. These initiatives have been supported by a change management program, helping teams to embrace new systems and ways of working. This combination of technology and cultural change has not only improved operational efficiency and quality, but also reduced costs and strengthened collaboration with sponsors and sites.
Looking ahead
Clinical trials continue to grow in scale and complexity, and applying systematic improvement methodologies will be key to meeting industry demands. Integrating LSS, robust change management and data-driven technology, can help organisations move from reactive problem-solving to proactive, continuous improvement. For sponsors, this means greater confidence in their CRO partners. For CROs, it represents an opportunity to build a lasting culture of operational excellence, one that positions them to meet the evolving needs of clinical research.
