BrazilAchieving better quality control in pharmaceutical companies
Pharmaceutical companies can gain major efficiency gains in their Quality Control (QC) department through an approach that combines operational excellence with data-driven change and people management, according to a benchmark by Valcon. While an efficiency gain of some 20% in cost base is “relatively straightforward” to achieve, pharma companies that manage to match best practice performance could nearly halve their current cost level.
Quality control is an essential part of the pharmaceutical industry’s primary process. Companies need to ensure that their drugs are safe, and comply with all quality standards and regulations set by local and/or international bodies. In realising this, pharma companies conduct a range of activities, including performing chemical experiments in so-called quality control laboratories, running complex data modelling and analysis on drug performance, and conducting external assessment.
Despite the importance of quality control of drugs and supporting processes, the function is in practice often overlooked in favour of more tangible areas of business, such as supply chain. However, in a new white paper by Birgitte Røddik from Scandinavian consulting firm Valcon, the senior consultant highlights that ignoring quality control can come at a hefty cost. “Inefficient operations can disturb the flow in the entire value stream,” she said, which down the line can frustrate processes such as marketing, sales & operations planning and supply chain.
Against a backdrop of a growing need for speed and cost-effectiveness, amid growing competition from globalised players and more agile market entrants, it is nowadays key “for all departments to perform optimally if the company is to remain competitive.” In the case of quality control, “delays on control processes can have a negative impact on product supply operation and can, in worst-case scenario, result in late delivery to customers. Delays in production batch releases also hurt the bottom line.”
But what can pharma companies then do to stay in better control of their control function? Røddik: “By combining operational excellence with data-driven insight and powerful change leadership practices, Quality Control departments can achieve productivity improvements of 15% to 20% or more.” And, strikingly, “these improvements can be achieved within months.”
In one example, a leading pharmaceutical company ran a number of lean initiatives in its function, and “without any increase in absence or stress levels,” a productivity gain of 17% was realised within less than a year. “It was not about working harder, just smarter, with more efficient processes as the fundament and more control on the outset,” explained Røddik.
Unlocked by data
The role of data is key in revealing such improvement potential. “Data creates transparency, and this enable the Quality Control department to optimise capacity and processes. And more importantly, to inject crucial previously unidentified insights into strategic planning.”
One of the improvement areas unravelled by data is likely to be in the activities of employees. Commonly, around three quarter of time spent is on tasks not directly related to quality analysis, such as validation of methods, qualification of equipment, support for the lab technicians, maintenance of equipment, and resourcing. “This usually comes as a surprise to lab leaders and employees,” said Røddik. “By optimising the bulk of time-spent, and aligning it to value-added, major potential can be gained.”
Another area lies in the improved alignment that can be found with key stakeholders. Quality Control operates within a chain of commands, and the department should be well aware of the needs from the business, while being reliable in its quality and planning. Typical to a scientific process, and lab environment, the workload that hits Quality Control’s frontiers is volatile.
“By applying an approach that combines operational excellence with data-driven change and people management, costs of quality control can be slashed by 20%.”
At one major international pharma company, the gap between the peak and trough workload was over 300%. “Data analysis can bring order to planning and time spent, and thus optimise the planning of resources.” At this specific company, the hypothesis was that additional resources were needed, however, thanks to intelligent algorithms the insight was provided that reshuffling planning was more than enough to cover all workload and even work away backlogs.”
To fully reap the benefits form data analysis, it is key to integrate the approach “consistently on both a strategic, tactical and operational level,” pointed out Røddik. Data can be captured beautifully in labs, but if managers subsequently don’t act upon the insights, the value gained is lost in translation. “Weekly or bi-weekly follow-up meetings with the leaders help ensure the realisation of the benefits.”
Similarly, well-designed processes and data roadmaps are nowhere without the people at their helm. “Change is actually the glue that binds the entire improvement process together. Data and operations management alone are not sufficient to achieve the necessary improvements. The key is to combine these with sustainment and anchoring as well as visualisation.”
Making change stick is notoriously challenging for managers anywhere in the world. In the eyes of Valcon’s expert Røddik: “The first action is to ensure that all leaders on all levels are involved and engaged from start to end. Then, make sure that project teams represent all tasks and functions, including lab techs, scientists and supporting staff, and that they work as one integrated team. Third, make sure that employees are fully engaged – communicate to/with them, empower them and foster involvement.”
