Measurement conflict in Pharma labs

Measurement conflict in Pharma labs

When postmortems reviews are made of rampant order delays and the consequent customer complaints, at any typical pharmaceutical company, it is common for manufacturing teams to point fingers at Quality Control (QC)ⁱ. This could be for lack of timely availability of tested raw materials, holdup owing to delays in in-process testing, or for dispatch delays due to pending finished batch releases.

Stricter tracking of the performance matrices of each section in QC is the usual strategy arrived at to prevent the situation form arising again. However, this not only do not address the root cause of these problems but also triggers a vicious loop of actions that aggravates them. Let us see how.

QC performance metrics and their impact

At the heart of QC's performance evaluation lie two critical metrics: Service Level (Turnaround Time - TAT ⁱⁱ ) and Efficiency. TAT encompasses not only the actual testing time but also the time required for any campaign formation and is managed with SLAs (Service level agreements) for the testing protocols. Failure to meet TAT SLAs in some tests in this highly regulated industry, with stringent quality standards and regulations can even lead to non-compliance with regulatory requirements. And any violation involves significant scrutiny, laborious documentation of the deviations, and corrective actions taken. Moreover, TAT is often linked to vendor paymentsⁱⁱⁱ and enables the department to serve the manufacturing process in a timely manner. So, this parameter is closely monitored.

Efficiency, on the other hand, quantifies the quantity of samples released relative to the available resources within a given month. With a growing backlog of samples to be cleared with the limited resources, this parameter too is of paramount interest. Moreover, improved efficiency often leads to cost savings^iv and also generally translates into shorter TAT for sample testing.

SLAs or TAT commitments underscore the importance of testing pending samples promptly after these are received. This is because it is strongly held that if QC begins testing early, it will be able to release the sample on or before the target date.

However, increasing campaign size is seen as a means to enhance efficiency by making the most of existing resources. Let’s take a hypothetical example to understand how this works. Let’s say planner Ajay is having 2 batch of product X on day 1 and another batch is going to come in QC on day 5. Ajay has 2 options.

As can be seen in this example, a typical test only needs a small amount of extra capacity to release an additional sample. So, the output from the same resources increases dramatically if QC increases batching or forms campaigns.

But waiting to form testing campaigns can sometimes be time-consuming. Further, once these campaigns are planned, lab planners open multiple samples in an effort to optimize the testing process. This creates a significant level of Work-in-Progress (WIP) within the lab. This is planned for^v and wouldn’t be an issue if all goes well. However, things don’t go as planned. For instance, fluctuations in the monthly production plan, in response to market changes, can force the Quality Control (QC) department to redirect its resources towards urgent samples needed for immediate production in RM testing. The new tests required adds to the existing WIP, increasing Work-in-Progress (WIP) and prolonging lead times. To compound matters, operational inefficiencies like instrument breakdowns, absenteeism, and the need for retesting due to Quality Management System (QMS) issues, priority changes to tests needed to help expedite vendor payments can disrupt workflow. These disruptions result in a growing number of pending samples, leading to increased delays in the release process and a rise in Turnaround Time (TAT). The tested RM output from QC too is poor.

The vicious loop of RM-QC

While the extent of problems is less pronounced, similar issues persist in both in-process testing and finished goods (FG) testing. In FG testing, Work in Progress (WIP) accumulates due to frequent changes in priorities prompted by the urgency to test samples with imminent airfreight deadlines. Conversely, in in-process testing, this occurrence may be attributed to emergent variability or quality failures. All these lead to expansion of TAT/poor efficiency.

When faced with these challenges and mounting complaints from the production team, management heightens its expectations of QC. They emphasize the need to simultaneously improve efficiency and TAT to regain control over the mounting delays.

Core issue

Clearly at the crux of this vicious loop is the dilemma of the section planner who manages the show at QC faces. While he may want to start testing available samples in small batches in hand to meet the SLA, the need for efficiency drives him to wait and form large campaigns. This and along with inherent uncertainties and variability results in long and variable lead times, hence the delays.

The way out!

A viable solution becomes evident when we can precisely define the primary objective of the quality department within a pharmaceutical manufacturing facility. The quality department primarily serves as a support team, tasked with facilitating the timely production and dispatch of pharmaceuticals, all while maintaining rigorous compliance with regulatory standards and requirements, notably Good Manufacturing Practices (GMP). Consequently, while the efficiency is not to be dismissed, the primary focus of quality control (QC) is TAT.

A support department like QC, is in fact, expected to operate with surplus capacity. Efforts to enhance efficiency though attempts to maximise capacity utilization in service departments or non-constraints by reducing resources or streamlining operations often risk transforming them into bottlenecks, ultimately hindering the overall performance of the system (in this case, the plant as a whole). This is because these departments may then lack the necessary capacity cushion to handle fluctuations in workload, which is a common occurrence in pharmaceutical manufacturing facilities.

The variability in workload within the laboratory is not solely a result of fluctuations in production but is also influenced by various external factors, such as changes in Standard Operating Procedures (SOPs) mandated by regulatory authorities, global Corrective and Preventive Actions (CAPA) initiatives, and testing errors, among others. Therefore, the lab requires sufficient capacity to effectively accommodate these workload fluctuations. Clearly QC has to balance the need for efficiency with the primary need of ensuring TAT commitments.

So, what can we monitor at each of the different QC labs to ensure that production/dispatch is never delayed due to the availability of tested material while also achieving good enough efficiency? What parameters should be observed to detect early signals of delays, and what can we track to drive continuous improvement in lab processes?

As mentioned earlier, the solution lies in further defining the purpose of each type of QC laboratory

RM/PM Lab:

The RM (Raw Material) lab has a clear purpose: to prevent production lines from experiencing shortages and to avoid delaying vendor payments. If the primary goal of the RM lab is to maintain a continuous supply of tested raw materials, its performance should be assessed based on how many days of production coverage it can consistently provide to each production line.

Creating RM QC Buffer: To facilitate seamless dispensing irrespective of changes in production plan, one can define the RM-QC cleared inventory level (norm) for each item that should be available at any given point in time all the production lines. This buffer serves two purposes. First, it provides QC teams with clear and specific signals to prioritize which SKUs should undergo testing. The prioritization is based on the availability of tested raw materials (RM) for each production line. Consequently, this approach helps decouple QC from the daily urgencies of manufacturing.

Secondly, monitoring of the buffer can give the central procurement visibility for replenishing them as consumption occurs. This ensures that procurement orders only what is necessary for the plant's operations, reducing the likelihood of SKUs being delivered but remaining unused. As a result, timely vendor payments can be made, and the RM lab can maintain focus without the constant switching between materials required for production and those needed for vendor payments.

This decoupling both from manufacturing and from the demands of procurement, in turn, enables the RM QC department to allocate sufficient waiting time for strategic planning and efficient execution of testing campaigns, ultimately leading to a reduction in Turnaround Time (TAT).

Finish Product and In-Process Lab:

The purpose of the Finished Product (FP) and In-Process (IP) labs is to guarantee the availability of the necessary quantity of Quality Control (QC) approved finished products before the scheduled dispatch date. Days of dispatch coverage is then the primary metric of interest here.

To achieve this a high coverage, buffers for finished products (on similar lines to what was discussed for RM) can be established based on market demand. Both production and the FP/IP lab can align to these defined buffers to coordinate production and testing efforts. For this plant production can use replenishment signals provided by these buffers for production. Simultaneously, FP/IP lab can ensure that a bank of QC-tested materials is consistently ready for dispatch from the plant.

This decoupling both from manufacturing for IP and from the demands of dispatch for FG, allows the QC department to allocate sufficient waiting time for making testing campaigns, again, ultimately leading to a reduction in Turnaround Time (TAT).

Stability Lab:

The Stability lab functions uniquely compared to other labs as it operates independently of plant operations and maintains a consistent, predictable flow of samples. Its primary objective is to ensure that samples are tested within the specified time frames, as detailed in the Standard Operating Procedures (SOP) for stability testing. This approach reduces the need to report exceptions, making Turnaround Time (TAT) the most suitable metric for assessing the lab's performance.

However, pharmaceutical companies often impose stricter timelines for stability sample testing compared to the guidelines set forth in Good Manufacturing Practice (GMP) by regulatory authorities. These tighter Service Level Agreements (SLAs) can hinder the stability lab's ability to plan testing campaigns effectively. As a result, the lab may be compelled to conduct tests under suboptimal campaign conditions, leading to significant inefficiencies without delivering substantial value within aggressive timelines. Therefore, enabling the stability lab to formulate campaigns while still adhering to regulatory requirements would enhance its overall effectiveness.

Conclusion

Mindlessly adhering to industry-standard metrics for evaluating lab performance is a recipe for disaster. The wisest course of action is to tailor lab metrics to match the precise expectations for the lab. Once established continuous improvement in processes can focus on delivering better results in these new lab matrics. Remarkably, when this is done, both efficiency and Turnaround Time (TAT) emerge harmoniously, without any compromise.

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