Exploring the value of lab KPIs: due date adherence

5 August, 2022

Adam Lester- George

Content Manager

Pharmaceutical manufacturing operates to tight deadlines, with regulatory approvals and supply chain logistics planned to a fixed schedule months or even years in advance. A pharma company’s Quality Control (QC) function serves a critical role in that manufacturing cycle, analyzing samples of the product to ensure that it adheres to safety standards and composition specifications.

QC labs must also balance final product testing against the validation of raw materials, test methods, and instrumentation, all of which are in competition for limited resources. As such the labs are engaged in a constant battle to maintain strict turnaround times and meet their analysis due dates.

Why due date adherence is business critical

A batch’s analysis due date is defined before it is even received in the lab and is calculated to allow sufficient time for downstream processes to be completed ahead of delivery to market. A delay in one batch test can cause delays in subsequent tests. If the overall product release schedule is delayed even slightly, it can have significant impacts on the rest of the delivery supply chain, with associated cost implications.

The analysis due date is used in combination with test method standard lead times to calculate the last day that analysis can begin while remaining within the agreed delivery window. There are many reasons why a batch might be delivered late:

  • The analysis may identify problems with the product, requiring retesting or even a new manufacturing run;
  • The analysis might start late due to scheduling conflicts or upstream delays (e.g. late batch reception);
  • The analysis might start on time but take longer to complete than the expected lead time.

Until the full analysis has been completed and the product has been signed-off, it cannot be released for packaging, cannot be shipped to market, and consequently will not be available to patients (who, in some cases, are in need of life-saving medication).

Adherence to analysis due dates and on-time delivery in QC labs is therefore business-critical.

Improving due date adherence

QC teams can improve their due date adherence if they have a clear and quantitative understanding of the situation in their lab via various key performance indicators, or KPIs. These can include:

  • Delivery performance:
    • The volume of tests/batches being delivered in a specific time frame
    • The rate of tests/batches being delivered on or before the due date
  • Productivity performance:
    • Turnaround time (number of days from batch reception to batch release)
    • Cycle time (number of days spent in analysis)
  • Analysis bottlenecks associated with specific:
    • Methods
    • Instruments
    • Product types
    • Teams

By understanding these performance metrics, teams can identify strategies for improving due date adherence. For instance:

  • If a particular product analysis has a longer than expected cycle time, standard lead times may need to be adjusted so that analysis start dates can be adjusted accordingly to avoid late delivery;
  • If a specific analysis consistently results in reduced on-time delivery performance, the associated test method may need to be reviewed;
  • If analyses are being delayed due to an item of equipment, the lab may need to invest in additional instrumentation to streamline throughput.

In identifying barriers to due date adherence and planning suitable resolutions, a QC lab can help to refine its value within the product release chain and reduce costly impacts on the wider organization.

How BINOCS can help

Are you looking for ways to reduce costs associated with your QC operations? BINOCS comes equipped with a range of standard  KPI dashboards that have been designed to help labs stay on top of the performance metrics, identify areas of improvement and take action.. You can use our Business Case Calculator to get a ballpark figure estimation of just how much costs reduction you could achieve, taking into account your unique lab parameters.

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