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Author
García Blanch, Elisa
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Abstract
The concept of quality has evolved over the years, representing, today, a key value for an organization. To be able to obtain a quality guarantee according to current regulations, it is important to implement statistical tools such as statistical process control (SPC), as they allow to detect special causes of variability of a process, reduce it and improve its capacity.
This work assesses the feasibility of applying SPC to the regulatory compliance study required for the manufacture of medicines. The study variables in this case are temperature and relative humidity, in rooms and equipment that store pharmaceuticals. These variables have been monitored by the SAVERIS system, obtaining a higher data set than previously obtained.
The study focuses on a first evaluation through graphs of variable behavior lines in 8 different facilities and on 5 teams (4 fridges and 1 freezer) over a period of approximately 5 months. Following the procedure in force in IQS, these two variables must not exceed the specification limits marked, therefore they are also represented in those charts. In this way, the degree of compliance is evaluated for each installation and in turn the possible instability and incidents are detected.
In order to understand why incidents or instabilities have been obtained in the sampling period, a first investigation is carried out consisting of linking these incidents with external factors, such as weather conditions or the timetable and working hours..
Next, I-MR control charts are produced on equipment, i.e. refrigerators and freezers that automatically regulate their temperature, to evaluate whether this tool is possible to detect unusual behavior, which could incur the quality of the products they store. What is detected is periodic temperature behavior, which does not allow to discern between natural and special causes. So other I-MR charts are performed again but this time, with data added in the observed periods in order to observe the behavior of a model fridge and thus be able to anticipate a malfunction.
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