Measurement Systems Analysis (MSA) is one of the most important processes in contexts where measurements are to be done using technology-based devices or equipment. Generally, MSA is regarded as a critical element of the six-sigma methodology that is highly applicable in quality management systems (Sperl, Ptacek, & Trewn, 2013).
By definition, the term MSA refers to a special procedure aimed at the determination of the components of variations in a measurement process (Sperl, Ptacek, & Trewn, 2013). Thus, the Measurement Systems Analysis is regarded as a quality control process for measuring devices and equipment.
MSA is often performed in various concepts involving equipment-based measurement to help realize several objectives. Its principal objectives include the selection of correct measurement methods and approaches, assessment of measuring devices and instruments, evaluation of measurement procedures and institutional staff, assessment of any measurement interactions, and the calculation of uncertainties associated with different measuring devices (Sperl, Ptacek, & Trewn, 2013). MSA procedures generally target two types of variations. The first set of variations is known as the process variation, which refers to the changes that may occur within the process under investigation at the time when measurements are being taken (Sperl, Ptacek, & Trewn, 2013).
The other type of variation that is targeted by this process is the measurement systems variation, which characterizes the measurement process. This type of variation may be caused by environmental factors, gage fluctuations, changes in the procedures used in taking measurements, and software-related problems. Overall, the measurement systems analysis is done to minimize measurement variations that can affect the accuracies of the measurements made.
The gage repeatability and reproducibility (Gage R & R) is one of the most popular forms of measurement systems analysis. It refers to a procedure that is often used to quantify the level of variation that characterizes a given set of data as a result of the measurement systems used in taking such measurements (Sperl, Ptacek, & Trewn, 2013). The process then helps to define the compatibility of the assessed measurement system by benchmarking the detected variability with the associated variations in the measurements taken by such instruments (Sperl, Ptacek, & Trewn, 2013). The Gage R & R method defines variability using the concept, “repeatability” and “reproducibility.” The former results from the variations in the equipment while the latter is as a result of the variations in the instrument operator (Sperl, Ptacek, & Trewn, 2013). This process is useful to refine data in popular data analysis software such as SPSS, Minitab, and UNISTAT. It is often performed in the forms such as Analysis of Variables (ANOVA) and Average and Range analyses.
The Gage Repeatability and Reproducibility analysis can be performed in six basic steps. The first step involves taking measurements in a random order, a measure that is taken to ensure even or random distribution of drifts that may characterize such measurements (Mazu 2006). Even though it is important for the observer to stay aware of the process in order to take the required measurements, the it is imperative that the worker stays unaware of the measured parameters to avoid opinion inflation of the measurements taken. Linearity is then determined by measuring the consistency of bias within the measuring device used (Scutoski & Sekar 2008). The fourth step of the Gage R and R analysis deals with the testing the ability of a single appraiser to take several measurements of the same process, part or sample and produce the same results. Generally, the principal essence of the Gage R and R analysis is to improve measurement quality by boosting both the accuracy and precision with which such measurements are taken.
Since the Gauge R & R method is a quality measure aimed at ensuring that measurements are accurately taken, it finds several applications in evidence-based practice in different sectors. The healthcare sector is one of the key beneficiaries of the process as it can be used to improve the quality of various aspects of care practice, especially in cases where measurements and data science are involved. According to Gao, Moore, Smith, Doub, Westenberger, and Buhse (2007), this technique can be used for quality control in the pharmaceutical industry. Since the ability to maintain the precision of measurement has been the key problem facing dissolution testing in the pharmaceutical industry, these scholars suggest that Gauge R & R process can be used to assess the sources of variation in measurement systems and determine the sources of contributions made by various factors to the identified variabilities (Gao et al. 2007). Therefore, the concept of Gauge R & R analysis can be used to improve measurement precision in the pharmaceutical industry.
Gao, Z., Moore, T., Smith, P. A., Doub, W., Westenberger, B., & Buhse, L. (2007). Gauge repeatability and reproducibility for accessing variability during dissolution testing: a technical note. AAPS PharmSciTech, 8(4), E1-E5.
Mazu, J. M. (2006). Design and analysis of Gauge R&R Studies. Technometrics, 48(2), 305.
Scutoski, H., & Sekar, C. (2008). Introduction to Gage R&R studies: The key to understanding measurement systems. Gilbert, AZ: Cerprobe Corporation.
Sperl, T., Ptacek, R., & Trewn, J. (2013). The Practical Lean Six Sigma Pocket Guide for Healthcare: Tools for the Elimination of Wastes in Hospitals, Clinics, and Physician Group Practices. Chelsea, UK: MCS Media, Inc.