Outline
- Introduction.
- Methods and Tools.
- Effectiveness.
- Company Example.
- Future of Quantitative Research.
- Conclusion.
Quantitative research is the type of study which uses an analysis based on mathematics and statistics. Various tests and models exist that helps to draw accurate conclusions about processes or events. The field of facilities maintenance is very technology-oriented, and many aspects require a mathematical approach. Although qualitative research proves to be useful when collecting opinions, this field needs the application of quantitative methods to measure the effectiveness of technology maintenance on each of the studied spots.
Methods and Tools
A typical quantitative research starts with the review of data that was either obtained through interviews or collected from measurements of technical performance. Both methods of collecting information are useful when addressing either human or technical performance. However, while qualitative research uses this data to compare and contrast different values, quantitative methods integrate it in various tests to prove a hypothesis. A hypothesis section is usually mentioned at the beginning of the study and includes two or more statements that are usually contradictory. The null hypothesis, as a general rule, implies that given variables do not have a significant impact on a studied value, while the alternative one insists on the opposite.
Models and tests are the essential part of any quantitative research. Some of the most popular are the ANOVA, the t-test, the Chi-square, and others. For instance, the study by Zhou, Kou, and Ergu (2014) features a proportional intensity model that was applied to measure the failure and repair rates as the part of a maintenance process in a particular site. Two composite models were also used, as well as geometrical calculations of graphic functions. The harmonic analysis method was used to measure the weekly failure level.
Effectiveness
Quantitative research proves to be very effective regarding prediction and evaluation of results. Statistical methods help to calculate the possibility of results that were acquired by an occurrence. Besides, errors can be tested by manipulating the size of the studied pool of data or the other variables that are independent. For instance, if the population is small and some people have provided answers that do not fit in the general picture, the number of questioned individuals should be increased to see if the initial deviation is a trend or an occurrence. Moreover, the way of representing data via linear graphs helps researchers to make predictions for the future if there is a clear trend seen on the model. This is probably the most valuable feature of quantitative research that differentiates it from qualitative studies.
Company Example
One of the companies that used quantitative research in the facilities maintenance field was PetroSA GTL refinery. The case study by Mahlangu and Kruger (2015) identified several objectives that had to be addressed, which included the connection between production output, profitability, and maintenance management. The Pearson product moment correlation coefficient was used to calculate the link between these objectives.
Some qualitative methods were also addressed to help build the overall picture of performance based on the interview answers of employees. The statistical approach helped to define a positive trend of a well-performed maintenance management style affecting the profitability rate of the company. This research was focused on the management impact, yet quantitative methods could also be used to measure any risks or casualties related to the usage of facility’s technology.
Future of Quantitative Research
Despite the fact that quantitative research calls for collecting a lot of data, it can currently be obtained in an easy way even without visiting a facility. For instance, interviews could be conducted via phones or the Internet, the latter of which is currently the second most popular way after the face-to-face method. Although there are some risks associated with a certain level of bias, these results can be very useful in evaluating things like performance based on self-assessment, along with the level of work satisfaction.
Globalization is becoming more powerful nowadays, and quantitative research could help to maintain facilities overseas. For instance, a group of researchers has studied the performance of an offshore wind farm by applying statistical models to the data obtained from the facility (Pliego Marugan, Garcia Marquez, & Pinar Perez, 2016). The main result received at the end states that there is a high probability of technology failure in this type of facility, yet the smart management style could help to control the situation and keep the number of such events to a minimum.
Once again, this example demonstrates that quantitative research can be used to make predictions and propositions about management tools in the field of facilities maintenance, which is valuable taking into consideration the fact that many head employees in this sector are more specialized in technology rather than leadership.
Conclusion
Quantitative research offers tools and methods that help to make accurate results and draw conclusions based on mathematical modeling. The future of this type of research seems bright as it is the most applicable when studying technological processes, and this fields is rapidly growing. Moreover, the trend of globalizing business that opts for maintaining facilities overseas also needs quantitative tools to measure the effectiveness of distant operations.
References
Mahlangu, B., & Kruger, L. (2015). The impact of the maintenance management system: A case study of the PetroSA GTL refinery. The South African Journal of Industrial Engineering, 26(3), 167-182. Web.
Pliego Marugan, A., Garcia Marquez, F. P., & Pinar Perez, J. M. (2016). Optimal maintenance management of offshore wind farms. Energies, 9(1), 1-20. Web.
Zhou, Y., Kou, G., & Ergu, D. (2014). Analyzing operating data to measure the maintenance performance. Quality and Reliability Engineering International, 31(2), 251-263. Web.