Role of Quantitative Techniques in Decision Making

This article discusses the role of quantitative techniques in decision making process for managers in organizations. It explains how these techniques are used to analyze data and information, estimate meaningful measures, and make informed decisions. The article also explores specific quantitative techniques such as simulation, linear programming, decision trees, and payback analysis.

Introduction
In the current world, successful businesses must use quantitative methods that help in basic
leadership irrespective of whether the business was private or public organization. The technique
incorporates quantitative skills used by managers to make informed decisions which leads to
achievement of better results in the future Govindan, K., Rajendran, S., Sarkis, J., & Murugesan,
P. (2015). Private and public organizations and administration offices rely on quantitative
systems for them to improve their efficiency at work. People in different departments such as
bookkeepers, supervisors and financial experts also require the quantitative techniques to
effectively carryout their duties. It is therefore obvious that the chief managers need to learn
strategic procedures which will enable them to investigate and assess collected data. Logical
decision making often depends on quantitative techniques.
Roles of quantitative techniques for estimating meaningful measures using variables
Data is made up of variables which are always used in any kind of analysis when assessing and
evaluating collected data. In that regard, analysis that involves decision making will do so
through thorough evaluation and exploitation of variables in a particular dataset Mehmood, T.,
Liland, K. H., Snipen, L., & Sæbø, S. (2012).
Simulation technique is a quantitative technique that involves modelling of various situations
using variables to predict the end results considered in decision making by the mangers in
organizations Bulgakova et al, (2014). Additionally, linear programming model, inter
programming, sensitivity analysis, goal programming, dynamic programming, non-linear
programming, queuing theory, inventory management techniques, network analysis

(PERT/CPM), decision theory, games theory, transportation and assignment model are also some
of the quantitative techniques used.
Inputting data in a model
First identify the correct data which covers all useful information needed to solve defined
problems. Errors at this step will lead to incorrect final results in the model. Secondly, the
managers are required to understand the model where the schematics or plans and various
situations are checked and analyzed to minimize chances of any possible cause of error. In the
process, arising problems are solved through experiments to ensure better outcomes Kuik et al,
(2016).
Model is approved based on experimental results. Positive experimental results leads to approval
otherwise, problems found are corrected before the final approval which is done by the
authorities. Finally, the results are taken through actualization process. The process involves
company’s model preparation, modification and execution.
Role of quantitative techniques for estimating meaningful measures using available data
and information
Managers in different organizations use data and information in quantitative techniques based on
statistical figures such as mean and standard deviation to examine project and to make good
decisions.

Mean is the division of the total scores by the number of scores in a sample Yentes et al, (2013).
It is used to find the focal point of the scores. On the other hand, standard deviation is a measure
of dispersion that is used to measure how far the score vary from the mean Mertler, C. A., &
Reinhart, R. V. (2016).
Importance of quantitative methods to managers in decision making
Every manager in any organization needs to poses decision making skills due to their usefulness
because lack of these skills may lead to serious losses in the organizations. Other managers use
data analysis and information obtained in this process to make sound and informed decisions.

Decision tree and payback analysis are the commonly used techniques by managers in decision
making process.
Decision trees
This is a techniques used by the managers to create an image of the decision and also a
representation of other decision paths mostly in a graph to find out the best decision from the
decision tree Malliaris, A. G., & Malliaris, M. (2015). Decision tree helps managers in decision
making i.e. market analysis, purchases of equipment, checking prices and investments. It is also
used to find out the progress of the projects regarding decisions that have already been made and
to make other minor decisions in case there is need.
Decision tree is mostly used when there is risk, these risks require decision making process. It is
also a data quantitative technique used to analyze quantitative data. Future based decisions can
be well made in various conditions and other possible environments from the trunk of decision
tree. This is possible due to the tree’s involvement in graphical data representation in the analysis
process therefore enhancing decision making.
Decision tree is a propellant for manager to analyze the results and to monitor the state of the
organization for the future possible outcomes based on decisions made. It is a simple and
manageable technique that enables managers to select as best results as possible.
Payback analysis
Pay back analysis is a quantitative technique that enables managers to select the appropriate
products for the future. It guides them on the products they should purchase in order to increase
their profits Bem, D. J. (2017). It involves checking on important factors especially when buying

a product for the organization. These factors include warranty of the property, insurance cost,
expected life of the product and the demand for that particular product.
These factors are analyzed based on the data and information in the quantitative technique, this
helps the managers to make the best possible decisions when buying a product so as to bring
benefits in the future.
Conclusion
Quantitative techniques are very important in the daily operations of any business association as
discussed above. Managers apply these techniques in decision making process for the betterment
of the business organizations. It is therefore important for each individual in management role to
acquire the quantitative techniques especially those that enhance decision making process.

References
Bem, D. J. (2017). An experimental analysis of self-persuasion. In Attitude Change (pp. 177-
204). Routledge.
Brunsdon, C. (2016). Quantitative methods I: Reproducible research and quantitative
geography. Progress in Human Geography, 40(5), 687-696.
Bulgakova, N. M., Zhukov, V. P., Meshcheryakov, Y. P., Gemini, L., Brajer, J., Rostohar, D., &
Mocek, T. (2014). Pulsed laser modification of transparent dielectrics: what can be
foreseen and predicted by numerical simulations?. JOSA B, 31(11), C8-C14.
Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making
approaches for green supplier evaluation and selection: a literature review. Journal of
Cleaner Production, 98, 66-83.
Kuik, F., Lauer, A., Churkina, G., van der Gon, D., Hugo, A. C., Fenner, D., ... & Butler, T. M.
(2016). Air quality modelling in the Berlin–Brandenburg region using WRF-Chem v3. 7.1:
sensitivity to resolution of model grid and input data. Geoscientific Model
Development, 9(12), 4339-4363.
Malliaris, A. G., & Malliaris, M. (2015). What drives gold returns? A decision tree
analysis. Finance Research Letters, 13, 45-53.
Mehmood, T., Liland, K. H., Snipen, L., & Sæbø, S. (2012). A review of variable selection
methods in partial least squares regression. Chemometrics and Intelligent Laboratory
Systems, 118, 62-69.

Mertler, C. A., & Reinhart, R. V. (2016). Advanced and multivariate statistical methods:
Practical application and interpretation. Routledge.
Yentes, J. M., Hunt, N., Schmid, K. K., Kaipust, J. P., McGrath, D., & Stergiou, N. (2013). The
appropriate use of approximate entropy and sample entropy with short data sets. Annals of
biomedical engineering, 41(2), 349-365.