Quantitative Techniques in Management
Quantitative techniques in management refer to various approaches used to solve managerial problems. Wiley argues that quantitative techniques in business are useful in analyzing numerical data and in making good decisions. There are many quantitative techniques, which include graphical analysis, statistical methods, linear programming, regression analysis, probability, decision theories, algebra, matrices, and calculus. Decision making process is effective with application of quantitative techniques. Managers and their assistants apply quantitative techniques to derive information that enhance planning and other managerial roles.
Some quantitative techniques such as graphs give a pictorial representation of variables in organizations. Managers and other stakeholders can visualize trend of sales, profits, demand, and other factors. Graphical method combined with linear programming approach helps to determine optimum resources required to maximize profits or minimize cost. These two methods also help to predict the effect of changing variables such as increasing the number of workers. According to CFA Institute, quantitative techniques help in risk management. Without these techniques, managers would not make right decisions.
Today, statistical techniques are the most practical methods used by data workers, knowledge workers, and managers. Managers use statistical methods to collect data, to test hypothesis, to organize and summarize data. They also use this technique to measure and compare performance by creating relationships between variables in businesses. The statistical figures obtained assist managers to predict outcomes due to changes in the related variables.
Queuing theory approach is another quantitative technique that promotes service delivery by matching arrival rate with service rate. The average time an item spends in a queue or system is determined. This information helps to determine the required number of servers and lines. This approach helps managers to allocate business resources. Banks, supermarkets and transport sectors have already benefited from this application.
Quantitative techniques are, however, limited because they depend on availability of data and ability to represent it. The calculation of results involves use of statistical software such as SPSS, spreadsheets and databases that require adequate training. These techniques are also time-consuming and expensive. It may also be difficult to use with large numbers