Forecasting is the one of the important part of decision making process. It helps managers to identify short term and long term future trends in the business activities. It may help in forecasting demand in retail store, predicting customer traffic at the petrol pump, calculation of probable population in upcoming years etc. There are plenty of studies published on forecasting techniques which are just introductory or highly mathematical and lacks in providing managerial perspective of solving business problems to the students. This chapter elucidates various forecasting techniques and its application in the field of management. In addition, various examples of real life problems are solved and analyzed with multiple forecasting techniques. Through this chapter students will have a clear understanding of the various nuances of different forecasting models in one single data set. Students will be able to identify future trend and seasonality in real life data set and evaluate more appropriate forecasting technique for the decision-making process.
Top1. Learning Objectives
- •
This chapter will help students to learn two different forecasting techniques of stationary time series and its applications in service sector
- •
With the learning provided through the chapter, students will be able to identify the best forecasting technique for the dataset in hand.
- •
This chapter will help students in problem solving and identifying future trends in various domains of service industry.
Intended Audience
- 1.
MBA/Post Graduate Students specializing in the area of Operations Management, Information Systems & Quantitative Techniques who are interested in learning forecasting techniques and its applications.
- 2.
Faculty members in the area of Operations Management, Information Systems & Quantitative Techniques can use this chapter as a guideline in preparing their forecasting teaching pedagogy.
- 3.
Corporate Practitioners who are looking for simple forecasting techniques with its applications in the Service Sector
2. Introduction
Forecasting is the process of predicting future trends based on historical data. companies nowadays look for better forecasting techniques so that they can plan their activities in advance. For example a petrol pump owner can reduce his operational cost by forecasting number of vehicles that may arrive for the purpose of oil filling, a product manufacturer can harness maximum profit by forecasting product's demand, manager in the retail store can reduce his operational cost by assigning optimal number of employees which satisfies forecasted customer traffic. Forecasting can also help in getting future trends of economy, population, pollution, weather and traffic etc.
With rising competition in the market and to remain competitive, companies nowadays started to focus on better forecasting techniques to plan and schedule their activities. Since the success of any business depends heavily on how well its management can predict future trends and can devise appropriate strategies. The management can utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period. This is typically based on the projected demand for the goods and services they offer. The inappropriate forecasting strategies of a company can lead to bankruptcy due to shortage or excess of inventory, unnecessary labor expenses and lack of technological advancement.
There are ample of forecasting techniques developed in recent years but their use largely depends on the context requirement. It is the responsibility of forecaster to select efficient forecasting technique considering its use and data handling flexibility for particular application. The more appropriate forecasting technique will give the more accurate results and future trend In this way, the selection of best forecasting technique is based on many factors like: available historical data, time available for analysis, cost benefit analysis, context of the problem and desirable accuracy of the forecaster. These factors must be evaluated and analyzed on various level while selecting a better forecasting technique. For example, a forecaster should select a technique that fully utilizes the available data. The trade-off between available data and accuracy of forecast must be analyzed carefully. There can be multiple techniques which may provide accurate forecast but for those techniques the requirement of historical of data may be large. Therefore the forecaster must first identify the product for which forecasting is to be made and it's; life cycle stage, available historical data, factors effecting the production of the product and desirable accuracy of the forecast etc.
There are basically two types of forecasting techniques i.e qualitative forecasting techniques and quantitative forecasting techniques. The first one refers to the opinions and judgment of experts, consumers and senior level managers on the basis of their past experience. This type of forecasting techniques are used when no numerical data is available to analyze the past trend. These techniques are usually applied for the long term planning and decision making.