RAJEEV GANDHI COLLEGE OF MANAGEMENT STUDIES TEAM MEMBER VIKAS SUSHANT PRADEEP PREMNATH RAHUL
FORECASTING OF BUSINESS INPUT AND OUTPUT
What is Forecasting? FORECAST: A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design
What is Input ? Basically, an Input is anything you can "put in" to the economy that will help to produce an economic output which is hoped to be profitable. The four basic economic inputs are LAND, LABOUR, CAPITAL & ENTERPRISE.
What is Output ? In Economics, quantity of goods or service produce in given time period by a firm, industry. or enterprise.
Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services
Common in all forecasts Assumes casual system past ==> future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.
Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use
Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”
Methods of Forecasting Quantitative Method:- Time Series Forecasting. i.e. sequence observations like monthly demand for a product . Moving Average method i.e. simply averages of the last month observations.
Methods of Forecasting Qualitative Method Judgmental methods like Delphi method and suveys. Expert Decision. Market research
Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future
Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion
Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance
Techniques for Averaging Moving average Weighted moving average Exponential smoothing
MAn = n Ai Moving Averages A technique that averages a number of recent actual values, updated as new values become available. The demand for tires in a tire store in the past 5 weeks were as follows. Compute a three-period moving average forecast for demand in week 6. 83 80 85 90 94 MAn = n Ai i = 1
Moving Averages Weighted moving average – More recent values in a series are given more weight in computing the forecast. Example: For the previous demand data, compute a weighted average forecast using a weight of .40 for the most recent period, .30 for the next most recent, .20 for the next and .10 for the next. If the actual demand for week 6 is 91, forecast demand for week 7 using the same weights.
Moving Average Methods
Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1) The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting.
Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1) Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term, is the % feedback
Main Methods of Forecasting Extrapolation - The output capacity in past year is increasing like 85,90,95 units then in nest year we could expect the output production capacity requirement is 100 units.
Extrapolation
Selecting an appropriate Forecasting techniques Requirements :- Must understand the nature of the forecasting problem. Must understand the nature of the data source; errors & outliers; smooth or irregular; trends, seasonality, etc
Conclusion At the end, forecasting is the systematic and disciplined application of common sense. Common sense can help us to identify the opportunities and risks involved and review the quality of the forecasts.
Conclusion IO helps to evaluate, predict & assess goals and policies in an inter-connected system of sectors / industries in an economy When combined with qualitative frameworks, a dynamic, transparent and powerful tool is utilized to assist decision making across multiple objective
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