1. RAJEEV GANDHI COLLEGE OF MANAGEMENT STUDIES
TEAM MEMBER
VIKAS
SUSHANT
PRADEEP
PREMNATH
RAHUL

2. FORECASTING OF BUSINESS INPUT AND OUTPUT

3. 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

4. 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.

5. What is Output ?
In Economics, quantity of goods or service produce in given time period by a firm, industry. or enterprise.

6. 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

7. 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.

8. Elements of a Good Forecast
Timely
Accurate
Reliable
Meaningful
Written
Easy to use

9. 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”

10. 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.

11. Methods of Forecasting
Qualitative Method
Judgmental methods like Delphi method and suveys.
Expert Decision.
Market research

12. 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

13. Judgmental Forecasts Executive opinions Sales force opinions
Consumer surveys
Outside opinion

14. 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

15. Techniques for Averaging
Moving average
Weighted moving average
Exponential smoothing

16. 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.
MAn = n Ai
i = 1 

17. 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.

18. Moving Average Methods

19. 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.

20. 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

21. 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.

22. Extrapolation

23. 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

24. 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.

25. 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

26. Thank you ...