1. Mechanical Engineering Haldia Institute of Technology
Forecasting
Prepared by
Prof. T. K. JANA.
Mechanical Engineering
Haldia Institute of Technology

2. Forecasting
Forecasting in the context of production management refers to future prediction about the sales of product(s) as evaluated by certain techniques.
e.g. sales of
(i) Maruti Swift Dezire during April – June, 17
(ii) Voltas A/C during Jan – March, 2017

3. Basis of all business decisions
Types of Automation
Basis of all business decisions
Production
Inventory
Personnel
Facilities

4. Importance of Forecasting
Various departments in the organization formulate and execute their plans based on forecast.
Finance needs forecasts to project cash flows and capital requirements.
HR needs forecasts to establish and recruit the man power requirements.
Production unit or shop-floor requires forecasts to plan schedule, workforce, nos. of shifts, material requirements, inventories, lead time etc.
Design department initiate new product design and / or modification/improvement of the existing product

5. Short-range forecast Types of Forecast Usually < 3 months
Scheduling, worker assignments
Medium-range forecast
3 months to 2 years
Sales/production planning
Long-range forecast
> 2 years
New product development
Detailed
use of
system
Design
of system

6. Forecasting Techniques
Qualitative or Subjective:
Executive Judgment: Opinion of a group of high level experts is aggregated
Sales Force Composite: Each regional salesperson provides his/her sales estimates. The forecasts are then reviewed to make sure that these are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast.

7. Forecasting Techniques
Market Research/Survey: Inputs from customers pertaining to their future purchasing plans forms the basis of forecasting.
It involves the use of questionnaires, consumer panels and tests of new products and services.

8. Forecasting Techniques
Delphi Method: This method relies on opinions of a group constituted by individuals from inside as well as outside the organization in such a way so that each member is unaware about the identity or credentials of other members.

9. Delphi Method The procedure consists of the following steps:
Each expert in the group makes his/her own forecasts in the form of statements based on certain questionnaire. The questionnaire should be free from any ambiguity.
The coordinator collects all group statements and summarizes them.
The coordinator prepares a summary and gives another set of questions to each group member including feedback of other experts as input.
The above steps are repeated until a consensus is reached.

10. Forecasting Techniques
Quantitative Techniques
Time Series model Regression
Naive Moving Average Exponential Smoothing
(a) Simple (a) level
(b) Weighted (b) trend
(c) seasonality

11. Forecasting Techniques
Attempts to predict the future based on past data
The philosophy is that
“the factors influencing the past will continue to influence the future”.

12. Time Series models: Components
Random
Seasonal
Trend
Composite

13. Product demand over time
Trend component
Seasonal peaks
Demand for product or service
Actual demand line
Random variation
1st Year
2nd Year
3rd Year
4th Year

14. Forecasting Techniques
Naive approach
Demand in next period is the same as demand in the most recent period.
Mathematically,
It is too simple and not very accurate

15. Forecasting Techniques
Simple Moving Average:
It is considered that an average is a good estimator of future behavior.
While computing the average, the specified numbers of the most recent data are used.
Ft+1 = Forecast for the next period, t+1
n = Number of periods to be averaged
D t = Actual demand in period t

16. Forecasting Techniques
Weighted Moving Average:
In this approach, more importance is given to the recent data.
Such that

17. Forecasting Techniques
Simple Exponential Smoothing:
This is also a weighted moving average that automatically considers exponentially declining weights to the older data.
Ft+1 = Forecast value for time t+1
Dt = Actual value at time t
Ft = Forecast value at time t
= Smoothing constant and varies as 0 < a < 1 and usually is small (around 0.1 to 0.2) for stability of forecasts.
N = No. of period

18. Forecasting Techniquesw1 w2 w3

19. Forecasting Techniques

20. Forecasting Techniques
Adjusted Exponential Smoothing
(Corrections due to Trend)
In reality, some form of trend exists that requires due considerations.
Adjusted exponential smoothing forecast predicts the next period by adding a trend component to the current period smoothed forecast.

21. Forecasting Techniques
Adjusted Exponential Smoothing:
Where,
where, and are smoothing constants. The trend adjustments utilizes a second co-efficient, .

22. Forecasting Techniques
Linear Regression: The simplest type of the relationship is linear regression which is in the form

23. Accuracy of Forecasting
The accuracy of forecast is determined by the attributes:
Mean Absolute Deviation (MAD): It is the mean absolute deviation of the forecast value and the actual demand.
2. Mean Squared Error (MSE): It is the mean of the squares of the deviations of the forecast value and the actual demand.

24. Accuracy of Forecasting
Mean Forecast Error (MFE): It is computed as the mean of the deviations of the forecast value and the actual demand.
Mean Absolute Percentage Error (MAPE): It is the mean of the % deviations of the forecast value and the actual demand.
5. Root Mean Squared Error (RMSE): It is the square root of MSE.

25. Thank You