FOR DIPLOMA STUDENTS

1. OPERATIONS MANAGEMENT
FORECASTING
Rashmi Panwar

2. IMPORTANCE OF FORECASTING
• Forecasts are estimates of the occurrence, timing, or magnitude of uncertain future events.
• Forecasts are essential for the smooth operations of business organizations.
• They provide information that can assist managers in guiding future activities toward organizational goals.
• Managers also use forecasts to estimate raw material prices, plan for appropriate levels of personnel, help decide
how much inventory to carry, and a host of other activities.
• This results in better use of capacity, more responsive service to customers, and improved profitability.
• Types of Forecasting:
• Short Term Forecasting: made for short term objectives covering less than one year. Ex. Material
Requirement Planning (MRP), scheduling, sequencing, budgeting etc.
• Medium Term: Which is done for a period in between short and long
• Long Term Forecasting
• Long Term Forecasting is the forecasting that made for that made for long term objectives covering
more than five years. Ex. Product diversification, sales and advertisement.

3.  Time series:
 is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a
sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.
• Time series are very frequently plotted via line charts.
• Time series analysis comprises methods for analyzing time series data in order to extract meaningful
statistics and other characteristics of the data.
• Time series forecasting is the use of a model to predict future values based on previously observed
values.

4. Elements of Forecasting:
• Forecasting consists basically of analysis of the following elements.
• Internal factors
• External factors
• Controllable
• Non-Controllable (Organizing with national economy, governments,
customers and Competitors)

5. Forecasting methods
There are numerous methods to forecasting depending on the need of the decision-maker. These
can be categorized in two ways:
1. Opinion and Judgmental Methods or Qualitative Methods.: Qualitative Forecasts uses such
factors like decision makers intuition, emotions, personal experiences, and value system.
2.Time Series or Quantitative Forecasting Methods.: Quantitative Forecasts uses one or more
mathematical models that rely on historical data and/or causal variable to forecast demand

6. QUALITATIVE Forecasting Approaches
• Educated Guess : The opinions of a group of high-level experts or managers, often in combination
with statistical models, are pooled to arrive at an estimate of demand.
• DELPHI Method: Three different kinds of participants are included:
• Decision makers,
• Staff personnel
• Respondents
• Decision makers consists of a group of 5 to 10 experts who will be making the actual
forecast.
• Staff personnel assist decision makers by preparing, distributing collecting and summarizing
a series of questionnaires and survey results.
• The respondents are a group of people, often located in different places, whose judgments are
valued. They provide inputs to the decision makers before the forecast is made.

7. QUALITATIVE Forecasting Approaches
• Consumer market survey:
• This method uses input from customers or
potential customers regarding future purchasing plans.
• It can help not only in preparing a forecast but also in improving product design and planning for
new products.
• Sales force composite:
• Each salesperson estimates what sales will be in his or her region.
• The forecasts are then reviewed to ensure that they are realistic. Then they are combined at district and
national levels to reach an overall forecast.

8. QUANTITATIVE Forecasting Approaches
The various forecasting methods are for quantitative data forecasting:
1. Naive Approach
2. Moving Averages
3. Exponential Smoothing
4. Trend Projection
5. Linear Regression
TIME SERIES MODELS
ASSOCIATIVE MODEL

9. QUANTITATIVE Forecasting Approaches
TIME SERIES MODELS:
• A time series is a set of observations of a variable at regular intervals over time. In decomposition analysis, the components of a time
series are generally classified as trend T, cyclical C, seasonal S, and random or irregular R.
• Time series are tabulated or graphed to show the nature of the time dependence. The forecast value (Ye) is commonly expressed as a
multiplicative or additive function of its components.
• YC =T. S. C. R
• YC =T + S+ C+R
multiplicative model
additive model
• Where T is Trend, S is Seasonal, C is Cyclical, and R is Random components of a series.
• Trend is a gradual long-term directional movement in the data (growth or decline).
• Seasonal effects are similar variations occurring during corresponding periods, e.g., December retail sales. Seasonal can be quarterly,
monthly, weekly, daily, or even hourly indexes.
• Cyclical factors are the long-term swings about the trend line. They are often associated with business cycles and may extend out to
several years in length.
• Random component are sporadic (unpredictable) effects due to chance and unusual occurrences. They are the residual after the trend,
cyclical, and seasonal variations are removed.

10. • Naive Approach:
• It is simplest way to forecast.
• It is a technique that assumes demand in the next period is equal to demand in the most recent period
Associative Models: Models like linear regression, incorporate the variables or factors that might
influence the quantity being forecast. E.g. Advertising budget, competitors price etc.

12. TIME-SERIES FORECASTING
Moving Averages:
• It is a method which uses a number of historical data values to generate a forecast.
• Moving averages are useful if we assume that market demands are fairly steady over time.
• Mathematically: Ft=Forecast in time period t
• Dt=Demand in time period t
• n=Number of values to be considered for forecasting .
• A quantitative method of forecasting or smoothing a time series by averaging each successive
group (no. of observations) of data values.
• term MOVING is used because it is obtained by summing and averaging the values from a given no of
periods, each time deleting the oldest value and adding a new value.
•
• Dt-1+Dt-2+-----Dt-n
Ft= ___________________________
n

13. • For applying the method of moving averages the period of moving
averages has to be selected
• This period can be 3- yearly moving averages 5yr moving averages 4yr
moving averages etc.
• For ex:- 3-yearly moving averages can be calculated from the data : a,
b, c, d, e, f can be computed as :

14. Goals : –
• Smooth out the short-term fluctuations.
• Identify the long-term trend.

15. MERITS Of Moving average method
• simple method.
• flexible method
• If the period of moving averages coincides with the period of
cyclic fluctuations in the data , such fluctuations are
automatically eliminated
• This method is used for determining seasonal, cyclic and
irregular variations beside the trend values.

16. LIMITATIONS OF MOVING AVERAGE METHOD
• No trend values for some year.
• M.A is not represented by mathematical function - not helpful in
forecasting and predicting.
• The selection of the period of moving average is a difficult task.
• In case of non-linear trend the values obtained by this method are
biased in one or the other direction.

17. year Sales,(millions of
rupees)
3-yearly totals 3-yearly moving
averages(trends)
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
3
4
8
6
7
11
9
10
14
12
15
18
21
24
27
30
33
36
5=(15/3)
6=(18/3)
7=(21/3)
8=(24/3)
9=(27/3)
10=(30/3)
11=(33/3)
12=(36/3)

18. sales
12
10
8
6
Actual line
4
3
2
1
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
years
In Figure, 3-yrs MA plotted on graph fall on a straight line, and the cyclic
f luctuation have been smoothed out. The straight Line is the
required trend line.

21. TIME-SERIES FORECASTING
Exponential Smoothing:
• It is more advanced method than moving average.
• It uses a very little record of past data
• New Forecast = last period forecast + α(last period’s actual demand – last period’s forecast)
• α is the smoothing constant. It has values between 0 and 1.
• Ft = Ft-1 + α(At-1 – Ft-1)
• Where Ft =New Forecast
Ft-1= previous period forecast
α = smoothing constant ( 0 ≤ α ≤ 1)
At-1 = previous period’s actualdemand

27. TIME-SERIES FORECASTING
Measuring the forecasting Error:
• Comparing forecasted values with the actual or observed values.
• It uses a very little record of past data
• Forecast Error = Actual Demand – Forecast Values
• Fe = At -Ft
• Where Ft = Forecast demand for period t
At = Actual Demand
MEAN ABSOLUTE DEVIATION: The first measure of the overall forecast error for a model is the mean
absolute deviation (MAD).
MAD =
Actual −Forecast
n

28. The standard error of the estimate
 The standard error of the estimate is closely related to this quantity and is
defined below:
 is a measure of the accuracy of predictions
est is the standard error of the estimate,
Y - actual score
Y' - predicted score
Y-Y' - differences between the actual scores and the
predicted scores.
Σ(Y-Y')2 - SSE
N - number of pairs of scores

29. Example:Assume the data below are the
data from a population of five X-Y pair
Assume the data below are the data from a population of five X-Y pairs
•The last column shows that the sum of the squared errors of prediction is 2.791.
•Therefore, the standard error of the estimate is:

30. THANK YOU