This document discusses various forecasting methods used for services where demand is unpredictable. It describes subjective or qualitative methods like the Delphi method and cross-impact analysis that are used when historical data is limited. Quantitative time series methods like moving averages, weighted moving averages, and exponential smoothing are explained. These methods use past demand data to forecast future demand. Regression models are also covered, using an example of how linear regression can relate independent variables like employee hours to a dependent variable like company revenues.

1. FORCASTING METHODS
SUBTITLE

2. MANAGING DEMAND IN SERVICES
1. There is no option of inventory buffer to meet variations in service demand .
• Perishable nature of service : simultaneous production and consumption of services .
2. Fixed capacity of service system restricts flexibility to entertain demand .
• Rooms in a hotel and seats in airplane
3. Seasonality in demand for some services & spur of the moment decisions of customers
that is unpredictability of demand .
• Heart attack emergency cannot can be planned
• Visiting hill station during summer season can be planned
4. Personalized service take varying service times

3. VARIOUS FORECASTING METHODS

4. SUBJECTIVE OR QUALITATIVE
FORECASTING METHODS ARE USED
WHERE
• No past data is available .
• If some data is available, cannot be used for long run forecast .
• Mostly used for new technology or new products introduced .

5. The patterns can be trend, seasonality, cycle,
regular and irregular variations
Trend: A gradual increase (upward
movement) or decrease (downward
movement) in observations over time.
Cycle: An unpredictable long-term cycling
behavior. This behavior may be due to
business cycle or service product life cycle
Seasonality: A predictable short-term
cycling behavior due to time of day, week,
month, season or year.
• Random error: Remaining variation that
cannot be explained by the other four
components also called residual
variations.
• Irregular variations: Variations due to
irregular circumstances which do not
reflect any typical behavior.
• Level: Short term patterns that are not
repetitive in nature.

7. DELPHI METHOD
• An expert opinion based forecasting method proposed by Olaf Helmer
• Repeatedly or iteratively asking questions to the diverse experts independently till the
experts arrive at a consensus
Steps in Delphi Method
1. The administrator prepares some questions using scale like likert scale and some open ended
questions.
2. Send the questionnaire to the experts in the area. The experts are not allowed to interact with
each other.
3. The experts are expected to give numerical estimates as per the proposed scale.
4. The test administrator tabulates the responses into quartiles. This completes the round 1 of
Delphi method.
5. The administrator send the findings from round 1 along with some updated questions based
on the open ended responses to the experts.
6. The experts are expected to reconsider their answers and to justify their opinions.
The steps (2) to (6) are repeated till all the experts arrive at a consensus

8. CROSS IMPACT ANALYSIS
The main assumption in this method is that some future event to be occurred is related to
the occurrence of an earlier event.
• The earlier event & future events are correlated.
• The conditional probabilities are estimated for the events, which are revised over a series
of iterations by the experts.
HISTORICAL ANALOGY
To forecast the growth pattern of new service it is assumed that it may show the pattern of
a similar concept for which data are available.

9. QUANTITATIVE FORECASTING METHODS
• Short term forecasts where future of a data set is assumed to be function of the past of
that set .
• An ordered sequence of observations taken at regular intervals of time .
• The past data set presents an identifiable pattern over time .
• Cannot include new factor in future .

10. TIME SERIES FORECASTING: MOVING
AVERAGES
• Let’s forecast the demand for a service
• N- Period moving average for time period t found by adding the actual observation or
demand during past recent N- periods and dividing by N .
•For each next time period forecast, the most recent observation of previous forecast is
added and the oldest observation is dropped.
• It helps in smoothing out short term irregularities, also called Level.
•Each observation is weighted equally. If there is 3-period moving average then all three
recent observation will have weight of 1/3.

11. EXAMPLE
• A hospital wants to forecast the number of
surgeries to be performed for the month of
December. The observed number of
surgeries for the same year from January
to November is given in the Table . What
is the forecasted number of surgeries a
hospital can expect for December?

12. • The number of surgeries forecasted
for the month of December with 3
month moving average is
• 𝑭𝑫𝒆𝒄𝒆𝒎𝒃𝒆𝒓=(𝟐𝟑+𝟐𝟖+𝟏𝟗)/𝟑=𝟐𝟑
• The number of surgeries forecasted
for the month of December with 4
month moving average is
• 𝑭𝑫𝒆𝒄𝒆𝒎𝒃𝒆𝒓=(𝟑𝟑+𝟐𝟑+𝟐𝟖+𝟏𝟗)/𝟑=𝟐𝟔

13. Time Series Forecasting: Weighted Moving
Average
• The demand data or observations when follow some trend or pattern
• Give different weights to different observations
• Respond to changes where recent observations are more emphasized or given
more importance
• In the above example, the weights given to the most recent observation,
wt-1=3, next most recent, wt-2= 2 and next to next most recent, wt-3= 1.
• The forecast for the month of December is
• 𝑭𝑫𝒆𝒄𝒆𝒎𝒃𝒆𝒓=(𝟑×𝟏𝟗+𝟐×𝟐𝟖+𝟏×𝟐𝟑)𝟔 =23
• In this example more weight is given to the most recent occurring observation
that is of November month.

14. TIME SERIES FORECASTING: EXPONENTIAL
SMOOTHING
• Smooth’s out blips in the data
• Required most recent observation
• At the same time old data or observations are never dropped or lost, in fact, older observations are
given progressively less weight
Where Ft 𝑖𝑠 𝑡ℎ𝑒 smoothed forecast value for period t, Ot is actual observed value for period t and is
smoothing constant assigned value mostly between 0.1 and 0.5.
• The term (Ot-1 – Ft-1) represents the forecast error (Difference between the actual observation and
forecast value that was calculated in the prior period)
• Hence, also called feeding back system where forecast error is considered to corrected the previous
smoothed or forecast value.

15. EXAMPLE
In January, the number of surgeries to be performed were predicted for February to be 100.
Actual number of surgeries performed were 120. Using = 0.3, the forecast for the month of
March using exponential smoothing tool is
Forecast(March) = 100 + 0.3 (120-100)
= 100 + 0.3 (20)
= 106

16. ASSOCIATION OR CAUSAL FORECASTING
METHOD
• Association or causal forecasting method helps in capturing trend in data.
• Consider several independent variables that are related to the dependent variable being
predicted.
• Independent variables can be many factors, which relates with the dependent variable.
• Linear regression analysis is the most commonly used quantitative casual forecasting model.
Example: The sales of spare parts of auto vehicles depend on the age of vehicle, seasonal
changes, distance covered etc.
• The forecast expression for exponential smoothing can also be written as
F=αOt+(1-α)Ft
If we substitute Ft in the above expression we get

17. And similarly we can substitute for Ft-1 . That
means in exponential smoothing forecast method
the last period forecast captures the entire
information about the past demand.
It can also be seen that maximum weightage is
given to the last period demand and lower
weightages are given to the individual demand
points as one goes down (past data) in time.

18. REGRESSION MODEL
• In linear regression model, there can be n independent variables Xi related to the dependent variable Y, as
expressed below
Y = a0 + a1X1 + a2 X2 + …..anXn
Where a0, a1, a2…..an , are the coefficients by using regression equations .
• Least squares method can be utilized to forecast the dependent Y∧ variable, related to independent variable
X,
Y∧ = a0 + a1X
a0 represents y-axis intercept
a1 represents slope of the regression line
• The values of a0 and a1 are so determined which can represent Y using best fit line
Y∧ =a0 + a1X within the range of observations of Yi and Xi

19. LEAST SQUARE METHOD
• We have the data on Yi and Xi
Define error Ei as
Ei = (a0 + a1 Xi - Yi)
• Determine a0 and a1 in such a way that sum
of the squared errors over all the
observations is minimized i.e.,
• To minimize we need to determine partial
derivative of SS with respect to a0 and a1
which gives following equation
• Equations (1) and (2) gives two linear
equations in ao and a1, which can be solved to
get

20. EXAMPLE
A software developer company wants to
forecast the revenues for the next year.
The manager of the company wants to
conduct casual analysis to analyze if the
number of hours spend by employee per
day has impact on revenues. Manger
collects data for past six years and
applied linear regression analysis in the
following manner. Every year he/she kept
on increasing the number of working
hours by one hour.

21. In this example, the dependent variable is revenues and the independent variable is number
of hours. We will apply least square method to following regression equation.
where,
Ῡ is the average of revenues for last six years Y
Ẋ is the average of number of hours per day to get the forecast for next year with 12 hours
per day, represented by X
• We need to determine a0 and a1