DEMAND FORECASTING
DEMAND FORECASTING
 Demand forecasting means estimation of the
demand for the good in the forecast period.
 It is a process of estimating a future event by casting
forward past data.
 The past data are systematically combined in a
predetermined way to obtain the estimate of future
demand.
Demand forecasting
is the activity of estimating the quantity of a
product or service that consumers will
purchase. Demand forecasting involves
techniques including both informal
methods, such as educated guesses, and
quantitative methods, such as the use of
historical sales data or current data from test
markets. Demand forecasting may be used
in making pricing decisions, in assessing
future capacity requirements, or in making
decisions on whether to enter a new market.
Necessity for forecasting demand
 Often forecasting demand is confused with forecasting
sales. But, failing to forecast demand ignores two
important phenomena.[1] There is a lot of debate in
demand-planning literature about how to measure
and represent historical demand, since the historical
demand forms the basis of forecasting. The main
question is whether we should use the history of
outbound shipments or customer orders or a
combination of the two as proxy for the demand.
Forecasting Horizons
Long Term
5+ years into the future
R&D, plant location, product planning
Principally judgment-based
Medium Term
1 season to 2 years
Aggregate planning, capacity planning,
sales forecasts
Mixture of quantitative methods and
judgment
Short Term
1 day to 1 year, less than 1 season
Demand forecasting, staffing levels,
purchasing, inventory levels
Quantitative methods
PURPOSE OF
FORECASTING
Purpose of short-term forecasting
 Appropriate production scheduling so as to avoid the
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problem of over-production & the problem of shortsupply.
Helping the firm to reducing costs of purchasing raw
materials.
Determining appropriate price policy.
Setting sales targets & establishing controls &
incentives.
Evolving a suitable advertising & promotion programme.
Forecasting short-term financial requirements.
PURPOSES OF LONGTERM FORECASTING
 Planning of a new unit or expansion of an existing unit. A
multi-product firm must ascertain not only the total
demand situation, but also the demand for different items
separately.
 Planning long-term financial requirements. As planning
for raising funds requires considerable advance notice,
long –term sales forecasting are quite essential to assess
long-term financial requirements.
 Planning man-power requirements. Training & personnel
development are long-term propositions, taking
considerable time to complete.
IMPORTANCE OF
DEMAND FORECASTING
 Demand forecasts are necessary since the basic
operations process, moving from the suppliers' raw
materials to finished goods in the customers' hands,
takes time. Most firms cannot simply wait for demand to
emerge and then react to it. Instead, they must anticipate
and plan for future demand so that they can react
immediately to customer orders as they occur. In other
words, most manufacturers "make to stock" rather than
"make to order" – they plan ahead and then deploy
inventories of finished goods into field locations
General Approaches to
Forecasting
1)
JUDGEMENTAL APPROACHES: The essence of the judgmental
approach is to address the forecasting issue by assuming that someone else
knows and can tell you the right answer.
2)
EXPERIMENTAL APPROACHES: When an item is "new" and
when there is no other information upon which to base a forecast, is to
conduct a demand experiment on a small group of customers
3)
RELATIONAL/CAUSAL APPROCHES: There is a reason why
people buy our product. If we can understand what that reason (or set of
reasons) is, we can use that understanding to develop a demand forecast.
4)
TIME SERIES APPROACHES: A time series is a collection of
observations of well-defined data items obtained through repeated
measurements over time.
Types of Forecasting Models
 Types of Forecasts
 Qualitative --- based on experience, judgment, knowledge
 Quantitative --- based on data, statistics
 Methods of Forecasting
 Naive Methods --- eye-balling the numbers
 Formal Methods --- systematically reduce forecasting errors
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time series models (e.g. exponential smoothing)
causal models (e.g. regression)
 Focus here on Time Series Models
 Assumptions of Time Series Models
 There is information about the past
 This information can be quantified in the form of data
 The pattern of the past will continue into the future
Forecasting Examples
 Examples from student projects
 Demand for tellers in a bank
 Traffic on major communication switch
 Demand for liquor in bar
 Demand for frozen foods in local grocery warehouse
 Example from Industry: American Hospital Supply Corp.
 70,000 items
 25 stocking locations
 Store 3 years of data (63 million data points)
 Update forecasts monthly
 21 million forecast updates per year
METHOD USED FOR FORCATING
least square method..
Least square method
Formulas used
∑y= ha+b∑x
∑xy=a∑x + b∑x^2
Given Past Data
Year
Demand
Deviation (x)
x2
xy
2007
120
-2
4
-240
2008
140
-1
1
-140
2009
120
0
0
0
2010
150
1
1
150
2011
180
2
4
360
Next Three Year Data
Year
Demand
Deviation (x)
x2
xy
2012
181
3
9
543
2013
194
4
16
776
2014
207
5
25
1035
Experimental Approaches
 Customer Surveys are sometimes conducted over
the telephone or on street corners, at shopping
malls, and so forth. The new product is displayed or
described, and potential customers are asked
whether they would be interested in purchasing the
item. While this approach can help to isolate
attractive or unattractive product features,
experience has shown that "intent to purchase" as
measured in this way is difficult to translate into a
meaningful demand forecast. This falls short of
being a true “demand experiment”.
TIME SERIES
APPROACHES
SIMPLE MOVING AVERAGE
 In a moving average, the forecast would be calculated
as the average of the last “few” observations. If we let M
equal the number of observations to be included in the
moving average, then:
Z’t+1 =1/M ∑i=t+M-1 Zi
 For example, if we let M=3, we have a "three period
moving average", and so, for example, at t = 7:
Z’8= (Z7+Z6+Z5) /3
AN ILLUSTRATION OF THE
EFFECT OF M
T
Z
M=2
M=3
M=4
M=5
M=6
M=7
1
98
2
110
3
100
104
4
94
105
103
5
100
97
101
101
6
92
97
98
101
100
7
96
96
95
97
99
99
8
102
94
96
96
96
99
99
9
105
99
97
98
97
97
99
10
96
104
101
99
99
98
98
Simple Exponential Smoothing
 A popular way to capture the benefit of the weighted
moving average approach while keeping the
forecasting procedure simple and easy to use is
called exponential smoothing, or occasionally, the
“exponentially weighted moving average”. In its
simple computational form, we make a forecast for
the next period by forming a weighted combination of
the last observation and the last forecast:
Z’ t+1 =aZt +(1-a)Zt
 Where α is a parameter called the “smoothing
coefficient”, “smoothing factor”, or “smoothing
constant”. Values of α are restricted such that 0 < α
< 1. The choice of α is up to the analyst. In this form,
α can be interpreted as the relative weight given to
the most recent data in the series.
Electricity forcasting for 8 years
Gold price forecast for the next 10 years
 An analysis by the Standard Chartered bank suggests that the gold
price will triple due to shortages in gold production. The bank's
research team looked at the production levels of 345 gold mines and
came to the conclusion that the gold production will be only 3.6%
annualy over the next five years. The demand for gold, however, has
been growing at a much faster pace, driven by purchases of gold by
Asian central banks. This forecast is unique for two reasons: first, most
gold price predictions are based on inflationary and crisis scenarios,
while this one looks at the supply-demand equation. Second, banks
usually tend to be rather conservative in their gold price predictions.
An interesting read, indeed.

 Superfund's Aaron Smith expects gold to increase 50% to
100% by 2014, as measured in major currencies. He also
thinks that an ounce of silver will trade for around $100
within the same timeframe.
 Reuter's analyst Wang Tao predicts silver to be worth $55
within the next few years.
 Recent developments, such as the legalization of gold and
silver as official currency in Utah or the purchase of $1bn
worth of gold by the University of Texas and similar big
institutions may accelerate this development.
OIL FORCASTING IN INDIA