Production and Project

Management
ChEg5211
Sales forecasting
 Sales forecasting
Forecasts are essential for the smooth operations of business
organizations.They provide information that can assist managers
in guiding future activities toward organizational goals
Objectives of Forecasting and Uses
Forecasts are estimates of the occurrence, timing, or magnitude of
uncertain future events.
Operations managers are primarily concerned with forecasts of
demand which are often made by (or in conjunction with)
marketing.
However, managers also use forecasts to estimate raw material
prices, plan for appropriate levels of personnel, decide how much
inventory to carry, and a host of other activities.
 Sales forecasting
“Sales forecasting is on important tool for the success of an
entrepreneurial or industrial set up. Therefore, it must be given
due respect while planning an industrial organization.”
 Market Research
Sales Forecasting Defined
 Need for Forecasting
 Types of forecasting
 Methods Used for Sales Forecasting
 Solved Problems
 Questions
 MARKET RESEARCH
It is the analysis of the project or Industrial enterprise to be
started, modified or expanded or it is a method of finding out facts
which must be known before a decision regarding market policy is
taken.
Thus market research is a scientific method of determining:
what to produce,
 who the purchasers are,
 where are these located,
 how much products/goods to manufacture,
 when to sell and how to sell
in order to maximize the service rendered and profits earned.
Cont’’’
This is a dynamic world in which profit opportunities are constantly
changing and
efficient market research can only enable a producer/manufacturer to earn
maximum while providing maximum satisfaction to consumer.
SALES FORECASTING DEFINED
Sales forecast is the estimate of amount of sales to be expected for an item/a
product or products for a future period of time.
Except the industries based on job order almost all the enterprises produce
in advance to meet the future requirements.
Thus, accurate sales forecasting is essential for a enterprise to enable it to
produce the required number of items at right time.
 It further makes arrangement in advance for raw materials, machines and
man-power, etc.
 NEED FOR FORECASTING
I. The management of the enterprise can take decision regarding
operations planning, scheduling, production programming,
inventories of various types, physical distribution and projecting
cash generation and operating profits on the basis of sales
forecasts.
II. Long term sales forecasts can help in deciding investment
proposals such as modernization, expansion of existing units,
diversification of product lines, etc.
III. Sales forecasts are essential to make proper arrangement for
training the man-power in its own unit or sending them to other
industries in the country or abroad to meet the future needs of
expertise.
TYPES OF FORECASTING (FORECASTING
HORIZONS)
There are three types of forecasting:
i. Short term forecasting (0 – 3 months)
ii. Medium term (3 months – 2 years )
iii. Long term forecasting (≥ 2 years)
Short Term Forecasting
It may be defined as forecasting done for a relatively shorter period.
The period may be one month to three months depending upon the
nature of the product.
Generally this type of forecasting is done for a period of three
months but if the market demand fluctuates, forecasting may be
done only for a short period.
 Purpose of Short Term Forecasting
a) Production Policy: By knowing the future demand, the decision
regarding production policy can be taken so that there is no
problem of over production and short supply of input materials.
b) Material requirement planning: By knowing the future
demand, the availability of right quantity and quality of materials
could be ensured.
c) Purchase procedure: The purchase program could be decided
depending upon the material requirements.
d) Inventory Control: Proper control of inventory could be
ensured, so that inventory carrying cost is minimum or optimum.
Cont’’’’
e)Equipment requirement: The decision regarding procurement
of new equipment in view of the capacity and capability of the
existing equipment can be taken.
f) Man-Power requirement: The decision regarding recruitment
of extra labor on full time or part time could be taken.
g) Finance: The arrangement of funds for purchase of raw
materials, machines and parts could be made.
 Long Term forecasting
The forecasting that covers a considerable period of time, such as 5, 10, 20
years is called long term forecasting.
The period no doubt depends upon the nature of business or type of the
product the firm is engaged in manufacturing.
In many industries like steel plants, petroleum refinery or paper mills where
the total investment for the equipment/infrastructure is quite high, long term
forecasting is needed.
Purpose of Long-term Forecasting
a. To plan for the new unit of production, or expansion of the existing unit or
diversification of lines of production or shut down of the existing units
depending upon the level of demand.
b. To plan the long term financial requirement for various needs.
c. To make proper arrangement for training the personnel so that man-
power requirement of desired expertise can be met in future.
FORECASTING DECISION VARIABLES
Forecasting activities are a function of:
1. the type of forecast (e.g., demand, technological),
2. the time horizon (short, medium, or long range),
3. the database available, and
4. the methodology employed (qualitative or quantitative).
Forecasts of demand are based primarily on non-random trends and
relationships, with an allowance for random components.
Forecasts for groups of products tend to be more accurate than those for
single products, and short-term forecasts are more accurate than long-term
forecasts (greater than five years).
Quantification also enhances the objectivity and precision of a forecast.
FORECASTING METHODS
There are numerous methods to forecasting depending on the need
of the decision-maker. These can be categorized in two ways:
1. Qualitative (Opinion and Judgmental) Methods
2. Quantitative (Time Series) Forecasting Methods.
Qualitative (Opinion and Judgmental) Methods
Some opinion and judgment forecasts are largely intuitive, whereas
others integrate data and perhaps even mathematical or statistical
techniques.
Judgmental forecasts often consist of:
1. Forecasts by individual sales people,
2. Forecasts by division or product-line managers, and
3. Combined estimates of the two
Cont’’’
Historical analogy relies on comparisons;
Delphi relies on the best method from a group of forecasts.
All these methods can incorporate experiences and personal
insights. However, results may differ from one individual to the next
and they are not all amenable to analysis. So there may be little
basis for improvement over time.
Quantitative (Time Series) Forecasting Methods
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. (Note: Autocorrelation
effects are sometimes included as an additional factor.)
Cont’’
Time series are tabulated or graphed to show the nature of the
time dependence.
 The forecast value (Yc) is commonly expressed as a multiplicative
or additive function of its components;
examples here will be based upon the commonly used
multiplicative model.
Yc = T. S. C. R multiplicative model
Yc = T + S + C + R additive model
where T is Trend, S is Seasonal, C is Cyclical, and R is Random
components of a series.
Cont’’’
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.
 Summary of Forecasting Methods

• Key: L = low, M = medium, H = high, SR = short range, MR = medium

range, LR = long range.
 Method of Least Squares
The method of least squares provides an equation which gives two
characteristics of the line of best fit.
A straight line can be described in terms of two things i.e., its slope
and Y-intercept. Y - intercept is the point on Y-axis of graph
between two variables where the line intersects the y-axis.
If we know the Y-intercept and slope of the line, the equation of the
line can be determined from the general expression for the
equation of any line which is as follows: Yc‘ = mx + a
Where ,Yc' = is the calculated value of the dependent variable
which is to be forecasted.
a = Y - intercept of the line of best fit.
m = slope of the line of best fit.
x = given value of independent variable in terms of which value of
dependent variable is to be forecasted.
Cont’’’’
The method of least squares can help us to find out the equation of
the line by working directly with the original data of dependent
and independent variables by making appropriate substitutions in
the following expressions.
Where, x = given values of the independent variable, which may be the
economic indicator Y = Given value of the dependent variable which
may be sales of the product in this case.
 n = number of given paired observations.
 a = Y - intercept of the line of best fit. m = slope of the line of best fit.
Cont’’’
cont’’’
 Time Series Analysis
This method of sales forecasting is considered similar to
economic indicator method since it also requires regression
analysis.
A time series is a chronological data which has some quantity
such as sales volume or sales in cashes as the dependent
variable and time as independent variable.
These time series data available with an established
organization are analyzed before making the forecast.
There is a common technique which is generally employed
termed as "project the trend." In this method the trend line
is projected by least squares method.
Cont’’’
The variations of the dependent variable may be segregated as
(a) Long period changes.
(b) Short period changes.
The long period tendency of data to change i.e., increase or
decrease is called basic trend which may be linear or non linear .
The short period changes may be of two types:
i. Regular
ii. Irregular
Regular fluctuations are those which occur at regular intervals of
time. These may be:
a) Seasonal variations.
b) Cyclic variations.
 Seasonal Variations
The most common periodic variation is the seasonal variation
which occurs with some regularity in a span of time.
 These variations are caused by climatic conditions such as
effect of the sun and weather conditions, social customs and
festivals, etc. These affect sales of various products (normally
of consumer utilization).
Cyclic Variations
These changes show periodicity and occur over a shorter
period of time. Like seasonal variations, cyclic variations are
also regular . But
whereas seasonal variations occur within a period of one year
or less, cyclic variations repeat at intervals of 5 to 10 years.
 Irregular Variations
These variations occur without any particular rhythm. They can be
caused by causes operating in a casual and irregular fashion.
Causes may be like droughts, floods, wars, strikes and earth quakes,
etc. In time series analysis technique of sales forecasting, an
organization analyses its past sales to find out if there is some trend.
This trend is then projected into the future and the resultant indicated
sales are used as a basis for a sales forecast. This method will be clear
with the help of following illustrations.
Suppose a manufacturer of painting equipment (may be paint
roller frames) decides to forecast next year sales of its product.
The firm starts by collecting the data for the last four/five years.
 The manufacturer knows from the past experience that sales of
its product fluctuates due to seasonal variations.
Cont’’’’’
 In fact, the firm has found from the past data that the market
demand for the product is at its minimum during the first quarter of
the year and this results in an increase in sales due to improved
weather conditions during the 2nd quarter of the year .
Example 2: A departmental store decides to forecast the demand for an
item it sells by means of a time series analysis. Quarterly sales for the
past three years were as follows:
Cont’’’
• In spite of the limited amount of data available, determine the equation
of the trend line . With the equation, calculate the trend values of
quarterly sales for the 4th year . Then, adjust these values to provide for
expected seasonal variations.
Cont’’
Cont’’’
The simplest and most straight forward method of measuring
total, variation around the trend line is by expressing actual
sales Ya as percentage of calculated sales Yc, In this case:

Advantages of Time Series Analysis

1. This technique is less subjective than collective opinion
method and method of economic indicators since its
application is not dependent on the organizations ability to
find appropriate indicator (s).
Cont’’’’
2. In comparison with method of collective opinion and method of
economic indicators, which may yield only an annual forecast
that must thereby be broken down into shorter periods,
the organization can forecast sales by year by analyzing past
annual sales, by month by analyzing past monthly sales or
even by week by analyzing past weekly sales.
Limitations of Time Series Analysis
1. This technique can not be used for forecasting the sales of
a new or relatively new product since no past data or insufficient
past data are available.
2. If the significant fluctuation in level of demand occur
month to month in a year due to seasonal variations, there
are adjustment factors may be required for adjusting the
forecasting in a year .
3. The impact of changes in selling prices, product quality,
economic conditions, marketing methods and sales promotional
efforts made by the organizations cannot be incorporated into
the method in a satisfactory way .