forecasting of diesel

2. Price forecasting of diesel

3. Team member
 Abdullah Saeed khan BBSE-14-01
 Ahtisham Zafar BBSE-14-02
 Zaima Fahim BBSE-14-17
 Hamza Saleem BBSE-14-34
 M Hamza Latif BBSE-14-40
 M Khubaib Almas BBSE-14-42

4. Diesel fuel
Types:
 Petroleum diesel
 Synthetic diesel
 Biodiesel
 Hydrogenated oils and facts
 DME (Diethyl ether)
Uses:
 Trucks
 Railroad
 Aircraft
 Military vehicles
 Cars
 Tractors and heavy equipment

5.  Forecasting is the process of estimating a variable,
such as the sale of the firm at some future date.
 Forecasting is important to business firm,
government, and non-profit organization as a
method of reducing the risk and uncertainty inherent
in most managerial decisions.
 A firm must decide how much of each product to
produce, what price to charge, and how much to
spend on advertising, and planning for the growth of
the firm.
What is meant by Forecasting and Why?

6. The aim of forecasting
 The aim of forecasting is to reduce the risk or
uncertainty that the firm faces in its short-term
operational decision making and in planning for its
long term growth.
 Forecasting the demand and sales of the firm’s
product usually begins with macroeconomic
forecast of general level of economic activity for the
economy as a whole or GNP.

7. Price forecasting
The steps to be followed:
 Identification of objectives
 Nature of product and market
 Determinants of demand
 Analysis of factors
 Choice of technology
 Testing the accuracy
Criteria to choose a method of forecasting are:
 Accuracy
 Plausibility
 Durability
 Flexibility
 Availability

8. The following are needed for demand forecasting:
 Appropriate production scheduling
 Suitable purchase policy
 Appropriate price policy
 Setting realistic sales targets for salesmen
 Forecasting financial requirements

9. Method of forecasting
 Qualitative forecasting
 Quantitative forecasting

10. Qualitative forecasting Methods:
 The qualitative (or judgmental) approach can be useful in
formulating short-term forecasts and can also supplement
the projections based on the use of any of the quantitative
methods. Qualitative forecasting is an estimation
methodology that uses expert judgment, rather than
numerical analysis. This type of forecasting relies upon the
knowledge of highly experienced employees and
consultants to provide insights into future outcomes. Four
of the better-known qualitative forecasting methods are

11.  Executive Opinions
 Delphi Method
 Sales Force Polling
 Consumer Survey

12. Executive Opinions
The subjective views of executives or experts from
sales, production, finance, purchasing, and administration
are averaged to generate a forecast about future sales.
Usually this method is used in conjunction with some
quantitative method, such as trend extrapolation. The
management team modifies the resulting forecast, based
on their expectations.
Pros
 The forecasting is done quickly and easily, without need
of elaborate statistics.
Cons
 One person’s opinion can dominate the forecast.

13. Delphi Method:
 This is a group technique in which a panel of experts is questioned
individually about their perceptions of future events. The experts do
not meet as a group; in order to reduce the possibility that
consensus is reached because of dominant personality factors.
Instead, the forecasts and accompanying arguments are
summarized by an outside party and returned to the experts along
with further questions. This continues until a consensus is reached.
Pros
 This type of method is useful and quite effective for long-range
forecasting. The technique is done by questionnaire format and
eliminates the disadvantages of group think.
Cons
 Low reliability is cited as the main disadvantage of the Delphi
method, as well as lack of consensus from the returns.

14. Sales Force Polling
Some companies use as a forecast source salespeople who have
continual contacts with customers. They believe that the salespeople
who are closest to the ultimate customers may have significant
insights regarding the state of the future market. Forecasts based on
sales force polling may be averaged to develop a future forecast. Or
they may be used to modify other quantitative and/or qualitative
forecasts that have been generated internally in the company.
Pros
 It is simple to use and understand.
 It uses the specialized knowledge of those closest to the action.
 It can place responsibility for attaining the forecast in the hands of
those who most affect the actual results.
Cons
 The Salespeople’s being overly optimistic or pessimistic regarding
their predictions and inaccuracies due to broader economic events
that are largely beyond their control.

15. Consumer Surveys
 Some companies conduct their own market surveys
regarding specific consumer purchases. Surveys may
consist of telephone contacts, personal interviews, or
questionnaires as a means of obtaining data.
Extensive statistical analysis usually is applied to
survey results in order to test hypotheses regarding
consumer behavior.
Pros
 Good determinants of customer preference.
Cons
 It can be difficult to develop a good questionnair.

16. Quantitative forecasting Methods:
 Quantitative forecasting methods are used when
historical data on variables of interest are available these
methods are based on an analysis of historical data
concerning the time series of the specific variable of
interest. There are two quantitative forecasting methods.
The first uses the past trend of a particular variable in
order to make a future forecast of the variable. In
recognition of this method's reliance on time series, it is
commonly called the "time series method." The second
quantitative forecasting method also uses historical data.
This method is often referred to as the causal method
because it relies on the use of several variables and their
"cause-and-effect" relationships.

17. Quantitative Methods
Time Series Models:
 Assumes information needed to generate a
forecast is contained in a time series of data
 Assumes the future will follow same patterns as
the past
Causal Models or Associative Models
 Explores cause-and-effect relationships
 Uses leading indicators to predict the future
 Housing starts and appliance sales

18. Time Series Models
 Time Series can be defined as an ordered sequence of
values of a variable at equally spaced time intervals.
The motivation to study time series models is
twofold:
 Obtain an understanding of the underlying forces
and structure that produced the observed data
 Fit a model and proceed to forecasting, monitoring
or even feedback and feed forward control.

19. Time Series Models
 Forecaster looks for data patterns as
 Data = historic pattern + random variation
 Historic pattern to be forecasted:
 Level (long-term average) – data fluctuates around a
constant mean
 Trend – data exhibits an increasing or decreasing pattern
 Seasonality – any pattern that regularly repeats itself and is
of a constant length
 Cycle – patterns created by economic fluctuations
 Random Variation cannot be predicted

20. Cyclical Component
 a long wave in the time series.
 Any regular pattern of sequences of values above
and below the trend line lasting more than one year
can be attributed to the cyclical component.
 Usually, this component is due to multiyear cyclical
movements in the economy.

21. The Seasonal Component:
 Fluctuations in time series that recur during
specific time periods.
 The seasonal component accounts for regular
patterns of variability within certain time periods,
such as a year.
 The variability does not always correspond with the
seasons of the year (i.e. winter, spring, summer, and
fall).
 There can be, for example, within-week or within-
day “seasonal” behavior.

22. Irregular Component:
 That represents all the influences on the time series
that are not explained by the other three
components.
 The irregular component is caused by short-term,
unanticipated and non-recurring factors that affect
the values of the time series.
 This component is the residual, or “catch-all,” factor
that accounts for unexpected data values.
 It is unpredictable.

23. Trend:
 A long-term monotonic change of the average level
of the time series.
 The trend component accounts for the gradual
shifting of the time series to relatively higher or
lower values over a long period of time.
 Trend is usually the result of long-term factors such
as changes in the population, demographics,
technology, or consumer preferences

24. Time Series Models
Naive:
 The forecast is equal to the actual value observed during the last period – good for level
patterns
Simple Mean:
 The average of all available data - good for level patterns
Moving Average:
 The average value over a set time period
 (e.g.: the last four weeks)
 Each new forecast drops the oldest data point & adds a new observation
 More responsive to a trend but still lags behind actual data
n/AF t1t 
n/AF t1t 
tA1tF

25. Exponential Smoothing
 Most frequently used time series method because
of ease of use and minimal amount of data
needed
 Need just three pieces of data to start:
 Last period’s forecast (Ft)
 Last periods actual value (At)
 Select value of smoothing coefficient, ,between 0 and
1.0
 If no last period forecast is available, average the
last few periods or use naive method
  tt1t Fα1αAF 

26. Linear Trend Line
 A time series technique that computes a forecast
with trend by drawing a straight line through a set
of data using this formula:
Y = a + bx where
 Y = forecast for period X
 X = the number of time periods from X = 0
 A = value of y at X = 0 (Y intercept)
 B = slope of the line

27. Averaging Methods
 This method is based on the assumption that the future is the
average of past achievements.
 Hence based on past achievement, future is predicted.
 When the demand is stable this method can provide good
forecasts.
 The main issue in moving averages is determining the ideal
number of periods to include the average.
 Customer needs demand forecasts competition.
 Financial conditions of the firm.
 Labour training capacity.
 New products product design changes Machines.
 Suppliers capability storage capacity material availabity.
 Machine capacity, workforce capabilities.

28. conclusion
 Based on this report by analyzing all methods and
predicting prices of diesel by all method we
conclude that the exponential smoothing is most
effective and accurate method. So exponential
smoothing is best technique to predict future
prices of diesel.
 Accurate demand forecasting requires
 Product knowledge
 Knowledge about the customer
 Knowledge about the environment