Forecasting and Quantification - Presentation

1. OPERATIONS MANAGEMENT ASSIGNMENT
Student: Wilfred Jacob Gitaari
Reg. No.: HD334-033-1657/2009
Individual Class Presentation
Date: June 2010
Lecturer: Mr. Paul Sang
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2. TITLE: Forecasting and Planning
Presentation.
Presentation made In Partial Fulfilment of
the Requirements for the Award of the
Executive Master of Business Administration
Degree
Jomo Kenyatta University of
Agriculture and Technology
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3. QUESTION
• Forecasting is needed to predict the requirements for
materials, products, services, or other resources to
respond to changes in demand. In light of the above
statement:
(I) Outline the steps in forecasting process
(II) Describe at least Quantitative and Qualitative forecasting
techniques and the advantages and disadvantages of each
(III) Identify the major factors to consider when choosing a
forecasting technique.

4. INTRODUCTION
1. Introduction
• All organizations operate in an atmosphere of
uncertainty.
• The ever changing political, economic, social,
technological, environmental, legal and regulatory
(PESTEL) factors and other uncontrollable factors like
weather are responsible for this uncertainty.
• Unfortunately decisions must be made today that affect
the future of the organization.
• Educated guesses are more valuable to the decision
maker than uneducated guesses.
• Hence the need for forecasting.
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5. INTRODUCTION (CONTINUED)
• According to Hanke and Wichern, (2005) forecasting is
concerned with predicting the uncertain nature of business
trends in an effort to help managers make better decisions
and plans.
• These treads could relate to sales volumes, inventories,
employees turnover, revenues, etc.
• Forecasting procedures involve extending of the past into the
future.
• Thus forecasting techniques assume that the conditions that
generated the past data are indistinguishable from the
conditions of the future except for those variables explicitly
recognized by the forecasting model.

6. (I) STEPS IN FORECASTING PROCESS
Hanke and Wichern, (2005) identify five steps in
the forecasting process as detailed below:-
1. Problem formulation and data collection
 Problem formulation and data collection treated as a single step
because they are closely related i.e. problem determines the
appropriate data.
 Answers the question – why (purpose) and what should be
forecasted ?
 Decides data type. Will it be qualitative or quantitative – what is
the most appropriate forecasting methodology? Will accurate
data be available.
 Data collection - measurements, interviews questionnaire
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7. (I) STEPS IN FORECASTING PROCESS
2. Data manipulation and cleaning
 Is data too much or too little?
 Resolve issues of incomplete or irrelevant data.
 Remove irrelevant data.
 Estimate missing values for incomplete data.
 Re-express data in appropriate units as opposed to original units
e.g. convert from pounds to kilograms.
 Consolidate data from different sources.
 Consolidate data according to periods when it is applicable for
example sales seasonality.
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8. (I) STEPS IN FORECASTING PROCESS
3. Model building and evaluation
 Choose an appropriate forecasting model or technique
 Chosen model must :
 Minimizes the forecasting errors.
 Simple for understanding of decision makers to gain support.
 Fit data into chosen forecasting model.
4. Model implementation (the actual forecast)
 The actual forecast is generated once data is inputted into the
forecasting model.
 Tests using forecasts for recent period (where actual historical
data values are known) to validate the accuracy of the process.
 Observe and summarize forecasting errors.
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9. (I) STEPS IN FORECASTING PROCESS
5. Forecast evaluation
 Comparing actual values with actual historical values.
 Involves holding back a few of the most recent data values from
the data set being analyzed, and using the model to forecast for
these periods for comparison.
 Work out forecasting errors and record.
 Modify forecasting procedure where necessary.
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10. (II) Description of Quantitative and Qualitative
forecasting techniques
a) Quantitative techniques
• These techniques utilize data manipulation to produce
quantitative results.
• Utilize time series data which may be level, seasonal, cyclic or
random.
• Comprise simple average, last period, moving average,
weighted average, exponential smoothing, regression analysis,
Box Jenkins method and econometric modeling.
• In this assignment we shall discuss moving averages,
exponential smoothing and regression analysis.
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11. (II) Description of Quantitative and Qualitative forecasting
techniques
i. Moving averages
 Generate forecasts based on the average of the past observations.
 Each new observation used to compute a new mean and the oldest
observation is dropped.
Advantages
 Works where analyst is concerned with recent observations.
 Useful for short and intermediate horizon forecast.
 Simple to do.
Disadvantages
 Does not handle seasonality and cyclic trends well though better than
simple average.
 Not accurate for long forecasting horizons.

12. (II) Description of Quantitative and Qualitative forecasting
techniques
ii. Exponential smoothing
 Generate forecasts by averaging past values of a series with a
decreasing (exponential) series of weights.
 Forecasts are continuously revised in the light of more recent
experience.
 Aims at estimating the current level.
 Estimates are used for future values.
Advantages
 Suited for data with no predictable upward or downward trend.
 Useful for short and intermediate horizon forecast.
Disadvantages
 Not accurate for long forecasting horizons.
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13. (II) Description of Quantitative and Qualitative forecasting
techniques
iii. Regression analysis
Also called ‘‘Best line of fit’’, ‘‘Least square method’’, ‘‘Linear
regression’’ or ‘‘Trend line analysis’’
 Involves two series.
 Dependent series or variable (to be predicted) .
 Independent series or variable (the predictor).
 Each observation has two variables, dependent and independent.
 Trend line involves plotting each pair of data, then estimating the
straight line that shows the tend.
Advantages
 Appropriate for short term, medium term and long term forecasting
horizons
 Useful for linear relationship forecasting.

14. Description of Quantitative and Qualitative forecasting
techniques
iii. Regression analysis
Disadvantages
 Technique fails to account for other variables that may affect the dependent
variable
 Needs a large number of observations to be accurate.
 Assumes conditions remain the same outside the observation area. This
may not always be true.
 Not adaptable for stocks management.
 Need to do correlation analysis to confirm degree of association.
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15. (II) Description of Quantitative and Qualitative
forecasting techniques
b) Qualitative techniques
• These techniques rely on judgment, intuition or experience of experts.
• Are used in situations of increasing uncertainty.
• Areas of application include
 New product introduction.
 New technology.
 Special events.
 Prediction of impact of unusual developments on product sales.
• Appear unscientific but should be regarded as important as quantitative
techniques.
• Include management consensus, Dephi method, market research, historical
analysis and grass root method
• To discuss management consensus, Dephi method and historical analysis

16. (II) Description of Quantitative and Qualitative forecasting techniques
i. Management consensus
 Also called the nominal group technique .
 Small groups of experienced managers asked to give gut feel estimates .
 Differences in opinion discussed until consensus is built.
 Multi-disciplinary group used.
 There should be free expression of ideas.
Advantages
 Works well in areas of increased uncertainty.
 Encourage group think and involvement.
Limitations
 Risk of disagreement with less informed powerful managers.
 Power politics.

17. (II) Description of Quantitative and Qualitative forecasting techniques
ii. Dephi method
 As structured form of group consensus .
 Involves external experts specialized in forecast area.
 Views of experts are sought.
 Conducted in several iterations.
 Secret forecasts collected from several panelists and averaged
 Panelists submitting significantly differing results asked to either
• Provide supporting information for their estimates.
• Change their forecasts to align with group.
• All panelists are given the additional information.
 Panelists asked to make new forecast in light of additional information.
Advantages
 Good for technological forecasting.
 Best where uncertainty is high.
Limitations
 Long timelines.
 Expensive .

18. (II) Description of Quantitative and Qualitative forecasting techniques
iii. History analogy
 Uses experience to forecast the future.
 Outcomes after special event help predict future outcomes after similar events
take place.
 Example based Coca Cola sales at the 2009 JKUAT Graduation ceremony, what
will be the sales ?
 Information used to forecast demand for quantity to be stocked.
Advantages
 Easy to do.
 Cheap.
Limitations.
 May not be accurate when circumstances change e.g. due to
weather.
 Only applicable in relation to events.

19. (III) FACTORS TO CONSIDER WHILE CHOSING A FORECASTING TECHNIQUE
 According to Hanke and Wichern (2005)
 Nature of forecasting problem
 Historical patterns or not.
 Type of data series
 Stationery
 With a trend
 Seasonal
 Cyclical
 Horizon of forecast – short, medium long term.
 Expected accuracy levels.
 Acceptable forecast costs.
 Timelines for completion.

20. REFERENCES
1. Hanke, J.E. and Wichern, D.W. (2005),
Business Forecasting; Eighth edition, New
Jersey; USA, Pearson Prentice Hall
2. KIM (2009), Fundamentals of Production and
Operations Management; Nairobi; Kenya,
Macmillan
3. Waters, D. (2001), Quantitative Methods for
Business; Third edition, Essex; England,
Pearson Education Limited

21. Question and Answer
Thank You
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