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OUTLINE

• The role of forecasting in a supply chain• Characteristics of forecasts• Components of forecasts and forecasting methods• Basic approach to demand forecasting• Time series forecasting methods• Measures of forecast error• Forecasting demand at Tahoe Salt• Forecasting in practice

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Characteristics of Forecasts

• Forecasts are always wrong. Should include expected value and measure of error. Ex.100-1900 and 900-1100

• Long-term forecasts are less accurate (large standard deviation) than short-term forecasts (forecast horizon is important)-

• Aggregate forecasts are more accurate (small standard deviation) than disaggregate forecasts. Ex – GDP (2% accuracy) - Sector-Industry-Firm.

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Forecasting• Forecasting involves the projection of the past into

the future. • Forecast involves estimating the level of demand of

a product on the basis of factors that generated the demand in the past months.

• Forecasting is more scientific. • It is relatively free from personal bias. • It is more objective. • It is generally called as “Throw Ahead” technique. • Error analysis is possible.• Forecasting is reproducible, i.e., every time same

result would be obtained by any particular technique.

Prediction• Prediction involves judgment in management after

taking all available information into account.• Prediction involves the anticipated change into the

future. It may include even new factors that may affect future demand.

• Prediction is more intuitive.• It is more governed by personal bias and

preferences.• It is more subjective.• It is generally called as “Saying Beforehand”

technique.• Prediction does not contain error analysis.• It is non-producible.

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Factors Considered For Selection of a method • the context of the forecast• the relevance and availability of historical data•  the degree of accuracy desirable• the time period to be forecast• the cost/ benefit (or value) of the forecast to the company• the time available for making the analysis

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Forecasting Methods

• Qualitative: primarily subjective; rely on judgment and opinion-no or little historical data

• Time Series: use historical demand only• Causal: use the relationship between demand and some

other factor to develop forecast (Interest rates-cement, power cuts-inverter, coffee-tea)

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Qualitative Technique

• used when data is scarce• use human judgment and rating schemes to turn qualitative

information into quantitative estimates• frequently used in new-technology areas• Classified as1. Delphi Method2. Market Research3. Panel consensus4. Visionary forecast5. Historical Analogy

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Time Series Analysis

• Statistical techniques used when several years’ data for a product

• Relationships and trends are both clear and relatively stable.• Rates and trends are not immediately obvious; they are

mixed up with seasonal variations• Time series is a set of chronologically ordered points of raw

data• More likely to be correct over the short term than it is over

the long term• A period of slow growth in sales will suddenly change to a

period of rapid decay, such points are called turning points

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Time Series Analysis(contd..,)

• Types1. Moving Average2. Exponential Smoothing3. Box Jenkins4. X-115. Trend Projections

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Causal Method

• A causal model is the most sophisticated kind of forecasting tool

• When historical data are available and enough analysis has been performed to spell out explicitly the relationships between the factor to be forecast and other factors (such as related businesses, economic forces, and socioeconomic factors), the forecaster often constructs a causal model.

• May also directly incorporate the results of a time series analysis.

•  Continually revised as more knowledge about the system becomes available.

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Causal Method(contd..,)

• Types1. Regression Model2. Econometric Model3. Surveys4. Diffusion Index5. Input-output Model6. Leading Indicator7. Life-cycle Analysis

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Basic Forecastinag Techniques

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Basic Forecastinag Techniques

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Basic Forecastinag Techniques

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Basic Forecastinag Techniques

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Basic Forecastinag Techniques

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