Project management: Demand Forecast

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Project Planning and Management (EG853ME)Ram C. Poudel Pulchowk Campus November 1, 2009

Forecasting HorizonsLong Term5+ years into the future R&D, plant location, product planning Principally judgement-based

Medium Term1 season to 2 years Aggregate planning, capacity planning, sales forecasts Mixture of quantitative methods and judgement

Short Term1 day to 1 year, less than 1 season Demand forecasting, staffing levels, purchasing,

inventory levels Quantitative methods

Short Term Forecasting: Needs and UsesScheduling existing resourcesNEA for Load Dispatch Center

Acquiring additional resourcesHow much power stations needs to be

added?Determining what resources are neededRenewable Energy Nuclear Energy

Types of Forecasting ModelsTypes of Forecasts Qualitative --- based on experience, judgement, knowledge; Quantitative --- based on data, statistics;

Methods of Forecasting Naive Methods --- eye-balling the numbers; Formal Methods --- systematically reduce forecasting errors; time series models (e.g. exponential smoothing); causal models (e.g. regression).

Focus here on Time Series Models

Assumptions of Time Series ModelsThere 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.

Methods of demand forecasting1. 2. 3. 4. 5. 6.

Jury of experts opinion Delphi method: Individual experts act separately Consumers Survey Sales forecast composite Nave models Smoothing techniquesa. b.

Moving average Exponential smoothing

7. 8. 9. 10.

Analysis of time series and trend projections Use of economic indicators Controlled experiments Judgemental approach

Approach to forecasting1. Identify and clearly state the objectives of forecasting. 2. Select appropriate method of forecasting. 3. Identify the variables. 4. Gather relevant data. 5. Determine the most probable relationship. 6. For forecasting the companys share in the demand, two different assumptions may be made:(a) (b)

Ratio of company sales to the total industry sales will continue as in the past. On the basis of an analysis of likely competition and industry trends, the company may assume a market share different from that of the past. (alternative / rolling forecasts)

7. Forecasts may be made either in terms of units or sales in rupees. 8. May be made in terms of product groups and then broken for individual products. 9. May be made on annual basis and then divided month-wise, etc.

Statistical MethodsTrend AnalysisCurve fitting Moving Average method Weighted moving average method Exponential smoothing method (w/ Trend and

Seasonality) Time Series decomposition method

Curve Fitting

Method of Least Squares:

Principle of maxima and minima

Find the value of m and b that minimize the sum of square of

residuals.

How do we know how good the fit is?Correlation Coefficient, R260 50

y = 9x - 17.333 R2 = 0.9743

40

30

20

10

0 0 2 4 6 8

Simple Moving AverageForecast Ft is average of n previous observations or actuals

Dt :Note that the n past observations are equally weighted. Issues with moving average forecasts:

1 Ft +1 = ( Dt + Dt 1 + + Dt +1n ) All n past observations treated equally; n Observations older than n are not included at all; t n Requires that1 past observations be retained; Ft +1 = D n i Problem when 1000's of items are being forecast. n i =t +1

Simple Moving AverageInclude n most recent observations Weight equally Ignore older observationsweight1/n

n

...

3

2

1

today

Moving Averagen= 3

Exponential Smoothing IInclude all past observations Weight recent observations much more

heavily than very old observations:weight Decreasing weight given to older observations

today

Exponential Smoothing: ConceptInclude all past observations Weight recent observations much more

heavily than very old observations:weight

0