Download Forecast I

Group No :- 9
Chapter 7 :- Demand forecasting in a supply chain.
Members :
Roll No
Name
1118
Lema Juliet D
1136
Mwakatundu T
1140
Peter Naomi D
1143
Rwelamila Thobias
1144
Shetty Sachindra
1149
Vasu Lakshman
Date :- 4th Aug 2009.
Learning Objectives
• Understanding the role of forecasting for both an
enterprise and a supply chain;
• Identify the Components of a demand forecasts ;
• Forecasting demand in a supply chain given historical
demand data using time series methodologies;
• Analyze demand Forecasting to estimate error.
Outline
• The role of Forecasting in a supply chain;
• Characteristics of Forecasts
• Components of a forecast and Forecasting Methods;
• Basic Approach to Demand Forecasting;
• Time Series Forecasting Methods;
• Measure of Forecast Error; and
• Summary
The Role of Forecasting in a supply
chain
• Consider a push and pull view of the supply chain discussed in
previous classes, in each case the Manager must plan the level
of activity be it in:• Production: scheduling, inventory, aggregate planning
• Transportation
• Marketing: sales force allocation, promotions, new
production introduction
• Finance: plant/equipment investment, budgetary planning
• Personnel: workforce planning, hiring, layoffs
• All of these decisions are interrelated
Characteristics of Forecasts
Companies and Supply Chain Managers should be
aware of the following forecast characteristics:• Forecasts are always wrong. Should include expected
value and measure of error.
• Long-term forecasts are less accurate than short-term
forecasts (have larger standard deviation of error
relative to mean)
• Aggregate forecasts are more accurate than
disaggregate forecasts (as they tend to have smaller
standard deviation of error relative to the mean)
Components of Forecast
Before a company selects an appropriate forecast
method it need to understand the role of the following
factors which influence the future:-
• Past demand;
• Lead time of the product;
• Planned advert or marketing efforts;
• State of the economy;
• Planned discounts
• Actions of the competitors
Types of Forecasting Methods
Qualitative Methods: primarily subjective; rely on
judgment and opinion
• Jury of executives;
• Delphi method (participants: decision makers,
staff and respondents)
• Sales force composite (provide sales estimates)
• Consumer market survey (solicit inputs from
customers)
Quantitative Methods
• Time Series Methods:use historical demand to make
a forecast
• Static or Naive approach
• Moving Averages
Types of Forecasting Methods
(Cont…)
Associative model (Causal): use the relationship
between demand and some other factor to
develop forecast
• Linear regression
• Simulation
• Imitate consumer choices that give rise to demand
• Can combine time series and causal methods
Basic Approach to Demand
Forecasting
The following basic six step approach helps the
company to perform effective forecasting:• Understand the objective of forecasting;
• Integrate demand planning and forecasting
throughout the supply chain;
• Understand and identify customers segments;
• Identify the major factors that influence the demand
forecast (see slide 6);
• Determine the appropriate forecasting technique; and
• Establish performance and error measures for the
forecast.
Time Series
Forecasting Methods
• Goal is to predict systematic component of
demand
• Multiplicative: (level)(trend)(seasonal
factor)
• Additive: level + trend + seasonal factor
• Mixed: (level + trend)(seasonal factor)
• Static methods
• Adaptive forecasting
Static Methods
• Estimating level and trend
• Estimating seasonal factors
Adaptive Forecasting
• The estimates of level, trend, and seasonality
are adjusted after each demand observation
• General steps in adaptive forecasting
• Moving average
• Simple exponential smoothing
• Trend-corrected exponential smoothing
(Holt’s model)
• Trend- and seasonality-corrected exponential
smoothing (Winter’s model)
Moving Average
It is used when demand has no observable trend or
seasonality. It is useful if we can assume that market
demands will stay fairly steady overtime.
Mathematically:Moving Averages = Σ Demand in previous n periods
n
Where n is the number of periods in the moving
average
Exponential Smoothing
This can simply be represented in mathematical terms as:New forecast = Last period forecast + α (Last period’s actual
demand – Last period’s forecast)
Where α is a weight or smoothing constant chosen by forecaster
that has a value between 0 and 1
Ft = Ft-1 + α(At-1- Ft-1)
Where Ft = new forecast,
Ft-1 = previous forecast
α = smoothing constant
At-1= previous period actual demand
Trend Projection
This techniques fits a trend line to a series of historical
data points and then projects the line into the future
for medium to long range forecasts.
Mathematically;
Ŷ=a+bX
(will elaborate on the board)
Measure of forecast error
Mathematically
Forecast Error = Actual Demand – Forecast value
Popular measure are
• Mean absolute Value (MAD)
• Mean square error (MSE)
MAD = Σ I Actual- Forecast I
n
Forecasting Demand at Tahoe Salt
•
•
•
•
Moving average
Simple exponential smoothing
Trend-corrected exponential smoothing
Trend- and seasonality-corrected
exponential smoothing
Forecasting in Practice
• Collaborate in building forecasts
• The value of data depends on where
you are in the supply chain
• Be sure to distinguish between demand
and sales
Summary
• Understanding the role of forecasting for both an
enterprise and a supply chain;
• Identify the Components of a demand forecasts ;
• Forecasting demand in a supply chain given historical
demand data using time series methodologies;
• Analyze demand Forecasting to estimate error.
THANK YOU