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 Exponential Smoothing Trend projection Holt’s model (with trend) Winter’s model (with trend and seasonality

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 F t = F t-1 + α(A t-1 - F t-1 ) Where F t = new forecast, F t-1 = previous forecast α = smoothing constant A t-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 + b X (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.

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