Chapter 7 - Demand Forecasting in a Supply Chain

Factors related to the demand forecast

• Past demand • Lead time of product replenishment • Planned advertising or marketing efforts • Planned price discounts • State of the economy • Actions that competitors have taken

To forecast demand, first...

...identify the factors that influence future demand and then ascertain the relationship between these factors and future demand.

Mean Absolute Percent Error (MAPE)

the average of the absolute differences between the forecast and actual values, expressed as a percent of actual values

Characteristics of Forecasts

1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error. 2. Long-term forecasts are usually less accurate than short-term forecasts; that is, longterm forecasts have a larger standard deviation of error relative to the mean than short-term forecasts. 3. Aggregate forecasts are usually more accurate than disaggregate forecasts, as they tend to have a smaller standard deviation of error relative to the mean. 4. In general, the farther up the supply chain a company is (or the farther it is from the consumer), the greater the distortion of information it receives.

4 Steps in the Adaptive Forecasting Method

1. Initialize 2. Forecast 3. Estimate Error 4. Modify Estimates

Forecasting methods are classified according to the following four types:

1. Qualitative forecasting 2. Time-Series forecasting 3. Casual forecasting 4. Simulation forecasting

Simple Exponential Smoothing

is appropriate when demand has no observable trend or seasonality.

Forecast error

measures the difference between the forecast and actual demand. A good forecasting method has an error whose size is comparable to the random component of demand.

Systematic Component

measures the expected value of demand and consists of what we will call level, the current deseasonalized demand

using multiple forecasting methods to create a combined forecast is

more effective than using any one method alone.

Deseasonalized Demand

represents the demand that would have been observed in the absence of seasonal fluctuations.

Observed demand (O) =

systematic component (S) + random component (R) The goal of any forecasting method is to predict the systematic component of demand and estimate the random component. The systematic component of demand data contains a level, a trend, and a seasonal factor.

Mean Squared Error (MSE)

the average of the squared differences between the forecasted and observed values

Adaptive Forecasting Method

the estimates of level, trend, and seasonality are updated after each demand observation. The main advantage of adaptive forecasting is that estimates incorporate all new data that are observed.

The greater the aggregation,

the greater the forecast

Periodicity (p)

the number of periods after which the seasonal cycle repeats.

Random Component

the part of the forecast that deviates from the systematic part. A company cannot (and should not) forecast the direction of the random component.

Seasonality

the predictable seasonal fluctuations in demand.

Trend

the rate of growth or decline in demand for the next period

Tracking Signal

the ratio of cumulative forecast error to the corresponding value of MAD, used to monitor a forecast bias/MAD If the TS at any period is outside the range {6, this is a signal that the forecast is biased and is either underforecasting (TS 6 -6) or overforecasting (TS 7 +6).

Identify the major factors that influence the demand forecast.

a firm must identify demand, supply, and product-related phenomena that influence the demand forecast. On the demand side, a company must ascertain whether demand is growing or declining or has a seasonal pattern. These estimates must be based on demand, not on sales data.

Mean Absolute Deviation (MAD)

a measure of the overall forecast error for a model

Static Forecasting Method

assumes that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed. In this case, we estimate each of these parameters based on historical data and then use the same values for all future forecasts.

The objective of forecasting is to

filter out the random component (noise) and estimate the systematic component.

Integrate demand planning and forecasting throughout the supply chain.

A company should link its forecast to all planning activities throughout the supply chain. These include capacity planning, production planning, promotion planning, and purchasing, among others.

Casual Forecasting

Causal forecasting methods assume that the demand forecast is highly correlated with certain factors in the environment (the state of the economy, interest rates, etc.). Causal forecasting methods find this correlation between demand and environmental factors and use estimates of what environmental factors will be to forecast future demand.

Establish Performance and Error Measures for the Forecast

Companies should establish clear performance measures to evaluate the accuracy and timeliness of the forecast. These measures should be linked to the objectives of the business decisions based on these forecasts.

Understand the objective of forecasting

Every forecast supports decisions that are based on it, so an important first step is to identify these decisions clearly. Examples of such decisions include how much of a particular product to make, how much to inventory, and how much to order. All parties affected by a supply chain decision should be aware of the link between the decision and the forecast.

Forecast at an appropriate level of aggregation

Given that aggregate forecasts are more accurate than disaggregate forecasts, it is important to forecast at a level of aggregation that is appropriate, given the supply chain decision that is driven by the forecast.

L, T, S

Level, Trend, Seasonal Factors

Qualitative Forecasting

Qualitative forecasting methods are primarily subjective and rely on human judgment. They are most appropriate when little historical data are available or when experts have market intelligence that may affect the forecast.

Simulation Forecasting

Simulation forecasting methods imitate the consumer choices that give rise to demand to arrive at a forecast. Using simulation, a firm can combine time-series and causal methods to answer such questions as: What will be the impact of a price promotion? What will be the impact of a competitor opening a store nearby?

2 forecasting methods for any form of a systematic component (S)

Static & Adaptive

Time-Series Forecasting

Time-series forecasting methods use historical demand to make a forecast. They are based on the assumption that past demand history is a good indicator of future demand.