Supply Chain Management Chapter 7: Demand Forecasting in a Supply Chain

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 of adaptive forecasting

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

Forecast errors contain valuable information and must be analyzed carefully for two reasons: Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 192). Pearson Education. Kindle Edition.

1. Managers use error analysis to determine whether the current forecasting method is predicting the systematic component of demand accurately. For example, if a forecasting method consistently produces a positive error, the forecasting method is overestimating the systematic component and should be corrected. 2. All contingency plans must account for forecast error. Consider a mail-order company with two suppliers. The first is in the Far East and has a lead time of two months. The second is local and can fill orders with one week's notice. The local supplier is more expensive than the Far East supplier. The mail-order company wants to contract a certain amount of contingency capacity with the local supplier to be used if the demand exceeds the quantity the Far East supplier provides. The decision regarding the quantity of local capacity to contract is closely linked to the size of the forecast error with a two-month lead time.

The following five points are important for an organization to forecast effectively:

1. Understand the objective of forecasting. 2. Integrate demand planning and forecasting throughout the supply chain. 3. Identify the major factors that influence the demand forecast. 4. Forecast at the appropriate level of aggregation. 5. Establish performance and error measures for the forecast.

Static Methods

A static 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. In this section, we discuss a static forecasting method for use when demand has a trend as well as a seasonal component. We assume that the systematic component of demand is mixed; Systematic component = (level + trend) x seasonal factor

3. Causal

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. For example, product pricing is strongly correlated with demand. Companies can thus use causal methods to determine the impact of price promotions on demand. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Forecasting in Practice

Collaborate in building forecasts Share only the data that truly provide value Be sure to distinguish between demand and sales

The forecasting methods we have discussed and the situations in which they are generally applicable are as follows: Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 192). Pearson Education. Kindle Edition.

Moving average - No trend or seasonality Simple exponential smoothing -No trend or seasonality Holt's model - Trend but no seasonality Winter's model - Trend and seasonality Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 192). Pearson Education. Kindle Edition.

Observed demand equation

Observed demand (O) = systematic component (S) + random component (R) Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

1. Qualitative

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. Such methods may also be necessary to forecast demand several years into the future in a new industry. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 179). Pearson Education. Kindle Edition.

4. Simulation

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? Airlines simulate customer buying behavior to forecast demand for higher-fare seats when no seats are available at lower fares. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Moving Average

The moving average method is used when demand has no observable trend or seasonality. In this case, Systematic component of demand = level Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 188). Pearson Education. Kindle Edition.

Trend-Corrected Exponential Smoothing (Holt's Model) Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 190). Pearson Education. Kindle Edition.

The trend-corrected exponential smoothing (Holt's model) method is appropriate when demand is assumed to have a level and a trend in the systematic component, but no seasonality. In this case, we have Systematic component of demand = level + trend We obtain an initial estimate of level and trend by running a linear regression between demand, Dt, and time, Period t, of the form Dt = at + b Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 190). Pearson Education. Kindle Edition.

Trend- and Seasonality-Corrected Exponential Smoothing (Winter's Model) Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 191). Pearson Education. Kindle Edition.

This method is appropriate when the systematic component of demand has a level, a trend, and a seasonal factor. In this case we have Systematic component of demand = (level + trend) x seasonal factor Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 191). Pearson Education. Kindle Edition.

2, Time Series

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. These methods are most appropriate when the basic demand pattern does not vary significantly from one year to the next. These are the simplest methods to implement and can serve as a good starting point for a demand forecast. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Periodicity (p)

is the number of periods after which the seasonal cycle repeats. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 184). Pearson Education. Kindle Edition.

Random Component

is the part of the forecast that deviates from the systematic part. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Forecast Error

measures the difference between the forecast and actual demand. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Systematic component

measures the expected value of demand and consists of what we will call level, Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Deseasonalized Demand

represents the demand that would have been observed in the absence of seasonal fluctuations. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 184). Pearson Education. Kindle Edition.

Level

the current deseasonalized demand; Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Adaptive Forecasting

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. We now discuss a basic framework and several methods that can be used for this type of forecast. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 187). Pearson Education. Kindle Edition.

For push processes, a manager must plan Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 177). Pearson Education. Kindle Edition.

the level of activity, be it production, transportation, or any other planned activity. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 177). Pearson Education. Kindle Edition.

For pull processes, a manager must plan Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 177). Pearson Education. Kindle Edition.

the level of available capacity and inventory, but not the actual amount to be executed. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 177). Pearson Education. Kindle Edition.

Seasonality

the predictable seasonal fluctuations in demand. Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

Trend

the rate of growth or decline in demand for the next period; Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 180). Pearson Education. Kindle Edition.

The equation for calculating the systematic component may take a variety of forms:

• Multiplicative: Systematic component = level x trend x seasonal factor • Additive: Systematic component = level + trend + seasonal factor • Mixed: Systematic component = (level + trend) x seasonal factor

A company must be knowledgeable about numerous factors that are related to the demand forecast, including the following: Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 179). Pearson Education. Kindle Edition.

• Past demand • Lead time of product replenishment • Planned advertising or marketing efforts • Planned price discounts • State of the economy • Actions that competitors have taken Chopra, Sunil; Meindl, Peter. Supply Chain Management: Strategy, Planning, and Operation (Page 179). Pearson Education. Kindle Edition.