CHAPTER 11 FORECASTING AND DEMAND PLANNING AND DEMAND PLANNING… · OM, Ch. 11 Forecasting and Demand Planning 2 ©2009 South- Western, a part of Cengage Learning LO1 Describe the

1OM, Ch. 11 Forecasting and Demand Planning©2009 South-Western, a part of Cengage Learning

FORECASTING AND DEMAND PLANNING

CHAPTER 11

DAVID A. COLLIERAND

JAMES R. EVANS

OM


LO1 Describe the importance of forecasting to thevalue chain.

LO2 Explain basic concepts of forecasting and timeseries.

LO3 Explain how to apply single moving average andexponential smoothing models.

LO4 Describe how to apply regression as a forecastingapproach.

LO5 Explain the role of judgment in forecasting.

LO6 Describe how statistical and judgmental forecasting techniques are applied in practice.

Chapter 11 Learning Outcomes

l e a r n i n g o u t c o m e s


Chapter 11 Forecasting and Demand Planning

he demand for rental cars in Florida and other warm climates peaks during college spring break season. Callcenters and rental offices are flooded with customers

wanting to rent a vehicle. National Car Rental took a unique approach by developing a customer-identification forecasting model, by which it identifies all customers who are young and rent cars only once or twice a year. These demand analysis models allow National to call this target market segment in February, when call volumes are lower, to sign them up again. The proactive strategy is designed to both boost repeat rentals and smooth out the peaks and valleys in call center volumes.

What do you think? Think of a pizza delivery franchise located near a college campus. What factors that influence demand do you think should be included in trying to forecast demand for pizzas?


• Forecasting is the process of projecting the values of one or more variables into the future.

• Poor forecasting can result in poor inventory and staffing decisions, resulting in part shortages, inadequate customer service, and many customer complaints.



• Many firms integrate forecasting with value chain and capacity management systems to make better operational decisions.

• Accurate forecasts are needed throughout the value chain, and are used by all functional areas of the organization, including accounting, finance, marketing, operations, and distribution.



• One of the biggest problems with forecasting systems is that they are driven by different departmental needs and incentive systems.

• Demand planning software systems integrate marketing, inventory, sales, operations planning, and financial data.



Exhibit 11.1 The Need for Forecasts in a Value Chain


Basic Concepts in Forecasting

• The planning horizon is the length of time on which a forecast is based. This spans from short-range forecasts with a planning horizon of under 3 months to long-range forecasts of 1 to 10 years.




• A time series is a set of observations measured at successive points in time or over successive periods of time. A time series pattern may have one or more of the following five characteristics: Trend Seasonal patterns Cyclical patterns Random variation (or noise) Irregular (one time) variation



Exhibit 11.2 Example of Linear and Nonlinear Trend Patterns


Exhibit 11.3

Seasonal patterns are characterized by repeatable periods of ups and downs over short periods of time.

Seasonal Pattern of Home Natural Gas Usage


Cyclical patterns are regular patterns in a data series that take place over long periods of time.

Exhibit Extra Trend and Business Cycle Characteristics (each data point is

1 year apart)


Random variation (sometimes called noise) is the unexplained deviation of a time series from a predictable pattern, such as a trend, seasonal, or cyclical pattern.

Because of these random variations, forecasts are never 100 percent accurate.




I rregular variation is a one-time variation that is explainable. For example, a hurricane can cause a surge in demand for building materials, food, and water.



Exhibit 11.4

Call Center Volume


Exhibit 11.5

There is an increasing trend over the six years, along with seasonal patterns within each year.

Chart of Call Volume


• Forecast error is the difference between the observed value of the time series and the forecast, or At – Ft.

• Mean Square Error (MSE)

• Mean Absolute Deviation Error (MAD)

• Mean Absolute Percentage Error (MAPE)


Σ(At – Ft )2 MSE = [11.1]

T

Σ׀(At – Ft ) ׀MAD = [11.2]T

Σ׀(At – Ft )/At ׀ X 100MAPE = [11.3]T


Exhibit 11.6 Forecast Error of Example Time Series Data


Forecast Errors and Accuracy• A major difference between MSE and MAD is that MSE is

influenced much more by large forecasts errors than by small errors (because the errors are squared).

• MAPE is different in that the measurement scale factor is eliminated by dividing the absolute error by the time-series data value. This makes the measure easier to interpret.

• The selection of the best measure of forecast accuracy is not a simple matter; indeed, forecasting experts often disagree on which measure should be used.



Solved Problem: Develop three-period and four-period moving-average forecasts and single exponential smoothing forecasts with α = 0.5. Compute the MAD, MAPE, and MSE for each. Which method provides a better forecast?

Period Demand Period Demand1 86 7 912 93 8 933 88 9 964 89 10 975 92 11 936 94 12 95



Based on these error metrics (MAD, MSE, MAPE), the 3-month moving average is the best method among the three.

80

82

84

86

88

90

92

94

96

98

1 2 3 4 5 6 7 8 9 10 11 12

Period

Moving Average Forecasts

Chapter 11 Solved Problem


Types of Forecasting Approaches

• Statistical forecasting is based on the assumption that the future will be an extrapolation of the past.

• Judgmental forecasting relies upon opinions and expertise of people in developing forecasts.



Single Moving Average

• A moving average (MA) forecast is an average of the most recent “k” observations in a time series.

• MA methods work best for short planning horizons when there is no major trend, seasonal, or business cycle pattern.

• As the value of “k” increases, the forecast reacts slowly to recent changes in the time series data.



Exhibit 11.7 Summary of 3-Month Moving-Average Forecasts


Exhibit 11.8 Milk Sales Forecast Error Analysis


Single Exponential Smoothing (SES) is a forecasting technique that uses a weighted average of past time-series values to forecast the value of the time series in the next period.


• The forecast “smoothes out” the irregular fluctuations in the time series.


Exhibit 11.9 Summary of Single Exponential Smoothing Milk Sales

Forecasts with α = 0.2


Exhibit 11.10 Graph of Single Exponential Smoothing

Milk Sales Forecasts with α = 0.2


• Regression analysis is a method for building a statistical model that defines a relationship between a single dependent variable and one or more independent variables, all of which are numerical.

Yt = a + bt (11.7)

• Simple linear regression finds the best values of aand b using the method of least squares.

• Excel provides a very simple tool to find the best-fitting regression model for a time series by selecting the Add Trendline option from the Chartmenu.



Exhibit 11.11 Factory Energy Costs


Exhibit 11.12 Add Trendline Dialog


Exhibit 11.13 Add Trendline Options Tab


Exhibit 11.14 Least-Squares Regression Model for Energy Cost Forecasting


Exhibit 11.15 2004 Gasoline Sales Data


Exhibit 11.16 Chart of Sales versus Time


Exhibit 11.17 Multiple Regression Results


Judgmental Forecasting

• When no historical data is available, only judgmental forecasting is possible.

• The Delphi method consists of forecasting by expert opinion by gathering judgments and opinions of key personnel based on their experience and knowledge of the situation.



Judgmental Forecasting• Another common approach to gathering data is a

survey. Sample sizes are usually much larger than with Delphi, however, and the cost of such surveys can be high.

• The major reasons for using judgmental methods are: Greater accuracy Ability to incorporate unusual or one-time

events The difficultly of obtaining the data necessary

for quantitative techniques



Forecasting in Practice

• Managers use a variety of judgmental and quantitative forecasting techniques.

• Statistical methods alone cannot account for such factors as sales promotions, competitive strategies, unusual economic disturbances, new products, large one-time orders, natural disasters, or labor complications.



Forecasting in Practice

• The first step in developing a practical forecast is to understand the purpose, time horizon, and level of aggregation.

• Different forecasting methods require different levels of technical ability and understanding of mathematical principles and assumptions.



Exhibit 11.18 Example Call Volume Data by Day for BankUSA Case Study

Day CALL VOLUME12345678910111213141516

413536495451490400525490492519402616495527461370

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CHAPTER 11 FORECASTING AND DEMAND PLANNING AND DEMAND PLANNING… · OM, Ch. 11 Forecasting and Demand Planning 2 ©2009 South- Western, a part of Cengage Learning LO1 Describe the