FORECASTING AND DEMAND PLANNING

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CHAPTER 11
FORECASTING AND DEMAND PLANNING
DAVID A. COLLIER AND JAMES R. EVANS
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Chapter 11 Learning Outcomes

l e a r n i n g o u t c o m e s
LO1 Describe the importance of forecasting to
the value chain. LO2 Explain
basic concepts of forecasting and time
series. LO3 Explain how to apply single moving
average and exponential smoothing
models. LO4 Describe how to apply regression
as a forecasting approach. LO5
Explain the role of judgment in forecasting. LO6
Describe how statistical and judgmental
forecasting techniques are applied in
practice.
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Chapter 11 Forecasting and Demand Planning
he demand for rental cars in
Florida and other warm climates
peaks during college spring break season. Call
centers and rental offices are flooded
with customerswanting 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?
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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

• 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.

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Exhibit 11.1
The Need for Forecasts in a Value Chain
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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

• Basic Concepts in Forecasting
• 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

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Exhibit 11.2
Example of Linear and Nonlinear Trend Patterns
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Exhibit 11.3
Seasonal Pattern of Home Natural Gas Usage
Seasonal patterns are characterized by repeatable
periods of ups and downs over short periods of
time.
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Trend and Business Cycle Characteristics (each
data point is 1 year apart)
Exhibit Extra
Cyclical patterns are regular patterns in a data
series that take place over long periods of time.
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Chapter 11 Forecasting and Demand Planning

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.
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Chapter 11 Forecasting and Demand Planning

Basic Concepts in Forecasting Irregular 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.
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Exhibit 11.4
Call Center Volume
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Exhibit 11.5
Chart of Call Volume
There is an increasing trend over the six years,
along with seasonal patterns within each year.
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Chapter 11 Forecasting and Demand Planning

• 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)

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Exhibit 11.6
Forecast Error of Example Time Series Data
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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

Solved Problem Develop three-period and
four-period moving-average forecasts and single
exponential smoothing forecasts with a 0.5.
Compute the MAD, MAPE, and MSE for each. Which
method provides a better forecast?
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Chapter 11 Solved Problem
Based on these error metrics (MAD, MSE, MAPE),
the 3-month moving average is the best method
among the three.
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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

• 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.

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Exhibit 11.7
Summary of 3-Month Moving-Average Forecasts
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Exhibit 11.8
Milk Sales Forecast Error Analysis
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Chapter 11 Forecasting and Demand Planning

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.

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Summary of Single Exponential Smoothing Milk
SalesForecasts with a 0.2
Exhibit 11.9
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Graph of Single Exponential Smoothing Milk Sales
Forecasts with a 0.2
Exhibit 11.10
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Chapter 11 Forecasting and Demand Planning

• 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
  a and 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
  Chart menu.

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Exhibit 11.11
Factory Energy Costs
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Exhibit 11.12
Add Trendline Dialog
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Exhibit 11.13
Add Trendline Options Tab
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Exhibit 11.14
Least-Squares Regression Model for Energy Cost
Forecasting
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Exhibit 11.15
2004 Gasoline Sales Data
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Exhibit 11.16
Chart of Sales versus Time
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Exhibit 11.17
Multiple Regression Results
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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

• 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

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Chapter 11 Forecasting and Demand Planning

• 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.

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Chapter 11 Forecasting and Demand Planning

• 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.

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Exhibit 11.18
Example Call Volume Data by Day for BankUSA Case
Study
CALL VOLUME
Day
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
413 536 495 451 490 400 525 490 492 519 402 616 49
5 527 461 370