What is Forecasting?

1

• What is Forecasting?
• A forecast is an estimate of what is likely to
  happen in the future.
• Forecasts are concerned with determining what the
  future will look like planning is concerned with
  what it should look like.
• Forecasting provides a basis for coordinating
  activities in various parts of the company.
• Forecasts are an important input to both
  long-term, strategic decision-making, as well as
  for short-term planning for day-to-day operations.

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• Importance of Forecasting
• Forecasting is important for all of the
  functional areas of business
• Finance uses long-term forecasts for capital
  planning and short-term forecasts for budgeting.
• Marketing produces sales forecasts for market
  planning and market strategy.
• Operations develops and uses forecasts for
  scheduling, inventory management, and long-term
  capacity planning.
• Human Resource Management uses forecasts to
  estimate the need for employees.

3

• Types of Forecasts
• There are three major types of forecasts
• Demand Forecasts these are estimates of demand
  for a companys goods or services.
• Technological Forecasts These are forecasts
  concerned with the rate of change in technology
  and the impact on a companys revenues and/or
  costs.
• Economic Forecasts predict inflation rates,
  employment rates, money supply, housing starts,
  and other measures of the performance of an
  economy.

4

• Features of Forecasts
• All forecasting techniques have the same
  features
• Forecasting techniques assume that the same basic
  or original system that existed in the past will
  exist in the future.
• Forecasts are rarely perfect.
• Forecast accuracy decreases as the time horizon
  increases.
• Forecasts for groups of items are more accurate
  than forecasts for individual items.

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• Elements of a Good Forecast
• A properly prepared forecast should meet the
  following requirements
• The forecast should be accurate.
• The forecast should be timely.
• The forecast should be reliable.
• The forecasting technique should be simple to
  understand and use.
• The forecast should be expressed in meaningful
  units.
• The forecast should be in writing.

6

• Steps in the Forecasting Process
• There are seven basic steps in the forecasting
  process
• Determine the purpose of the forecast.
• Select the items to be forecast.
• Establish a time horizon.
• Select the forecasting technique.
• Gather and analyze relevant data.
• Prepare the forecast.
• Monitor the results.

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• Forecasting Approaches
• There are two major approaches to forecasting
  qualitative (judgmental) and quantitative
  methods.
• Further, quantitative methods can be divided into
  ones that use historical data (time series
  models) or ones that develop relationships
  between variables (associative models).
• Forecasts Based on Judgment Judgmental forecasts
  rely on analysis of subjective inputs from a
  variety of sources including customer surveys,
  sales staff, managers, and panels of experts.
• Forecasts Based on Time Series Data Some
  forecasting techniques use historical, or time
  series data, with the assumption that the future
  will be like the past.

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• Forecasts Based on Judgment
• Executive Opinion A forecasting method in which
  the opinions and experience of one or more
  managers are used to produce a forecast.
• Sales Force Opinion Forecasts compiled from
  estimates of demand made by members of a
  companys sales force.
• Customer Surveys A forecasting method that seeks
  input from customers regarding future purchasing
  plans for existing products or services.
• Market Research This method tests hypothesis
  about new products or services or new markets for
  existing products or services.
• Delphi Method A forecasting technique using a
  group process that allows experts to make
  forecasts.

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• Forecasts Based on Time Series Data
• A time series is a sequential series of
  observations taken at regular intervals over a
  period of time.
• The data may be demand (units or dollars), output
  (units), profits (dollars), or CPI (indices),
  among others.
• Analysis of time series data seeks to identify
  the underlying behavior of the series. The
  underlying behavior is made up of patterns such
  as
• Trend
• Cycles
• Seasonality
• Random variation

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Classification of Forecasting Methods
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• Overview of Time Series Forecasting
• A time series consists of a sequential set of
  data of a variable, such as demand.
• There are four possible components of demand
• Trend . A gradual upward or downward movement of
  the data over time.
• Cycles. Wavelike variations in the data that
  occur every several years.
• Seasonality. Short-term, fairly regular
  variations that are generally related to weather
  factors or to human-made factors such as
  holidays.
• Random Variations. Blips in the data caused
  by chance and unusual situations. They follow no
  discernable pattern, so they cannot be predicted.

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Figure 1 Product Demand Charted Over 4 Years
with a Growth Trend and Seasonality
Trend component
Seasonal peaks
Average demand over four years
Random variation
Year 1
Year 2
Year 3
Year 4
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• Naive Forecasts
• A forecast that assumes that demand in the next
  period will be equal to demand in the most recent
  period.
• Can handle the following components of demand
• Random variation. The last data point becomes
  the forecast for the next period.
• Seasonal variation. The forecast for this
  season is equal to the value of the series last
  season.
• Trend. The forecast is equal to the last value
  of the data series, plus or minus the difference
  between the last two values.

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• Techniques for Averaging
• These are techniques that are useful for data
  that has only random variation.
• These techniques smooth fluctuations in a time
  series.
• Forecasts that are based on an average are more
  stable than the original data.
• There are three popular averaging techniques
• Simple moving average
• Weighted moving average
• Simple exponential smoothing

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• Moving Average
• A technique that uses a number of historical data
  values to generate a forecast.
• Involves finding a series of successive averages
  by dropping the first data value in the series
  and adding the last data value.
• Useful for data without trend, seasonality, or
  cycles.

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• Simple Moving Average
• A key decision involves selecting the number of
  periods that will be included in the average.
• The larger the number of periods, the greater the
  smoothing the smaller the number of periods, the
  quicker the forecast reacts to changes in the
  data.

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Market Mixer, Inc. sells can openers. Monthly
sales for an eight-month period were as
follows Month Sales Month Sales 1
450 5 460 2 425 6 455
3 445 7 430 4 435 8
420 Forecast next months sales using a 3-month
moving average. Solution Period Sales Moving
Average Forecast 1 450 2 425 3
445 4 435 (450 425 445) / 3 440
5 460 (425 445 435) / 3 435 6
455 (445 435 460) / 3 447 7 430 (435
460 455) / 3 450 8 420 (460 455
430) /3 448 9 (455 430 420) / 3
Example 1 Simple Moving Average Illustration
Comments 1. Any forecasts beyond Period 9 will
have the same value as the Period 9 forecast
i.e., 435. 2. As a new actual value becomes
available, the forecast will be updated by adding
the newest value and dropping the oldest one. 3.
SMA gives equal weight to all values in the
average. Hence, the oldest value has the same
weight, or importance, as the newest.
435
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• Weighted Moving Average
• A model that applies different weights to each
  value in the moving average calculation.
• Two key decisions
• The number of periods that will be included in
  the average. The larger the number of periods,
  the greater the smoothing the smaller the number
  of periods, the quicker the forecast reacts to
  changes in the data.
• The weight that will be applied to each period.
  The higher the weight applied to more recent
  data, the quicker the model reacts to changes
  the lower the weight that is applied to the more
  recent data, the greater is the smoothing
  process.

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(Let us continue with the same problem as we
had in Example 1.) Market Mixer, Inc. sells can
openers. Monthly sales for an eight-month period
were as follows Month Sales Month Sales
1 450 5 460 2 425 6
455 3 445 7 430 4 435
8 420 Forecast next months sales using a
3-month weighted moving average, where the weight
for the most recent data value is 0.60 the next
most recent, 0.30 and the earliest,
0.10. Solution Period Sales Weighted Moving
Average Forecast 1 450 2 425 3
445 4 435 (450.10) (425.30)
(445.60) 440 5 460 (425.10)
(445.30) (435.60) 437 6 455
(445.10) (435.30) (460.60) 451 7
430 (435.10) (460.30) (445.60)
455 8 420 (460.10) (455.30)
(430.60) 441 9 (445.10)
(430.30) (420.60)
Example 2 Weighted Moving Average Illustration
Comments 1. Any forecasts beyond Period 9 will
have the same value as the Period 9 forecast,
i.e., 427. 3. WMA gives greater weight to more
recent values in the moving average and is more
responsive to recent changes in the data.
427
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• Simple Exponential Smoothing
• This is a variation of the weighted moving
  average model.
• Weights are determined by an exponential function
  which declines as the data gets older.
• The formula Ft1 aAt (1 a)Ft
• Where Ft1 forecast for next period
• a smoothing constant (0 lt a lt 1)
• At current periods actual demand
• Ft current periods forecast

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(Let us continue with the same problem as we
had in Example 1.) Market Mixer, Inc. sells can
openers. Monthly sales for an eight-month period
were as follows Month Sales Month Sales
1 450 5 460 2 425 6
455 3 445 7 430 4 435
8 420 Forecast next months sales using
exponential smoothing with alpha (a) 0.30 and
the first (starting) forecast
450. Solution Period Sales Exponential
Smoothing Forecast 1 450 450 2
425 (.30450) (1 - .30)450 450 3 445
(.30425) (1 - .30)450 443 4 435
(.30445) (1 - .30)443 443 5 460
(.30435) (1 - .30)443 441 6 455
(.30460) (1 - .30)441 447 7 430
(.30455) (1 - .30)447 449 8 420
(.30430) (1 - .30)449 443 9
(.30420) (1 - .30)443
Example 3 Simple Exponential Smoothing
Illustration
Comments 1. Any forecasts beyond Period 9 will
have the same value as the Period 9 forecast,
i.e., 436. 3. The higher the value of a, the
quicker the reaction to changes in the data and
the less the smoothing.
436