Chapter 9: Forecasting

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1

Long-range forecast (Asset Acquisition)

  • Yearly planning bucket
  • 3-10 years planning horizon
  • New product planning, facility construction, technology
2

Medium-range forecast (Asset Utilization)

  • Monthly/Quarterly planning bucket
  • 3 months to 2 years planning horizon
  • Seasonal production, inventory, employment, budgeting
3

Short-range forecast (Asset Execution)

  • Weekly/Monthly planning bucket
  • 1-26 week planning horizon
  • Job scheduling, worker assignments, inventory stocking
4

Demand Patterns

  • Level or Constant
  • Randomness
  • Trend
  • Seasonality
  • Cyclical
5

Level or Constant

Average value is relatively constant over time.

6

Randomness

Unpredictable movement from one time period to the next.

7

Trend

Long-term movement up or down in a time series.

8

Seasonality

A repeated pattern of spikes or drops associated with certain times of the year.

9

Cyclical

Downward and upward movement of gross domestic product (GDP) around its long-term growth trend.

10

Random Component

  • Unpredictable movement from one time period to the next
  • Due to random variation or unforeseen events
    • Varying number of daily shoppers
    • Rockets win Southwest Division
    • Rainy day
    • Natural disaster
    • Strikes
  • Often measured by variance or standard deviation of time series
11

Trend Component

  • Persistent, up or down movement in a time series
  • Due to changes in population, technology, product acceptance, etc.
  • Trends usually change over time.
  • Increase, decrease, remain constant
12

Patient Forecast

= a + bx, where

  • a = current number of patients
  • b = trend per month in patients
  • x = number of periods to forecast in the future

What is forecast if a = 80, b = 12, and x = 3?

Forecast = 80 + 12(3) = 116 patients

13

Seasonality Component

  • A repeated yearly pattern of spikes or dips in a time series.
  • Due to weather, holidays, promotions, etc.
  • Seasonal index is expressed as a portion of the average demand in a time period.
14

Forecasting Approaches

  • Qualitative Methods
  • Quantitative Methods
15

Qualitative Methods

  • Subjective
  • Used when situation is vague and little data exist
  • New products
  • New technology
  • Involves intuition, experience
  • e.g., forecasting sales of a new product
16

Quantitative Methods

  • Objective
  • Used when situation is stable and historical data exist
  • Existing products
  • Current technology
  • Involves mathematical techniques
  • e.g., forecasting sales of commodity products
17

Qualitative Forecasting Methods

  • Market survey
  • Build-up forecast
  • Panel consensus forecasting
  • Delphi method
  • Life cycle analogy method
18

Quantitative Forecasting Approaches

  • Time Series Models
  • Causal Models
19

Time Series Models

demand follows a pattern over time.

  • Naïve Approach
  • Moving average
  • Weighted moving average
  • Exponential smoothing
  • Trend-adjusted Exponential Smoothing
  • Linear Regression
20

Causal Models

demand is predicted by observing environmental factors such as economic indicators

  • Linear Regression
  • Multiple Regression
21

Time Series Forecasting Models

A quantitative model that uses a time series to develop forecasts.

  • Underlying demand pattern may change over time
  • Balance forecast responsiveness versus stability
  • Forecasting is typically automated with managerial override.
22

Time series

A set of observations arranged in chronological order

23

Assumption

past is a good predictor of the future.

24

Last Period

Current demand is next period’s forecast.

25

Naïve Approach

replicates random variation resulting in a highly unstable forecast.

26

Forecast objective

is to smooth out randomness to find underlying demand pattern.

27

Moving Average Model

MA t = Moving average forecast prepared at end of period t for use in period t + 1

  • n is the number of periods in the moving average
  • Smooths data randomness to illuminate data pattern.
  • Responsiveness (small n) versus stability (large n)
  • Does not consider trend or seasonality
28

Weighted Moving Average Method

A form of the moving average model that applies varying weights to past observations.

  • Highest weights usually placed on more recent past demand
  • Responsiveness determined by weight values.
29

Exponential Smoothing Model

A form of moving average model in which the forecast is calculated as the weighted average of the current period’s actual value and prior forecast.

Features

  • Weighted smoothing model with greater weight on most recent data.
  • Requires very little stored data and easy to automate.

The general rules for determining the a value:

  • Stability
  • The greater the randomness in the time series data, the lower the a value.
  • The less randomness in the time series data, the higher the a value.
  • Responsiveness
  • The greater the instability in the underlying data pattern, the higher the a value.
  • The less instability in the underlying demand pattern, the lower the a value.
  • Select a to minimize forecast error (MAD, MSE, MAPE).
  • Smoothing constant is typically 0.05 ≤ α ≤ 0.3
30

Adjusted Exponential Smoothing Model

An expanded version of the exponential smoothing model that includes a trend adjustment factor.

31

Measures of Forecast Accuracy

Measures of Forecast Accuracy are used to assess how well a model is performing or to compare multiple forecast models to one another.

  • Forecast error for period t
  • Running sum of forecast error
  • Tracking Signal
32

Forecast error for period t

FE i = D iF i

  • Note: Right-hand-side values are listed alphabetically. Positive FE i indicates that demand exceeded the forecast.
33

Running sum of forecast error

  • RSFE indicates tendency to over or under forecast
  • RSFE should equal 0 in long term.
34

Tracking Signal

TS provides a measure of the severity of forecast model bias.

  • General rule: If - 4 ≤ TS ≤ 4, then the forecast model is performing normally. Otherwise, alternative models should be considered.