08/08/02 SJSU Bus 140 - David Bentley 1

Course Part 2

Supply and Demand Management

Rev. 09/19/05 SJSU Bus 140 - David Bentley 2

Course Organization

Part 2: Supply & Demand Management Forecasting: Chapter 3 Inventory management: Chapter 11 Aggregate planning: Chapter 12 MRP…, ERP & JIT: Chapters 13, 14 Supply chain management: Chapter 16

Rev. 03/01/02 SJSU Bus 140 - David Bentley 3

Chapter 3 – Forecasting

Demand behavior, approaches to forecasting, measures of forecast error

Rev. 09/19/05 SJSU Bus 140 - David Bentley 4

Why Forecast? You’re wrong more than you’re right Often ignored or used as scapegoat Thankless job!

Examples of the downside of forecasting

Rev. 09/19/05 SJSU Bus 140 - David Bentley 5

Why Forecast – (the answer)

Rev. 02/12/02 SJSU Bus 140 - David Bentley 6

Demand Components Components or Elements or Behavior

Trend – long-term linear movement up or down

Seasonal – short term recurring variations Cyclical – long-term recurring variations Random & Irregular – doesn’t fit other

three components

Rev. 09/19/05 SJSU Bus 140 - David Bentley 7

Forecasting Approaches

Qualitative (“subjective) Judgment and Opinion

Quantitative (“objective”) Associative

External sources of data Historical

Internal sources of data used

Rev. 10/01/02 SJSU Bus 140 - David Bentley 8

Judgment and Opinion - 1 Sources

Executives Marketing & Sales Projections Customers Potential customers “Experts”

Rev. 10/01/02 SJSU Bus 140 - David Bentley 9

Judgment and Opinion - 2 Appropriate Use

Irregular or random demand New products Absence of historical data

Techniques Surveys, questionnaires, interviews,

focus groups, observation Delphi method

Rev. 10/01/02 SJSU Bus 140 - David Bentley 10

Associative Sources

External industry data Demographic and econometric data

Appropriate use Cyclical demand

Technique Leading indicator, and Linear regression, in conjunction with Correlation

Rev. 10/031/02 SJSU Bus 140 - David Bentley 11

Historical Sources

Historical (“time series”) data Appropriate use

Varies (see later slides) Technique types

Multi-period pattern projection Single period patternless projection

Rev. 10/01/02 SJSU Bus 140 - David Bentley 12

Multi-period Pattern Projection Techniques - Trend

Appropriate use Clear trend pattern over time

Techniques Best fit (“eyeball”) Linear trend equation or least squares

Yt = a + bt b = n (ty) – (t)(y)

n t2 – (t)2

a = y - b t n

Rev. 10/01/02 SJSU Bus 140 - David Bentley 13

Multi-period Pattern Projection Techniques - Seasonal

Appropriate use Seasonal demand Related to weather, holidays, sports,

school calendar, day of the week, etc. Techniques

Seasonal indexes or relatives Seasonally adjusted trend

Separate trend from seasonality

Rev. 10/01/02 SJSU Bus 140 - David Bentley 14

Single Period Patternless Projection - 1

Appropriate use Lack of clear data pattern Limited historical data

Techniques Moving Average (older method)

Ft = A

n Weighted moving average

Ft = a(At-1) + b(At-2) + … + x(At-n)

Rev. 10/01/02 SJSU Bus 140 - David Bentley 15

Single Period Patternless Projection - 2

Techniques (continued) Exponential Smoothing (newer method)

Ft = Ft-1 + ( At-1 – Ft-1 )

Naïve Forecast Simple

= last period’s actual (often used with seasonality) Ft = At-1

Advanced Ft-1 = At-1 ±(At-1 - At-2) / 2

Rev. 10/01/02 SJSU Bus 140 - David Bentley 16

Single Period Patternless Projection - 3

Techniques (continued) Double exponential smoothing

aka second order exponential smoothing Special case Incorporates some trend Uses exponential smoothing formula plus

second formula with additional smoothing constant

Rev. 03/04/02 SJSU Bus 140 - David Bentley 17

Multiperiod Pattern Projection

Behavior Technique Tools

Trend Trend line Linear regression or

Best fit (eyeball)

Seasonal Seasonal calculations

Seasonal relatives(aka indexes)

Trend and seasonal

Seasonally adjusted trend

Linear regression and seasonal relatives

Rev. 03/04/02 SJSU Bus 140 - David Bentley 18

Single Period Patternless ProjectionBehavior Technique Tools

Random and Irregular

Time series (historical)

Moving average, weighted average, exponential smoothing, ornaïve

Random with some trend

Time series(historical)

Double exponential smoothing (aka trend adjusted or second order exponential smoothing)

Rev. 09/19/05 SJSU Bus 140 - David Bentley 19

Other Forecasting Methods

Behavior Technique Tools

Cyclical Associative Leading indicator, regression and correlation

All behaviors Judgment and opinion

Executive opinion, sales and marketing estimates, and/or customer surveys

Rev. 10/04/01 SJSU Bus 140 - David Bentley 20

Measures of Forecast Error - 1

Forecast Error (e, E, or FE) Et = At - Ft

Average Error (AE) AE = E

n Mean Absolute Deviation (MAD)

MAD = |E| n

Rev. 03/02/05 SJSU Bus 140 - David Bentley 21

Measures of Forecast Error - 2

Mean Squared Error (MSE) MSE = E2

n-1 Standard Deviation (SD)

SD = square root of E2

n-1 Mean Absolute Percent Error (MAPE)

MAPE = (|E|/A) (100) n

Rev. 07/22/04 SJSU Bus 140 - David Bentley 22

Controlling the forecast Control charts

Upper and lower control limits (remember SPC?) – See Figure 3-11

Tracking Signal (TS) Reflects “bias” in the forecast TS = (A – F)

MAD

Look for values within ± 4

Rev. 09/19/05 SJSU Bus 140 - David Bentley 23

Forecast accuracy Aggregation

Would you rather forecast sales of all Ford automobiles or forecast a specific model?

Time Would you rather forecast Ford sales for

2005 or for 2010?

Rev. 09/19/05 SJSU Bus 140 - David Bentley 24

Choosing and … Choosing a forecasting technique

(T3.4) Nature of data (pattern?) Forecast horizon Preparation time Experience (may want to try several)

Choosing a measure of forecast error Ease of use Cost

Rev. 10/01/02 SJSU Bus 140 - David Bentley 25

… Using Using forecast information

Proactive vs. reactive Look at reasonability Assure everyone works off same

data “What – if”