Supply Chain Management

1
Supply Chain Management

• Lecture 11

2
Outline

• Today
• Homework 2
• Chapter 7
• Thursday
• Chapter 7
• Friday
• Homework 3
• Due Friday February 26 before 500pm

3
Announcements

• FEI Student Financial Awards Program
• Awards are presented to Finance or Accounting
  majors from schools in Colorado. Each award can
  either go to an undergraduate or graduate
  student. This year there are five awards for
  1,200 each.
• The criteria includes the following three
  factors
• Students who have performed well academically,
• Students who have potential leadership skills in
  the business field, and
• Students who have financial need.
• All applications are due to the committee no
  later than March 25, 2010. Applications and
  information are available in the office of Bonnie
  Beverly (KOBL S315A) or Consuelo Delval (KOBL
  S328) (paper applications only)

4
Announcements

• What?
• Tour the Staples Fulfillment Center in Brighton,
  CO
• Informal Lunch-and-Learn
• Up to 20 students with a Operations Management
  major
• When?
• Weeks of March 15 or March 29
• There is a fair amount of time involved in the
  activity
• Transit is close to an hour in each direction
• Probably 2 hours onsite 

5
Forecasting Examples

• Walt Disney World
• Daily forecast of attendance (weather forecasts,
  previous days crowds, conventions, seasonal
  variations)
• Add more cast members and add street activities
  to manage high demand
• Amazon Kindle
• Kindle sold out in 5.5 hours
• Kindle was not in stock for another 5 months
• FedEx customer service center
• Goal is to answer 90 of all calls within 20
  seconds
• Makes extensive use of forecasting for staffing
  decisions and to ensure that customer
  satisfaction stays high

6
Characteristics of Forecasts

• Forecasts are always wrong!
• Long-term forecasts are less accurate than
  short-term forecasts
• Aggregate forecasts are more accurate than
  disaggregate forecasts
• Information gets distorted when moving away from
  the customer

7
Types of Forecasts

• Qualitative
• Primarily subjective, rely on judgment and
  opinion
• Time series
• Use historical demand only
• Causal
• Use the relationship between demand and some
  other factor to develop forecast
• Simulation
• Imitate consumer choices that give rise to demand

8
Role of Forecasting
Manufacturer
Distributor
Retailer
Customer
Supplier
Push
Push
Push
Pull
Push
Push
Pull
Push
Pull
Is demand forecasting more important for a push
or pull system?
9
Time Series Forecasting
Observed demand Systematic component Random
component
The goal of any forecasting method is to predict
the systematic component of demand and estimate
the random component
10
Components of an Observation
Level (L)
Forecast(F) Ftn Lt
The moving-average method is used when demand has
no observable trend or seasonality
11
Example Moving Average Method

• A supermarket has experienced the following
  weekly demand of coffee over the last four weeks
• 120, 127, 114, and 122

Determine LevelLt (DtDt-1Dt-N1)/N
ForecastFtn Lt
12
Example Tahoe Salt
13
Example Tahoe Salt

• Demand forecasting using Moving Average

14
Components of an Observation
Level (L)
Forecast(F) Ftn Lt
The simple exponential smoothing is used when
demand has no observable trend or seasonality
15
Example Simple Exponential Smoothing Method

• A supermarket has experienced the following
  weekly demand of coffee over the last four weeks
• 120, 127, 114, and 122

Determine initial levelL0 (?i Di)/ n
Determine levelsLt1 ?Dt1 (1 ?)Lt
ForecastFtn Lt
? 0.1
16
Example Tahoe Salt
17
Example Tahoe Salt

• Demand forecasting using simple exponential
  smoothing

18
Components of an Observation
Trend (T)
Forecast(F) Ftn Lt nTt
Holts method is appropriate when demand is
assumed to have a level and a trend
19
Example Holts Method

• An electronics manufacturer has seen demand for
  its latest MP3 player increase over the last six
  months
• 8415, 8732, 9014, 9808, 10413, 11961

Determine initial levelL0 INTERCEPT(ys,
xs)T0 LINEST(ys, xs)
20
Example Holts Method

• An electronics manufacturer has seen demand for
  its latest MP3 player increase over the last six
  months
• 8415, 8732, 9014, 9808, 10413, 11961

Determine initial levelL0 INTERCEPT(ys,
xs)T0 LINEST(ys, xs)
Determine levelsLt1 ?Dt1 (1 ?)(Lt
Tt) Tt1 ?(Lt1 Lt) (1 ?)Tt
ForecastFtn Lt nTt
? 0.1, ? 0.2
21
Example Tahoe Salt
22
Example Tahoe Salt

• Demand forecasting using Holts method

23
Components of an Observation
Seasonality (S)
Forecast(F) Ftn (Lt Tt)Stn
24
Time Series Forecasting
Observed demand Systematic component Random
component
L Level (current deseasonalized demand)
T Trend (growth or decline in demand)
S Seasonality (predictable seasonal fluctuation)
25
Static Versus Adaptive Forecasting Methods

• Static
• Dt Actual demand
• L Level
• T Trend
• S Seasonal factor
• Ft Forecast

• Adaptive
• Dt Actual demand
• Lt Level
• Tt Trend
• St Seasonal factor
• Ft Forecast

26
Example Static Method

• A theme park has seen the following attendance
  over the last eight quarters (in thousands)
• 54, 87, 192, 130, 80, 124, 265, 171

Determine initial levelL INTERCEPT(ys, xs)T
LINEST(ys, xs)
ForecastFt (L Tt)Si
27
Example Tahoe Salt
28
Static Forecasting Method
29
Static Forecasting Method

• Deseasonalize demand
• Demand that would have been observed in the
  absence of seasonal fluctuations
• Periodicity p
• The number of periods after which the seasonal
  cycle repeats itself
• 12 months in a year
• 7 days in a week
• 4 quarters in a year
• 3 months in a quarter

30
Deseasonalize demand
31
Deseasonalize demand

• Periodicity p is odd

• Periodicity p is even

32
Deseasonalize demand
Deseasonalizing demand around t (2,4), that is,
year 2 and 4th quarter, when p is odd
33
Deseasonalize demand
Assume p 3, hence a seasonal cycle consists of
three periods
34
Deseasonalize demand
Deseasonalized demand for t(2,4) 18,000
23,000 38,000 26,333
35
Deseasonalize demand
Deseasonalizing demand around t (2,4), that is,
year 2 and 4th quarter, when p is even
36
Deseasonalize demand
Assume p 4, hence a seasonal cycle consists of
four periods
37
Deseasonalize demand
What happens if you take the average demand?
38
Deseasonalize demand
39
Deseasonalize demand
40
Deseasonalize demand

• Periodicity p is odd

• Periodicity p is even

41
Example Tahoe Salt
42
Static Forecasting Method
43
Static Forecasting Method
Deasonalize demandDepends on number periods in a
seasonal cycle
Determine initial levelL INTERCEPT(ys, xs)T
LINEST(ys, xs)
ForecastFt (L Tt)Si
44
Example Tahoe Salt

• Demand forecast using Static forecasting method

45
Example Winters Model

• A theme park has seen the following attendance
  over the last eight quarters (in thousands)
• 54, 87, 192, 130, 80, 124, 265, 171

Determine initial levelsL0 From static
forecastT0 From static forecastSi,0 From
static forecast
Determine levelsLt1 ?(Dt1/St1) (1 ?)(Lt
Tt) Tt1 ?(Lt1 Lt) (1 ?)Tt Stp1
?(Dt1/Lt1) (1 ?)St1
ForecastFt1 (Lt Tt)St1
46
Example Tahoe Salt
47
Example Tahoe Salt

• Demand forecast using Winters method