Transcript Document 7274316

Supply Chain Management
Lecture 11
Outline
• Today
– Homework 2
– Chapter 7
• Thursday
– Chapter 7
• Friday
– Homework 3
• Due Friday February 26 before 5:00pm
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)
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
Forecasting Examples
• Walt Disney World
– Daily forecast of attendance (weather forecasts, previous day’s
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
Characteristics of Forecasts
1. Forecasts are always wrong!
2. Long-term forecasts are less accurate than
short-term forecasts
3. Aggregate forecasts are more accurate than
disaggregate forecasts
4. Information gets distorted when moving away
from the customer
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
Role of Forecasting
Supplier
Manufacturer
Distributor
Push
Push
Push
Push
Push
Retailer
Customer
Pull
Push
Pull
Pull
Is demand forecasting more important
for a push or pull system?
Time Series Forecasting
Observed demand =
Systematic component + Random component
L
T
S
Level (current deseasonalized demand)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
The goal of any forecasting method is to predict
the systematic component of demand and
estimate the random component
Components of an Observation
3500
Level (L)
3000
Forecast(F)
Demand
2500
Ft+n = Lt
2000
1500
1000
500
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Quarter
The moving-average method is used when
demand has no observable trend or seasonality
Example: Moving Average Method
• A supermarket has experienced the following weekly
demand of coffee over the last four weeks
– 120, 127, 114, and 122
t
1
2
3
4
5
6
Demand
Level
Forecast
Dt
Lt
Ft
120
127
114
122
125
Determine Level
Lt = (Dt+Dt-1+…+Dt-N+1)/N
Forecast
Ft+n = Lt
120.75
122.00
120.75
122.00
Example: Tahoe Salt
Example: Tahoe Salt
• Demand forecasting using Moving Average
Actual
Forecast (Moving Avg)
50,000
Demand
40,000
30,000
20,000
10,000
0
1
2
3
4
5
6
7
8
9
Quarter
10 11 12 13 14 15 16
Components of an Observation
3500
Level (L)
3000
Forecast(F)
Demand
2500
Ft+n = Lt
2000
1500
1000
500
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Quarter
The simple exponential smoothing is used when
demand has no observable trend or seasonality
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
Demand
t
1
2
3
4
5
6
 = 0.1
Dt
120
127
114
122
125
Level
Lt
120.75
121.60
120.68
121.44
121.31
122.00
120.58
121.20
120.72
121.28
121.65
Forecast
Ft
121.60
120.75
120.68
121.44
121.31
122.00
120.58
121.20
120.72
121.28
120.72
121.65
Determine initial level
L0 = (∑i Di)/ n
Determine levels
Lt+1 = Dt+1 + (1 – )*Lt
Forecast
Ft+n = Lt
Example: Tahoe Salt
Example: Tahoe Salt
• Demand forecasting using simple exponential
smoothing
Actual
Forecast (Exp Smooth)
50,000
Demand
40,000
30,000
20,000
10,000
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16
Quarter
Components of an Observation
3500
Trend (T)
3000
Demand
2500
2000
Forecast(F)
1500
1000
Ft+n = Lt + nTt
500
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
Quarter
Holt’s method is appropriate when demand is
assumed to have a level and a trend
Example: Holt’s Method
• An electronics manufacturer has seen demand for its
latest MP3 player increase over the last six months
– 8415, 8732, 9014, 9808, 10413, 11961
t
1
2
3
4
5
6
7
8
9
10
Demand
Level
Trend
Forecast
Dt
Lt
Tt
Ft
8,415
8,732
9,014
9,808
10,413
11,961
Determine initial level
L0 = INTERCEPT(y’s, x’s)
T0 = LINEST(y’s, x’s)
Example: Holt’s Method
• An electronics manufacturer has seen demand for its
latest MP3 player increase over the last six months
– 8415, 8732, 9014, 9808, 10413, 11961
Demand
t
1
2
3
4
5
6
7
8
9
10
Dt
8,415
8,732
9,014
9,808
10,413
11,961
Level
Lt
7,367
8,078
8,756
9,394
10,040
10,677
11,401
 = 0.1,  = 0.2
Trend
Forecast
Tt
Ft
673
681
680
672
667
661
673
8040
8759
9436
10066
10707
11338
12074
12747
13420
14094
Determine initial level
L0 = INTERCEPT(y’s, x’s)
T0 = LINEST(y’s, x’s)
Determine levels
Lt+1 = Dt+1 + (1 – )*(Lt + Tt)
Tt+1 = (Lt+1 – Lt) + (1 – )*Tt
Forecast
Ft+n = Lt + nTt
Example: Tahoe Salt
Example: Tahoe Salt
• Demand forecasting using Holt’s method
Actual
Forecast (Holt)
50,000
Demand
40,000
30,000
20,000
10,000
0
1
2
3
4
5
6
7
8
9
Quarter
10 11 12 13 14 15 16
Components of an Observation
3500
Seasonality (S)
3000
Demand
2500
2000
1500
1000
Forecast(F)
500
0
1
2
3
4
5
6
7
8
9
Quarter
Ft+n = (Lt + Tt)St+n
10 11 12 13 14 15 16
Time Series Forecasting
Observed demand =
Systematic component + Random component
L
T
S
Level (current deseasonalized demand)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
Static Versus Adaptive Forecasting
Methods
• Static
• Adaptive
– Dt: Actual demand
– Dt: Actual demand
– L: Level
– T: Trend
– S: Seasonal factor
– Lt: Level
– Tt: Trend
– St: Seasonal factor
– Ft: Forecast
– Ft: Forecast
Example: Static Method
• A theme park has seen the following attendance over the
last eight quarters (in thousands)
Determine initial level
– 54, 87, 192, 130, 80, 124, 265, 171
L = INTERCEPT(y’s, x’s)
Demand
Level
Trend Deseason. Seasonal Seasonal Forecast
Demand Factor
Factor
T = LINEST(y’s, x’s)
t
Dt
L
T
Dt_bar
Si_bar
Si
Ft
59.3
1
2
3
4
5
6
7
8
54
87
192
130
80
124
265
171
17.3
76.6
93.9
111.2
128.5
145.8
163.1
180.4
197.7
0.70
0.93
1.73
1.01
0.55
0.76
1.47
0.86
0.63
0.84
1.60
0.94
48.0
79.2
177.7
120.6
91.4
137.6
288.2
185.5
Determine deason. demand
Dt = L + Tt
Determine seasonal factors
St = Dt / Dt
Determine seasonal factors
Si =AVG(Si)
Forecast
Ft = (L + Tt)Si
Example: Tahoe Salt
Static Forecasting Method
45,000
40,000
Demand
Demand
35,000
30,000
25,000
20,000
15,000
10,000
Demand
5,000
Demand
Lin.
Reg.
0
1
2
3
4
5
6
7
8
9
Quarter
Quarter
10 11 12 13 14 15 16
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
Deseasonalize demand
Deseasonalize demand
• Periodicity p is odd
Demand
t
1
2
3
4
5
6
7
8
9
10
11
12
Deseason.
Demand
Dt
8,000
13,000
23,000
10,000
18,000
23,000
12,000
13,000
32,000
• Periodicity p is even
Demand
t
14,667
15,333
17,000
17,000
17,667
16,000
19,000
1
2
3
4
5
6
7
8
9
10
11
12
Deseason.
Demand
Dt
8,000
13,000
23,000
34,000
10,000
18,000
23,000
38,000
12,000
13,000
32,000
41,000
19,750
20,625
21,250
21,750
22,500
22,125
22,625
24,125
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
Deseasonalizing demand around t= (2,4),
that is, year 2 and 4th quarter, when p is odd
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
Assume p = 3, hence a seasonal cycle
consists of three periods
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
Deseasonalized demand for t=(2,4)
= 18,000 + 23,000 + 38,000 = 26,333
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
Deseasonalizing demand around t= (2,4), that
is, year 2 and 4th quarter, when p is even
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
Assume p = 4, hence a seasonal cycle
consists of four periods
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
What happens if you take the average
demand?
Deseasonalize demand
50,000
Demand
40,000
30,000
20,000
10,000
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
50,000
50,000
40,000
40,000
Demand
Demand
Quarter
30,000
20,000
30,000
20,000
10,000
10,000
0
0
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
1, 2 1, 3 1, 4 2, 1 2, 2 2, 3 2, 4 3, 1 3, 2 3, 3 3, 4 4, 1 4, 2 4, 3 4, 4 5, 1
Quarter
Quarter
Deseasonalize demand
Deseasonalize demand
• Periodicity p is odd
Demand
t
1
2
3
4
5
6
7
8
9
10
11
12
Deseason.
Demand
Dt
8,000
13,000
23,000
10,000
18,000
23,000
12,000
13,000
32,000
• Periodicity p is even
Demand
t
14,667
15,333
17,000
17,000
17,667
16,000
19,000
1
2
3
4
5
6
7
8
9
10
11
12
Deseason.
Demand
Dt
8,000
13,000
23,000
34,000
10,000
18,000
23,000
38,000
12,000
13,000
32,000
41,000
19,750
20,625
21,250
21,750
22,500
22,125
22,625
24,125
Example: Tahoe Salt
Demand
Static Forecasting Method
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
Demand
Demand
Deseason.
Deseason.
Deseason. Lin. Reg.
1
2
3
4
5
6
7
8
9
Quarter
10 11 12 13 14 15 16
Static Forecasting Method
Deasonalize demand
Depends on number periods in a seasonal cycle
Determine initial level
L = INTERCEPT(y’s, x’s)
T = LINEST(y’s, x’s)
Determine deason. demand
Dt = L + Tt
Determine seasonal factors
St = Dt / Dt
Determine seasonal factors
Si =AVG(Si)
Forecast
Ft = (L + Tt)Si
Example: Tahoe Salt
• Demand forecast using Static forecasting method
50,000
Actual
Forecast (Static)
Demand
40,000
30,000
20,000
10,000
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16
Quarter
Example: Winter’s Model
• A theme park has seen the following attendance over the
last eight quarters (in thousands)
– 54, 87, 192, 130, 80, 124, 265, 171
t
1
2
3
4
5
6
7
8
Demand
Level
Trend
Dt
L
T
54
87
192
130
80
124
265
171
Seasonal Forecast
Factor
Si
Ft
Determine initial levels
L0 = From static forecast
T0 = From static forecast
Si,0 = From static forecast
Determine levels
Lt+1 = (Dt+1/St+1)+ (1 – )*(Lt + Tt)
Tt+1 = (Lt+1 – Lt) + (1 – )*Tt
St+p+1 = (Dt+1/Lt+1) + (1 – )*St+1
Forecast
Ft+1 = (Lt + Tt)St+1
Example: Tahoe Salt
Example: Tahoe Salt
• Demand forecast using Winter’s method
Actual
Forecast (Winter)
50,000
Demand
40,000
30,000
20,000
10,000
0
1
2
3
4
5
6
7
8
9
Quarter
10 11 12 13 14 15 16