Supply Chain Management

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Supply Chain Management

• Lecture 9

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Outline

• Homework 2 due
• Today
• Finish Chapter 6
• Start with Chapter 7
• Thursday
• Continue Chapter 7
• Bring your laptop if you can

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CUAccelerate

• Program
• 430 - 520 PM Networking and Dinner Buffet 
  Engebretson Quad, Leeds School of Business 
• 520 - 525 PM Welcome Zoya Voronovich,
  President, Colorado Chapter of Society for
  Information Management (SIM) 
• 525 - 550 PM Keynote Chris Laping, CIO, Red
  Robin Gourmet Burgers 
• 600 - 645 PM Breakout Session 1 (See speaker
  information and locations below) 
• 700 - 745 PM Breakout Session 2 (Each speaker
  will do a repeat performance) 
• 800 830 PM Drawing, Wrap Up and Networking
  over Dessert and Coffee
• Speakers from
• Rally Software, Riptide Games, Key Equipment
  Finance, Array BioPharm, Xcel Energy, Statera,
  KaiserPermanente

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Example Decision Tree Analysis

• Three options for Target.com
• Get all warehousing space from the spot market as
  needed
• Sign a three-year lease for a fixed amount of
  warehouse space and get additional requirements
  from the spot market
• Sign a flexible lease with a minimum change that
  allows variable usage of warehouse space up to a
  limit with additional requirement from the spot
  market

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Example Decision Tree Analysis

• Target.com input data
• Evaluate each option over a 3 year time horizon
  (1 period is 1year)
• Demand D may go up or down each year by 20 with
  probability 0.5
• Warehouse spot price p may go up or down by 10
  with probability 0.5
• Discount rate k 0.1

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Example

• Represent the tree, identifying all states

0.25
0.25
0.25
0.25
0.25
Period 0
0.25
D100
0.25
p1.20
0.25
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Example Option 1 (Spot)
Period 2

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• C(D 144,000, p 1.45, 2) 144,000 x 1.45
  208,800
• R(D 144,000, p 1.45, 2) 144,000 x 1.22
  175,680
• P(D 144,000, p 1.45, 2) R C
  175,680 208,800 33,120

D144
p1.45
D144
p1.19
D96
p1.45
D144
p0.97
D96
p1.19
D96
p0.97
D64
p1.45
D64
p1.19
D64
p0.97
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Example Option 1 (Spot)
Period 2

• Starting at period T, work back to period 0
  identify the expected cash flows at each step

D144
p1.45
D144
p1.19
D96
p1.45
D144
p0.97
D96
p1.19
D96
p0.97
D64
p1.45
D64
p1.19
D64
p0.97
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Example Option 1 (Spot)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• EP(D 120, p 1.22, 1) 0.25xP(D 144, p
  1.45, 2) 0.25xP(D 144, p 1.19, 2)
  0.25xP(D 96 p 1.45, 2) 0.25xP(D 96, p
  1.19, 2) 12,000
• PVEP(D 120, p 1.22, 1) EP(D 120, p
  1.22, 1)/(1k) 12,000/1.1
  10,909

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Example Option 1 (Spot)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D 120, p 1.32, 1) R(D 120, p 1.22,
  1) C(D 120, p 1.32, 1) PVEP(D 120, p
  1.22, 1) 146,400 - 158,400
  (10,909) 22,909

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Example Option 1 (Spot)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step

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Example Option 1 (Spot)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step

NPV(Spot) 5,471
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Example Option 2 (Fixed lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step

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Example Option 2 (Fixed lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D , p , 2) R(D , p , 2) C(D , p , 2)
• P(D , p , 2) Dx1.22 (100,000x1.00 Sxp)

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Example Option 2 (Fixed lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D , p , 1) R(D , p , 1) C(D , p , 1)
  PVEP(D , p , 1)
• P(D , p , 1) Dx1.22 (100,000x1.00 Sxp)
  EP(D , p , 1)/(1k)

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Example Option 2 (Fixed lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D , p , 0) R(D , p , 0) C(D , p , 0)
  PVEP(D , p , 0)
• P(D , p , 0) 100,000x1.22 100,000x1.00
  16,364/1.1

NPV(Fixed lease) 38,364
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Example Option 3 (Flexible lease)

• Flexible lease rules
• Up-front payment of 10,000
• Flexibility of using between 60,000 and 100,000
  sq.ft. at 1.00 per sq.ft. per year
• Additional space requirements from spot market

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Example Option 3 (Flexible lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step

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Example Option 3 (Flexible lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D , p , 2) R(D , p , 2) C(D , p , 2)
• P(D , p , 2) Dx1.22 (Wx1.00 Sxp)

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Example Option 3 (Flexible lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D , p , 1) R(D , p , 1) C(D , p , 1)
  PVEP(D , p , 1)
• P(D , p , 1) Dx1.22 (Wx1.00 Sxp) EP(D
  , p , 1)/(1k)

20,000
20,000
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Example Option 3 (Flexible lease)

• Starting at period T, work back to period 0
  identify the expected cash flows at each step
• P(D , p , 0) R(D , p , 0) C(D , p , 0)
  PVEP(D , p , 0)
• P(D , p , 0) 100,000x1.22 100,000x1.00
  38,198/1.1

NPV(Flexible lease) 56,725 10,000 46,725
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Example Decision Tree Analysis

• What product to make for the next three years
  using a discount factor k 0.1?
• Old product with certain demand (90 profit/unit)
• New product with uncertain demand (85
  profit/unit)

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Example Decision Tree Analysis

• New product with uncertain demand (85
  profit/unit)
• Annual demand expected to go up by 20 with
  probability 0.6
• Annual demand expected to go down by 20 with
  probability 0.4

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Example

• Represent the tree, identifying all states as
  well as all transition probabilities

Period 2
D144
Period 1
0.6
Period 0
D96
D120
0.6
0.4
D100
0.6
0.4
D80
D96
0.4
D64
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From Design to Planning

• Network design
• C4 ? Designing Distribution Networks
• C5 ? Network Design in the Supply Chain
• C6 ? Network Design in an Uncertain Environment
• Planning in a supply chain
• C7 ? Demand Forecasting in a Supply Chain
• C8 ? Aggregate Planning in a Supply Chain
• C9 ? Planning Supply and Demand

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Demand Forecasting

• How does BMW know how many Mini Coopers it will
  sell in North America?
• How many Prius cars should Toyota build to meet
  demand in the U.S. this year? Worldwide?
• When is it time to tweak production, upward or
  downward, to reflect a change in the market?

What factors influence customer demand?
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Factors that Affect Forecasts

• Past demand
• Time of year/month/week
• Planned advertising or marketing efforts
• Planned price discounts
• State of the economy
• Market conditions
• Actions competitors have taken

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Example Demand Forecast for Milk

• A supermarket has experienced the following
  weekly demand (in gallons) over the last ten
  weeks
• 109, 116, 108, 103, 97, 118, 120, 127, 114, and
  122

What is a reasonable demand forecast for milk for
the upcoming week?
When could using average demand as a forecast
lead to an inaccurate forecast?
If demand turned out to be 125 what can you say
about the demand forecast?
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1) Characteristics of Forecasts

• Forecasts are always wrong!
• Forecasts should include expected value and
  measure of error (or demand uncertainty)
• Forecast 1 sales are expected to range between
  100 and 1,900 units
• Forecast 2 sales are expected to range between
  900 and 1,100 units

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2) Characteristics of Forecasts

• Long-term forecasts are less accurate than
  short-term forecasts
• Less easy to consider other variables hard to
  include in a forecast such as the effect of
  weather
• Forecast horizon is important, long-term forecast
  have larger standard deviation of error relative
  to the mean

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2) Characteristics of Forecasts
0.25
P2u
Pu
P
Pud
0.5
Pd
P2d
0.25
Average 100Standand dev. 16.395 range
67.4 132.6Deviation 33
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2) Characteristics of Forecasts
0.016
P6u
P5u
P4u
0.094
P5ud
P3u
P4ud
P2u
P3ud
Pu
P4u2d
0.234
P2ud
P
P3u2d
Pud
P2u2d
Pd
0.313
P3u3d
Pu2d
P2u3d
P2d
Pu3d
0.234
P2u4d
P3d
Pu4d
P4d
0.094
Pu5d
P5d
Average 100Standand dev. 24.795 range
50.6 149.4Deviation 49
0.016
P6d
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3) Characteristics of Forecasts

• Aggregate forecasts are more accurate than
  disaggregate forecasts

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3) Characteristics of Forecasts

• Aggregate forecasts are more accurate than
  disaggregate forecasts
• They tend to have a smaller standard deviation of
  error relative to the mean

Monthly sales SKU
Monthly sales product line
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4) Characteristics of Forecasts

• Information gets distorted when moving away from
  the customer
• Bullwhip effect

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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

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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?
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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

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Components of an Observation

• Quarterly demand at Tahoe Salt

Actual demand (D)
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Components of an Observation

• Quarterly demand at Tahoe Salt

Level (L) and Trend (T)
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Components of an Observation

• Quarterly demand at Tahoe Salt

Seasonality (S)
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Components of an Observation
Observed demand Systematic component Random
component
L Level (current deseasonalized demand)
T Trend (growth or decline in demand)
S Seasonality (predictable seasonal fluctuation)
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Time Series Forecasting
Forecast demand for the next four quarters.