1. Supply Chain Management
SYST 4050 Slides
Supply Chain Management
Lecture 10
Chapter 1

2. Outline Today Tomorrow Next week
SYST 4050 Slides
Outline
Today
Finish Chapter 6 (Decision tree analysis)
Start chapter 7
Tomorrow
Homework 2 due before 5:00pm
Next week
Chapter 7 (Forecasting)
Chapter 1

3. Example: Decision Tree Analysis
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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
Use discount factor k = 0.1
Chapter 1

4. SYST 4050 Slides
Example
Represent the tree, identifying all states as well as all transition probabilities
Period 2
P = 120*85+(0.6* *8160)/1.1 = 19844
P = 12240
Period 1
D=144
0.6
Period 0
D=120
0.6
0.4
P = 8160
D=100
D=96
0.6
P = 100*85+ (0.6* *13229)/1.1 = 24135
0.4
D=80
0.4
P = 5440
D=64
P = 80*85+(0.6* *5440)/1.1 = 13229
Chapter 1

5. Calculate the NPV of each possible scenario separately
SYST 4050 Slides
Example
Represent the tree, identifying all states as well as all transition probabilities
Period 2
Period 1
D=144
0.6
Period 0
D=120
0.6
0.4
D=100
D=96
0.6
0.4
D=80
0.4
D=64
Calculate the NPV of each possible scenario separately
Chapter 1

6. Calculate the NPV of each possible scenario separately
SYST 4050 Slides
Example
Represent the tree, identifying all states as well as all transition probabilities
D=144
D=96
D=64
D=120
D=80
D=100
0.6
0.4
Period 0
Period 2
Period 1
Calculate the NPV of each possible scenario separately
Chapter 1

7. Decision Trees (Summary)
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Decision Trees (Summary)
A decision tree is a graphic device used to evaluate decisions under uncertainty
Identify the duration of each period and the number of time periods T to be evaluated
Identify the factors associated with the uncertainty
Identify the representation of uncertainty
Identify the periodic discount rate k
Represent the tree, identifying all states and transition probabilities
Starting at period T, work back to period 0 identify the expected cash flows at each step
(Alternatively, calculate the NPV of each possible scenario separately)
Decisions:
Sign a long-term warehousing contract or get space in the spot market?
How much capacity should the facility have? What fraction of this capacity should be flexible?
Multi-period decisions when uncertainty resolves after each period
Sources of uncertainty:
Prices
Demand
Inflation rate
Exchange rates
Chapter 1

8. SYST 4050 Slides
Decision Trees
Using decision trees to evaluate network design decisions
Should the firm sign a long-term contract for warehousing space or get space from the spot market as needed
What should the firm’s mix of long-term and spot market be in the portfolio of transportation capacity
How much capacity should various facilities have? What fraction of this capacity should be flexible?
Chapter 1

9. Example: Decision Tree Analysis
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Example: Decision Tree Analysis
Three options for Trips Logistics
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
Chapter 1

10. Example: Decision Tree Analysis
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Example: Decision Tree Analysis
Trips Logistics input data
Evaluate each option over a 3 year time horizon (1 period is 1 year)
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
Chapter 1

11. Example Represent the tree, identifying all states Period 2 Period 1
SYST 4050 Slides
Example
D=144
p=$1.45
p=$1.19
D=96
p=$0.97
D=64
Period 2
Represent the tree, identifying all states
D=120
p=$1.32
p=$1. 08
D=80
p=$1.08
Period 1
0.25
0.25
0.25
0.25
0.25
Period 0
0.25
D=100
0.25
p=$1.20
0.25
Chapter 1

12. Example – Option 1 (Spot)
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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 = $208,800
R(D = 144,000, p = 1.45, 2) = 144,000 x = $175,680
P(D = 144,000, p = 1.45, 2) = R – C = 175,680 – 208, = –$33,120
D=144
p=$1.45
D=144
p=$1.19
Cost
D=96
p=$1.45
D=144
Revenue
p=$0.97
D=96
p=$1.19
Profit
D=96
p=$0.97
D=64
p=$1.45
D=64
p=$1.19
D=64
p=$0.97
Chapter 1

13. Example – Option 1 (Spot)
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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
D=144
p=$1.45
D=144
p=$1.19
D=96
p=$1.45
D=144
p=$0.97
D=96
p=$1.19
D=96
p=$0.97
D=64
p=$1.45
D=64
p=$1.19
D=64
p=$0.97
Chapter 1

14. Example – Option 1 (Spot)
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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) = xP(D = 144, p = 1.45, 2) xP(D = 144, p = 1.19, 2) xP(D = 96 p = 1.45, 2) xP(D = 96, p = 1.19, 2) = –$12,000
PVEP(D = 120, p = 1.22, 1) = EP(D = 120, p = 1.22, 1)/(1+k) = –12,000/ = –$10,909
D=144
p=$1.45
p=$1.19
D=96
D=120
p=$1.32
0.25
Period 1
Period 2
Chapter 1

15. Example – Option 1 (Spot)
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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
D=144
p=$1.45
p=$1.19
D=96
D=120
p=$1.32
0.25
Period 1
Period 2
Chapter 1

16. Example – Option 1 (Spot)
SYST 4050 Slides
Example – Option 1 (Spot)
Starting at period T, work back to period 0 identify the expected cash flows at each step
D=144
p=$1.45
p=$1.19
D=96
p=$0.97
D=64
D=120
p=$1.32
p=$1. 08
D=80
0.25
Period 1
Period 2
Chapter 1

17. Example – Option 1 (Spot)
SYST 4050 Slides
Example – Option 1 (Spot)
Starting at period T, work back to period 0 identify the expected cash flows at each step
D=144
p=$1.45
p=$1.19
D=96
p=$0.97
D=64
D=120
p=$1.32
p=$1. 08
D=80
D=100
p=$1.20
0.25
Period 0
Period 1
Period 2
NPV(Spot) = $5,471
NPV(Spot) = $5,471
Chapter 1

18. Example: Decision Tree Analysis
SYST 4050 Slides
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
Get 100,000 sq ft. of warehouse space at $1 per square foot
Additional space purchased from 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
Chapter 1

19. Example – Option 2 (Fixed lease)
SYST 4050 Slides
Example – Option 2 (Fixed lease)
Starting at period T, work back to period 0 identify the expected cash flows at each step
Period 2
D=144
p=$1.45
Period 1
0.25
D=144
0.25
D=120
p=$1.19
0.25
0.25
p=$1.32
D=96
0.25
p=$1.45
D=144
Period 0
0.25
D=120
p=$0.97
p=$1. 08
D=100
D=96
0.25
p=$1.20
p=$1.19
D=80
D=96
p=$1.32
p=$0.97
0.25
D=64
D=80
p=$1.45
p=$1.32
D=64
p=$1.19
D=64
p=$0.97
Chapter 1

20. Example – Option 2 (Fixed lease)
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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 – (100,000x Sxp) 8
Chapter 1

21. Example – Option 2 (Fixed lease)
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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 – (100,000x Sxp) + EP(D =, p =, 1)/(1+k)
Chapter 1

22. Example – Option 2 (Fixed lease)
SYST 4050 Slides
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 – 100,000x ,364/1.1
NPV(Fixed lease) = $38,364
NPV(Fixed lease) = $38,364
Chapter 1

23. Example: Decision Tree Analysis
SYST 4050 Slides
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
$10,000 upfront payment
Use anywhere between 60,000 and 100,000 sq ft. at $1 per sq ft.
Additional space purchased from spot market
Chapter 1

24. Example – Option 3 (Flexible lease)
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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
Chapter 1

25. Example – Option 3 (Flexible lease)
SYST 4050 Slides
Example – Option 3 (Flexible lease)
Starting at period T, work back to period 0 identify the expected cash flows at each step
Period 2
D=144
p=$1.45
Period 1
0.25
D=144
0.25
D=120
p=$1.19
0.25
0.25
p=$1.32
D=96
0.25
p=$1.45
D=144
Period 0
0.25
D=120
p=$0.97
p=$1. 08
D=100
D=96
0.25
p=$1.20
p=$1.19
D=80
D=96
p=$1.32
p=$0.97
0.25
D=64
D=80
p=$1.45
p=$1.32
D=64
p=$1.19
D=64
p=$0.97
Chapter 1

26. Example – Option 3 (Flexible lease)
SYST 4050 Slides
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 – (Wx Sxp)
Chapter 1

27. Example – Option 3 (Flexible lease)
SYST 4050 Slides
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 – (Wx Sxp) + EP(D =, p =, 1)/(1+k)
20,000
20,000
Chapter 1

28. Example – Option 3 (Flexible lease)
SYST 4050 Slides
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 – 100,000x ,198/1.1
NPV(Flexible lease) = $46,725
NPV(Flexible lease) = 56,725 – 10,000 = $46,725
Chapter 1

29. From Design to Planning
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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
Chapter 1

30. What factors influence customer 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?
Chapter 1

31. Factors that Affect Forecasts
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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
Chapter 1

32. Example: Demand Forecast for Milk
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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?
Chapter 1

33. 1) Characteristics of Forecasts
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1) Characteristics of Forecasts
Forecasts are always wrong!
Forecasts should include an expected value and a 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
Chapter 1

34. 2) Characteristics of Forecasts
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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 the effects of weather in a forecast
Forecast horizon is important, long-term forecast have larger standard deviation of error relative to the mean
Chapter 1

35. 3) Characteristics of Forecasts
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3) Characteristics of Forecasts
Aggregate forecasts are more accurate than disaggregate forecasts
Chapter 1

36. 3) Characteristics of 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
Chapter 1

37. 4) Characteristics of Forecasts
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4) Characteristics of Forecasts
Information gets distorted when moving away from the customer
Bullwhip effect
Bullwhip effect: Because forecast errors are a given, companies often carry an inventory buffer called "safety stock". Moving up the supply chain from end-consumer to raw materials supplier, each supply chain participant has greater observed variation in demand and thus greater need for safety stock. In periods of rising demand, down-stream participants will increase their orders. In periods of falling demand, orders will fall or stop in order to reduce inventory. The effect is that variations are amplified as one moves upstream in the supply chain (further from the customer).
Chapter 1

38. Characteristics of Forecasts
SYST 4050 Slides
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
Chapter 1

39. Is demand forecasting more important for a push or pull system?
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Role of Forecasting
Supplier
Manufacturer
Distributor
Retailer
Customer
Push
Push
Push
Pull
Push
Push
Pull
Push: in anticipation of customer demand
Pull: in response of customer demand
Forecasting is used for both push and pull processes
Push processes: Managers must plan the level of activity, be it production, transportation, or any other planned activity.
Pull processes: Managers must plan the level of available capacity and inventory
Always: Managers must forecast what customer demand will be
Manufacturer: Production scheduling decisions, materials requirement planning, purchasing
Distributor: Ordering, warehouse capacity
Retailer: Shelf space
Dell orders PC components in anticipation of customer orders (inventory)
Dell performs assembly in response to customer orders (capacity)
Intel needs forecasts to determine production and inventory levels. Intel’s suppliers need forecasts to determine production and inventory levels.
Mature products with stable demand (milk, paper towels) are usually the easiest to forecast.
Scheduling existing resources
How many employees do we need and when?
How much product should we make in anticipation of demand?
Acquiring additional resources
When are we going to run out of capacity?
How many more people will we need?
How large will our back-orders be?
Determining what resources are needed
What kind of machines will we require?
Which services are growing in demand? declining?
What kind of people should we be hiring?
Push
Pull
Is demand forecasting more important for a push or pull system?
Chapter 1

40. Types of Forecasts Qualitative Time series Causal Simulation
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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
Qualitative
They are primarily subjective; rely on judgment and opinion, intuition, surveys, or comparative techniques. They produce information typically no quantitative, and subjective. Its non-scientific nature makes it difficult to standardize or validate for accuracy, Appropriate when there is little historical data or there is market intelligence.
Time Series
It uses historical demand only; appropriate when the basic demand pattern varies little between years, the basic premise is that the future demand pattern will be mostly a replication. It uses mathematician and statistical models as forecasting tools which can be static or adaptive to new demand patterns.
Causal
It uses the relationship between demand and some other factor (e.g. the state of the economy, interest rates) to develop forecast. For example, if is know how level service can influence sales, then by knowing the level of service provided, the level of sales can be projected. We can say that service causes sales. Another example is forecasting the impact of price promotion on demand. Usually it is very difficult to find good cause-and-effect relationships.
Simulation
This method imitates consumer choices that give rise to demand. It can combine time series and causal methods
Chapter 1

41. Components of an Observation
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Components of an Observation
Quarterly demand at Tahoe Salt
Actual demand (D)
Chapter 1

42. Components of an Observation
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Components of an Observation
Quarterly demand at Tahoe Salt
Level (L) and Trend (T)
Chapter 1

43. Components of an Observation
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Components of an Observation
Quarterly demand at Tahoe Salt
Seasonality (S)
Chapter 1

44. Components of an Observation
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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)
Chapter 1

45. Time Series Forecasting
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Time Series Forecasting
Forecast demand for the
next four quarters.
Chapter 1