1. Supply Chain Management
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Supply Chain Management
Lecture 22
Chapter 3

2. Outline Today Thursday Next week Finish Chapter 12
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Outline
Today
Finish Chapter 12
Sections 1, 2, 3
Section 2 up to and including Example 12.2
Thursday
Homework 5 due before class
Start with Chapter 14
Sections 1, 2, 3, 4, 6, 7, 8, 9
Section 6 buyback and revenue sharing contracts only
Next week
Guest speaker: Paul Dodge
SVP Supply Chain, ProBuild
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3. Guest Lecture Date Speaker Subject Tuesday April 13
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Guest Lecture
Date
Tuesday April 13
Speaker
Paul Dodge (Senior Vice President – Supply Chain)
Subject
Today’s Supply Chain
Chapter 3

4. Semester Outline Tuesday April 6 Chap 12 Thursday April 8 Chap 14
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Semester Outline
Tuesday April 6 Chap 12
Thursday April 8 Chap 14
Tuesday April 13 Paul Dodge guest lecture
Thursday April 15 Chap 14, 15
Tuesday April 20 Chap 15
Thursday April 22 Simulation Game briefing
Tuesday April 27 Review, buffer
Thursday April 29 Simulation Game
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5. The Newsboy/Newsvendor Problem
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The Newsboy/Newsvendor Problem
Matching supply with demand is particularly challenging when supply must be chosen before observing demand (and demand is uncertain). Suppose you are the owner of a simple business: selling newspapers. Each morning you purchase a stack of papers with the intention of selling them at your newsstand at the corner of a busy street. Even though you have some idea regarding how many newspaper you can sell on any given day, you never can predict demand for sure.
Chapter 3

6. The Newsboy/Newsvendor Problem
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The Newsboy/Newsvendor Problem
Order quantity (O)
Uncertain demand (D)
Cost of overstocking (Co = c – s)
The loss incurred by a firm for each unsold unit at the end of the selling season
Cost of understocking (Cu = p – c)
The margin lost by a firm for each lost sale because there is no inventory on hand
Includes the margin lost from current as well as future sales if the customer does not return
Chapter 3

7. O’Neill PsychoFreak 3347 The “too much/too little problem”
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O’Neill PsychoFreak 3347
The “too much/too little problem”
Order too much and inventory is left over at the end of the season
Order too little and sales are lost
Submit order to Manufacturer
Selling seaon
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Receive order from Manufacturer
Discount leftovers
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8. O’Neill PsychoFreak 3347 Gather economic data Forecast demand
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O’Neill PsychoFreak 3347
Gather economic data
Selling price (p = $180)
Procurement cost (c = $110)
Discount price (s = $90)
Forecast demand
Empirical demand distribution
Normal demand distribution
Order quantity (so as to maximize profits)
Chapter 3

9. Example: Parkas at L.L. Bean
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Example: Parkas at L.L. Bean
What is the expected demand?
Notes: L.L. Bean is a mail order company deciding on the number of units of a red parkas to order. An estimate of demand using past information and expertise of buyers is given here. What should the appropriate order quantity be? In general distribution may not be known.
Under the old policy of ordering, the buyers would have ordered 1,000 parkas. However, demand is uncertain and the table shows that there is a 51% chance that demand will be 1,000 or less. Hence, ordering 1,000 parkas results in a cycle service level (CSL) of 51%. The buyers must decide on an order size and CSL that maximizes profits.
Expected demand = ∑Dipi = 1,026 parkas
Chapter 3

10. Example: Parkas at L.L. Bean
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Example: Parkas at L.L. Bean
What is the expected overstock?
What is the expected understock?
Notes: L.L. Bean is a mail order company deciding on the number of units of a red parkas to order. An estimate of demand using past information and expertise of buyers is given here. What should the appropriate order quantity be? In general distribution may not be known.
Under the old policy of ordering, the buyers would have ordered 1,000 parkas. However, demand is uncertain and the table shows that there is a 51% chance that demand will be 1,000 or less. Hence, ordering 1,000 parkas results in a cycle service level (CSL) of 51%. The buyers must decide on an order size and CSL that maximizes profits.
Expected understock = ∑Understockipi = 111 parkas
Expected overstock = ∑Overstockipi = 85 parkas
Chapter 3

11. Example: Parkas at L.L. Bean
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Example: Parkas at L.L. Bean
Cost c = $45
Price p = $100
Salvage value s = $40
What is the expected profit?
Notes: L.L. Bean is a mail order company deciding on the number of units of a red parkas to order. An estimate of demand using past information and expertise of buyers is given here. What should the appropriate order quantity be? In general distribution may not be known.
Under the old policy of ordering, the buyers would have ordered 1,000 parkas. However, demand is uncertain and the table shows that there is a 51% chance that demand will be 1,000 or less. Hence, ordering 1,000 parkas results in a cycle service level (CSL) of 51%. The buyers must decide on an order size and CSL that maximizes profits.
Expected profit = ∑Profitipi = $49,900
Chapter 3

12. Example: Parkas at L.L. Bean
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Example: Parkas at L.L. Bean
(1 – CSL)(p – c)
CSL(c – s)
Notes: L.L. Bean is a mail order company deciding on the number of units of a red parkas to order. An estimate of demand using past information and expertise of buyers is given here. What should the appropriate order quantity be? In general distribution may not be known.
Under the old policy of ordering, the buyers would have ordered 1,000 parkas. However, demand is uncertain and the table shows that there is a 51% chance that demand will be 1,000 or less. Hence, ordering 1,000 parkas results in a cycle service level (CSL) of 51%. The buyers must decide on an order size and CSL that maximizes profits.
Cost of understocking p – c = $55
Cost of overstocking c – s = $5
What is the optimal order quantity?
Chapter 3

13. Example: Parkas at L.L. Bean
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Example: Parkas at L.L. Bean
(1 – CSL)(p – c)
CSL(c – s)
Notes: L.L. Bean is a mail order company deciding on the number of units of a red parkas to order. An estimate of demand using past information and expertise of buyers is given here. What should the appropriate order quantity be? In general distribution may not be known.
Under the old policy of ordering, the buyers would have ordered 1,000 parkas. However, demand is uncertain and the table shows that there is a 51% chance that demand will be 1,000 or less. Hence, ordering 1,000 parkas results in a cycle service level (CSL) of 51%. The buyers must decide on an order size and CSL that maximizes profits.
What is the safety stock?
Safety stock = Order quantity – Expected Demand
Chapter 3

14. Optimal Level of Product Availability
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Optimal Level of Product Availability
Expected marginal contribution of raising the order size from O* to O*+1 (1 – CSL*)(p – c) – CSL*(c – s)
p – c
p – s Cu
Cu + Co
CSL* = Prob(Demand  O*) = =
O* = F-1(CSL*, , ) = NORMINV(CSL*, , )
Chapter 3

15. Example 12-1: Evaluating the optimal service level for seasonal items
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Example 12-1: Evaluating the optimal service level for seasonal items
The manager at Sportmart, a sporting goods store, has to decide on the number of skis to purchase for the winter season. Based on past demand data and weather forecasts for the year, management has forecast demand to be normally distributed, with a mean 350 and a standard deviation of 100. Each pair of skis costs $100 and retails for $250. Any unsold skis at the end of the season are disposed of for $85. Assume that it costs $5 to hold a pair of skis in inventory for the season. How many skis should the manager order to maximize expected profits?
Chapter 3

16. Example 12-1: Evaluating the optimal service level for seasonal items
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Example 12-1: Evaluating the optimal service level for seasonal items
Average demand (mean)
 =
Standard deviation of demand (stdev)
 =
Material cost
c =
Price
p =
Salvage value
s =
Cost of understocking
Cu =
Cost of overstocking
Co =
Optimal cycle service level
CSL* =
Optimal order size
O* =
350
100
$100
$250
85 – 5 = $80
p – c = 250 – 100 = $150
c – s = 100 – 80 = $20
Cu/(Cu + Co) = 150/170 = 0.88
NORMINV(CSL*, , ) = 468
Chapter 3

17. When Demand is Normally Distributed
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When Demand is Normally Distributed
Expected profits = (p – s)Fs((O – )/) – (p – s)fs((O – )/) – O(c – s)F(O, , ) + O(p – c)[1 – F(O, , )] Expected overstock = (O – )Fs((O – )/) + fs((O – )/)
Expected understock = ( – O)[1 – Fs((O – )/)] + fs((O – )/)
Chapter 3

18. Example 12-1: Evaluating the optimal service level for seasonal items
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Example 12-1: Evaluating the optimal service level for seasonal items
Expected profits = (p – s)Fs((O – )/) – (p – s)fs((O – )/) – O(c – s)F(O, , ) + O(p – c)(1 – F(O, , )) 59,500*NORMDIST(1.18,0,1,1) – 17,000*NORMDIST(1.18,0,1,0) – 9,360*NORMDIST(468,350,100,1) + 70,200(1 – NORMDIST(468,350,100, 1)) = $49,146
Expected overstock = (O – )Fs((O – )/) + fs((O – )/) = (450 – 350)*NORMDIST((450 – 350)/100,0,1,1) *NORMDIST((450 – 350)/100,0,1,0) = 108
Expected understock = ( – O)[1 – Fs((O – )/)] + fs((O – )/) = (350 – 450)*[1 – NORMDIST(((450 – 350)/100,0,1,1)] + 100*NORMDIST((450 – 350)/100,0,1,0) = 8
Chapter 3

19. Factors Affecting the Optimal Level of Product Availability
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Factors Affecting the Optimal Level of Product Availability
Consider two products with the same margin. Any leftover units of one product are worthless. Leftover units of the other product can be sold to outlet stores. Which product should have a higher level of product availability?
Chapter 3

20. Higher salvage value leads to lower Co
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Intermezzo
CSL* 1
Higher salvage value leads to lower Co
Higher salvage value will have lower Co and thus higher CSL
Co/Cu
Chapter 3

21. Factors Affecting the Optimal Level of Product Availability
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Factors Affecting the Optimal Level of Product Availability
Consider two products with the same margin. Any leftover units of one product are worthless. Leftover units of the other product can be sold to outlet stores. Which product should have a higher level of product availability?
Consider two products with the same cost but different margins. Which product should have a higher level of product availability?
Chapter 3

22. Intermezzo CSL* Nordstrom Discount store Co/Cu 1 SYST 4050 Slides
Co/Cu
Chapter 3

23. Maximizing Expected Profits
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Maximizing Expected Profits
Cost of over- and understocking have a direct impact on both the optimal cycle service level and profitability
How could one improve profitability?
Chapter 3

24. Improving Supply Chain Profitability
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Improving Supply Chain Profitability
Two obvious ways to improve profitability
Increase salvage value of each unit
Sport Obermeyer sells winter clothing in south America during the summer.
Buyback contracts with manufacturer
Decrease the margin lost from a stock out
Arrange for backup sourcing or provide substitute product
Car part suppliers, McMaster-Carr and W.W.Grainger, are competitors but they buy from each other to satisfy the customer demand during a stockout
Cost of over- and understocking have a direct impact on both the optimal cycle service level and profitability
Chapter 3

25. Improving Supply Chain Profitability
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Improving Supply Chain Profitability
Another way to improve profitability
Reduce demand uncertainty
Improved forecasting: Use better market intelligence and collaboration to reduce demand uncertainty
Quick response: Reduce replenishment lead time so that multiple orders may be placed in a selling season
Postponement: In a multiproduct setting, postpone product differentiation until closer to point of sale
Tailored sourcing: Use a low lead time, but perhaps an expansive supplier as a backup for a low-cost, but perhaps long lead time supplier
Chapter 3

26. Example: Impact of Improved Forecasting
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Example: Impact of Improved Forecasting
Demand is Normally distributed with a mean of = 350 and standard deviation of  = 150
Purchase price c = $100
Retail price p = $250
Salvage value s = $80
How many units should be ordered as  changes?
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27. Example: Impact of Improved Forecasting
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Example: Impact of Improved Forecasting
Increase in forecast accuracy increases a firm’s profits
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28. Impact of Improved Forecasting
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Impact of Improved Forecasting
Better forecasts leads to reduced uncertainty
Decreases both the overstocked and understocked quantity
Increases a firm’s profits
Chapter 3

29. Impact of Quick Response
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Impact of Quick Response
Quick response is a set of actions a supply chain takes to reduce replenishment lead time
Lead time ~30 weeks
Selling season ~14 weeks
Selling season ~14 weeks
Lead time ~14 weeks
Selling season ~14 weeks
Lead time ~4 weeks
Chapter 3

30. Impact of Quick Response
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Impact of Quick Response
If quick response (reduction in replenishment lead time) allows multiple orders in the season
A buyer can usually improve forecast accuracy after observing demand
Less overstock, less understock
Higher profits
Chapter 3

31. Example: Impact of Quick Response
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Example: Impact of Quick Response
Mattel was hurt last year by inventory cutbacks at Toys “R” Us, and officials are also eager to avoid a repeat of the 1998 Thanksgiving weekend. Mattel had expected to ship a lot of merchandise after the weekend, but retailers, wary of excess inventory, stopped ordering from Mattel. That led the company to report a $500 million sales shortfall in the last weeks of the year ... For the crucial holiday selling season this year, Mattel said it will require retailers to place their full orders before Thanksgiving. And, for the first time, the company will no longer take reorders in December, Ms. Barad said. This will enable Mattel to tailor production more closely to demand and avoid building inventory for orders that don't come.
Will Mattel’s action help or hurt profitability?
Quick response is clearly advantageous to a retailer in a supply chain. As the manufacturer reduces replenishment lead times, the retailer’s order size drops. In effect, the manufacturer sells less to the retailer. Thus quick response results in the manufacturer making a lower profit in the short term if all else is unchanged. This is an important point to consider, because decreasing replenishment lead times requires tremendous effort from the manufacturer, yet seems to benefit the retailer at the expense of the manufacturer. Hence, the benefits resulting from quick response should be shared appropriately across the supply chain.
Wall Street Journal, Feb. 18, 1999
Chapter 3

32. Did Mattel’s action help or hurt profitability at Toys “R” Us?
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Mattel Inc. & Toys “R” Us
Decreasing replenishment lead times requires tremendous effort from the manufacturer, yet seems to benefit the retailer at the expense of the manufacturer
Hence, the benefits resulting from quick response should be shared appropriately across the supply chain
Did Mattel’s action help or hurt profitability at Toys “R” Us?
Chapter 3

33. Impact of Postponement
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Impact of Postponement
Postponement is delaying product differentiation (customization) until closer to the time of the sale of the product
Delaying the commitment of the work-in-process inventory to a particular product
Examples
Dell delivers customized PC in a few days after customer order
HP printer places power supply modules, labels in appropriate language on to printers after the demand is observed
Motorola cell phones are customized for different service providers after demand is materialized
McDonalds assembles meal menus after customer order
Chapter 3

34. Example: Impact of Postponement
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Example: Impact of Postponement
Benetton sells knit sweaters in four colors at a retail price p = $50
Option 1: (Long lead time) Dye the threat then knit the garment. Results in manufacturing cost c = $20.
Option 2: (Short lead time). Knit the garment then dye the garment. Results in manufacturing cost c = $22
Benetton disposes any unsold sweaters at the end of the season in clearance for s = $10.
For each color 20 weeks in advance demand forecast
Normally distributed with a mean of  = 1000 and a standard deviation of  = 500
Chapter 3

35. Example: Impact of Postponement
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Example: Impact of Postponement
 = 1000,  = 500
 = 4000,  = 1000
p = 50 c = 20 s = 10
CSL = (p – c)/(c – s)
O* = NORMINV(CSL*,,)
p = 50 c = 22 s = 10
CSL = (p – c)/(c – s)
O* = NORMINV(CSL*,,)
CSL = 0.75
O* = 1,337*4 = 5,348
CSL = 0.70
O* = 4,524
Expected profits $94,576
Expected profits $98,092
Chapter 3

36. Tailored Postponement
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Tailored Postponement
By postponing all garment types, production cost of each product goes up
When this increase is substantial or a single product’s demand dominates all other’s (causing limited uncertainty reduction via aggregation), a partial postponement scheme is preferable to full postponement.
Tailored postponement allows a firm to increase profits by postponing differentiation only for products with the most uncertain demand; products with more predictable demand are produced at lower cost without postponement
Chapter 3

37. Tailored (Dual) Sourcing
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Tailored (Dual) Sourcing
Tailored sourcing is a business strategy where a firm uses a combination of two supply sources
The two sources must focus on different capabilities
Volume-based tailoring: Benetton subcontracts 65% to low-cost sources. The other 35% is manufactured in a flexible plant owned by Benetton (overseas – long lead time, and local – short lead times)
Product-based tailoring: Levi Strauss sells standard-size jeans as well as jeans that can be customized to fit
Chapter 3