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Designing Global Supply Chain Networks

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Chopra and Meindl Supply Chain Management, 5e

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Learning Objectives

Identify factors that need to be included in total cost when making global sourcing decisions.

Define uncertainties that are particularly relevant when designing global supply chains.

Explain different strategies that may be used to mitigate risk in global supply chains.

Understand decision tree methodologies used to evaluate supply chain design decisions under uncertainty.

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Impact of Globalization on Supply Chain Networks

Opportunities to simultaneously grow revenues and decrease costs

Accompanied by significant additional risk

Difference between success and failure often ability to incorporate suitable risk mitigation into supply chain design

Uncertainty of demand and price drives the value of building flexible production capacity

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Impact of Globalization on Supply Chain Networks

Risk Factors	Percentage of Supply Chains Impacted
Natural disasters	35
Shortage of skilled resources	24
Geopolitical uncertainty	20
Terrorist infiltration of cargo	13
Volatility of fuel prices	37
Currency fluctuation	29
Port operations/custom delays	23
Customer/consumer preference shifts	23
Performance of supply chain partners	38
Logistics capacity/complexity	33
Forecasting/planning accuracy	30
Supplier planning/communication issues	27
Inflexible supply chain technology	21

Table 6-1

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The Offshoring Decision: Total Cost

Comparative advantage in global supply chains

Quantify the benefits of offshore production along with the reasons

Two reasons offshoring fails

Focusing exclusively on unit cost rather than total cost

Ignoring critical risk factors

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The Offshoring Decision: Total Cost

Performance Dimension	Activity Impacting Performance	Impact of Offshoring
Order communication	Order placement	More difficult communication
Supply chain visibility	Scheduling and expediting	Poorer visibility
Raw material costs	Sourcing of raw material	Could go either way depending on raw material sourcing
Unit cost	Production, quality (production and transportation)	Labor/fixed costs decrease; quality may suffer
Freight costs	Transportation modes and quantity	Higher freight costs
Taxes and tariffs	Border crossing	Could go either way
Supply lead time	Order communication, supplier production scheduling, production time, customs, transportation, receiving	Lead time increase results in poorer forecasts and higher inventories

Table 6-2

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The Offshoring Decision: Total Cost

Performance Dimension	Activity Impacting Performance	Impact of Offshoring
On-time delivery/lead time uncertainty	Production, quality, customs, transportation, receiving	Poorer on-time delivery and increased uncertainty resulting in higher inventory and lower product availability
Minimum order quantity	Production, transportation	Larger minimum quantities increase inventory
Product returns	Quality	Increased returns likely
Inventories	Lead times, inventory in transit and production	Increase
Working capital	Inventories and financial reconciliation	Increase
Hidden costs	Order communication, invoicing errors, managing exchange rate risk	Higher hidden costs
Stock-outs	Ordering, production, transportation with poorer visibility	Increase

Table 6-2

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The Offshoring Decision: Total Cost

A global supply chain with offshoring increases the length and duration of information, product, and cash flows

The complexity and cost of managing the supply chain can be significantly higher than anticipated

Quantify factors and track them over time

Big challenges with offshoring is increased risk and its potential impact on cost

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The Offshoring Decision: Total Cost

Key elements of total cost

Supplier price

Terms

Delivery costs

Inventory and warehousing

Cost of quality

Customer duties, value added-taxes, local tax incentives

Cost of risk, procurement staff, broker fees, infrastructure, and tooling and mold costs

Exchange rate trends and their impact on cost

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Risk Management In Global Supply Chains

Risks include supply disruption, supply delays, demand fluctuations, price fluctuations, and exchange-rate fluctuations

Critical for global supply chains to be aware of the relevant risk factors and build in suitable mitigation strategies

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Risk Management In Global Supply Chains

Category	Risk Drivers
Disruptions	Natural disaster, war, terrorism Labor disputes Supplier bankruptcy
Delays	High capacity utilization at supply source Inflexibility of supply source Poor quality or yield at supply source
Systems risk	Information infrastructure breakdown System integration or extent of systems being networked
Forecast risk	Inaccurate forecasts due to long lead times, seasonality, product variety, short life cycles, small customer base Information distortion

Table 6-3

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Risk Management In Global Supply Chains

Category	Risk Drivers
Intellectual property risk	Vertical integration of supply chain Global outsourcing and markets
Procurement risk	Exchange-rate risk Price of inputs Fraction purchased from a single source Industry-wide capacity utilization
Receivables risk	Number of customers Financial strength of customers
Inventory risk	Rate of product obsolescence Inventory holding cost Product value Demand and supply uncertainty
Capacity risk	Cost of capacity Capacity flexibility

Table 6-3

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Risk Management In Global Supply Chains

Good network design can play a significant role in mitigating supply chain risk

Every mitigation strategy comes at a price and may increase other risks

Global supply chains should generally use a combination of rigorously evaluated mitigation strategies along with financial strategies to hedge uncovered risks

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Risk Management In Global Supply Chains

Risk Mitigation Strategy	Tailored Strategies
Increase capacity	Focus on low-cost, decentralized capacity for predictable demand. Build centralized capacity for unpredictable demand. Increase decentralization as cost of capacity drops.
Get redundant suppliers	More redundant supply for high-volume products, less redundancy for low-volume products. Centralize redundancy for low-volume products in a few flexible suppliers.
Increase responsiveness	Favor cost over responsiveness for commodity products. Favor responsiveness over cost for short–life cycle products.

Table 6-4

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Risk Management In Global Supply Chains

Risk Mitigation Strategy	Tailored Strategies
Increase inventory	Decentralize inventory of predictable, lower value products. Centralize inventory of less predictable, higher value products.
Increase flexibility	Favor cost over flexibility for predictable, high-volume products. Favor flexibility for unpredictable, low-volume products. Centralize flexibility in a few locations if it is expensive.
Pool or aggregate demand	Increase aggregation as unpredictability grows.
Increase source capability	Prefer capability over cost for high-value, high-risk products. Favor cost over capability for low-value commodity products. Centralize high capability in flexible source if possible.

Table 6-4

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Flexibility, Chaining, and Containment

Three broad categories of flexibility

New product flexibility

Ability to introduce new products into the market at a rapid rate

Mix flexibility

Ability to produce a variety of products within a short period of time

Volume flexibility

Ability to operate profitably at different levels of output

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Flexibility, Chaining, and Containment

Figure 6-1

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Flexibility, Chaining, and Containment

As flexibility is increased, the marginal benefit derived from the increased flexibility decreases

With demand uncertainty, longer chains pool available capacity

Long chains may have higher fixed cost than multiple smaller chains

Coordination more difficult across with a single long chain

Flexibility and chaining are effective when dealing with demand fluctuation but less effective when dealing with supply disruption

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Discounted Cash Flow Analysis

Supply chain decisions should be evaluated as a sequence of cash flows over time

Discounted cash flow (DCF) analysis evaluates the present value of any stream of future cash flows and allows managers to compare different cash flow streams in terms of their financial value

Based on the time value of money – a dollar today is worth more than a dollar tomorrow

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Discounted Cash Flow Analysis

Compare NPV of different supply chain design options

The option with the highest NPV will provide the greatest financial return

where

C0, C1,…,CT is stream of cash flows over T periods

NPV = net present value of this stream

k = rate of return

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Trips Logistics Example

Demand = 100,000 units

1,000 sq. ft. of space for every 1,000 units of demand

Revenue = $1.22 per unit of demand

Sign a three-year lease or obtain warehousing space on the spot market?

Three-year lease cost = $1 per sq. ft.

Spot market cost = $1.20 per sq. ft.

k = 0.1

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Trips Logistics Example

Expected annual profit if warehouse space is obtained from the spot market

= =

100,000 x $1.22 – 100,000 x $1.20 $2,000

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Trips Logistics Example

Expected annual profit with three year lease

= =

100,000 x $1.22 – 100,000 x $1.00 $22,000

NPV of signing lease is $60,182 – $5,471 = $54,711 higher than spot market

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Using Decision Trees

Many different 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?

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Using Decision Trees

During network design, managers need a methodology that allows them to estimate the uncertainty in demand and price forecast and incorporate this in the decision-making process

Most important for network design decisions because they are hard to change in the short term

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Basics of Decision Tree Analysis

A decision tree is a graphic device used to evaluate decisions under uncertainty

Identify the number and duration of time periods that will be considered

Identify factors that will affect the value of the decision and are likely to fluctuate over the time periods

Evaluate decision using a decision tree

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Decision Tree Methodology

Identify the duration of each period (month, quarter, etc.) and the number of periods T over which the decision is to be evaluated

Identify factors whose fluctuation will be considered

Identify representations of uncertainty for each factor

Identify the periodic discount rate k for each period

Represent the decision tree with defined states in each period as well as the transition probabilities between states in successive periods

Starting at period T, work back to Period 0, identifying the optimal decision and the expected cash flows at each step

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Decision Tree – Trips Logistics

Three warehouse lease options

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 charge that allows variable usage of warehouse space up to a limit with additional requirement from the spot market

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Decision Tree – Trips Logistics

1000 sq. ft. of warehouse space needed for 1000 units of demand

Current demand = 100,000 units per year

Binomial uncertainty: Demand can go up by 20% with p = 0.5 or down by 20% with 1 – p = 0.5

Lease price = $1.00 per sq. ft. per year

Spot market price = $1.20 per sq. ft. per year

Spot prices can go up by 10% with p = 0.5 or down by 10% with 1 – p = 0.5

Revenue = $1.22 per unit of demand

k = 0.1

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Decision Tree

Figure 6-2

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Decision Tree – Trips Logistics

Analyze the option of not signing a lease and using the spot market

Start with Period 2 and calculate the profit at each node

For D = 144, p = $1.45, in Period 2:

C(D = 144, p = 1.45,2) = 144,000 x 1.45

= $208,800

P(D = 144, p = 1.45,2) = 144,000 x 1.22

– C(D = 144, p = 1.45, 2)

= 175,680 – 208,800

= –$33,120

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Decision Tree – Trips Logistics

	Revenue	Cost C(D =, p =, 2)	Profit P(D =, p =, 2)
D = 144, p = 1.45	144,000 × 1.22	144,000 × 1.45	–$33,120
D = 144, p = 1.19	144,000 × 1.22	144,000 × 1.19	$4,320
D = 144, p = 0.97	144,000 × 1.22	144,000 × 0.97	$36,000
D = 96, p = 1.45	96,000 × 1.22	96,000 × 1.45	–$22,080
D = 96, p = 1.19	96,000 × 1.22	96,000 × 1.19	$2,880
D = 96, p = 0.97	96,000 × 1.22	96,000 × 0.97	$24,000
D = 64, p = 1.45	64,000 × 1.22	64,000 × 1.45	–$14,720
D = 64, p = 1.19	64,000 × 1.22	64,000 × 1.19	$1,920
D = 64, p = 0.97	64,000 × 1.22	64,000 × 0.97	$16,000

Table 6-5

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Decision Tree – Trips Logistics

Expected profit at each node in Period 1 is the profit during Period 1 plus the present value of the expected profit in Period 2

Expected profit EP(D =, p =, 1) at a node is the expected profit over all four nodes in Period 2 that may result from this node

PVEP(D =, p =, 1) is the present value of this expected profit and P(D =, p =, 1), and the total expected profit, is the sum of the profit in Period 1 and the present value of the expected profit in Period 2

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Decision Tree – Trips Logistics

From node D = 120, p = $1.32 in Period 1, there are four possible states in Period 2

Evaluate the expected profit in Period 2 over all four states possible from node D = 120, p = $1.32 in Period 1 to be

EP(D = 120, p = 1.32,1) = 0.2 x [P(D = 144, p = 1.45,2) +

P(D = 144, p = 1.19,2) +

P(D = 96, p = 1.45,2) +

P(D = 96, p = 1.19,2)

= 0.25 x [–33,120 + 4,320 –

22,080 + 2,880

= –$12,000

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Decision Tree – Trips Logistics

The present value of this expected value in Period 1 is

PVEP(D = 120, p = 1.32,1) = EP(D = 120, p = 1.32,1) / (1 + k)

= –$12,000 / (1.1)

= –$10,909

The total expected profit P(D = 120, p = 1.32,1) at node D = 120, p = 1.32 in Period 1 is the sum of the profit in Period 1 at this node, plus the present value of future expected profits possible from this node

P(D = 120, p = 1.32,1) = 120,000 x 1.22 – 120,000 x 1.32 +

PVEP(D = 120, p = 1.32,1)

= –$12,000 – $10,909 = –$22,909

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Decision Tree – Trips Logistics

For Period 0, the total profit P(D = 100, p = 120,0) is the sum of the profit in Period 0 and the present value of the expected profit over the four nodes in Period 1

EP(D = 100, p = 1.20,0) = 0.25 x [P(D = 120, p = 1.32,1) +

P(D = 120, p = 1.08,1) +

P(D = 96, p = 1.32,1) +

P(D = 96, p = 1.08,1)]

= 0.25 x [–22,909 + 32,073 –

15,273) + 21,382]

= $3,818

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Decision Tree – Trips Logistics

PVEP(D = 100, p = 1.20,1) = EP(D = 100, p = 1.20,0) / (1 + k)

= $3,818 / (1.1) = $3,471

P(D = 100, p = 1.20,0) = 100,000 x 1.22 – 100,000 x 1.20 +

PVEP(D = 100, p = 1.20,0)

= $2,000 + $3,471 = $5,471

Therefore, the expected NPV of not signing the lease and obtaining all warehouse space from the spot market is given by NPV(Spot Market) = $5,471

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Decision Tree – Trips Logistics

Node	EP(D =, p =, 1)	P(D =, p =, 1) = D x 1.22 – D x p + EP(D =, p =, 1) / (1 + k)
D = 120, p = 1.32	100,000 sq. ft.	–$22,909
D = 120, p = 1.08	100,000 sq. ft.	$32,073
D = 80, p = 1.32	100,000 sq. ft.	–$15,273
D = 80, p = 1.08	100,000 sq. ft.	$21,382

Table 6-6

Fixed Lease Option

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Decision Tree – Trips Logistics

Node	Leased Space	Warehouse Space at Spot Price (S)	Profit P(D =, p =, 2) = D x 1.22 – (100,000 x 1 + S x p)
D = 144, p = 1.45	100,000 sq. ft.	44,000 sq. ft.	$11,880
D = 144, p = 1.19	100,000 sq. ft.	44,000 sq. ft.	$23,320
D = 144, p = 0.97	100,000 sq. ft.	44,000 sq. ft.	$33,000
D = 96, p = 1.45	100,000 sq. ft.	0 sq. ft.	$17,120
D = 96, p = 1.19	100,000 sq. ft.	0 sq. ft.	$17,120
D = 96, p = 0.97	100,000 sq. ft.	0 sq. ft.	$17,120
D = 64, p = 1.45	100,000 sq. ft.	0 sq. ft.	–$21,920
D = 64, p = 1.19	100,000 sq. ft.	0 sq. ft.	–$21,920
D = 64, p = 0.97	100,000 sq. ft.	0 sq. ft.	–$21,920

Table 6-7

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Decision Tree – Trips Logistics

Node	EP(D =, p =, 1)	Warehouse Space at Spot Price (S)	P(D =, p =, 1) = D x 1.22 – (100,000 x 1 + S x p) + EP(D =, p = ,1)(1 + k)
D = 120, p = 1.32	0.25 x [P(D = 144, p = 1.45,2) + P(D = 144, p = 1.19,2) + P(D = 96, p = 1.45,2) + P(D = 96, p = 1.19,2)] = 0.25 x (11,880 + 23,320 + 17,120 + 17,120) = $17,360	20,000	$35,782
D = 120, p = 1.08	0.25 x (23,320 + 33,000 + 17,120 + 17,120) = $22,640	20,000	$45,382
D = 80, p = 1.32	0.25 x (17,120 + 17,120 – 21,920 – 21,920) = –$2,400	0	–$4,582
D = 80, p = 1.08	0.25 x (17,120 + 17,120 – 21,920 – 21,920) = –$2,400	0	–$4,582

Table 6-8

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Decision Tree – Trips Logistics

Using the same approach for the lease option, NPV(Lease) = $38,364

Recall that when uncertainty was ignored, the NPV for the lease option was $60,182

However, the manager would probably still prefer to sign the three-year lease for 100,000 sq. ft. because this option has the higher expected profit

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Decision Tree – Trips Logistics

Node	Warehouse Space at $1 (W)	Warehouse Space at Spot Price (S)	Profit P(D =, p =, 2) = D x 1.22 – (W x 1 + S x p)
D = 144, p = 1.45	100,000 sq. ft.	44,000 sq. ft.	$11,880
D = 144, p = 1.19	100,000 sq. ft.	44,000 sq. ft.	$23,320
D = 144, p = 0.97	100,000 sq. ft.	44,000 sq. ft.	$33,000
D = 96, p = 1.45	96,000 sq. ft.	0 sq. ft.	$21,120
D = 96, p = 1.19	96,000 sq. ft.	0 sq. ft.	$21,120
D = 96, p = 0.97	96,000 sq. ft.	0 sq. ft.	$21,120
D = 64, p = 1.45	64,000 sq. ft.	0 sq. ft.	$14,080
D = 64, p = 1.19	64,000 sq. ft.	0 sq. ft.	$14,080
D = 64, p = 0.97	64,000 sq. ft.	0 sq. ft.	$14,080

Table 6-9

Flexible Lease Option

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Decision Tree – Trips Logistics

Node	EP(D =, p =, 1)	Warehouse Space at $1 (W)	Warehouse Space at Spot Price (S)	P(D =, p =, 1) = D x 1.22 – (W x 1 + S x p) + EP(D =, p = ,1)(1 + k)
D = 120, p = 1.32	0.25 x (11,880 + 23,320 + 21,120 + 21,120) = $19,360	100,000	20,000	$37,600
D = 120, p = 1.08	0.25 x (23,320 + 33,000 + 21,120 + 21,120) = $24,640	100,000	20,000	$47,200
D = 80, p = 1.32	0.25 x (21,120 + 21,120 + 14,080 + 14,080) = $17,600	80,000	0	$33,600
D = 80, p = 1.08	0.25 x (21,920 + 21,920 + 14,080 + 14,080) = $17,600	80,000	0	$33,600

Table 6-10

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Decision Tree – Trips Logistics

Option	Value
All warehouse space from the spot market	$5,471
Lease 100,000 sq. ft. for three years	$38,364
Flexible lease to use between 60,000 and 100,000 sq. ft.	$46,545

Table 6-11

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Onshore or Offshore

D-Solar demand in Europe = 100,000 panels per year

Each panel sells for €70

Annual demand may increase by 20 percent with probability 0.8 or decrease by 20 percent with probability 0.2

Build a plant in Europe or China with a rated capacity of 120,000 panels

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D-Solar Decision

European Plant		Chinese Plant
Fixed Cost (euro)	Variable Cost (euro)	Fixed Cost (yuan)	Variable Cost (yuan)
1 million/year	40/panel	8 million/year	340/panel

Table 6-12

Period 1		Period 2
Demand	Exchange Rate	Demand	Exchange Rate
112,000	8.64 yuan/euro	125,440	8.2944 yuan/euro

Table 6-13

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D-Solar Decision

European plant has greater volume flexibility

Increase or decrease production between 60,000 to 150,000 panels

Chinese plant has limited volume flexibility

Can produce between 100,000 and 130,000 panels

Chinese plant will have a variable cost for 100,000 panels and will lose sales if demand increases above 130,000 panels

Yuan, currently 9 yuan/euro, expected to rise 10%, probability of 0.7 or drop 10%, probability of 0.3

Sourcing decision over the next three years

Discount rate k = 0.1

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D-Solar Decision

Period 0 profits = 100,000 x 70 – 1,000,000 – 100,000 x 40 = €2,000,000

Period 1 profits = 112,000 x 70 – 1,000,000 – 112,000 x 40 = €2,360,000

Period 2 profits = 125,440 x 70 – 1,000,000 – 125,440 x 40 = €2,763,200

Expected profit from onshoring = 2,000,000 + 2,360,000/1.1 +

2,763,200/1.21

= €6,429,091

Period 0 profits = 100,000 x 70 – 8,000,000/9 – 100,000 x 340/9

= €2,333,333

Period 1 profits = 112,000 x 70 – 8,000,000/8.64 – 112,000 x 340/8.64

= €2,506,667

Period 2 profits = 125,440 x 70 – 8,000,000/7.9524 – 125,440 x 340/7.9524 = €2,674,319

Expected profit from off-shoring = 2,333,333 + 2,506,667/1.1 +

2,674,319/1.21

= €6,822,302

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Decision Tree

Figure 6-3

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D-Solar Decision

Period 2 evaluation – onshore

Revenue from the manufacture

and sale of 144,000 panels = 144,000 x 70

= €10,080,000

Fixed + variable cost

of onshore plant = 1,000,000 + 144,000 x 40

= €6,760,000

P(D = 144, E = 10.89,2) = 10,080,000 – 6,760,000

= €3,320,000

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D-Solar Decision

D	E	Sales	Production Cost Quantity	Revenue (euro)	Cost (euro)	Profit (euro)
144	10.89	144,000	144,000	10,080,000	6,760,000	3,320,000
144	8.91	144,000	144,000	10,080,000	6,760,000	3,320,000
96	10.89	96,000	96,000	6,720,000	4,840,000	1,880,000
96	8.91	96,000	96,000	6,720,000	4,840,000	1,880,000
144	7.29	144,000	144,000	10,080,000	6,760,000	3,320,000
96	7.29	96,000	96,000	6,720,000	4,840,000	1,880,000
64	10.89	64,000	64,000	4,480,000	3,560,000	920,000
64	8.91	64,000	64,000	4,480,000	3,560,000	920,000
64	7.29	64,000	64,000	4,480,000	3,560,000	920,000

Table 6-14

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D-Solar Decision

Period 1 evaluation – onshore

EP(D = 120, E = 9.90, 1) = 0.24 x P(D = 144, E = 10.89, 2) +

0.56 x P(D = 144, E = 8.91, 2) +

0.06 x P(D = 96, E = 10.89, 2) +

0.14 x P(D = 96, E = 8.91, 2)

= 0.24 x 3,320,000 + 0.56 x 3,320,000 +

0.06 x 1,880,000 + 0.14 x 1,880,000

= €3,032,000

PVEP(D = 120, E = 9.90,1) = EP(D = 120, E = 9.90,1)/(1 + k)

= 3,032,000/1.1 = €2,756,364

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D-Solar Decision

Period 1 evaluation – onshore

Revenue from manufacture

and sale of 120,000 panels = 120,000 x 70 = €8,400,000

Fixed + variable cost of onshore plant = 1,000,000 + 120,000 x 40

= €5,800,000

P(D = 120, E = 9.90, 1) = 8,400,000 – 5,800,000 +

PVEP(D = 120, E = 9.90, 1)

= 2,600,000 + 2,756,364

= €5,356,364

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D-Solar Decision

D	E	Sales	Production Cost Quantity	Revenue (euro)	Cost (euro)	Profit (euro)
120	9.90	120,000	120,000	8,400,000	5,800,000	5,356,364
120	8.10	120,000	120,000	8,400,000	5,800,000	5,356,364
80	9.90	80,000	80,000	5,600,000	4,200,000	2,934,545
80	8.10	80,000	80,000	5,600,000	4,200,000	2,934,545

Table 6-15

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D-Solar Decision

Period 0 evaluation – onshore

EP(D = 100, E = 9.00, 1) = 0.24 x P(D = 120, E = 9.90, 1) +

0.56 x P(D = 120, E = 8.10, 1) +

0.06 x P(D = 80, E = 9.90, 1) +

0.14 x P(D = 80, E = 8.10, 1)

= 0.24 x 5,356,364 + 0.56 x 5,5356,364 +

0.06 x 2,934,545 + 0.14 x 2,934,545

= € 4,872,000

PVEP(D = 100, E = 9.00,1) = EP(D = 100, E = 9.00,1)/(1 + k)

= 4,872,000/1.1 = €4,429,091

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D-Solar Decision

Period 0 evaluation – onshore

Revenue from manufacture

and sale of 100,000 panels = 100,000 x 70 = €7,000,000

Fixed + variable cost of onshore plant = 1,000,000 + 100,000 x 40

= €5,000,000

P(D = 100, E = 9.00, 1) = 8,400,000 – 5,800,000 +

PVEP(D = 100, E = 9.00, 1)

= 2,000,000 + 4,429,091

= €6,429,091

6-‹#›

D-Solar Decision

Period 2 evaluation – offshore

Revenue from the manufacture

and sale of 130,000 panels = 130,000 x 70

= €9,100,000

Fixed + variable cost

of offshore plant = 8,000,000 + 130,000 x 340

= 52,200,000 yuan

P(D = 144, E = 10.89,2) = 9,100,000 – 52,200,000/10.89

= €4,306,612

6-‹#›

D-Solar Decision

D	E	Sales	Production Cost Quantity	Revenue (euro)	Cost (yuan)	Profit (euro)
144	10.89	130,000	130,000	9,100,000	52,200,000	4,306,612
144	8.91	130,000	130,000	9,100,000	52,200,000	3,241,414
96	10.89	96,000	100,000	6,720,000	42,000,000	2,863,251
96	8.91	96,000	100,000	6,720,000	42,000,000	2,006,195
144	7.29	130,000	130,000	9,100,000	52,200,000	1,939,506
96	7.29	96,000	100,000	6,720,000	42,000,000	958,683
64	10.89	64,000	100,000	4,480,000	42,000,000	623,251
64	8.91	64,000	100,000	4,480,000	42,000,000	–233,805
64	7.29	64,000	10,000	4,480,000	3,560,000	–1,281,317

Table 6-16

6-‹#›

D-Solar Decision

Period 1 evaluation – offshore

EP(D = 120, E = 9.90, 1) = 0.24 x P(D = 144, E = 10.89, 2) +

0.56 x P(D = 144, E = 8.91, 2) +

0.06 x P(D = 96, E = 10.89, 2) +

0.14 x P(D = 96, E = 8.91, 2)

= 0.24 x 4,306,612 + 0.56 x 3,241,414 +

0.06 x 2,863,251 + 0.14 x 2,006,195

= € 3,301,441

PVEP(D = 120, E = 9.90,1) = EP(D = 120, E = 9.90,1)/(1 + k)

= 3,301,441/1.1 = €3,001,310

6-‹#›

D-Solar Decision

Period 1 evaluation – offshore

Revenue from manufacture

and sale of 120,000 panels = 120,000 x 70 = €8,400,000

Fixed + variable cost of offshore plant = 8,000,000 + 120,000 x 340

= 48,800,000 yuan

P(D = 120, E = 9.90, 1) = 8,400,000 – 48,800,000/9.90 +

PVEP(D = 120, E = 9.90, 1)

= 3,470,707 + 3,001,310

= €6,472,017

6-‹#›

D-Solar Decision

D	E	Sales	Production Cost Quantity	Revenue (euro)	Cost (yuan)	Expected Profit (euro)
120	9.90	120,000	120,000	8,400,000	48,800,000	6,472,017
120	8.10	120,000	120,000	8,400,000	48,800,000	4,301,354
80	9.90	80,000	100,000	5,600,000	42,000,000	3,007,859
80	8.10	80,000	100,000	5,600,000	42,000,000	1,164,757

Table 6-17

6-‹#›

D-Solar Decision

Period 0 evaluation – offshore

EP(D = 100, E = 9.00, 1) = 0.24 x P(D = 120, E = 9.90, 1) +

0.56 x P(D = 120, E = 8.10, 1) +

0.06 x P(D = 80, E = 9.90, 1) +

0.14 x P(D = 80, E = 8.10, 1)

= 0.24 x 6,472,017 + 0.56 x 4,301,354

+ 0.06 x 3,007,859 + 0.14 x 1,164,757

= € 4,305,580

PVEP(D = 100, E = 9.00,1) = EP(D = 100, E = 9.00,1)/(1 + k)

= 4,305,580/1.1 = €3,914,164

6-‹#›

D-Solar Decision

Period 0 evaluation – offshore

Revenue from manufacture

and sale of 100,000 panels = 100,000 x 70 = €7,000,000

Fixed + variable cost of onshore plant = 8,000,000 + 100,000 x 340

= €42,000,000 yuan

P(D = 100, E = 9.00, 1) = 7,000,000 – 42,000,000/9.00 +

PVEP(D = 100, E = 9.00, 1)

= 2,333,333 + 3,914,164

= €6,247,497

6-‹#›

Decisions Under Uncertainty

Combine strategic planning and financial planning during global network design

Use multiple metrics to evaluate global supply chain networks

Use financial analysis as an input to decision making, not as the decision-making process

Use estimates along with sensitivity analysis

6-‹#›

Summary of Learning Objectives

Identify factors that need to be included in total cost when making global sourcing decisions

Define uncertainties that are particularly relevant when designing global supply chains

Explain different strategies that may be used to mitigate risk in global supply chains

Understand decision tree methodologies used to evaluate supply chain design decisions under uncertainty

6-‹#›

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Printed in the United States of America.

6-‹#›

discount factor = 1

1+k

NPV =C0 + 1

1+k ⎛

⎝ ⎜

⎞

⎠ ⎟

Ct t=1

∑

discount factor=

1+k

NPV=C

1+k

t=1

NPV(No lease) =C0 + C1

1+k +

C2 (1+k)2

NPV(No lease)=C

1+k

(1+k)

= 2,000+ 2,000 1.1

+ 2,000 1.12

= $5,471

=2,000+

2,000

1.1

2,000

1.1

=$5,471

NPV(Lease) =C0 + C1 1+k

+ C2

(1+k)2

NPV(Lease)=C

1+k

(1+k)

= 22,000+ 22,000 1.1

+ 22,000 1.12

= $60,182

=22,000+

22,000

1.1

22,000

1.1

=$60,182

164 Chapter 6 • Designing Global Supply Chain Networks

a variable cost of !40 and sell each panel for revenue of !70. Revenues and costs are evaluated as follows:

In Period 2, the total profit for D-Solar at the node D ! 144, E ! 10.89 for the onshore option is thus given by

Using the same approach, we can evaluate the profit in each of the nine states (represented by the corresponding value of D and E) in Period 2 as shown in Table 6-14.

P1D = 144, E = 10.89, 22 = 10,080,000 - 6,760,000 = !3,320,000 = ! 6,760,000

Fixed + variable cost of onshore plant = 1,000,000 + 144,000 * 40

= 144,000 * 70 = !10,080,000 Revenue from the manufacture and sale of 144,000 panels

D = 100 E = 9.00

D = 120 E = 9.90

D = 120 E = 8.10

D = 80 E = 9.90

D = 80 E = 8.10

D = 144 E = 10.89

D = 144 E = 8.91

D = 96 E = 10.89

D = 96 E = 8.91

D = 144 E = 7.29

D = 96 E = 7.29

D = 64 E = 10.89

D = 64 E = 8.91

D = 64 E = 7.29

0. 8

! 0 .3

0.8 ! 0.3

0.2 ! 0.3

0. 8

! 0 .3

0.8 ! 0.7

0.2 ! 0.7

0.8 ! 0

0.8 ! 0.7

0.2 ! 0.7

0.8 !

0.3

0.2 ! 0.3

0.2 ! 0.7

0.8 !

0.3

0.8 ! 0

0.8 ! 0.7

0.2 ! 0.3

0.2 ! 0.7

0.2 ! 0.3

Period 0 Period 1 Period 2

FIGURE 6-3 Decision Tree for D-Solar

M06_CHOP3952_05_SE_C06.QXD 10/20/11 10:01 PM Page 164