Homework 6

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Supply Chain Management: Strategy, Planning, and Operation

Seventh Edition

Chapter 6

Designing Global Supply Chain Networks

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Learning Objectives

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

6.2 Define relevant risks and explain different strategies that may be used to mitigate risk in global supply chains.

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

6.4 Use decision tree methodologies to value flexibility and make onshoring/offshoring decisions under uncertainty.

Impact of Globalization on Supply Chain Networks (1 of 2)

Opportunities to simultaneously increase revenues and decrease costs

Accompanied by significant additional risk and uncertainty

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

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

Impact of Globalization on Supply Chain Networks (2 of 2)

Table 6-1 Results of Accenture Survey on Sources of Risk That Affect Global Supply Chain Performance

Risk Factors	Percentage of Supply Chains Affected
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

Importance of Total Cost (1 of 4)

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

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

Importance of Total Cost (2 of 4)

Table 6-2 Dimensions to Consider When Evaluating Total Cost from Offshoring

Performance Dimension	Activity Affecting 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

Importance of Total Cost (3 of 4)

Table 6-2 [Continued]

Performance Dimension	Activity Affecting 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
Stockouts	Ordering, production, transportation with poorer visibility	Increase

Importance of Total Cost (4 of 4)

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

Summary of Learning Objective 1

It is critical that global sourcing decisions be made while accounting for total cost. Besides unit cost, total cost should include the impact of global sourcing on freight, inventories, lead time, quality, on-time delivery, minimum order quantity, working capital, and stock- outs. Other factors to be considered include the impact on supply chain visibility, order communication, invoicing errors, and the need for currency hedging. Offshoring typically lowers labor and fixed costs but increases risk, freight costs, and working capital.

Risk Management in Global Supply Chains (1 of 6)

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

Risk Management in Global Supply Chains (2 of 6)

Table 6-3 Supply Chain Risks to Be Considered During Network Design

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

Risk Management in Global Supply Chains (3 of 6)

Table 6-3 [Continued]

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

Risk Management in Global Supply Chains (4 of 6)

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

Risk Management in Global Supply Chains (5 of 6)

Table 6-4 Tailored Risk Mitigation Strategies During Network Design

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.

Risk Management in Global Supply Chains (6 of 6)

Table 6-4 [Continued]

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.

Flexibility, Chaining, and Containment (1 of 3)

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

Flexibility, Chaining, and Containment (2 of 3)

Figure 6-1 Different Flexibility Configurations in Network

Flexibility, Chaining, and Containment (3 of 3)

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

Summary of Learning Objective 2

The performance of a global supply chain is affected by risk and uncertainty in a number of input factors such as supply, demand, price, exchange rates, and other economic factors. These risks can be mitigated by building suitable flexibility in the supply chain network. Operational strategies that help mitigate risk in global supply chains include carrying excess capacity and inventory, flexible capacity, redundant suppliers, improved responsiveness, and aggregation of demand. Hedging fuel costs and currencies are financial strategies that can help mitigate risk. It is important to keep in mind that no risk mitigation strategy will always pay off. These mitigation strategies are designed to guard against certain extreme states of the world that may arise in an uncertain global environment.

Using Decision Trees (1 of 2)

Several 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?

Using Decision Trees (2 of 2)

Executives need a methodology that allows them to estimate global currency instability, unpredictable commodities costs, uncertainty about customer demand, political or social unrest in key markets, and potential changes in government regulations the uncertainty in demand and price forecast

Discounted Cash Flows

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

Discounted cash flow (D C F) 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

Discounted Cash Flow Analysis

Where

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

N P V = net present value of this stream

K = rate of return

Compare N P V of different supply chain design options

The option with the highest N P V will provide the greatest financial return

Trips Logistics Example (1 of 3)

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

Trips Logistics Example (2 of 3)

Expected annual profit if Warehousing space is obtained	=	(100,000 × $1.22) − (100,000 × $1.20)
from spot market	=	$2,000

Trips Logistics Example (3 of 3)

Expected annual profit with Three year lease	=	(100,000 × $1.22) − (100,000 × $1.00)
Blank	=	$22,000

N P V of signing lease is $60,182 − $5,471 = $54,711 higher than spot market

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 (T)

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

Evaluate decision using a decision tree

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

Decision Tree – Trips Logistics (1 of 3)

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

Decision Tree – Trips Logistics (2 of 3)

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

Decision Tree (1 of 2)

Figure 6-2 Decision Tree for Trips Logistics, Considering Demand and Price Fluctuation

Evaluating the Spot Market Option (1 of 9)

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 × 1.45

= $208,800

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

− C(D = 144, p = 1.45, 2)

= 175,680 − 208,800

= −$33,120

Evaluating the Spot Market Option (2 of 9)

Table 6-5 Period 2 Calculations for Spot Market Option

Blank	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

Evaluating the Spot Market Option (3 of 9)

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

P V E P(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

Evaluating the Spot Market Option (4 of 9)

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

E P(D = 120, p = 1.32,1) = 0.2 × [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 × [−33,120 + 4,320

− 22,080 + 2,880]

= −$12,000

Evaluating the Spot Market Option (5 of 9)

The present value of this expected value in Period 1 is

Evaluating the Spot Market Option (6 of 9)

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

Evaluating the Spot Market Option (7 of 9)

Table 6-6 Period 1 Calculations for Spot Market Option

Node	E P(D =, p =, 1)	P(D =, p =, 1) = D × 1.22 – D x p + start fraction E P at left parenthesis D =, p =, 1 right parenthesis over left parenthesis 1 + k right parenthesis end fraction
D = 120, p = 1.32	−$12,000	−$22,909
D = 120, p = 1.08	$16,000	$32,073
D = 80, p = 1.32	−$8,000	−$15,273
D = 80, p = 1.08	$11,000	$21,382

Evaluating the Spot Market Option (8 of 9)

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

E P(D = 100, p = 1.20,0) = 0.25 × [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 × [−22,909 + 32,073

− 15,273) + 21,382]

= $3,818

Evaluating the Spot Market Option (9 of 9)

P(D = 100, p = 1.20,0) = (100,000 × 1.22) − (100,000 × 1.20)+ P V E P(D = 100, p = 1.20,0)

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

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

Evaluating the Fixed Lease Option (1 of 5)

Table 6-7 Period 2 Profit Calculations at Trips Logistics for Fixed Lease Option

Node	Leased Space	Warehouse Space at Spot Price (S)	Profit P(D =, p =, 2) = D × 1.22 − (100,000 × 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

Evaluating the Fixed Lease Option (2 of 5)

Table 6-8 Period 1 Profit Calculations at Trips Logistics for Fixed Lease Option

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

Evaluating the Fixed Lease Option (3 of 5)

Using the same approach for the lease option, N P V (Lease) = $38,364

E P(D = 100, p = 1.20,0) = 0.25 × [P(D = 120, p = 1.32,1) + P(D = 120, p = 1.08,1) + P(D = 80, p = 1.32,1) + P(D = 80, p = 1.08,1)]

= 0.25 × [35,782 + 45,382 − 4,582 − 4,582]

= $18,000

Evaluating the Fixed Lease Option (4 of 5)

P(D = 100, p = 1.20,0) = (100,000 × 1.22) − (100,000 × 1) + P V E P(D = 100, p = 1.20,0)

= $22,000 + $16,364 = $38,364

Evaluating the Fixed Lease Option (5 of 5)

Recall that when uncertainty was ignored, the N P V 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

Evaluating the Flexible Lease Option (1 of 2)

Table 6-9 Period 2 Profit Calculations at Trips Logistics with Flexible Lease Contract

Node	Warehouse Space at $1 (W)	Warehouse Space at Spot Price (S)	Profit P(D =, p =, 2) = D × 1.22 − (W× 1 + S × 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

Evaluating the Flexible Lease Option (2 of 2)

Table 6-10 Period 1 Profit Calculations at Trips Logistics with Flexible Lease Contract

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

Decision Tree – Trips Logistics (3 of 3)

Table 6-11 Comparison of Different Lease Options for 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

Summary of Learning Objective 3

Uncertainty in demand and economic factors should be included in the financial evaluation of supply chain design decisions. Decision trees can be used to evaluate supply chain decisions under uncertainty. Uncertainty along different dimensions over the evaluation period is represented as a tree with each node corresponding to a possible scenario. Starting at the last period of the evaluation interval, the decision tree analysis works back to Period 0, identifying the optimal decision and the expected cash flows at each step. The inclusion of uncertainty typically decreases the value of rigidity and increases the value of flexibility.

Onshore or Offshore

The value of flexibility under uncertainty

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

D-Solar Decision (1 of 21)

Table 6-12 Fixed and Variable Production Costs for D-Solar

European Plant	European Plant	Chinese 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 6-13 Expected Future Demand and Exchange Rate

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

D-Solar Decision (2 of 21)

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

D-Solar Decision (3 of 21)

Period 0 profits = (100,000 × 70) – 1,000,000 − (100,000 × 40) = €2,000,000

Period 1 profits = (112,000 × 70) − 1,000,000 − (112,000 × 40) = €2,360,000

Period 2 profits = (125,440 × 70) − 1,000,000 − (125,440 × 40) = €2,763,200

D-Solar Decision (4 of 21)

Decision Tree (2 of 2)

Figure 6-3 Decision Tree for D-Solar

D-Solar Decision (5 of 21)

Period 2 evaluation – onshore

Revenue from the manufacture and sale of 144,000 panels

= 144,000 × 70 = €10,080,000

Fixed + variable cost of onshore plant

= 1,000,000 + (144,000 × 40)

= €6,760,000

P(D = 144, E = 10.89,2)

= 10,080,000 − 6,760,000

= €3,320,000

D-Solar Decision (6 of 21)

Table 6-14 Period 2 Profits for Onshore Option

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

D-Solar Decision (7 of 21)

Period 1 evaluation – onshore

E P(D = 120, E = 9.90, 1) = 0.24 × P( D = 144, E = 10.89, 2)+ 0.56 × P( D = 144, E = 8.91, 2)+ 0.06 × P( D = 96, E = 10.89, 2)+ 0.14 × P( D = 96, E = 8.91, 2)

= (0.24 × 3,320,000) + (0.56 × 3,320,000)+ (0.06 × 1,880,000)

+ (0.14 × 1,880,000)

= €3,032,000

D-Solar Decision (8 of 21)

D-Solar Decision (9 of 21)

Period 1 evaluation – onshore

Revenue from manufacture and sale of 120,000 panels

= 120,000 × 70 = €8,400,000

Fixed + variable cost of onshore plant

= 1,000,000 + (120,000 × 40)

= €5,800,000

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

+ P V E P(D = 120, E = 9.90, 1)

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

= €5,356,364

D-Solar Decision (10 of 21)

Table 6-15 Period 1 Profits for Onshore Option

D	E	Sales	Production Cost Quantity	Revenue (euro)	Cost (euro)	Expected 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

D-Solar Decision (11 of 21)

Period 0 evaluation – onshore

E P(D = 100, E = 9.00, 1) = 0.24 × P(D = 120, E = 9.90, 1)+ 0.56 × P(D = 120, E = 8.10, 1)+ 0.06 × P(D = 80, E = 9.90, 1)+ 0.14 × P(D = 80, E = 8.10, 1)

= (0.24 × 5,356,364) + (0.56 × 5,5356,364)

+ (0.06 × 2,934,545) + (0.14 × 2,934,545)

= € 4,872,000

D-Solar Decision (12 of 21)

Period 0 evaluation – onshore

Revenue from manufacture and sale of 100,000 panels

= 100,000 × 70 = €7,000,000

Fixed + variable cost of onshore plant

= 1,000,000 + (100,000 × 40)

= €5,000,000

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

+ P V E P(D = 100, E = 9.00, 1)

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

= €6,429,091

D-Solar Decision (13 of 21)

Period 2 evaluation – offshore

Revenue from the manufacture and sale of 130,000 panels

= 130,000 × 70

= €9,100,000

Fixed + variable cost of offshore plant

= 8,000,000 + (130,000 × 340)

= 52,200,000 yuan

D-Solar Decision (14 of 21)

Table 6-16 Period 2 Profits for Offshore Option

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

D-Solar Decision (15 of 21)

Period 1 evaluation – offshore

E P(D = 120, E = 9.90, 1) = 0.24 × P(D = 144, E = 10.89, 2)

+ 0.56 × P(D = 144, E = 8.91, 2)

+ 0.06 × P(D = 96, E = 10.89, 2)

+ 0.14 × P(D = 96, E = 8.91, 2)

= (0.24 × 4,306,612) + (0.56 × 3,241,414) + (0.06 × 2,863,251) + (0.14 × 2,006,195)

= € 3,301,441

D-Solar Decision (16 of 21)

D-Solar Decision (17 of 21)

Period 1 evaluation – offshore

Revenue from manufacture and sale of 120,000 panels

= 120,000 × 70 = €8,400,000

Fixed + variable cost of offshore plant

= 8,000,000 + (120,000 × 340)

= 48,800,000 yuan

D-Solar Decision (18 of 21)

Table 6-17 Period 1 Profits for Offshore Option

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

D-Solar Decision (19 of 21)

Period 0 evaluation – offshore

E P(D = 100, E = 9.00, 1) = 0.24 × P(D = 120, E = 9.90, 1)

+ 0.56 × P(D = 120, E = 8.10, 1)

+ 0.06 × P(D = 80, E = 9.90, 1)

+ 0.14 × P(D = 80, E = 8.10, 1)

= (0.24 × 6,472,017) + (0.56 × 4,301,354)

+ (0.06 × 3,007,859) + (0.14 × 1,164,757)

= € 4,305,580

D-Solar Decision (20 of 21)

D-Solar Decision (21 of 21)

Period 0 evaluation – offshore

Revenue from manufacture and sale of 100,000 panels

= 100,000 × 70 = €7,000,000

Fixed + variable cost of onshore plant

= 8,000,000 + (100,000 × 340)

= €42,000,000 yuan

Summary of Learning Objective 4

Relying solely on expected trends can lead to flawed decisions when designing global sup- ply chains under uncertainty. It is important to use an approach such as decision trees that accounts for future uncertainty. In the presence of uncertainty, flexibility can be valued as a real option using decision trees. Decision trees allow the valuation of different flexibility alternatives for each potential outcome of an uncertain future. This provides an accurate value of flexibility and other real options such as onshoring. In general, the value of real options such as flexibility and onshoring increases with an increase in uncertainty, while the value of inflexible choices decreases with an increase in uncertainty.

Chapter 6 • Designing Global Supply Chain Networks 151

Dedicated Network

Chained Network with One Long

Chain

Chained Network with Two Short

Chains

Fully Flexible Network

FIGURE 6-1 Different Flexibility Configurations in Network

Given that some form of flexibility is often used to mitigate risks in global supply chains, it is important to understand the benefits and limitations of this approach. When dealing with demand uncertainty, Jordan and Graves (1995) make the important observation that as flexibility is increased, the marginal benefit derived from the increased flexibility decreases. They suggest operationalizing this idea in the concept of chaining, which is illustrated as follows. Consider a firm that sells four distinct products. A dedicated supply network with no flexibility would have four plants, each dedicated to producing a single product, as shown in Figure 6-1. A fully flexible network configuration would have each plant capable of producing all four products. The production flexibility of plants is beneficial when demand for each of the four products is unpredictable. With dedicated plants, the firm is not able to meet demand in excess of plant capacity. With flexible plants, the firm is able to shift excess demand for a product to a plant with excess capacity. Jordan and Graves define a chained network with one long chain (limited flexibility), configured as shown in Figure 6-1. In a chained configuration, each plant is capable of producing two products with the flexibility organized so that the plants and their products form a chain. Jordan and Graves show that a chained network mitigates the risk of demand fluctuation almost as effectively as a fully flexible network. Given the higher cost of full flexibility, the results of Jordan and Graves indicate that chaining is an excellent strategy to lower cost while gaining most of the benefits of flexibility.

The desired length of chains is an important question to be addressed when designing chained networks. When dealing with demand uncertainty, longer chains have the advantage of effectively pooling available capacity to a greater extent. Long chains, however, do have a few disadvantages. The fixed cost of building a single long chain can be higher than the cost of multiple smaller chains. With a single long chain, the effect of any fluctuation ripples to all facilities in the chain, making coordination more difficult across the network. It has also been observed by several researchers that flexibility and chaining are effective when dealing with demand fluctuation but less effective when dealing with supply disruption. In the presence of supply disruption, Lim et al. (2008) have observed that designing smaller chains that contain or limit the impact of a disruption can be more effective than designing a network with one long chain. An example of containment is shown in the last example in Figure 6-1, which shows four plants with the flexibility to produce the four products in the form of two short chains. In this design, any disruption in one of the chains does not impact the other chain. A simple example of containment is hog farming: The farms are large to gain economies of scale, but the hogs are kept separated in small groups to ensure that the risk of disease is contained within a group and does not spread to the entire farm.

Key Point

Appropriate flexibility is an effective approach for a global supply chain to deal with a variety of risks and uncertainties. Whereas some flexibility is valuable, too much flexibility may not be worth the cost. Strategies like chaining and containment should be used to maximize the benefit from flexibility while keeping costs low.

M06_CHOP3952_05_SE_C06.QXD 10/20/11 10:01 PM Page 151

æö

ç÷

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discount factor

NPV

(

)

=++

NPV(No lease)

=++=

2,0002,000

2,000$5,471

1.11.1

(

)

=++

NPV(Lease)

=++=

22,00022,000

22,000$60,182

1.11.1

156 Chapter 6 • Designing Global Supply Chain Networks

Period 0 Period 1 Period 2

D = 100 p = $1.20

D = 120 p = $1.32

D = 120 p = $1.08

D = 80 p = $1.32

D = 80 p = $1.08

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

0.25

FIGURE 6-2 Decision Tree for Trips Logistics Considering Demand and Price Fluctuation

Evaluating the Spot Market Option

The manager first analyzes the option of not signing a lease and obtaining all warehouse space from the spot market. He starts with Period 2 and evaluates the profit for Trips Logistics at each node. At the node D ! 144, p ! $1.45, Trips Logistics must satisfy a demand of 144,000 and faces a spot price of $1.45 per square foot for warehouse space in Period 2. The cost incurred by Trips Logistics in Period 2 at the node D ! 144, p ! $1.45 is represented by C(D ! 144, p !1.45, 2) and is given by

The profit at Trips Logistics in Period 2 at the node D ! 144, p ! $1.45 is represented by P(D ! 144, p ! 1.45, 2) and is given by

= 175,680 - 208,800 = - $33,120

P1D = 144, p = $1.45, 22 = 144,000 * 1.22 - C1D = 144, p = 1.45, 22 C1D = 144, p = 1.45, 22 = 144,000 * 1.45 = $208,800

M06_CHOP3952_05_SE_C06.QXD 10/20/11 10:01 PM Page 156

(

)

()=

(120,1.32,1)

120,

)

$12,

1.32,1

10,

000

1.1

PVEPD

(

)

(

)

–

–––

==´´

+==

= 120, 1.32,1 120,000 1.22 120,000 1

.32

120, 1.32,1

$12,000 $10,909 $22,

()

(

)

PDp

PVEPDp

(

)

(,,1)

EPDp

(

)

(

)

(

)

100,1.20,0

()

$3,818

100, 1.20

,471

1.1

EPDp

PVEPDp

(

)

(

)

(

)

100,1.20,1

()

$18,000

$16,364

100, 11

.20,

EPD

2,000,0002,360,000

Expectedprofit from onshoring

2,763,200

1.21

€6,4

29,091

(

)

(

)

(

)

æöæö

=´--=

ç÷ç÷

èøèø

æöæö

=´--=

ç÷ç÷

èøèø

æö

=´-

èø

8,000,000100,000340

Period 0 profits100,00070€2,333,333

8,000,000112,000340

Period 1 profits112,00070€2,506,667

8.648.64

8,000,000

Period2profits125,44070

7.9524

æö

÷ç÷

èø

-=++=

125,440340

€2,674,319

7.9524

2,506,6672,674,319

Expected profit fromoffshoring2,333,333

€6,822,302

1.11.21

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

(

)

(

)

120,9.90,

()

20, 9.90,1

3,032,000

€2,756,364

1.1

PVEPDE

EPDE

(

)

(

)

100,9.00,1

4,872,000

€4,429,09

()

PVDE

52,200,000

144,10.89,29,100,000

10.89

€4,306,6

)

(

PDE

æö

===-

ç÷

èø

(

)

(

)

120,9.90,1

()

120,9.90,1

3,301,441

€3,001,3

EPDE

PVEP

48,800,000

120,9.90,18,400,000

9.90

120,9.90, 1

3,470,7073,001,310

€6,472,0

()

PDE

PVEPDE

æö

==-

ç÷

èø

+==

(

)

(

)

100,9.00,1

4,305,580

€3,914,16

()

PVDE

42,000,000

100,9.00, 17,000,000

9.00

100, 9.00, 1

2,333,333 3,914,16

()

€6,247,497

PDE

PVEPDE

æö

==-

ç÷

èø