scm_lessius chapter 4 - supply contracts

Supply Chain Management 1112 – supply contracts Roel Leus

SUPPLY CONTRACTS


Procurement

Planning

Manufacturing

Planning

Distribution

Planning Demand

Planning

Sequential Optimization

Supply Contracts/Collaboration/Information Systems and DSS

Procurement

Planning

Manufacturing

Planning

Distribution

Planning Demand

Planning

Global Optimization

Sequential optimization vs. global optimization


Supply contracts

A contract is an agreement between two parties. The raison d’être for contracts

is two parties with conflicting objectives.

Differences in costs at the buyer and supplier can lead to decisions that increase

total supply chain costs. E.g.: replenishment order placed by the buyer. The

buyer’s EOQ ignores the supplier’s costs.

A quantity discount contract may encourage the buyer to purchase a larger

quantity. This may result in lower total supply chain costs.

[ but misleading demand information because of order batching ]

A contract is said to be coordinating a supply chain if the sum of the profits of

various decision makers under the contract is “globally optimal”

Important especially for strategic components, not for commodities.

Last few years, significant increase in level of outsourcing; many leading

brand-name manufacturers outsource complete manufacturing (to OEMs*) and

design (to ODM’s) of their products (Apple, Dell, Sony and Toshiba to

Quanta). The procurement function in OEMs* becomes critical to remain in

control of their destiny.

*http://en.wikipedia.org/wiki/Original_equipment_manufacturer


SUPPLY CONTRACTS

1. Contracts for MTO supply chains

2. Contracts for MTS

3. Other issues

Coordination


Case swimsuit production Chapter 2

Consider a company that designs, produces, and sells summer fashion items

such as swimsuits. About six months before summer, the company must

commit itself to specific production quantities. Demand is forecasted and

certain probabilities are attached to specific quantities. Overestimating

demand will result in unsold inventory while underestimating it will lead to

inventory stockouts and loss of potential customers. The probabilistic

forecast suggests that average customer demand is 13 100 units for the

summer season.

Demand Probability Weighted Demand

8000 11% 880

10000 11% 1100

12000 28% 3360

14000 22% 3080

16000 18% 2880

18000 10% 1800

Average 13100

Demand Scenarios

0%

5%

10%

15%

20%

25%

30%

8000 10000 12000 14000 16000 18000

Sales

Pro

ba

bil

ity


Manufacturer Manufacturer DC Retail DC

Stores

Fixed Production Cost =$100,000

Variable Production Cost=$35

Selling Price=$125

Salvage Value=$20

Wholesale Price =$80

Swimsuits Chapter 2


Swimsuits Chapter 2 (2)

To start production, the manufacturer has to invest $100 000 independent

of the amount produced. This is fixed production cost.

The variable production cost per unit equals $80.

During the summer season, the selling price of a swimsuit is $125 per unit.

Any swimsuit not sold during summer is sold to a discount store for $20.

To identify the best production quantity, the firm needs to understand the

relationship between the production quantity, customer demand, and profit.

Suppose the manufacturer produces 10 000 units while demand realises at

12 000 units. Profit equals revenue from summer sales less variable and

fixed production costs:

Profit = 125 (10 000) – 80 (10 000) – 100 000 = $350 000

and the probability of realising this profit is 28%



In similar fashion, one can calculate the profit associated with each demand

scenario, given that the manufacturer produces 10 000 swimsuits. This

allows us to calculate the expected profit associated with producing 10 000

units.

We would like to find the production quantity that maximizes expected

profit. Should the optimal production quantity be equal to, more than or

less than the average demand?

With $45 understocking cost vs. $60 overstocking cost, the best production

quantity will probably be less than average demand for these particular cost

parameters.

Demand Probability Production = 10 000

8000 11% $140,000.00

10000 11% $350,000.00

12000 28% $350,000.00

14000 22% $350,000.00

16000 18% $350,000.00

18000 10% $350,000.00

Average $326,900.00



Expected profit is maximised for a production quantity of 12 000 units

We can also do this without graph:

Expected Profit

$370,700.00

$0

$50,000

$100,000

$150,000

$200,000

$250,000

$300,000

$350,000

$400,000

5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000

Production Quantity

Profit



Let cu = 60, co = 45. We look for the smallest Q such that

Note that this probability is a measure of the risk the manufacturer is

willing to take. We use an inequality in this case, because the demand

scenarios we have are discrete, i.e. demand is not continuous.

(while average demand is 13 100)

5714.0105

60

4560

60]Pr[

uo

o

cc

cQD

Demand Probability Q Pr(D>Q)

8000 0.11 8 000 0.89

10000 0.11 10 000 0.78

12000 0.28 12 000 0.50

14000 0.22 14 000 0.28

16000 0.18 16 000 0.10

18000 0.10 18 000 0.00



As we increase the production quantity, the risk – that is, the probability of

large losses – always increases. At the same time, the probability of large

gains also increases. This is the risk/reward trade-off. This is clear from

comparing the figures for production quantities 9 000 and 16 000, which

bring about the same average profit but have a different risk profile.

Of course, a production quantity of 9 000 does not make much sense, since

the probability of demand being equal to 9 000 is zero, unless we have an

initial inventory of 1 000 and should bring it up to 10 000.

Demand Probability

9000 12000 16000

8000 11% $200,000.00 $20,000.00 -$220,000.00

10000 11% $305,000.00 $230,000.00 -$10,000.00

12000 28% $305,000.00 $440,000.00 $200,000.00

14000 22% $305,000.00 $440,000.00 $410,000.00

16000 18% $305,000.00 $440,000.00 $620,000.00

18000 10% $305,000.00 $440,000.00 $620,000.00

Average Profit $293,450.00 $370,700.00 $294,500.00

Profit for a Given Production Level

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pro

ba

bil

ity

Revenue

Q=9000

Q=16000



Suppose now that the swimsuit under consideration is a model produced

last year, and that the manufacturer has an initial inventory of 5 000 units.

Assume that demand follows the same pattern as before.

Fixed production costs ($100 000) are charged independent of the amount

produced.

Should the manufacturer start production and if so, how many swimsuits

should be produced?

– If nothing is produced, average profit is equal to the sales of the 5000 initial

inventory, where no fixed production cost and no variable production costs are

taken into account: profit is 5000 ×125 = $625 000

– If the manufacturer decides to produce to bring inventory to 12 000 units (what

we found is the optimum), the profit he gains would be:

0.11(8000×125 + 4000×20) + 0.11(10 000×125 + 2000×20) +

0.28(12 000×125 + 0×20) + 0.22(12 000×125 + 0×20) +

0.18(12 000×125 + 0×20) + 0.10(12 000×125 + 0×20) – 7000×80 – 100 000 =

$770 700

– In conclusion: the optimal policy is to produce 7000 = 12000 – 5000 units


Case swimsuit production Chapter 4

In the analysis of the case in Chapter 2 it is assumed that the manufacturer

has adequate supply of raw materials, delivered on time. In order to ensure

this, buyers and suppliers agree on supply contracts. Supply contracts are

very powerful tools that can be used for far more than to ensure adequate

supply of, and demand for goods. In a supply contract, the buyer and the

supplier may agree on:

– Pricing and volume discounts.

– Minimum and maximum purchase quantities.

– Delivery lead times.

– Product or material quantity.

– Product return policies.

We assume now that there are two companies involved in the supply chain:

a retailer who faces customer demand and a manufacturer. Demand

follows the same pattern as before.



Stores



Selling Price=$125

Salvage Value=$20

Wholesale Price =$??

Swimsuits Chapter 4


Case swimsuit production (cnt’d)

For the retailer:

– Selling price of swimsuit during the summer season: $125

– Wholesale price paid to the manufacturer: $80

– Salvage value: $20

For the manufacturer:

– Fixed production cost: $100 000

– Variable production cost: $35

Manufacturer Retailer

Seller Buyer

selling

price: 125

salvage value: 20

fixed

production

cost: 100 000

variable

production

cost: 35

wholesale

price: 80


Case swimsuit production (cnt’d)

Marginal profit (understocking cost) for both actors are:

– retailer: 125 – 80 = $45

– manufacturer: 80 – 35 = $45

Retailer’s marginal cost (overstocking cost) is 80 – 20 = $60 and following

the analysis of the case in Chapter 2, it is optimal for the retailer to order

12 000 swimsuits.

Expected profit of retailer = 370 700 (supra) + 100 000 = $470 700

Expected profit of manufacturer = 12 000 (80 – 35) – 100 000 = $440 000

Total for Supply Chain = 470 700 + 440 000 = $910 700

In the previous example, we had a sequential supply chain, where the

manufacturer reacts to decisions made by the retailer. The retailer bears all

risk (of having excess inventory), the supplier does not bear any risk.

It is natural to look for mechanisms that the supply chain parties can use to

improve profits, which means they would move to global optimization.


Swimsuit production Buy-back contract

Buy-back contracts – the seller (manufacturer) agrees to buy back unsold

goods from the buyer (retailer) for some agreed-upon price.

Suppose the manufacturer offers to buy unsold swimsuits from the retailer

for $55. The retailer can either salvage goods, or the manufacturer will buy

them back and salvage them himself.

Such a construction is valuable when the increase in order quantity placed

by the retailer more than compensates the supplier’s increase in risk.

Item Price

Retailer sells for: $125.00

Manufacturer sells for: $80.00

Salvage: $20.00

Manufacturer buy back: $55.00

Fixed Production Cost: $100,000.00

Variable Production Cost: $35.00

M

R

Seller Buyer

125

20

100 000

35

80

55


Swimsuit production Buy-back contract (2)

profit retailer:

– demand = 12 000, order level = 10 000

profit = min{10 000, 12 000}*125 – 10 000 * 80

+ max{10 000 – 12 000, 0}*max{20, 55}

= 10 000 * 125 – 10 000 * 80 + 0 * 55 = 1 250 000 – 800 000 = $450 000


profit = min{14 000, 12 000}*125 – 14 000 * 80

+ max{14 000 – 12 000, 0}*max{20, 55}

= 12 000 * 125 – 14 000 * 80 + 2 000 * 55

= 1 500 000 – 1 120 000 + 110 000 = $490 000

Retailer’s expected profit:

Demand Probability

8000 10000 12000 14000 16000

8000 11% $360,000.00 $310,000.00 $260,000.00 $210,000.00 $160,000.00

10000 11% $360,000.00 $450,000.00 $400,000.00 $350,000.00 $300,000.00

12000 28% $360,000.00 $450,000.00 $540,000.00 $490,000.00 $440,000.00

14000 22% $360,000.00 $450,000.00 $540,000.00 $630,000.00 $580,000.00

16000 18% $360,000.00 $450,000.00 $540,000.00 $630,000.00 $720,000.00

18000 10% $360,000.00 $450,000.00 $540,000.00 $630,000.00 $720,000.00

Expected Profit $360,000.00 $434,600.00 $493,800.00 $513,800.00 $503,000.00

Profit for a Given Retailer Order Level



profit manufacturer:


profit = 12 000 * 80 – 100 000 – 12 000 * 35

– max{12 000 – 14 000, 0} * (55 – 20) [ aangezien 55 > 20 ]

= 960 000 – 100 000 – 420 000 – 0 = $440 000


profit = 14 000 * 80 – 100 000 – 14 000 * 35

– max{14 000 – 10 000, 0} * (55 – 20)

= 1 120 000 – 100 000 – 490 000 – 4 000*55 + 20*4 000

=120 000 – 100 000 – 490 000 – 220 000 + 80 000 = $390 000

Manufacturer’s expected profit:

Demand Probability

10000 12000 14000 16000 18000

8000 11% $280,000.00 $300,000.00 $320,000.00 $340,000.00 $360,000.00

10000 11% $350,000.00 $370,000.00 $390,000.00 $410,000.00 $430,000.00

12000 28% $350,000.00 $440,000.00 $460,000.00 $480,000.00 $500,000.00

14000 22% $350,000.00 $440,000.00 $530,000.00 $550,000.00 $570,000.00

16000 18% $350,000.00 $440,000.00 $530,000.00 $620,000.00 $640,000.00

18000 10% $350,000.00 $440,000.00 $530,000.00 $620,000.00 $710,000.00

Expected Profit $342,300.00 $416,900.00 $471,900.00 $511,500.00 $538,500.00



$,000

$200000,000

$400000,000

$600000,000

$800000,000

$1000000,000

$1200000,000

5000,0 8000,0 11000,0 14000,0 17000,0

Profit ($)

Quantity

retailer's profit

manufacturer's profit

total profit


Retailer: profit from $470 700 to $513 800;

quantity from 12 000 to 14 000

Manufacturer: profit from $440 000 to $ 471 900.

The total average profit increases from $910 700 with sequential

optimization to $985 700 (= $ 513 800 + $471 900) with buy-back contract

The buy-back contract is effective because it allows the manufacturer to

share some of the risk and thus incites the retailer to increase order quantity


Buyback

Downsides:

– effective reverse logistics needed

– Incentive for retailer for selling competing products

– Surplus inventory for the supplier that must be disposed of, which increases

supply chain costs

– Inflated retail orders, not actual customer demand

Most effective for products with low variable cost, such as music, software,

books, magazines and newspapers so that

– profit margin is high, product availability is critical

– consequence of supplier’s surplus inventory is little (or proof of destruction)

Which of these are true? Buyback contract increases the expected

supply chain profit / supplier profit / retailer profit / sales to the market /

sales to the retailer / demand


Swimsuit production Revenue-sharing contract

In the sequential supply chain, one important reason for the retailer to order

only 12 000 units is the high wholesale price. If somehow the retailer can

convince the manufacturer to reduce the wholesale price, then clearly the

retailer would order more. Of course, price reduction will decrease

manufacturer’s profit if the retailer is unable to sell more units. This issue is

addressed by revenue-sharing contracts.

Suppose the swimsuit manufacturer and retailer have a revenue-sharing

contract with the following conditions: the manufacturer reduces the price

he charges from $80 to $60 and the retailer transfers 15% of the sales

revenue back to the manufacturer in return.

Item Price



Salvage: $20.00

Revenue Sharing: 15%



Seller Buyer

8060

Manufacturer Retailer

15% of sales revenue

Transfer


Swimsuit production Revenue-sharing contract (2)

Profit retailer:


profit = min{10 000,12 000}*125*85% – 10 000*60

+ max{10 000 – 12 000,0}*20

= 1 062 500 – 600 000 = $462 500


profit = min{14 000, 12 000)*125*85% – 14 000*60

+ max{14 000 – 12 000, 0}*20

= 1 275 000 – 840 000 + 40 000 = $475 000

Retailer’s expected profit:

Demand Probability

8000 10000 12000 14000 16000

8000 11% $370,000.00 $290,000.00 $210,000.00 $130,000.00 $50,000.00

10000 11% $370,000.00 $462,500.00 $382,500.00 $302,500.00 $222,500.00

12000 28% $370,000.00 $462,500.00 $555,000.00 $475,000.00 $395,000.00

14000 22% $370,000.00 $462,500.00 $555,000.00 $647,500.00 $567,500.00

16000 18% $370,000.00 $462,500.00 $555,000.00 $647,500.00 $740,000.00

18000 10% $370,000.00 $462,500.00 $555,000.00 $647,500.00 $740,000.00

Expected Profit $370,000.00 $443,525.00 $498,075.00 $504,325.00 $472,625.00




Profit manufacturer:


profit = 12 000*60 – 100 000 – 12 000*35 + min{12 000,10 000}*125*15%

= 720 000 – 100 000 – 420 000 + 187 500 = $387 500


profit = 10 000*60 – 100 000 – 10 000*35 + min{10 000, 12 000}*125 *15%

= 600 000 – 100 000 – 350 000 + 187 500 = $337 500

Manufacturer’s expected profit:

Demand Probability

10000 12000 14000 16000 18000

8000 11% $300,000.00 $350,000.00 $400,000.00 $450,000.00 $500,000.00

10000 11% $337,500.00 $387,500.00 $437,500.00 $487,500.00 $537,500.00

12000 28% $337,500.00 $425,000.00 $475,000.00 $525,000.00 $575,000.00

14000 22% $337,500.00 $425,000.00 $512,500.00 $562,500.00 $612,500.00

16000 18% $337,500.00 $425,000.00 $512,500.00 $600,000.00 $650,000.00

18000 10% $337,500.00 $425,000.00 $512,500.00 $600,000.00 $687,500.00

Expected Profit $333,375.00 $412,625.00 $481,375.00 $541,875.00 $595,625.00




Retailer: profit from $470 700 to $504 325;

quantity from 12 000 to 14 000

Manufacturer: profit from $440 000 to $ 481 375.

The total average profit increases from $910 700 in the sequential supply

chain to $985 700 (= $504 325 + $481 375) with the revenue-sh. contract.

The reduction of the wholesale price coupled with revenue sharing leads to

increased profits for both parties.

$,000

$200000,000

$400000,000

$600000,000

$800000,000

$1000000,000

$1200000,000

5000,0 8000,0 11000,0 14000,0 17000,0

Profit ($)

Quantity

retailer's profit

manufacturer's profit

total profit


Revenue sharing

The buyer pays a minimal amount for each unit purchased from the

supplier but shares a fraction of the revenue for each unit sold

Decreases the cost per unit charged to the retailer, which effectively

decreases the cost of overstocking

When the overstocking cost drops, retailer’s order quantity rises

Misleading for the supply chain as it reacts to (inflated) retail orders, not to

actual customer demand

Supplier needs to monitor buyer’s revenue

Incentive for buyer for pushing competing products with higher margins


Blockbuster case

Demand for a newly released movie typically starts high and decreases

rapidly; peak demand lasts about 10 weeks

– Blockbuster purchases a copy from a studio for $65 and rents for $3.

Blockbuster (retailer) must rent the tape at least 22 times before earning profit

– Retailers cannot justify purchasing a movie (cassette) by covering the peak

demand. In 1998, 20% of surveyed customers reported that they could not rent

the movie they wanted because the Blockbuster stores did not have that movie.

In 1998, Blockbuster started revenue sharing with the major movie studios

– Studio charges $8 per copy.

– Blockbuster (retailer) shares a portion (30-45%) of of the sales revenue (rental

income) with the supplier

– Even if Blockbuster keeps only half of the rental income, the breakeven point is

6 rentals per copy

– The impact of revenue sharing on Blockbuster was dramatic. Rentals increased

by 75% in test markets due to higher video availability. Market share increased

from 25% to 31% (the 2nd largest retailer only has 5% market share)

http://www.blockbuster.com.br/


Swimsuit production Global optimization

Rather than investigate contracts modifying initial sales terms between two

parties, we now consider supplier and buyer as two partners / two members

of the same organization. In other words: we ignore the transfer of money

between the parties and an unbiased decision maker will maximize the

supply-chain profit.

The only relevant data in this case are the selling price, the salvage value,

the variable production costs, and the fixed production costs. The cost that

the manufacturer charges the retailer becomes meaningless, since we

consider them as one and we are only interested in external costs and

revenues.

Manufacturer

Retailer

Seller Buyer

125

20

100 000

35

80



Stores



Selling Price=$125

Salvage Value=$20

Wholesale Price =$80

Swimsuit production Global optimization (2)



Evidently, the supply chain marginal profit of 90 (= 125 – 35) is

significantly higher than the marginal loss of 15 (= 35 – 20), and hence the

supply chain will probably produce more than average demand.

Overall profit:

Stel demand = 10 000, order level = 12 000:

profit = min{12 000,10 000}*125 – 12 000 * 35 – 100 000

+ max{12 000 – 10 000, 0}*20

= 10 000*125 – 12 000*35 – 100 000 + 2 000*20

= 1 250 000 – 420 000 – 100 000 + 40 000 = $770 000

Item Price


Salvage: $20.00





Profit vs Order Quantity

$0.00

$200,000.00

$400,000.00

$600,000.00

$800,000.00

$1,000,000.00

$1,200,000.00

5,000 6,000 7,000 8,000 9,000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000

Quantity

System Profit ($)



Or

The risk the manufacturer can take should be smaller than 0.1429. The first

smaller value is 0.10 and it corresponds to production quantity of 16 000.

This is exactly the result we got using expected profit as a measure.

In this global optimization strategy, the optimal production quantity is

16 000, which implies an expected supply chain profit of $1 014 500.

1429.0105

15

9015

15]Pr[

uo

o

cc

cQD

Demand Probability Q Pr(D>Q)

8000 0.11 8 000 0.89

10000 0.11 10 000 0.78

12000 0.28 12 000 0.50

14000 0.22 14 000 0.28

16000 0.18 16 000 0.10

18000 0.10 18 000 0.00

Demand Probability Profit for a Given Order Level

10000 12000 14000 16000 18000

8000 11% $590,000.00 $560,000.00 $530,000.00 $500,000.00 $470,000.00

10000 11% $800,000.00 $770,000.00 $740,000.00 $710,000.00 $680,000.00

12000 28% $800,000.00 $980,000.00 $950,000.00 $920,000.00 $890,000.00

14000 22% $800,000.00 $980,000.00 $1,160,000.00 $1,130,000.00 $1,100,000.00

16000 18% $800,000.00 $980,000.00 $1,160,000.00 $1,340,000.00 $1,310,000.00

18000 10% $800,000.00 $980,000.00 $1,160,000.00 $1,340,000.00 $1,520,000.00

Expected Profit $776,900.00 $910,700.00 $985,700.00 $1,014,500.00 $1,005,500.00


Swimsuit production Globally optimal buy-back contract

The difficulty with global optimization is that it requires the firm to

surrender decision-making power to an unbiased (external) decision maker.

It can be shown, however, that carefully designed supply contracts achieve

exactly the same profit as global optimization.

Illustration: See Excel-sheet DMSCe3.xls

Buy back (bis): the retailer can either salvage goods or the manufacturer

will buy them back and salvage himself. Consider these parameters:

The wholesale price has decreased from $80 to $75 and the buy back value

has increased from $55 to $65. In this case, the retailer’s individual

optimum quantity and the globally optimum quantity coincide.

Item Price



Salvage: $20.00

Manufacturer buy back: $65.00




Other constructions

Quantity-flexibility contracts = buy back with full refund / allows the

buyer to modify the order (within limits) as demand visibility increases

towards the point of sale

– Better matching of supply and demand

– Increased overall supply chain profits if the supplier has flexible capacity

– Lower levels of misleading demand information than either buyback contracts

or revenue sharing contracts

– Benetton: 40% allowance on colors, 10% on aggregate quantity across colors;

guaranteed portion is manufactured by Benetton with an inexpensive but long-

lead-time process; the flexible part (about 35%) is manufactured using

postponement

Sales rebate contracts = rebate beyond certain quantity


SUPPLY CONTRACTS



3. Other issues


MTS instead of MTO

Fashion item “ski jackets”

Short life cycles, one production opportunity

Same data as before, but now MTS at supplier

Time line:

Contrary to the swimsuit example, the supplier now assumes all of the risk

of building more capacity than sales!

Jan 00 Jan 01 Jan 02

Feb 00 Sep 00 Sep 01

Design Production Retailing

Feb 01

Distributor

places order


Ski jackets

It is now the manufacturer who needs to decide a (production) quantity, and

thus faces a newsboy problem: lowest Q such that Pr[ D ≤ Q ] ≥ 0.2941 ?

Q = 12 000

Again, a variety of supply contracts enable risk sharing and hence reduce

manufacturer’s risk and motivate him to increase production.

– Pay-back contract: buyer pays a price for each unit produced but not purchased

– Cost-sharing contract: the buyer shares some of the production cost (e.g. set %)

in return for a discount on the wholesale price

– An issue here is the sharing of production-cost information. A possible

solution to this is for the retailer to purchase one or more components.

– Again, globally optimal solutions can be achieved

Item Price

Manufacturer sells for: $80,00

Distributor sells for: $125,00

Salvage: $20,00

Fixed Production Cost: $100.000,00

Variable Production Cost: $55,00

Demand Probability

8000 11%

10000 11%

12000 28%

14000 22%

16000 18%

18000 10%


SUPPLY CONTRACTS



3. Other issues


Asymmetric information

Implicit assumption so far: buyer and supplier share the same forecast

– Inflated forecasts from buyers a reality

– How to design contracts such that the information shared is credible?

Capacity Reservation Contract

– Buyer pays to reserve a certain level of capacity at the supplier

– A menu of prices for different capacity reservations provided by supplier

– Buyer signals true forecast by reserving a specific capacity level

Advance Purchase Contract

– Supplier charges special price before building capacity

– When demand is realized, price charged is different

– Buyer’s commitment to paying the special price reveals the buyer’s true

forecast


Contracts for non-strategic components

Traditionally, buyers have focused on long-term contracts for many of their

purchasing needs

Recently, trend towards more flexible contracts for non-strategic

components:

– Variety of suppliers

– Market conditions dictate price

– Flexibility more important than long-term relationship:

Reduce supply chain costs

Be more responsive and flexible to market conditions

Effective procurement strategy for commodity products has to focus on

both driving costs down and reducing risks:

– Inventory risk due to uncertain demand

– Price, or financial, risk due to volatile market price

– Shortage risk due to limited component availability


Contracts for non-strategic components (2)

Long-term contract = forward contract = fixed-commitment contract

– Supplier and buyer agree on both price and quantity (at a future time)

– Buyer bears no financial risk but takes huge inventory risks

Option contract:

– Supplier commits to reserve capacity up to a certain level

– “reservation price” / “premium” up front

– Buyer can purchase any amount of supply up to the option level

(execution price or exercise price)

– “flexible” contract: fixed amount of supply; can differ by given %

Spot market:

– Additional supply in the open market; “here and now”


Contracts for non-strategic components (3)

Portfolio approach to supply contracts: appropriate mix of the foregoing:

– “Base commitment” = long-term contracts;

– “option level” = options;

– remainder = uncommitted

LOW HIGH

LOW Price & shortage

risks for buyer

Inventory risk for

buyer

HIGH Inventory risk for

supplier n/a

base commitment level

option

level

50%

35% (Hewlett-Packard)

rest

scm_lessius chapter 4 - supply contracts

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