SCM_Lessius Chapter 4 - Supply Contracts

SUPPLY CONTRACTS

Sequential optimization vs. global optimization Sequential Optimization

Procurement Planning

Manufacturing Planning

Distribution Planning

Demand Planning

Global Optimization Supply Contracts/Collaboration/Information Contracts/Collaboration/Information Systems and DSS

Procurement Planning

Manufacturing Planning

Distribution Planning

Demand Planning

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

Coordination

1. Contracts for MTO supply chains 2. Contracts for MTS 3. Other issues

SUPPLY CONTRACTS

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 Scenarios

Demand Probability Weighted Demand

8000 10000 12000 14000 16000 18000 Average

11% 11% 28% 22% 18% 10%

880 1100 3360 3080 2880 1800 13100

30% 25%

y t i l i 20% b a 15% b o 10% r P

5% 0% 8000 10000 12000 14000 16000 18000 Sales

Swimsuits Chapter 2

Fixed Production Cost =$100,000 Variable Production Cost=$35

Wholesale Price =$80 Selling Price=$125 Salvage Value=$20 Manufacturer

Manufacturer DC

Retail DC

Stores

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. Demand Probability

8000 10000 12000 14000 16000 18000 Average

11% 11% 28% 22% 18% 10%

Production = 10 000

$140,000.00 $350,000.00 $350,000.00 $350,000.00 $350,000.00 $350,000.00 $326,900.00



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.


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

Expected Profit $370,700.00

$400,000 $350,000 $300,000 $250,000

$200,000 $150,000 $100,000 $50,000 $0 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 Production Quantity



We can also do this without graph:


Let cu = 60, co = 45. We look for the smallest Q such that Pr[ D  Q] 

co co

 cu



60 60  45



60 105



0.5714

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. Demand

Probability

8000 10000 12000 14000 16000 18000

0.11 0.11 0.28 0.22 0.18 0.10

(while average demand is 13 100)

Q

8 000 10 000 12 000 14 000 16 000 18 000

Pr(D>Q)

0.89 0.78 0.50 0.28 0.10 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. Demand

8000 10000 12000 14000 16000 18000 Average Profit

Probability 11% 11% 28% 22% 18% 10%

Profit for a Given Production Level 9000 12000 16000 $200,000.00 $20,000.00 -$220,000.00 $305,000.00 $230,000.00 -$10,000.00 $305,000.00 $440,000.00 $200,000.00 $305,000.00 $440,000.00 $410,000.00 $305,000.00 $440,000.00 $620,000.00 $305,000.00 $440,000.00 $620,000.00 $293,450.00 $370,700.00 $294,500.00

100% 90% y t i l i b a b o r P

80% 70%

Q=9000

60%

Q=16000

50% 40% 30% 20% 10% 0% Revenue



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.


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.

Swimsuits Chapter 4


Wholesale Price =$?? Selling Price=$125 Salvage Value=$20 Manufacturer

Manufacturer DC

Retail DC

Stores

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

Seller fixed production cost: 100 000

wholesale price: 80

Manufacturer variable production cost: 35

Buyer

Retailer

selling price: 125 salvage value: 20

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

Buyer

Seller 100 000 35 20

M

Retailer sells for: Manufacturer sells for: Salvage: Manufacturer buy back: Fixed Production Cost: Variable Production Cost:

80 55

R

125

Price

$125.00 $80.00 $20.00 $55.00 $100,000.00 $35.00

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 – demand = 12 000, order level = 14 000

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 8000 10000 12000 14000 16000 18000 Expected Profit

Probability 11% 11% 28% 22% 18% 10%

8000 $360,000.00 $360,000.00 $360,000.00 $360,000.00 $360,000.00 $360,000.00 $360,000.00

Profit for a Given Retailer Order Level 10000 12000 14000 $310,000.00 $260,000.00 $210,000.00 $450,000.00 $400,000.00 $350,000.00 $450,000.00 $540,000.00 $490,000.00 $450,000.00 $540,000.00 $630,000.00 $450,000.00 $540,000.00 $630,000.00 $450,000.00 $540,000.00 $630,000.00 $434,600.00 $493,800.00 $513,800.00

16000 $160,000.00 $300,000.00 $440,000.00 $580,000.00 $720,000.00 $720,000.00 $503,000.00


profit manufacturer: – demand = 14 000, order level = 12 000

profit = 12 000 * 80 – 100 000 – 12 000 * 35 [ aangezien 55 > 20 ] – max{12 000 – 14 000, 0} * (55 – 20) = 960 000 – 100 000 – 420 000 – 0 = $440 000 – demand = 10 000, order level = 14 000

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 8000 10000 12000 14000 16000 18000 Expected Profit

Probability 11% 11% 28% 22% 18% 10%

10000 $280,000.00 $350,000.00 $350,000.00 $350,000.00 $350,000.00 $350,000.00 $342,300.00

Profit for a 12000 $300,000.00 $370,000.00 $440,000.00 $440,000.00 $440,000.00 $440,000.00 $416,900.00

Given Retailer Order Level 14000 16000 $320,000.00 $340,000.00 $390,000.00 $410,000.00 $460,000.00 $480,000.00 $530,000.00 $550,000.00 $530,000.00 $620,000.00 $530,000.00 $620,000.00 $471,900.00 $511,500.00

18000 $360,000.00 $430,000.00 $500,000.00 $570,000.00 $640,000.00 $710,000.00 $538,500.00




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 Profit ($) $1200000,000 $1000000,000 $800000,000

retailer's profit $600000,000

manufacturer's profit total profit

$400000,000 $200000,000 $,000 5000,0

8000,0

11000,0

Quantity

14000,0

17000,0

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. 8060

Item

Seller

Buyer

Manufacturer

Retailer Transfer

15% of sales revenue

Retailer sells for: Manufacturer sells for: Salvage: Revenue Sharing: Fixed Production Cost: Variable Production Cost:

Price

$125.00 $60.00 $20.00 15% $100,000.00 $35.00

Swimsuit production Revenue-sharing contract (2) 

Profit retailer: – demand = 12 000, order level = 10 000

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 – demand = 12 000, order level = 14 000

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 8000 10000 12000 14000 16000 18000 Expected Profit

Probability 11% 11% 28% 22% 18% 10%

8000 $370,000.00 $370,000.00 $370,000.00 $370,000.00 $370,000.00 $370,000.00 $370,000.00


16000 $50,000.00 $222,500.00 $395,000.00 $567,500.00 $740,000.00 $740,000.00 $472,625.00


Profit manufacturer: – demand = 10 000, order level = 12 000

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 – demand = 12 000, order level = 10 000

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 8000 10000 12000 14000 16000 18000 Expected Profit

Probability 11% 11% 28% 22% 18% 10%

10000 $300,000.00 $337,500.00 $337,500.00 $337,500.00 $337,500.00 $337,500.00 $333,375.00


18000 $500,000.00 $537,500.00 $575,000.00 $612,500.00 $650,000.00 $687,500.00 $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. Profit ($) $1200000,000 $1000000,000 $800000,000

retailer's profit

$600000,000

manufacturer's profit total profit

$400000,000 $200000,000 $,000 5000,0

8000,0

11000,0

14000,0

Quantity

17000,0

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)

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. Seller 100 000 35

Manufacturer

Buyer 80

Retailer

125 20

Swimsuit production Global optimization (2)


Wholesale Price =$80 Selling Price=$125 Salvage Value=$20 Manufacturer

Manufacturer DC

Retail DC

Stores

Swimsuit production Global optimization (3) 

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. Item

Retailer sells for: Salvage: Fixed Production Cost: Variable Production Cost:



Price

$125.00 $20.00 $100,000.00 $35.00

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

Swimsuit production Global optimization (4)

System Profit ($)

Profit vs Order Quantity

$1,200,000.00 $1,000,000.00 $800,000.00 $600,000.00 $400,000.00 $200,000.00 $0.00 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

Swimsuit production Global optimization (5) Demand 8000 10000 12000 14000 16000 18000 Expected Profit



Or

Probability Profit for a Given Order Level 10000 12000 14000 11% $590,000.00 $560,000.00 $530,000.00 11% $800,000.00 $770,000.00 $740,000.00 28% $800,000.00 $980,000.00 $950,000.00 22% $800,000.00 $980,000.00 $1,160,000.00 18% $800,000.00 $980,000.00 $1,160,000.00 10% $800,000.00 $980,000.00 $1,160,000.00 $776,900.00 $910,700.00 $985,700.00

Pr[ D  Q] 

co co

Demand

Probability

8000 10000 12000 14000 16000 18000

0.11 0.11 0.28 0.22 0.18 0.10

 cu Q

8 000 10 000 12 000 14 000 16 000 18 000



15 15  90



16000 $500,000.00 $710,000.00 $920,000.00 $1,130,000.00 $1,340,000.00 $1,340,000.00 $1,014,500.00

15 105



18000 $470,000.00 $680,000.00 $890,000.00 $1,100,000.00 $1,310,000.00 $1,520,000.00 $1,005,500.00

0.1429

Pr(D>Q)

0.89 0.78 0.50 0.28 0.10 0.00

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.

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: Item

Retailer sells for: Manufacturer sells for: Salvage: Manufacturer buy back: Fixed Production Cost: Variable Production Cost:

Price

$125.00 $75.00 $20.00 $65.00 $100,000.00 $35.00

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.

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 longlead-time process; the flexible part (about 35%) is manufactured using postponement 

Sales rebate contracts = rebate beyond certain quantity

1. Contracts for MTO supply chains

2. Contracts for MTS 3. Other issues

SUPPLY CONTRACTS

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:

Jan 00

Jan 01

Feb 00

Design Sep 00

Jan 02

Production Feb 01

Retailing

Sep 01

Distributor places order



Contrary to the swimsuit example, the supplier now assumes all of the risk of building more capacity than sales!

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 ? Item

Price

Manufacturer sells for: Distributor sells for: Salvage: Fixed Production Cost: Variable Production Cost:

$80,00 $125,00 $20,00 $100.000,00 $55,00

Demand Probability 8000 11% 10000 11% 12000 28% 14000 22% 16000 18% 18000 10%

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

1. Contracts for MTO supply chains 2. Contracts for MTS

3. Other issues

SUPPLY CONTRACTS

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”

SCM_Lessius Chapter 4 - Supply Contracts

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