Supply Chain Management

Supply Chain Management 


Produkt- und Prozessdesign 

Informationsverarbeitung 

Versorgungssicherheit und Lagerhaltung 

Simchi Levi (2005) Managing the Supply chain,… 

Unternehmensnetzwerke 

1

Supply 

Sources: 

plants 

vendors 

ports 

Production/ 

purchase 

costs 

Regional 

Warehouses: 

stocking 

points 

Inventory & 

warehousing 

costs 

Field 

Warehouses: 

stocking 

points 

Transportation 

costs 

Inventory & 

warehousing 

costs 


Customers, 

demand 

centers 

sinks 

Transportation 

costs 

2

Inventory 

Where do we hold inventory? 

Suppliers and manufacturers 

warehouses and distribution centers 

retailers 

Types of Inventory 

WIP 

raw materials 

finished goods 

Why do we hold inventory? 

Economies of scale 

Uncertainty in supply and demand 


3

Understanding Inventory 

The inventory policy is affected by: 

Demand Characteristics 

Lead Time 

Number of Products 

Objectives 

Service level 

Minimize costs 

Cost Structure 


4

Cost Structure 

Order costs 

Fixed 

Variable 

Holding Costs 

Insurance 

Maintenance and Handling 

Taxes 

Opportunity Costs 

Obsolescence 


5

EOQ: A Simple Model* 

Book Store Mug Sales 

Demand is constant, at 20 units a week 

Fixed order cost of $12.00, no lead time 

Holding cost of 25% of inventory value annually 

Mugs cost $1.00, sell for $5.00 

Question 

How many, when to order? 


6

EOQ: A View of Inventory* 

Note: 

• No Stockouts 

• Order when no inventory 

• Order Size determines policy 

Order 

Size 

Inventory 


Avg. Inven 

Time 

7

EOQ: Calculating Total Cost* 

Purchase Cost Constant 

Holding Cost: (Avg. Inven) * (Holding Cost) 

Ordering (Setup Cost): 

Number of Orders * Order Cost 

Goal: Find the Order Quantity that Minimizes These Costs: 


8

EOQ:Total Cost* 

Cost 

160 

140 

120 

100 

80 

60 

40 

20 

0 

Total Cost 

0 500 1000 1500 

Order Quantity 


Order Cost 

Holding Cost 

9

EOQ: Optimal Order Quantity* 

Optimal Quantity = 

Q= 2∗Demand∗Setup Cost 

holding cost 

So for our problem, the optimal quantity is 316 


10

EOQ: Important Observations* 

Tradeoff between set-up costs and holding costs when determining 

order quantity. In fact, we order so that these costs are equal per unit 

time 

Total Cost is not particularly sensitive to the optimal order quantity 

Order Quantity 50% 80% 90% 100% 110% 120% 150% 200% 

Cost Increase 125% 103% 101% 100% 101% 102% 108% 125% 


11

The Effect of Demand Uncertainty 

Most companies treat the world as if it were predictable: 

Production and inventory planning are based on forecasts of demand 

made far in advance of the selling season 

Companies are aware of demand uncertainty when they create a 

forecast, but they design their planning process as if the forecast 

truly represents reality 

Recent technological advances have increased the level of demand 

uncertainty: 

Short product life cycles 

Increasing product variety 


12

Demand Forecasts 

The three principles of all forecasting techniques: 

Forecasting is always wrong 

The longer the forecast horizon the worst is the forecast 

Aggregate forecasts are more accurate 


13

SnowTime Sporting Goods 

Fashion items have short life cycles, high variety of competitors 

SnowTime Sporting Goods 

New designs are completed 

One production opportunity 

Based on past sales, knowledge of the industry, and economic 

conditions, the marketing department has a probabilistic forecast 

The forecast averages about 13,000, but there is a chance that 

demand will be greater or less than this. 


14

SnowTime Demand Scenarios 

Probability 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

8000 

Demand Scenarios 

10000 

12000 

14000 

Sales 

16000 


18000 

15

SnowTime Costs 

Production cost per unit (C): $80 

Selling price per unit (S): $125 

Salvage value per unit (V): $20 

Fixed production cost (F): $100,000 

Q is production quantity, D demand 

Profit = 

Revenue - Variable Cost - Fixed Cost + Salvage 


16

SnowTime Scenarios 

Scenario One: 

Suppose you make 12,000 jackets and demand ends up being 

13,000 jackets. 

Profit = 125(12,000) - 80(12,000) - 100,000 = $440,000 

Scenario Two: 

Suppose you make 12,000 jackets and demand ends up being 

11,000 jackets. 

Profit = 125(11,000) - 80(12,000) - 100,000 + 20(1000) = $ 335,000 


17

SnowTime Best Solution 

Find order quantity that maximizes weighted average profit. 

Question: Will this quantity be less than, equal to, or greater than 

average demand? 


18

What to Make? 

Question: Will this quantity be less than, equal to, or greater than 

average demand? 

Average demand is 13,100 

Look at marginal cost Vs. marginal profit 

if extra jacket sold, profit is 125-80 = 45 

if not sold, cost is 80-20 = 60 

So we will make less than average 

Optimal quantity? 

 


19

SnowTime Expected Profit 

Profit 

$400,000 

$300,000 

$200,000 

$100,000 

Expected Profit 

$0 

8000 12000 16000 20000 



20


Profit 

$400,000 

$300,000 

$200,000 

$100,000 


$0 

8000 12000 16000 20000 



21


Profit 

$400,000 

$300,000 

$200,000 

$100,000 


$0 

8000 12000 16000 20000 



22

SnowTime: 

Important Observations 

Tradeoff between ordering enough to meet demand and ordering too 

much 

Several quantities have the same average profit 

Average profit does not tell the whole story 

Question: 9000 and 16000 units 

lead to about the same average 

profit, so which do we prefer? 


23

Probability of Outcomes 

Probability 

100% 

80% 

60% 

40% 

20% 

0% 

-300000 

-100000 

100000 

300000 

Cost 

500000 


Q=9000 

Q=16000 

24

Key Points from this Model 

The optimal order quantity is not necessarily equal to average 

forecast demand 

The optimal quantity depends on the relationship between marginal 

profit and marginal cost 

As order quantity increases, average profit first increases and then 

decreases 

As production quantity increases, risk increases. In other words, the 

probability of large gains and of large losses increases 


25

Initial Inventory 

Suppose that one of the jacket designs is a model produced last year. 

Some inventory is left from last year 

Assume the same demand pattern as before 

If only old inventory is sold, no setup cost 

Question: If there are 7000 units remaining, what should SnowTime do? 

What should they do if there are 10,000 remaining? 


26

Initial Inventory and Profit 

Profit 

500000 

400000 

300000 

200000 

100000 

0 

5000 

6500 

8000 

9500 

11000 

12500 


14000 

Production Quantity 

15500 

27


Profit 

500000 

400000 

300000 

200000 

100000 

0 

5000 

6500 

8000 

9500 

11000 

12500 

14000 



15500 

28


Profit 

500000 

400000 

300000 

200000 

100000 

0 

5000 

6500 

8000 

9500 

11000 

12500 

14000 



15500 

29


Profit 

500000 

400000 

300000 

200000 

100000 

0 

5000 

6000 

7000 

8000 

9000 

10000 

11000 

12000 

13000 

14000 



15000 

16000 

30

(s, S) Policies 

For some starting inventory levels, it is better not to start production 

If we start, we always produce to the same level 

Thus, we use an (s,S) policy. If the inventory level is below s, we 

produce up to S. 

s is the reorder point, and S is the order-up-to level 

The difference between the two levels is driven by the fixed costs 

associated with ordering, transportation, or manufacturing 


31

A Multi-Period Inventory Model 

Often, there are multiple reorder opportunities 

Consider a central distribution facility which orders from a manufacturer 

and delivers to retailers. The distributor periodically places orders to 

replenish its inventory 


32

Case Study: Electronic Component Distributor 

Electronic Component Distributor 

Parent company HQ in Japan with world-wide manufacturing 

All products manufactured by parent company 

One central warehouse in U.S. 


33

Case Study:The Supply Chain 


34

Demand Variability: Example 1 

Demand 

(000's) 

250 

200 

150 

100 

50 

0 

Apr 

150 

75 

Jun 

Product Demand 

225 

150 

100 

Aug 

50 

Oct 

125 

Month 


61 48 53 

Dec 

Feb 

104 

45 

35

Demand Variability: Example 1 

Frequency 

25 

20 

15 

10 

5 

0 

$25,000 

Histogram for Value of Orders Placed in a Week 

$50,000 

$75,000 

$100,000 

$125,000 


$150,000 

Value of Orders Placed in a Week 

$175,000 

$200,000 

36

Reminder: 

The Normal Distribution 

Standard Deviation = 10 

Average = 30 

0 10 20 30 40 50 60 


Standard Deviation = 5 

37

The distributor holds inventory to: 

Satisfy demand during lead time 

Protect against demand uncertainty 

Balance fixed costs and holding costs 


38

The Multi-Period Inventory Model 

Normally distributed random demand 

Fixed order cost plus a cost proportional to amount ordered. 

Inventory cost is charged per item per unit time 

If an order arrives and there is no inventory, the order is lost 

The distributor has a required service level. This is expressed as the the 

likelihood that the distributor will not stock out during lead time. 

Intuitively, what will a good policy look like? 


39

A View of (s, S) Policy 

Inventory Level 

S 

s 

0 

Lead 

Time 

Inventory Position 


Time 

40

The (s,S) Policy 

(s, S) Policy: Whenever the inventory position drops below a certain 

level, s, we order to raise the inventory position to level S. 

The reorder point is a function of: 

The Lead Time 

Average demand 

Demand variability 

Service level 


41

Notation 

AVG = average daily demand 

STD = standard deviation of daily demand 

LT = replenishment lead time in days 

h = holding cost of one unit for one day 

SL = service level (for example, 95%). This implies that the probability of 

stocking out is 100%-SL (for example, 5%) 

Also, the Inventory Position at any time is the actual inventory plus 

items already ordered, but not yet delivered. 


42

Analysis 

The reorder point has two components: 

To account for average demand during lead time: 

LT×AVG 

To account for deviations from average (we call this safety stock) 

z *STD * LT 

where z is chosen from statistical tables to ensure that the probability 

of stockouts during leadtime is 100%-SL. 

Popular z-values?? 

0.95 

0.99 


43

Example 

The distributor has historically observed weekly demand of: 

AVG = 44.6 STD = 32.1 

Replenishment lead time is 2 weeks, and desired service level SL = 97% 

Average demand during lead time is: 

44.6 × 2 = 89.2 

Safety Stock is: 

1.88 × 32.1 × 2 

= 85.3 

Reorder point is thus 175, or about 3.9 weeks of supply at warehouse 

and in the pipeline 


44

Safety Stocks for HP Deskjet 


45

Model Two: 

Fixed Costs* 

In addition to previous costs, a fixed cost K is paid every time an order is 

placed. 

We have seen that this motivates an (s,S) policy, where reorder point 

and order quantity are different. 

The reorder point will be the same as the previous model, in order to 

meet meet the service requirement: 

s = LT * AVG + z * AVG * L 

 

What about the order up to level? 


46

Model Two: 

The Order-Up-To Level* 

We have used the EOQ model to balance fixed, variable costs: 

 

 

If there was no variability in demand, we would order Q when inventory 

level was at LT×AVG. Why? 

There is variability, so we need safety stock 

 

Q= 2*K*AVG 

h 

z * AVG * LT 

The total order-up-to level is: 

S=max{Q, AVG * LT} + z * AVG * LT 


47

Model Two: Example* 

Consider the previous example, but with the following additional info: 

fixed cost of $4500 when an order is placed 

$250 product cost 

holding cost 18% of product 

Weekly holding cost: 

h = (.18 × 250) / 52 = 0.87 

Order quantity 

Q = 2∗4500∗44.6/0.87=679 

Order-up-to level: 

safety stock + Q = 85 + 679 = 765 


48

Risk Pooling 

Consider these two systems: 

Supplier 

Supplier 

Warehouse One 

Warehouse Two 

Warehouse 


Market One 

Market Two 

Market One 

Market Two 

49

Risk Pooling 

For the same service level, which system will require more inventory? 

Why? 

For the same total inventory level, which system will have better service? 

Why? 

What are the factors that affect these answers? 


50

Risk Pooling Example 

Compare the two systems: 

two products 

maintain 97% service level 

$60 order cost 

$.27 weekly holding cost 

$1.05 transportation cost per unit in decentralized system, $1.10 in 

centralized system 

1 week lead time 


51


Week 1 2 3 4 5 6 7 8 

Prod A, 

Market 1 

Prod A, 

Market 2 

Prod B, 

Market 1 

Product B, 

Market 2 

33 45 37 38 55 30 18 58 

46 35 41 40 26 48 18 55 

0 2 3 0 0 1 3 0 

2 4 0 0 3 1 0 0 


52


Warehouse Product AVG STD CV 

Market 1 A 39.3 13.2 .34 

Market 2 A 38.6 12.0 .31 

Market 1 B 1.125 1.36 1.21 

Market 2 B 1.25 1.58 1.26 


53


Warehouse Product AVG STD CV s S Avg. 

Inven. 

Market 1 A 39.3 13.2 .34 65 158 91 

Market 2 A 38.6 12.0 .31 62 154 88 

Market 1 B 1.125 1.36 1.21 4 26 15 

Market 2 B 1.25 1.58 1.26 5 27 15 


% 

Dec. 

Cent. A 77.9 20.7 .27 118 226 132 26% 

Cent B 2.375 1.9 .81 6 37 20 33% 

54

Risk Pooling: 

Important Observations 

Centralizing inventory control reduces both safety stock and average 

inventory level for the same service level. 

This works best for 

High coefficient of variation, which reduces required safety stock. 

Negatively correlated demand. Why? 

What other kinds of risk pooling will we see? 


55

Risk Pooling: 

Types of Risk Pooling* 

Risk Pooling Across Markets 

Risk Pooling Across Products 

Risk Pooling Across Time 

Daily order up to quantity is: 

LT×AVG + z × AVG × √LT 

Orders 

10 11 12 13 14 15 


Demands 

56

To Centralize or not to Centralize 

What is the effect on: 

Safety stock? 

Service level? 

Overhead? 

Lead time? 

Transportation Costs? 


57

Centralized Systems* 

Centralized Decision 

Supplier 

Warehouse 


Retailers 

58

Centralized Distribution Systems* 

Question: How much inventory should management keep at each 

location? 

A good strategy: 

The retailer raises inventory to level S r each period 

The supplier raises the sum of inventory in the retailer and 

supplier warehouses and in transit to S s 

If there is not enough inventory in the warehouse to meet all 

demands from retailers, it is allocated so that the service level at 

each of the retailers will be equal. 


59

The Value of Information 

Information age: potential availability of more information 

throughout the supply chain 

in modern supply chains, information replaces inventory 

customers needs products not just information 

how can information affect the design and operation of 

supply chains? 


60

The Value of Information 

Information 

helps reduce variability in the SC 

helps suppliers make better forecasts 

enables the coordination of manufacturing and distribution 

systems 

enables retailes for locating desired items 

enables retailers to react more rapidly 

enables lead time reductions 


61

The Value of Information - 

The Bullwhip Effect 

Observation: while customer 

demand for specific products does 

not vary much, inventory and backorder 

levels fluctuate considerably 

across their supply chains 

ORDER LT 

ORDER LT 

ORDER LT 


EXTERNAL DEMAND 

RETAILER 

WHOLESALER 

ORDER LT 

DISTRIBUTOR 

FACTORY 

DELIVERY LT 

DELIVERY LT 

DELIVERY LT 

DELIVERY LT 

62


Demand 

70 

60 

50 

40 

30 

20 

10 

0 

Order Variations 

1 5 9 13 17 21 25 29 33 37 41 45 

Period 


Sales 

Orders 

63



Main factors contributing to the increase in variability 

Demand forecasting 

forecasting and inventory control (min-max) 

Lead Time 

with long lead times a small change in the estimate of demand variability 

implies a significant change in in safety stock 

why? 

Batch Ordering 

distorted high variable pattern of orders (min-max) 

transportation discounts 

sales quotas 


64



Price fluctuations 

stock up 

Inflated Orders 

shortage periods 


65


(Christopher, 1992, S.173): nicht koordinierte Bestellungen 


66



67



Methods for Coping with the Bullwhip Effect 

Reducing Uncertainty 

Reducing Variability 

Every Day Low Pricing 

Lead Time Reduction 

Order Lead Time 

Information Lead Time 

Strategic Partnership 

VMI 


68

Bullwhip Reduktion: 

Erklärungszugänge 

Varianz Reduktion 

Schock 

Filter 


69



Effective Forecasts 

Cooperative Forecasts systems 

CPFR “Collaborative planning, forecasting and 

replenishment systems” 

Information for the coordination of systems 

consider the entire system and coordinate decisions 

POS data 

need for cooperation 

who will optimize??? 

How will the savings be split between the different 

supply chain facilities 

Locating desired Products 


70



Lead Time Reduction 

ability to quickly fill customer orders 

reduction in the bullwhip effect 

more accurate forecasts 

reduction in finished goods inventory 


71

Strategic Partnering 

Quick Response: Vendors receive POS data from retailers, and use this 

information to synchronize production and inventory activities at the supplier. In this 

strategy, the retailer still prepares individual orders, but the POS data is used by 

the supplier to improve forecasting and scheduling. 

Continuous Replenishment: Vendors receive POS data and use it prepare 

shipments at previously agreed upon intervals to maintain agreed to levels of 

inventory. 

Advanced Continuous Replenishment: Suppliers may gradually decrease 

inventory levels at the retailer’s store or distribution center as long as service levels 

are met. Inventory levels are thus continuously improved in a structured way. 

Vendor Managed Inventory (VMI):JITD 


72

Requirements for Effective SP 

Advanced information systems 

Top management commitment 

Mutual trust 


73

Important SP Issues 

Inventory ownership: 

Supplier owns the goods until they are sold 

Retailer owns the goods 

Performance measures: Fill rate, inventory level, inventory turns 


74

Important SP Issues 

Confidentiality 

Communication and cooperation 

When First Brands started partnering with Kmart, Kmart often 

claimed that its supplier was not living up to its agreement to 

keep two weeks of inventory at all times. It turned out that this 

was due to the fact that the two companies employed different 

forecasting methods. 


75

Steps in SP Implementation 

Contractual negotiations 

Ownership 

Credit terms 

Ordering decisions 

Performance measures 

Develop or integrate information systems 

Develop effective forecasting techniques 

Develop a tactical decision support tool to assist in coordinating inventory management 

and transportation policies 


76

Case Study: Barilla SpA 

1) Barilla SpA Part A 

2) Barilla SpA Parts B, C and D 

3) Strategic Partnership 

Reading: 

1. Barilla SpA, Harvard Business School, 9-694-046 


77

Barilla SpA Part A 

Barilla SpA is the world’s largest pasta manufacturer 

The company sells to a wide range of Italian retailers, primarily through third party distributors 

During the late 1980s, Barilla suffered increasing operational inefficiencies and cost penalties that 

resulted from large week-to-week variations in its distributors’ order patterns 


78


79

Exhibit 12 Weekly Demand for Barilla Dry Products from Cortese’s Northeast Distribution Center to the 

Pedrignano CDC, 1989. 


80

• What exactly is causing the distributor’s order 

pattern to look this way? 

• What are the underlying drivers of the 

fluctuations? 


81

Causes for Demand Fluctuations 

Transportation discounts 

Volume discount 

Promotional activity 

No minimum or maximum order quantities 

Product proliferation 

Long order lead times 

Poor customer service rates 

Poor communication 


82

Demand Fluctuations 

The extreme fluctuation in Exhibit 12 is truly remarkable when one considers the underlying 

aggregate demand for pasta in Italy. 

What does the underlying consumer demand pattern for pasta look like in Italy? 


83

Demand Fluctuations 

What are the differences and similarities between the Barilla channel and the beer distribution 

channel? 


84

What is the impact of demand fluctuation seen in Exhibit 12? 

Because the plant has high product change over costs, Barilla has either inefficient 

production or excess finished goods inventory 

Utilization of central distribution is low 

Workers 

Equipment 

Transportation costs are higher than necessary 


85

What is the impact of demand fluctuation seen in Exhibit 12? 

The distributor must build excess capacity to hold goods bought on any type of 

promotion, including quantity discounts, truckload discounts and canvass period 

discounts 

What if the distributor passes the discount along to the retailers? 

What is the value of the promotion game? 


86

Barilla SpA Part A (continued) 

To address this problem, the director of logistics suggests the implementation of Just-in-Time 

Distribution (JITD), with Barilla’s distributors. 

Under the proposed JITD system, decision-making authority for determining shipments from 

Barilla to a distributor would transfer from the distributor to Barilla. 

Specifically, rather than simply filling orders specified by the distributor, Barilla would monitor 

the flow of its product through the distributor’s warehouse, and then decide what to ship to the 

distributor and when to ship it. 


87

Evaluation of the JITD Proposal 

Clearly the variation in demand is imposing additional costs on the channel. What do you think of 

the JITD proposal as a mechanism for reducing these costs? 

Why should this work? 

How does it work? 

What makes Barilla think that it can do a better job of determining a good product/delivery 

sequence than its distributors? 


88

Two Key Concepts Behind JITD 

Replace Sequential optimization with Joint optimization 

Who will optimize? 

Eliminate some of the ‘false’ economics that drive traditional ordering processes 


89

Implementation Issues 

Resistance from the Distributors 

“Managing stock is my job; I don’t need you to see my warehouse or my figures.” 

“I could improve my inventory and service level myself if you would deliver my orders more 

quickly; I would place my order and you would deliver within 36 hours.” 

“We would be giving Barilla the power to push products into our warehouse just so that Barilla 

can reduce its costs.” 

? 


90

Implementation Issues 

Resistance from Sales and Marketing 

“Our sales levels would flatten if we put this program in place.” 

“How can we get the trade to push Barilla product to retailers if we don’t offer some sort of 

incentive?” 

“If space is freed up in our distributors’ warehouses…the distributors would then push our 

competitors’ product more than ours.” 

“…the distribution organization is not yet ready to handle such a sophisticated relationship.” 

? 


91

How Can Maggiali 

Solve the Implementation Problems? 

Demonstrate that JITD benefits the distributors (lowering inventory, improving their service levels 

and increasing their returns on assets); Run experiment at one or more of Barilla’s 18 depots 

Maggiali needs to look at JITD not as a logistics program, but as a company-wide effort; Get top 

management closely involved 

Trust 


92

Barilla (B) Case 

What did Barilla learn from the experiments in Florence and Milan? 

How should Barilla change the way it attempts to sell the JITD concept to its distributors? 

If you were a Barilla distributor, would you sign onto the program after seeing these results? 


93


94


95


96


97

How do you evaluate the implementation process Barilla used with Cortese? 


98


- Vertragsdesign 

Lieferant nennt zuerst den Preis und der Händler entscheidet über 

die Menge,die er zum Verkauf übernimmt. 

Händler kann Menge q ≥ 

p( 

q) 

≥ 

Lieferant hat Herstellkosten 

von 

zum Preis w 

0 zu einem Preis 

0 verkaufen, 

maximale Menge : q : p( 

q) 

= 0 

c ≤ 

p( 

0) 

und verkauft 

Ziel: maximiere Gewinn der Wertschöpfungskette 

•Gewinn der gesamten Kette 

•isolierte Betrachtung 


∧ 

∧ 

99

100 



Kette 

gesamte 

die 

für 

Menge 

optimale 

0 

) 

( 

) 

( 

) 

) 

( 

( 

) 

( 

) 

( 

) 

) 

( 

( 

) 

( 

) 

( 

) 

( 

) 

( 

0 

0 

' 

0 

0 

 

q 

q 

p 

q 

c 

q 

p 

c 

q 

p 

q 

q 

c 

w 

q 

w 

q 

p 

q 

q 

q 

q 

q s 

r 

= 

+ 

− 

− 

= 

Π 

− 

+ 

− 

= 

Π 

Π 

+ 

Π 

= 

Π

101 



* 

0 

0 

' 

0 

0 

* 

* 

' 

* 

* 

0 

) 

( 

) 

( 

: 

Händler 

den 

für 

g 

Betrachtun 

isolierter 

bei 

Menge 

optimale 

0 

) 

( 

) 

( 

) 

) 

( 

( 

) 

( 

: 

Händler 

q 

q 

q 

p 

q 

w 

q 

p 

c 

w 

q 

q 

p 

q 

w 

q 

p 

w 

q 

p 

q 

q 

r 

> 

< 

+ 

− 

> 

= 

+ 

− 

− 

= 

Π

102 



Beispiel: 

24 

; 

8 

32 

3 

8 

5) 

- 

(6 

8 

(8) 

8 

q 

0 

q/2 

- 

2 

- 

q/2 

- 

10 

: 

Kette 

gesamte 

2 

/ 

1 

) 

( 

2 

5 

2 

/ 

10 

) 

( 

: 

Daten 

r 

= 

Π 

= 

Π 

= 

⋅ 

+ 

⋅ 

= 

Π 

= 

= 

− 

= 

′ 

= 

= 

− 

= 

s 

q 

p 

c 

w 

q 

q 

p

103 



Händler: 

; 

2 

5 

. 

27 

15 

3 

5 

) 

( 

5 

. 

12 

) 

5 

5 

. 

7 

( 

5 

) 

) 

( 

( 

) 

( 

5 

0 

2 

/ 

5 

2 

/ 

10 

) 

) 

( 

( 

) 

( 

* = 

= 

⎭ 

⎬ 

⎫ 

= 

⋅ 

= 

Π 

= 

− 

⋅ 

= 

− 

= 

Π 

= 

= 

− 

− 

− 

− 

= 

Π 

w 

q 

w 

q 

p 

q 

q 

q 

q 

q 

w 

q 

p 

q 

q 

s 

r 

r 

Lieferant: 

0 

) 

( 

; 

32 

) 

( 

; 

6 

32 

) 

2 

( 

8 

? 

* 

= 

Π 

= 

Π 

= 

= 

− 

= 

q 

q 

w 

w 

w 

r 

s


Lösung: Vertrag zur Gewinnaufteilung 

Supply chain Verträge: 

Festlegung der Entscheidungsrechte (VMI) 

Preisgestaltung 

Mindestabnahmemenge 

Mengenflexibilität 

Rückkauf und Rückgabe 

Zuteilungsregeln (Rationing game) 

Beschaffungszeit 

Qualität 


104

What is a process reference model? 

Process reference models integrate the well-known concepts of business process 

reengineering, benchmarking, and process measurement into a cross-functional framework 

Business Process 

Reengineering 

Capture the “as-is” state of 

a process and derive the 

desired “to-be” future state 

Benchmarking 

Quantify the operational 

performance of similar 

companies and establish 

internal targets based on 

“best-in-class” results 

Best Practices 

Analysis 

Characterize the 

management practices and 

software solutions that 

result in “best-in-class” 

performance 


Process Reference 

Model 

Capture the “as-is” state 

of a process and derive 

the desired “to-be” future 

state 

Quantify the operational 

performance of similar 

companies and establish 

internal targets based on “bestin-class” 

results 

Characterize the 

management practices 

and software solutions 

that result in “best-inclass” 

performance 

105

SCOR is founded on four distinct management 

processes 

“From your supplier’s supplier to your customer’s customer” 

SCOR spans 

All customer interactions, from order entry through paid invoice 

All physical material transactions, from your supplier’s supplier to your 

customer’s customer, including: 

Equipment, supplies, spare parts, bulk product, software, etc. 

All market interactions, from the understanding of aggregate demand to 

the fulfillment of each order 

Deliver 

Supplier’s 

Supplier 

Source 

Supplier 

Plan 

Source Make Source Make Deliver 

Make Deliver 

Deliver 

Your Company 


Customer 

Internal or External Internal or External 

Source 

Customer’s 

Customer 

106

Processes that balance aggregate demand and supply to 

develop a course of action which best meets the business rules 

processes 

Plan 

• Demand/supply planning 

– Assess supply resources, aggregate 

and prioritize demand requirements, 

plan inventory, distribution 

requirements, production, material, 

and rough-cut capacity for all 

products and all channels 

• Manage planning infrastructure 

– Make/buy decisions, supply-chain 

configuration, long-term capacity 

and resource planning, business 

planing, product phase-in/phase-out, 

manufacturing ramp-up, end-of-life 

management, product-line 

management 


107

Processes that procure goods and services to meet planned or 

actual demand 

Continued 

Source 

• Sourcing/material acquisition 

– Obtain, receive, inspect, hold, and 

issue material 

• Manage sourcing infrastructure 

– Vendor certification and feedback, 

sourcing quality, in-bound freight, 

component engineering, vendor 

contracts, initiate vendor payments 


108

Processes that transform goods to a finished state to meet planned 

or actual demand 

Continued 

Make 

• Production execution 


– Request and receive 

material, manufacture and 

test product, package, hold 

and/or release product 

• Manage make infrastructure 

– Engineering changes, 

facilities and equipment, 

production status, 

production quality, shop 

scheduling/sequencing, 

short-term capacity 

109

Processes that provide finished goods and services to meet planned 


Continued 

Deliver 

• Order management 

– Enter and maintain orders, generate quotations, configure product, 

create, and maintain customer database, manage allocations, 

maintain product/price database, manage accounts receivable, 

credits, collections, and invoicing 

• Warehouse management 

– Pick, pack, and configure products, create customer specific 

packaging/labeling, consolidate orders, 

ship products 

• Transportation and installation management 

– Manage traffic, manage freight, manage product import/export 

– Schedule installation activities, perform installation, verify 

performance 

• Manage deliver infrastructure 

– Manage channel business rules, order rules, manage deliver 

inventories, manage deliver quality 


110

Each basic supply chain is a “chain” of Source, Make, 

and Deliver Execution processes 

. . . 

Plan 

Source Make Deliver 

Customer & Supplier 

Plan Plan Plan 

A Supply Chain 




Each intersection of two execution processes (Source-Make-Deliver) 

is a “link” in the supply chain 

Planning processes manage these customer-supplier links 

111

At Level 1, SCOR is based on four core management processes 

SCOR 

Process 

Plan 

Source 

Make 

Deliver 

Definitions 

Processes that balance aggregate demand and supply to 

develop a course of action which best meets the established 

business rules 

Processes that procure goods and services to meet planned or 

actual demand 

Processes that transform goods to a finished state to meet planned 


Processes that provide finished goods and services to meet 

planned or actual demand, typically including order management, 

transportation management, and distribution management 


1 

112

SCOR Level 1 metrics characterize performance 

from customer-facing and internal-facing perspectives 

SCOR Level 1 

Supply-Chain Management Metrics Delivery 

Cost Assets 

Performance/ 

Quality 

Delivery performance ✓ 

Order fulfillment performance ✓ 

• Fill rate (Make-to-stock) 

• Order fulfillment lead time (ETO, MTO, CTO) 

Perfect order fulfillment ✓ 


Flexibility & 

Responsiveness 

Supply-chain response time ✓ 

Production flexibility ✓ 

Customer-Facing Internal-Facing 

Total supply-chain management cost ✓ 

Value-added productivity ✓ 

Warranty cost or returns processing cost ✓ 

Cash-to-cash cycle time ✓ 

Inventory days of supply ✓ 

Asset turns ✓ 

1 

113

At Level 2, each SCOR process can be 

further described by process type 

SCOR 

Process Type 

Planning 

Execution 

Characteristics 

A process that aligns expected resources to meet expected demand 

requirements. Planning processes: 

• Balance aggregated demand and supply 

• Consider consistent planning horizon 

• (Generally) occur at regular, periodic intervals 

• Can contribute to supply-chain response time 

A process triggered by planned or actual demand that changes the state of 

material goods. Execution processes: 

• Generally involve 

1. Scheduling/sequencing, 

2. Transforming goods, and/or 

3. Moving goods to the next process 

• Can contribute to the order fulfillment cycle time 

Infrastructure A process that prepares, maintains, or manages information or relationships on 

which planning and execution processes rely 


2 

114

For each “SCOR Process,” Level 2 “Process Categories” represent 

supply-chain variations 

SCOR process categories reflect distinctions in how 

products are planned, sourced, made, and delivered 

Type SCOR 

Process 

Process Category Characteristics 

Planning Plan Which execution process is being planned 

(Source, Make, Deliver, Supply Chain)? 

Execution Source 

Execution Make 

Execution Deliver 

Is the sourced part standard or custom? 

Is the sourced part stocked by suppliers, or not? 

Is the manufacturing process discrete or process-based? 

What triggers the “Make” signal? 

Is the product standard or custom? 

Is the product stocked in finished goods, or not? 


2 

115

Process categories are defined by the relationship between a SCOR 

process and a process type 

“SCOR Configuration Toolkit” 

Process 

Type 

Planning 

Execution 

Infrastructure 

SCOR Process 

Plan Source Make Deliver 

P1 P2 P3 P4 

S1 – S3 M1 – M3 D1 – D3 

P0 S0 M0 D0 

Practitioners select appropriate process categories from the SCOR 

configuration toolkit to represent their supply-chain configuration(s) 


Process 

Category 

2 

116

Level 4: Implementierungsebene Wareneingang 


117

Supply Chain Management

Create successful ePaper yourself

Delete template?

Save as template?