Order Fulfillment - WueCampus2 · For a number of companies, so-called “online configurators”...

Richard Pibernik

Lehrstuhl für Logistik und Quantitative Methoden

Order Fulfillment

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Remember!

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Monitoring/Event Management

MaterialsManagement Production

PlanningSales & Distribution ERP*

Purchasing

*Relevant modules for Supply Chain Management

Strategic Network Design

SC Master Planning

Material Requirements

Planning&

Purchasing

ProductionPlanning

DistributionPlanning

SchedulingTransportation

Planning

DemandPlanning

OrderFulfillment

APS

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Monitoring/Event ManagementMonitoring/Event Management



Purchasing




Purchasing



Purchasing



Purchasing



SC Master Planning


Planning&

Purchasing

ProductionPlanning



Planning

DemandPlanning

OrderFulfillment

APS


SC Master Planning


Planning&

Purchasing

ProductionPlanning



Planning

DemandPlanning

OrderFulfillment

APS


SC Master Planning


Planning&

Purchasing

ProductionPlanning



Planning

DemandPlanning

OrderFulfillment

APS

Pla

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You are here!

Learning Objectives

• So far, we have addressed planning problems that occur upstream

relative to the decoupling point (i.e., that are based on forecasts)

• Now, we consider decisions that have to be taken downstream (i.e.

when customer orders/demand have materialized)

• Once customer orders/demand are known, we have to decide how

to best fulfill them given our available supply (i.e., capacity &

inventory) – this is commonly referred to as “order fulfillment” or

“demand fulfillment”

• After completing this part of our course, you should

• Know the basic tasks and functionalities of order fulfillment (systems)

• Understand key objectives and relevant trade-offs

• Know different modes of order fulfillment and when they should be employed

• Have a thorough understanding of the logic of order fulfillment systems and

state-of-the-art quantitative methods

• Have a good knowledge of how to cope with stock-out situations 3

Order Fulfillment

…balances supply and demand in the short run

• Some example (industry) problems:

Dell receives a customer order for 20 high-grade servers. For when should they promise

delivery?

An OTC pharmaceuticals company runs short on stock. Which customers should receive their deliveries first, which customers have to wait?

A hard disk manufacturer runs short but has the option to „upgrade“ or expedite certain orders. Should the manufacturer do so?

Alcatel-Lucent has promised certain customers a specific service level for DSL equipment. How much capacity and inventory should they reserve for these customers?

A paper manufacturer wants to incentivize customers to order earlier. What should they offer?

4

Important to understand: Decoupling Point

5

Fleischmann/Meyr, 2003

Order fulfillment occurs downstream (i.e., on the right hand side)

of the decoupling point

Order Management/Order Fulfillment

The order fulfillment process includes all

relevant activities from placing an order by the

customer to its fulfillment/completion

• Objectives

Enable easy to use (electronic) order placement and

order entry

Short response time to customer orders and

requests,

Promise reliable due dates and ensure on time

delivery

“Optimal” allocation of orders to resources

(administrative resources, inventory,

manufacturing, distribution and transportation

resources)

Order Entry

Order Promising

Scheduling

(Production)

Execution &

Monitoring

Scheduling

(Shipping)

6

Different Terms

Note: different terms are being used in industry and academia, which

are not precisely defined and are often overlapping:

• Order fulfillment

• Demand fulfillment

• Order management

• Order promising

• Available to promise (ATP)/Capable to promise (CTP)/Profitable to Promise

(PTP)

• Due date Quoting

7

The Fulfillment Process

8

Order entry

Customer order

received

Order has been

entered

Check availablitiy

Order has been

confirmed

Execution and

monitoring

Order fulfilled

Reuested

quantity

is available

Quantity not

available on

time

Determine alternative

strategies

Alternative

strategies

determined

XOR

Confirm order and

delivery dates

XOR

Tasks and Objectives

Tasks of order fulfillment systems:

• Receive/handle orders coming in through multiple channels

• Quote due-dates/deliver dates (order promising)

• Reserve capacity/schedule order

• Order execution and monitoring/rescheduling

Objectives:

• Enable easy to use electronic order placement and order entry

• Short response time to customer orders and requests

• Ensure on time delivery

• “Optimal” allocation of incoming orders to inventory, manufacturing, distribution and

transportation resources

To which criterion/criteria does the term “optimal” refer to?

9

Receiving Orders

Today, systems have to be able to support order entry through:

• conventional channels (fax, e-mail)

• phone (call center support)

• website

• Electronic Data Interchange

For a number of companies, so-called “online configurators” are essential to their business model (ATO/MTO)

• Most prominent: Dell, started in 1996

• Features include

compatibility constraints

quoting

approximate delivery time

Although still relatively young, these functions are now mature; state of the art systems are fairly easy to implement and run stable

10

More Interesting: Order Promising

Relevant questions:

• When can we fulfill an order?

• Are we meeting the customer’s requirements in terms of delivery times?

• What can we do if we cannot meet the customer’s requirements? Any alternatives?

• If we have alternatives, how much do they cost us? Is the customer worth the additional cost?

• In upcoming stock-out situations, which customer orders are fulfilled, which orders are rejected or have to wait? What are short and long term consequences?

Order promising links sales and CRM to core SCM functions (manufacturing, inventory management, distribution)

11

Order Promising

Let’s have a closer look at the order promising process described previously

12

Check availablitiy

Reuested

quantity

is available

Quantity not

available on

time

Determine alternative

strategies

Alternative

strategies

determined

XOR

Confirm order and

delivery dates

XOR

Conventional ATP

13

Conventional “Available to Promise” (ATP) implemented in ERP

Provides information on uncommitted portion of inventory on hand + master schedule

Simple bookkeeping function:

t=1 t=2 t=3

Initial inventory 100

Planned receipts 300 500 500

Committed quantities 200 400 200

ATP 200 300 600

ERP module: PP

ERP module: MM

ERP module: SD

Order Promising – Make to Stock

Slightly advanced order promising includes:

• Simultaneous allocation of orders to ATP quantity and due date quoting

• When can an order of size be fulfilled?

• Most commonly, due date quoting against available to promise inventory is called ATP

• Easy to implement standard function

14

ATP

t

Order Promising – Assemble to Order

ATO order promising includes:

• Simultaneous allocation of orders to component ATP quantities and due date quoting

• When can an order be fulfilled if?

• capacity and quantities

• of A,B,C are required?

• Capacity and all components can be bottleneck

• Most commonly referred to as Capable to Promise (CTP)

• Relatively easy to implement standard function

15

t ATP Component B

t ATP Component C

t Assembly Capacity

ATP Component A

Order Promising – Make to Order

If we want to consider a multi-stage production process, order promising gets

very complicated if bottlenecks can occur on more than one stage

If only one bottleneck, ATO logic can be applied

We will look at MTO order promising later

16

Real-Time Order Promising (1)

Real-time order promising

• For every incoming order, delivery dates are immediately quoted

• Customer instantly receives information about availability and expected delivery date

17

21 1 1

ˆ( , );q d d

42 2 2

ˆ( , );q d d

315 15 15

ˆ( , );q d d

116 16( , )q d

Incoming Orders

Allocation of resources; due date quoting

16d̂

Order book (with quoted due dates)

Inventory of component mM

t

t

Capacity

invm,t

capt

1, ,, ,

1, ,

min ,..., ,d M d d

p d dp M p p

inv inv capctp

Real-Time Order Promising (2)

Real-time “Capable to Promise” (CTP) algorithm:

Quantity of product p that can be produced in time interval [tb+1,d]

Inventory component 1,…,M in period d

Capacity in period d

Resource coefficients for product p

, , ,1

b

d

p d p t dt t

ctp ctp

1, 1 , 1 1, ,, ,

1, , 1, ,

min ,..., , ,..., ,

net nett M t t M t t

p t dp M p p M p p

inv inv inv inv capctp

1,..., 1bt d t

, 1,..., 1bm M t d t

,for 0m p

18

, , , 1 , , ,min ,net netm t m t m t p t d m pinv inv inv ctp

pmddpdm

net

dm ctpinvinv ,,,,, γ

Batch Order Promising (1)

First we collect all incoming orders during a “batching-interval”, e.g. one day

At the end of the batching-interval, orders are allocated to inventory/

capacity and due dates are determined

19

Order book

(end of batching-

interval)

1 2 15ˆ ˆ ˆ, ,...,d d d

Inventory of component

mM

t

t

Capacity

invm,t

capt

Allocation of resources;

due date quoting

21 1( , )q d

42 2( , )q d

315 15( , )q d

21 1 1

ˆ( , );q d d

42 2 2

ˆ( , );q d d

315 15 15

ˆ( , );q d d Order book

(with quoted

due dates)

Order Promising – Real Time vs. Batch

„It should be noted that there are very few true real-time ATP systems operating today;

most systems that give an immediate response (including most web-based retail sites)

produce an initial „soft“ promise, run a batch module later and then produce a „hard“

promise.“

(Ball et al., 2002)

What are the advantages/disadvantages of real-time and batch order promising?

When employing batch order promising, the length of the batching-interval is a decision variable

We will look at models supporting batch order promising!

20

Batch Order Promising (2)

Alternative mechanisms:

• sequential (e.g. first come first served, highest priority first)

• optimization models

Let’s look at a simple optimization model

21

Batch Order Promising Model – Notation

22

,pi tx

,,p cumi tx

,

1 if order is shipped in

0 elsei t

i ty

1 if order is promised

0 elsei

iz

,pi tx

i

,( )i i tc y

Additional variables

production quantity product p for order i in period t

cumulated production quantity for order i in period t

quantity shipped for order i in period t

Additional parameters:

profit margin order i

penalty cost if order cannot be fulfilled

Model Formulation (1)

23

Model Formulation (2)

24

pm

p

ti γx ,,

Active vs. Passive Order Promising

Passive order promising

• Utilizes information about available inventory/capacity for due date quoting

• Returns due dates for execution to manufacturing, warehouse management, and

distribution systems

• Does not generate detailed schedules for order execution (e.g. manufacturing

schedules)

Active order promising

• Utilizes information about available inventory/capacity for due date quoting

• Simultaneously determines schedules for execution (e.g. manufacturing schedules)

When are active/passive systems feasible?

What are the advantages/disadvantages?

25

Classification of Order

Promising Types and Methods

26

Aktive (A) Passive (P) Active (A) Passive (P)

Execution Level

Availability Level

Finished goods Resources

optimization models ? ?

Rea l- time (RT)

- time

Batch (B)

Response

disposition rules disposition rules RT/R/A

B/R/A

Simultaneous generation

of due dates and

schedules

RT/R/P

B/R/P

Determiniation of due dates

first, then scheduling

RT/FG/A RT/FG/P

B/FG/A B/FG/P

disposition rules disposition rules

optimization models ? ?

Search rules in case of shortage

27

Search rules for supply alternatives:

(„Rule-based search“, „Multi-dimensional search“), e.g. SAP-APO:

4

2

3 1

Products

Locations

CTP

2

3

4

1 substitute products

same product at other

locations

substitute products at

other locations

CTP: create new

supply order

Order Promising with substitute products

In certain cases substitute products can be delivered instead of the product,

originally ordered by the customer

Depends on the availability of a product acceptable to the customer

Customer will only accept the substitute if it provides at least the same utility as

the original, and if its use is not limited, e.g. by technical restrictions

Necessary to determine additional fulfillment cost. Do we really want to carry

these?

28

Order Promising with multiple locations

If order cannot be fulfilled with the finished goods or capacity at a certain

location, order can be shifted to other locations

Order promising is then applied to a distribution or manufacturing network

rather than only to a single location

Multi-location order promising should take different manufacturing and

transportation lead times and costs into account

29

Order Promising with partial deliveries

If ordered quantity is not available within requested delivery time window, order

can be fulfilled with two or more partial deliveries

First partial delivery should be scheduled within the given time window

Customer must agree to partial deliveries

If partial deliveries are possible, then order promising should determine the

quantities and delivery dates for each partial delivery

Necessary to determine additional fulfillment cost. Do we really want to carry

these?

30

Assessment of Fulfillment Alternatives

The previously outlined “Add-ons” can be utilized in case of temporary stock-

out situations

They can be employed simultaneously and can also be combined (Note the

increase in complexity)

System should then be able to assess the fulfillment alternatives in terms of

• fulfillment cost

• customer satisfaction?

Critical question: How well does a system support generation and assessment

of these fulfillment alternatives?

31

Order Promising/Fulfillment in hierarchies

• So far, we have mainly looked at order fulfillment for a

single location

• In many companies, however, we find different geographical

(sales) regions across which product quantities are first allocated

in a hierarchical manner

• The allocated quantities are the basis for order promising

• In the following we will address this problem and see how this is

handled in SAP APO

• Slides come from Kilger/Meyr, Chapter 9 in

Stadtler/Kilger (2010)

32

The Customer Hierarchy

To allocate supply to customers the following is required:

• a model of the customer structure

aligned with the geographic dimension in demand planning

• a forecast of the future customer demand

33

Forecast

Aggregation World-wide

Sales

America Europe

Germany France Italy

West East

200 200

400 200

400

1400

400

1000

top

area

region

country

1400

The allocation process – first step

• Aggregate all forecast quantities to the root of the hierarchy.

• This gives the total forecast of the specific product (or product

group).

• Transfer total forecast to Master Planning (MP) to check which

portion of the forecast can be fulfilled.

• Example: 1400 total forecast; 1200 can be fulfilled (according to

MP)

34

The allocation process – second step

Allocate MP quantity top down to the leaves of the customer hierarchy

This top-down allocation is controlled by allocation rules:

• rank based:

higher priorities (ranks) between customers

available quantity is allocated to higher rank up to the original forecast

helpful to support sales to specific markets

• per committed:

allocation according to the forecast the customers have committed to

split appropriate to the fraction of the original forecast

support a „fair share“ allocation according to the forecast

caution: rationing gaming may occur !

• fixed split:

apply predefined split factors

independent of the individual forecast 35

Example for second allocation step

36

ATP

Allocation

World-wide

Sales

America Europe


West East

split 0.7

224 (200)

320

(400)

160

(200)

rank 1

400 (400) 1200 (1400)

320 (400)

800 (1000)

top

area

region

country

1200 (1400)

rank based

per committed

fixed split

rank 2

800 (1000)

split 0.3

96 (200)

Search process

General ATP-based order promising process:

• search for ATP according to a set of search rules

• if ATP is found

reduce accordingly and generate a quote for the order

• if no ATP is found

no quote is generated and the order must be rejected or confirmed

manually at the end of the allocation planning horizon

(SC is not able to fulfill the order within the allocation planning horizon)

ATP is searched along several dimensions (as shown before)

• time dimension

• customer dimension

• product dimension

37

Example of an ATP search procedure

38

America Europe


West East

World-wide

Sales

2. Customer

3. Product

1. Time

Three dimensions of ATP search paths

Request

Search rules for example

Following search rules are applied:

(assume that ATP is on finished goods level)

1. The leaf node in the customer hierarchy, to which the customer belongs, the

product being requested by the order and the time bucket containing the

customer requested date are determined.

ATP at this point is consumed – if available

2. If ATP is not sufficient, then the time dimension is searched backwards in time

for additional ATP (still for the same customer and product)

all ATP found up to a predefined number of time buckets back in time is

consumed

(consumption in earlier time buckets leeds to pre-built of an order = inventory)

3. If ATP is not sufficient, steps 1. and 2. are repeated for the next higher node

(parent node) in the customer hierarchy, then for the next higher and so on

up to the root

4. If ATP is not sufficient, steps 1. to 3. are repeated for all alternate products

that may substitute the original requested product (e.g. downgrading)

5. If ATP is not sufficient, steps 1. to 4. are repeated, but searched forward in

time, up to a predefined number of time buckets.

(order is made late) 39

ATP Consumption – steps 1 & 2

Order:

• 300 units

• from customer in East Germany

• requested date in week 4

40

70 50 60 10

Request

300 units

week 4

World_Wide_Sales -> Europe -> Germany -> East

weeks 1 2 3 4

1. Time

1. ATP check for customer group East Germany for week 4

2. ATP check for week 3 and for week 2

(allowed to consume ATP 2 weeks backwards in time)

ATP found:

10 in week 4, 60 in week 3, 50 in week 2 = 120 in total

ATP Consumption – step 3

41

West East

Germany

Request

300 units

week 4

weeks

weeks weeks

1 2 3 4

1 2 3 4 1 2 3 4

20 10 30 80

30 20 40 30 70 50 60 10

2. Customer

3. ATP check in next higher customer node Germany

(but no ATP in Europe or World-Wide is available)

ATP found

80 in week 4, 30 in week 3, 10 in week 2 = 120 in total

ATP Consumption – step 4

4. ATP search for alternate products

(alternates are sorted by priority)

• alternate with highest priority is considered first and the same steps are

applied as for the original product

(first search back in time and second search up the customer hierarchy)

• same steps to the alternate with second priority and so on

42

East

East

East East, Product A

East, Product A, alternate 1



Request

300 units

week 4

70 50 60 10

1 2 3 4 weeks

3. Product

Managing Stock-outs with

Order Fulfillment Systems

43

Contents/Objectives

One of the key criteria to evaluate order fulfillment systems is their capability to

handle temporary stock-out situations

If a company is facing a temporary stock-out situation, it will want to minimize the

overall (i.e. short and long term) negative consequences

In this session we will analyze, which negative consequences result from stock-

out situations and how they can be mitigated through the use of order fulfillment

systems

A case study from the pharmaceuticals industry will give you insight into specific

problems regarding the management of stock-out situations and will facility a

discussion about suitable models and techniques for handling these situations

effectively

44

Impact of Order Fulfillment on Profitability

Order promising is trivial if we always have enough capacity/inventory

available to fulfill customer’s requirements!

But,

• we want systems to be tight (i.e. minimize excess inventory, maximize capacity

utilization)

• we have to cope with uncertainties (supply, manufacturing, demand)

How can we handle situations in which not all customer requirements

(quantity and delivery date) can be fulfilled?

45


If inventory/capacity is not sufficient for fulfilling all customer requirements:

• Allocation of available inventory/capacity to incoming customer orders

• More appropriate: allocation of stock-outs to customer orders

Allocation has impact on short and long term profitability:

46

Short term: Lost sales, penalties, additional cost for expediting or upgrading

Immediate impact on profitability

Long term: Customer satisfaction, loss of goodwill, (loss of reputation)

Impact on Customer Lifetime Value, i.e. on long term

profitability

Possible trade-off

Traditional Inventory Theory

Stocking decisions based on:

• Demand forecasts/probability distribution of (anonymous) demand

• Based on aggregate data (e.g. demand in cycle time)

Suggests: Determine inventory policy that minimizes

47

holding cost + fixed set-up/ordering cost + penalty cost

holding cost + fixed set-up/ordering cost

subject to: (/) service level constraint

Implemented!

Determines potential for order fulfillment

Traditional Inventory Theory vs.

Order Fulfillment

Inventory Management Order Fulfillment

SC Planning SC Execution

Based on Forecasts/aggregate anonymous

demand

Based on detailed order information

Stochastic Deterministic

Demand fill rate (represented ex ante

through /-service level)

Order fill rate based on required due dates

and quantities

Penalizes unfilled fraction of aggregate

demand

Penalty should depend on customer

(order)

48

Order fulfillment is especially concerned about temporary stock-outs, i.e.

• materialization of 1-/1-

• situations where aggregate but not disaggregate demand can be fulfilled

• situations caused by SC disruptions (supply shortages, yield problems, …)


49

Temporary stock-outs – ways to

make our customers unhappy

Different short and

long term consequences

(highly customer specific!)

• Reject order

• Quote unrealistic delivery date

• Quote late delivery date

• Quote less than ordered quantity

• Partial deliveries (first delivery meets

delivery date requirements)

• Partial deliveries (first delivery late)

Better, but not necessarily

feasible/available to all customers

Different impact on

fulfillment cost

Has to be decided for every individual order!

• Meet customer requirements with

remaining capacity/inventory

• Upgrade

• Expedite

•…


What are the long term consequences of not meeting a customer’s requirements in regard to a specific order?

Customer’s reaction:

• No reaction ( CLV= CLVb - CLVa = 0)

• Chooses different supplier ( CLV = CLVb)

• Chooses different supplier, may return after a while ( CLV > 0)

• Procures portion of products from different suppliers ( CLV > 0)

Customer’s reaction depends on:

• Availability of alternative suppliers (not order specific)

• Contractual relationship (not order specific)

• Utility/need/criticality of order (i.e. impact on customer side)

• Deviation from customer’s requirements (tardiness)

• Fulfillment history

50

Objective of Order Fulfillment in

Stock-out Situations

In a temporary stock-out situation, order fulfillment should allocate

available inventory/capacity to customer orders in such a way,

that the overall negative impact is minimized

Take into account impact of individual orders and importance of customer’s

(represented by CLV)

Exploit all options (e.g. partial deliveries, upgrades, expediting) if economically

feasible (i.e. additional cost less than negative consequences CLV)

51

Case Data

General information:

• Pharmaceuticals company, operating mainly in Europe, 1000 employees, € 378 M revenue in 2003/2004

• Products: Variety of prescription and over the counter drugs

• Relevant products for case: over the counter pharmaceuticals (e.g. cough drops, herbal laxatives, vitamins)

Production process:

• Large batch production, make to stock

• Procurement of ingredients mainly from local suppliers

• Quality control (batch) with uncertain outcome, 2-3 days

Distribution:

• Company supplies large and medium retailers, drugstore chains, individual chemists

• For German market, all products are stored at the production facility

• Shipping is outsourced

• Due to (increasing) demand variance, supply shortages and uncertain manufacturing yields, company increasingly experiences stock-outs

52

Case Data

Customer specific contracts and conditions:

• When negotiating contracts with large customers (large retailers and large drugstore

chains), company has little bargaining power

• Virtually all requirements have to be met, sometimes drastic penalties or consequences

if orders are not fulfilled according to requirements, examples:

• Complete shipments are being sent back

• penalty = sales volume of order

• price discounts enforced in next round of negotiations

• Large customers can order whatever and whenever they want and can specify delivery

date

53

Case Data

Order processing:

• Orders received through multiple channels (phone, fax, E-mail), now also online portal

• Most orders are printed and entered manually in SAP system

• Orders processed first come first served, some orders expedited, if very short delivery

lead-time

• Availability is being checked in SAP system, customer receives confirmation after order

has been processed

• No “booking levels”, only certain quantity is reserved for one “very nasty” customer

54

Case Data

One specific stock-out situation:

• October/November 2002

• Product: Cough drops

• Reason for stock-out: supply shortage (quality issues of one supplier) in combination

with high demand due to wave of flu

• Upcoming stock-out situation could have been anticipated one week prior, but no active

management of remaining inventory

• At 10/23, 150 orders (4071 units) committed with delivery dates

• Between 10/23 and 11/11 656 orders (8660 units) received

55

Case Data

56

0

200

400

600

800

1000

1200

1400

1600

1800

23

-10

24

-10

25

-10

28

-10

29

-10

30

-10

31

-10

1-1

1

4-1

1

5-1

1

6-1

1

7-1

1

8-1

1

11

-11

12

-11

13

-11

14

-11

15

-11

18

-11

19

-11

Ordered QuantityQuantity Committed Previously

Units

Dates

Inventory and Fulfillment Situation –

Company’s Results

57

-2000

-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

23

-10

24

-10

25

-10

28

-10

29

-10

30

-10

31

-10

1-1

1

4-1

1

5-1

1

6-1

1

7-1

1

8-1

1

11

-11

12

-11

13

-11

14

-11

15

-11

18

-11

19

-11

Dates

Un

its

Planned Receipts Total Quantity Ordered Available Quantity

Rejected Orders – Company’s Results

58

Is this the profile we want?

0

100

200

300

400

500

1 2 3 4 5

Customer Priority

Total number of orders Rejected

Alternatives

SAP’s methodology:

• Availability is checked through ATP-function in SAP

• For main customers, future orders are forecasted

• If forecasted orders exceed available quantity, certain incoming orders are blocked

(rejected?)

• Only orders from high priority customers can be processed

What are advantages/disadvantages of this approach?

59

Alternatives

-2000

-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

23

-10

24

-10

25

-10

28

-10

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-10

30

-10

31

-10

1-1

1

4-1

1

5-1

1

6-1

1

7-1

1

8-1

1

11

-11

12

-11

13

-11

14

-11

15

-11

18

-11

19

-11

Dates

Un

its

Planned Receipts Total Quantity Ordered Available Quantity

60

Similar: Booking levels

• Reserve portions of available inventory for certain customer classes

• Dynamic service level approach – how do we determine quantities to be reserved?

• What are the prerequisites?

What are the advantages/disadvantages of a booking level approach?

A Different Approach:

Batching and Partial Deliveries

Apply batching and partial deliveries: Utilize customers’ flexibility in terms of

• response time and

• delivery quantities

Customers do not require same day order confirmation

Some customers are willing to accept partial deliveries

61

Experiment 1: 1-Day-Batching,

Partial Deliveries Allowed

62

Partial deliveries: 1.22 %

Not very exciting! What are the reasons? What are the

implications?

0

10

20

30

40

50

60

70

1 2 3 4 5 Overall

Customer Priority

Company's results Model's results

Unfille

d o

rders

in %


Partial Deliveries Enforced

Partial deliveries are enforced: If customer generally agrees to partial deliveries,

all orders will be fulfilled with two deliveries if possible (see model assumptions

and constraints)

Impact on available inventory:

63

-2000

-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

23

-10

24

-10

25

-10

28

-10

29

-10

30

-10

31

-10

1-1

1

4-1

1

5-1

1

6-1

1

7-1

1

8-1

1

11

-11

12

-11

13

-11

14

-11

15

-11

18

-11

19

-11

Dates

Un

its

Planned Receipts Committed and Unfulfilled Quantity Available Quantity Partial Delivery Quantities


Partial Deliveries Enforced

Results: Unfilled orders

Partial deliveries:

64

0

10

20

30

40

50

60

70

1 2 3 4 5 Overall

Customer Priority

Company's resultsResults Experiment 1Results Experiment 2

Customer Priority 1 2 3 4 5 Overall

Partial Deliveries in % 0,00 3,96 0,00 9,52 48,28 7,62

Further Analysis

Impact of batching: Two and three day intervals • Accepted by customers? Is the achieved benefit worth it?

• Problem: Some orders have to be fulfilled next day. How do we handle these?

How strong is the impact of partial deliveries? • Does it make sense to try to negotiate partial deliveries with customers?

• Do we want to compensate them for accepting these?

Are there any other ways of creating flexibility in terms of customers’ requirements which can be utilized in stock-out situations?

Are there more intelligent ways of allocating the available inventory to customers?

To which extent do these results apply to other stock-out situations? Can they be generalized?

65

Readings

• Pibernik, R. (2005). Advanced available-to-promise: Classification,

selected methods and requirements for operations and inventory

management. International Journal of Production Economics, 93-94(1),

239-252.

• Kilger, C./Meyr, R. (2010). Demand Fulfilment and ATP, in: Stadtler,

H./Kilger, C. (eds.): Supply Chain Management and Advanced Planning,

Berlin & Heidelberg, Chapter 9.

• Pibernik, R. (2006). Managing stock-outs effectively with order fulfillment

systems. Journal of Manufacturing Technology Management, 17(6), 721-

736.

(Note: This article provides the details of the case study presented on

slides 52-65; you are not required to study the formal details)

66

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