pibernik, richard classification, selected methods and requirements for inventory management

8/3/2019 Pibernik, Richard Classification, Selected Methods and Requirements for Inventory Management

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Int. J. Production Economics 9394 (2005) 239252

Advanced available-to-promise: Classification,

selected methods and requirements for operations

and inventory management

Richard Pibernik

School of Business and Economics, Goethe-University, Mertonstrasse 17, D-60054 Frankfurt, Germany

Abstract

Advanced available-to-promise (AATP) comprises of an assortment of methods and tools to enhance order

promising responsiveness and order fulfillment reliability. This paper contributes to a theoretical framework for the

development of models and algorithms supporting order quantity and due date quoting. At first, alternative generic

AATP systems will be identified on the basis of relevant classification criteria. Based upon this classification, the AATP

planning mechanisms will be detailed for two generic AATP types. On the basis of the introduced AATP types and the

description of selected models we finally derive requirements, which operations and inventory management have to

meet in order to ensure a successful application of AATP.r 2004 Elsevier B.V. All rights reserved.

Keywords: Order promising; Available-to-promise; Order management; Capable to promise; Demand management

1. Introduction

Advanced available-to-promise (AATP) refers to a variety of methods and tools to enhance the

responsiveness of order promising and the reliability of order fulfillment. Based on customer requests (i.e.

requested product, order quantity and delivery time window) they support order quantity and order

due date quoting. AATP directly links available resources, i.e. finished goods and work in progress, as well

as raw materials, production and distribution capacity with customer orders in order to improve the overallperformance of a supply chain (Chen et al., 2001).

The major goals pursued with the implementation of AATP are (1) the improvement of on time delivery

by generating reliable quotes, (2) the reduction of the number of missed business opportunities by

employing more effective methods for order promising and (3) an enhancement of revenue and profitability

by increasing the average sales price (Kilger and Schneeweiss, 2000).

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www.elsevier.com/locate/dsw

0925-5273/$ - see front matterr 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.ijpe.2004.06.023

E-mail address: [email protected] (R. Pibernik).
http://www.elsevier.com/locate/dswhttp://www.elsevier.com/locate/dsw


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AATP is usually integrated in ERP systems and Advanced Planning Systems (APS). Their functional

scope can vary significantly. Conventional ATP, commonly implemented in ERP, merely determines the

availability of finished goods at certain points of time in the future. Advanced ATP provides a broader

scope of functions, such as order quantity and due date quoting on the basis of available supply chainresources and alternative measures in case of an anticipated shortage of finished goods or manufacturing

resources.

The development of methods and their application to support order promising has primarily been driven

by providers of ERP and APS. Until now, a limited number of theoretically founded contributions have

been made. Publications addressing AATP either discuss the needs or propose features for AATP Systems

from a general and rather unspecific perspective or present methods applicable in a particular company-

specific setting only.

This paper provides a framework supporting the successful development and implementation of AATP

and points out the requirements, AATP implementation imposes on operations and inventory manage-

ment. At first, alternative generic AATP types will be identified on the basis of relevant classification

criteria. Based upon this classification, the AATP planning mechanisms will be detailed for two generic

AATP types. We introduce a mixed-integer programming model as well as a planning mechanism suitable

for batch and real-time order quantity and due date quoting on the basis of finished goods inventory. We

derive the requirements, operations and inventory management have to meet in order to enable a successful

application of AATP.

2. Literature review

The relevant literature on order promising and ATP can be broadly subdivided into textbook and

management-oriented publications (e.g. e[B2]x, 2002; Fischer, 2001; Kilger and Schneeweiss, 2000;

Knolmayer et al., 2002) as well as research papers introducing AATP methods in regard to specific practical

ATP applications. The former only discuss features of ATP in a rather unspecific and general way and

briefly comment on the potential benefits of advanced ATP. However, they usually neither consider the

different company-specific requirements or different configuration alternatives for AATP nor do they

demonstrate suitable methods supporting order quantity and due date quoting. Very few contributions

provide quantitative methods for quantity and due date promising. Taylor and Plenert (1999) present a

basic approach for finite capacity promising. Their approach supports a continuous monitoring of the

manufacturing capacity that can be utilized to fulfill additional incoming customer orders. Major

contributions have been made by Chen et al. (2002, 2001). The authors develop mixed-integer programming

models that allocate resources among customer orders that arrive within a pre-determined time interval

(batching-interval). The proposed model is focused on a particular AATP type: Batch AATP for a

configure-to-order case in computer manufacturing. Jeong et al. (2002) consider a similar AATP type

(batch AATP for the assembly of TFT Displays) and present an algorithm which subsequently allocatescustomer orders to relevant resources on the basis of customer-specific due dates. Fischer (2001) proposes a

mixed-integer programming model for allocating available finished goods inventory to a set of customer

orders. Allocation is based on a coefficient cij which is supposed to represent suitability of an available

portion iof inventory on hand for the fulfillment of customer order j. The coefficients values are calculated

on the basis of individual indicators reflecting customer priorities, order-specific sales and profit margins, as

well as penalty costs for early or late delivery.1 Kilger and Schneeweiss (2000) as well as Fischer (2001)

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1As depicted in Pibernik (2002), the proposed methodology for the determination of the coefficients and consequently also for the

allocation of finished goods inventory to customer orders can lead to significant problems in regard to consistency and traceability of

the results.

R. Pibernik / Int. J. Production Economics 9394 (2005) 239252240


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describe in a very general way, how a real-time planning algorithm should be designed in order to support

quantity and due date quoting.

Considering the previously described research in regard to order promising and fulfillment, this paper

aims at a theoretical foundation of AATP. We present a well-founded classification of alternative AATPtypes and derive requirements for operations and inventory management on the basis of generic AATP

methods which are appropriate for implementation in supply chain management software suites and have

not yet been considered in the relevant literature.

3. A classification of advanced ATP

3.1. An outline of advanced ATP functions

A manufacturer or retailer has to decide whether to accept or deny a potential customer order. This

decision must be based upon the ordered quantity, customers delivery time window, finished goodsinventory, supply chain lead times and supply chain resources. If the manufacturer or retailer anticipates

the availability of the ordered quantity within the customers delivery time window, he confirms the

customers order, determines a reliable due date within the customers delivery time window and assigns the

required quantity of finished goods in supply or the requisite amount of supply chain resources (semi-

finished goods, raw materials, production capacity). In case of an anticipated shortage of finished goods or

supply chain resources, feasible alternative strategies to fulfill the customers order, e.g. partial deliveries

and deliveries of substitute products, should be generated and assessed. The customer order will have to be

denied if no feasible alternative strategies can be identified and employed. As we will demonstrate, AATP

methods and systems need to be classified on the basis of relevant criteria in order to facilitate the

implementation of models and algorithms for order quantity and due date quoting. In the following

section, the relevant characteristics will be described in detail and used for specifying generic AATP types.

3.2. Generic advanced ATP types

The initial characteristic used for classifying AATP is, what we shall call the availability level. As

mentioned previously, quantity and due date quoting can either be performed on the basis of finished goods

inventory or on the basis ofsupply chain resources, including raw materials, work-in-process, finished goods,

and even production and distribution capacities (see Chen et al., 2001). Conventional ATP, implemented in

ERP systems, is always based on finished goods inventory. It provides information regarding product

availability by determining the uncommitted portion of a companys inventory and planned production,

maintained in the master schedule (APICS, 1987) While conventional ATP is simply a monitoring of the

uncommitted portion of current and future available finished goods, advanced ATP provides a decisionmaking mechanism for allocating available finished goods inventory to customer orders and concluding

order quantities and due date quotes. AATP based on finished goods inventory is applicable in a make-to-

stock manufacturing environment only. Advanced ATP based on supply chain resources represents a

systematic resource allocation process. It provides a decision making mechanism for allocating available

supply chain resources to customer orders and determining order quantity and due date quotes (see e.g.

Chen et al., 2001). A pre-condition for AATP based on supply chain resources is detailed information

regarding supply chain capacity requirements for each product included in the product range. Therefore,

the bill of material, the routing plan as well as information on manufacturing and distribution capacity

requirements must be available to perform the resource allocation. AATP based on supply chain resources

is appropriate in make-to-order production.

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The second characteristic used for classifying AATP is its operating mode. AATP can run in real-time or

batch mode. When employed in real-time mode, order quantity and due date quoting is completed at the

time of a customers request (see e.g. Chen et al., 2002, 2001). The request triggers the AATP planning

mechanism. If AATP is operated in batch mode, the potential customer orders arriving within a pre-

determined time interval (batching-interval) are first collected and then processed together by a model or

algorithm, that simultaneously or sequentially determines order quantities and due dates. Thus, the AATP

planning mechanism is executed periodically, e.g. at the end of each day or week. On the one hand, the

operating mode of AATP affects customer response times and therefore the customers perception of the

service provided by the company, on the other hand it has impact on models and algorithms employed for

performing order quantity and due date quoting (see Section 4).

The last characteristic used for classifying AATP is the interaction with manufacturing resource

planning. So-called passive AATP Systems receive information regarding finished goods and supply chain

resource availability from the manufacturing resource planning system. Based on this information, order

quantities and due dates are quoted. Passive AATP Systems do not have direct impact on manufacturing

resource planning apart from determining the accepted orders and their due dates. Active AATP isintegrated in the companys manufacturing resource planning. Whilst performing the usual order quantity

and due date quoting, active AATP generates or modifies the master schedule. Thus AATP simultaneously

determines and adjusts the master schedule as well as the order quantity and due date quotes. Active AATP

is limited to small batch and single unit production. Its application in a large batch or bulk production

opposes the generally pursued goal of high and even capacity utilization. Passive AATP does not have any

limitations in regard to the production type and is therefore also applicable in large batch and bulk

production. Furthermore, it has to be considered, that active AATPs application is especially suitable in a

make-to-order production environment, whereas passive AATP has greater applicability in a make-to-

stock environment.

Using the main characteristics outlined above, eight generic AATP types can be derived. These are

illustrated in Table 1. In Section 4, it will become clear that this categorization is essential for an accuratecharacterization and also implementation of different AATP methods.

3.3. Additional advanced ATP functionalities

In addition to the functionalities, identified in the previous section, further functionalities of AATP are

currently discussed (see e.g. Fischer, 2001; Kilger and Schneeweiss, 2000). These functionalities mainly refer

to strategies applied in case of an anticipated shortage of finished goods or supply chain resources. They

are, however, independent of the employed AATP type and can therefore be considered as general add-

on features to the generic AATP types, derived above. Three different strategies can be supported by

AATP:

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Table 1

Generic AATP types

Availability level

Finished goods (FG) Supply chain resources (SCR)

Real-time

(RT)RT/FG/A RT/FG/P RT/SCR/A RT/SCR/P

Operating

mode Batch (B) B/FG/A B/FG/P B/SCR/A B/SCR/P

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

Interaction with manufacturing resource planning



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AATP with substitute products: In certain cases substitute products can be delivered within the given

delivery time window instead of the product, originally ordered by the customer. This depends, of course,

on the availability of a product, the customer will accept instead of the ordered product. The 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.

Multi-location AATP: If the customer order cannot be fulfilled with the finished goods or supply chain

resources available at a certain location, available finished goods and resources can be sourced at other

locations. Therefore, the AATP planning mechanism has to be applied to a distribution or manufacturing

network rather than only to a single location. Multi-location AATP should take different manufacturing

and transportation lead times and costs into account, depending on the proposed locations.

AATP with partial delivery: If the ordered quantity is not available within the given delivery time window,

the customer order can be fulfilled with two or more partial deliveries, where the first partial delivery is

carried out within the given time window. This option is, of course, only valid if the customer generally

accepts partial deliveries. If partial deliveries are taken into account, then AATP should determine the

quantities and delivery dates for each partial delivery. The customer can receive partial deliveries from

different locations; they can also include substitute products.

These different strategies can be combined in any possible sequence in the AATP planning mechanism. For

instance, it is possible to first check whether finished goods or manufacturing resources are available at any

of the locations in the distribution or manufacturing network. If this is not the case, substitute availability

and acceptance can be considered and if necessary, partial deliveries can be taken into account. Besides

generating these strategies sequentially, they can be combined in the AATP planning mechanism in such a

way, that all feasible solutions are determined and assessed simultaneously. The decision maker then selects

a solution on the basis of data regarding relevant costs and customers priority. This procedure will be

detailed in Section 4.2.

4. Selected methods for advanced ATP on the basis of finished goods inventory

From the characterization and classification of different AATP types in the previous section, it becomes

clear that companies have to identify the generic AATP type and the additional functionalities which meet

their specific requirements. These requirements mainly depend upon the design of the manufacturing

system (e.g. make-to-stock or make-to-order) and on customers requisites and preferences (e.g. if

customers require real-time information on availability and delivery dates). Selecting an appropriate AATP

configuration determines the specific methods employed for generating due dates and order quantities.

However, the successful application of appropriate methods also imposes specific requirements on bothinformation processing and the performance of operations, inventory and transportation management:

Reliable order promises will only be generated if the relevant information is provided and processed

accurately and on time and if operations, inventory and transportation management are actually capable of

producing and delivering the ordered quantities in accordance with the due dates and order quantities

determined by AATP. In this section we describe adequate methods supporting two of the generic AATP

types, introduced in the previous section: Batch and real-time AATP on the basis of finished goods

inventory. This will facilitate an analysis of the specific requirements which have to be met in order to

effectively fulfill order quantity and due date quotes (see Section 6). We will also utilize these results in

order to derive special requirements associated with the application of the remaining AATP types

introduced in the previous section.

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4.1. A model for batch AATP based on finished goods inventory

When applying a batch AATP, the potential customer orders arriving within a pre-determined time

interval (batching-interval) are first collected and then processed together in order to determine orderacceptance or denial as well as quantities and due dates for accepted orders (see Section 3.2). The AATP

planning mechanism is executed at the end of the batching interval. It generates a schedule that specifies the

quantities and due dates of whole or partial deliveries for every accepted customer order in the batch.

Uncommitted inventory and planned production is reserved according to the committed due dates.

Therefore, an assignment problem has to be solved: The customer orders in the batch have to be assigned to

those quantities of finished goods, which are available-to-promise. In general, this assignment problem

can either be solved simultaneously for all orders in the batch by employing an optimization model or by

applying an adequate algorithm which successively processes the orders on the basis of a pre-determined

order sequence. In the following, we present an optimization model which can be employed for a

simultaneous generation of order quantities and due dates for a given set of potential customer orders. To

exemplarily illustrate the impact of the additional functionalities, described in Section 3.3, the model also

accounts for partial deliveries.

Let ta; ta 1; ta 1; ta 2; . . . ; ta T 1; ta T te 1; te be the AATP planning horizon for one

batch, consisting ofTdiscrete time periods. The point of time t denotes the beginning and t 1 the end of a

time period t; t 1 2 fta; ta 1; . . . ; te 1; teg. We assume that the customer orders were collected

during the batching-interval ta t; ta, where toT represents the length of the batching-interval, e.g. 24 h.

t determines the points of time ta, ta t; ta 2t; . . ., at which the AATP planning mechanism is executed.

We therefore assume, the model is run on a rolling horizon basis (see e.g. Chen et al., 2001).

Let A(ta) denote the set of potential customer orders for a single product, collected during the most recent

batching-interval ta t; ta. Every potential customer order i 2 Ata can be characterized by a quadruple

dui ; doi ; z

ui ; z

oi of minimum order quantity d

ui, maximum order quantity d

oi , earliest date of delivery z

ui 2

fta 1; . . . ; teg and latest date of delivery zoi 2 fta 1; . . . ; teg; z

oiXz

ui . The customer requires delivery of

quantity doi within the time window zui ; zoi . He will, however, accept a partial delivery of the minimumquantity dui within z

ui ; z

oi and delivery of the remainder at a point of time t4z

oi . We assume, that at every

point of time t t ta 1; . . . ; te a given quantity ofqt units of the product is produced and put into stock.

qt is determined by the master schedule. We further assume, that the qt units can be delivered to a customer

at the point of time t. Let bt and rt denote the inventory on hand and the committed quantity of finished

goods at point of time t. Both inventory on hand bta1 at ta 1 and the committed quantities rt are

determined by AATP runs executed at ta t; ta 2t; . . .. With given values for qt, bt and rt, we can

calculate the uncommitted (available-to-promise) quantity of finished goods at point of time t, denoted by

atpt. Let lk be the holding costs for finished products per unit and time period, based on the quantity bt of

inventory on hand at point of time t t ta 1; . . . ; te. We further assume that we can calculate a profit dbifor every potential order i 2 Ata. We consider a penalty fki, associated with the denial of a potential

customer order i 2 Ata. fki accounts for contract penalties and the loss of future profits if the customerswitches to a different supplier.

To formulate a mixed-integer programming model for the determination of order quantities and due

dates we define the following decision variables:

x1it: quantity of the first (partial) delivery at point of time t for order i 2 Ata

x2it: quantity of the second partial delivery at point of time t4zoi for order i 2 Ata

u1it 1; if the due date of the first partial delivery of order i 2 Ata is t

0; else

& ';

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u2it 1; if the due date of the second partial delivery of order i 2 Ata is t

0; else

( );

vi 1; if order i 2 Ata is fulfilled

0; else

( ):

By tkwe denote the additional handling and shipping costs associated with the second delivery. These have

to be considered ifx2it40. For simplicity and without loss of generality, we assume a delivery time of zero

for every order i 2 Ata.

The decision maker has to solve the following interrelated problems: (1) Determine the potential orders

i 2 Ata which will be fulfilled within the planning horizon; (2) Determine quantities and due dates of

partial deliveries for all accepted orders. For solving these problems simultaneously, we can employ the

following mixed-integer programming model:

max PX

i2Ata

dbi vi Xte

tta1

lk bt Xte

tta1

Xi2Ata

tk u2it X

i2Ata

fki1 vi 1

s.t.

x1itXdui u

1it for all i 2 Ata; t ta 1; . . . ; te; 2

x1itpdoi u

1it for all i 2 Ata; t ta 1; . . . ; te; 3

Xzoi

tzui

x1it Xte

tzoi 1

x2it doi vi for all i 2 Ata; 4

bt1 bt qt X

i2Ata

x1it x2i t; t ta 1; . . . ; te 1; 5

x2itpdoi d

ui u

2i t for all i 2 Ata; t ta 1; . . . ; te; 6

atpt bt rt; t ta 1; . . . ; te; 7

u1it 2 f0; 1g for all i 2 Ata; t 2 zui ; z

oi ; 8

u1it 0 for all i 2 Ata; tezui ; z

oi ; 9

u2it 2 f0; 1g for all i 2 Ata; t 2 zoi ; te; 10

u1it 0 for all i 2 Ata; tezoi ; te; 11Xzoi

tzui

u1it vi for all i 2 Ata; 12

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Xtetzo

i

u2itp1 for all i 2 Ata; 13

vi 2 f0; 1g for all i 2 Ata; 14

bt; atptX0; t ta 1; . . . ; te: 15

The objective function (1) accounts for profits from promised orders, inventory costs for finished goods,

additional handling and shipping costs resulting from partial deliveries as well as (intangible) penalties

associated with order denial. The model generates an ATP schedule that can be represented by a jAtajT-

matrix x. Components of this matrix are the tuples x1it; x2it i 2 Ata; t ta 1; . . . ; te. The models

objective is to determine an optimal schedule x* which maximizes overall profit P.

Constraints (2) and (3) ensure that the quantity of the first partial delivery is within the given interval

dui ; doi . Constraint (4) ensures that the demand d

oi is met for every accepted order. Balance of finished

goods inventory is provided by constraint (5). Constraint (6) links the variables u2it, indicating the second

partial delivery taking place at point of time t, and x2i t, the quantity of the second partial delivery. Theuncommitted (available-to-promise) quantity of finished goods is provided by constraint (7). Constraints

(8)(14) define the domains of the models integer decision variables.

This model can be further customized to meet company-specific demands. We can, e.g. easily include

constraints, which ensure that certain customers do not receive any partial deliveries and a latest possible

delivery date for the second partial delivery can also be taken into account without complications.

Furthermore, a decrease in customer service, resulting from partial deliveries instead of complete deliveries

can be incorporated in the model by including additional penalties in the objective function. The model can

also be enhanced in order to account for the multi-product case (see Pibernik, 2003), different customer

priorities and the strategies considered in case of an anticipated shortage of finished goods.

The model is currently being employed for order quantity and due date quoting at Merz

Pharmaceuticals. A corresponding Lingo 8.0 file as well as experimental data can be obtained fromhttp://data.dsmserver.net/atp.

4.2. A planning mechanism for real-time AATP based on finished goods inventory

Customer requirements and preferences in regard to the response time (see Section 3.2) may necessitate

AATP to be operated in real-time mode. Customer requirements mainly depend upon the product subject

to order quantity and due date quoting and the relevant distribution channel. When, e.g. products are

highly standardized and sold via a web-based retail site, customers may not be willing to accept the (longer)

response times associated with AATP operated in batch mode. In this case, the application of real-time

AATP is feasible. Employing real-time AATP implies that order quantities and due dates are determined at

the time an order or request is received. Order promising then requires a planning mechanism whichinstantly determines product availability, generates order quantities and due dates and considers alternative

fulfillment strategies in case of shortages for every incoming order. It should be noted, that processing each

order separately, which is inevitable when operating AATP in real-time mode, generally leads to a decrease

in performance compared to AATP operated in batch mode. Neither customer priorities nor the

consequences of order acceptance on the fulfillment of orders arriving at a later point in time can

adequately be considered. However, firms may be forced or may be willing to put up with a loss of AATP

performance in order to meet customer requirements or enhance customer service in regard to the response

time.

We will now outline an appropriate planning mechanism for a multi-location real-time AATP based on

finished goods inventory and substitute products. Real-time order quantity and due date quoting for a

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single incoming order is illustrated in Fig. 1. The planning mechanism is triggered by the arrival of a

prospective customer order i0. We pre-suppose that every customer is assigned to a definite warehouse

location. At first, the uncommitted quantity of finished goods atpt is determined for every point of time t

within the customers delivery time window Zui0 ; Zoi0

: Thereupon, it is assessed whether the order can be

fulfilled within the customers delivery time window, i.e. if atptXdoi0 applies for any 2 Z

ui0 ; Z

oi0

: If the

ordered quantity is available, a due date t 2 Zui0 ; Zoi0

is determined. The due can be determined on the basis

of a specific assignment rule, e.g. t min tjatptXdoi0 ; t 2 Z

ui0 ; Z

oi0

:

(earliest date of delivery) or t

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Customer order has beenreceived

Determine uncommited quantitiesof product at assigned location

Check if order can be fulfilled onthe basis of uncommitted quantity

XOR

Order can be fulfilled

within time window

Order cannot be fulfilled

within time window

AND

Determine uncommited quantities

at alternative locations

Determine availability of

substitute products

AND

Generate feasible fulfillment strategies

XOR

Feasible fulfillment-strategies available

No feasible fulfillmentstrategies available

Assessment of alternative strategies

Propose first strategy to customer

XOR

Customer accepts strategy Customer does not acceptstrategy

XOR

Order denial

XOR

Propose second strategy to customer

XOR

Customer accepts strategy Customer does not accept

strategy

Confirmation, reservation andrecalculation of uncommited quantity

XOR

End of AATP run

Fig. 1. Real-time AATP planning mechanism.



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max tjatptXdoi0 ; t 2 Z

ui0 ; Z

oi0

:

(latest date of delivery) in order to maintain flexibility for the fulfillment of

orders arriving at a later point in time. After due date determination, the customer is informed accordingly,

the required quantity is reserved and the uncommitted quantity of finished goods is recalculated for a

subsequent execution of the planning mechanism.In the event of a shortage of finished goods at the assigned warehouse location, availability of the original

product as well as substituted products is assessed at alternative locations in the companys distribution

network. If the product or substitute is available, feasible fulfillment strategies are generated.

Determination of alternative strategies has to be defined by a set of rules complying to company-specific

conditions, i.e. cost and required time for shipping and handling, unit profit margins for regular and

substitute products as well as customer-specific information such as delivery time requirements and the

potential acceptance of partial deliveries and substitute products. By this set of rules it must be ensured that

only feasible strategies are generated from the companys and the customers viewpoint.

If a multiple number of alternative fulfillment strategies are available, the planning mechanism has to

support their assessment on the basis of detailed information on handling and shipping costs. This

assessment then determines the sequence in which the fulfillment strategies are proposed to the customer. If

the customer accepts the first approach in this sequence, the corresponding quantities of finished goods are

reserved, delivery is confirmed and the uncommitted quantity recalculated as described previously.

Otherwise, the second strategy in sequence will be proposed, etc. If the customer does not accept any of the

alternative fulfillment strategies, the order will be rejected.

The depicted planning mechanism can be further customized in order to meet company-specific

requirements. These modifications can, e.g. include specific rules for identifying and assessing alternative

strategies in case of a temporary shortage of finished goods. From the description in Fig. 1 we can derive a

formal algorithm for real-time order quantity and due date quoting which can be directly executed by a

software application supporting real-time AATP. A corresponding formulation of an algorithm is provided

in the appendix of this paper.

5. Advanced ATPs requirements for operations and inventory management

The characterization of AATP types in Section 3 and the description of adequate methods in Section 4

facilitate an analysis of the requirements operations and inventory management have to meet in order to

successfully quote order quantities and due dates. We will first focus on AATP based on finished

goods inventory for which suitable methods were introduced in the previous section. Thereupon we will

also consider additional requirements resulting from active and passive AATP based on supply chain

resources.

From the AATP methods shown in Section 4 it becomes clear that models and algorithms generating

order quantity and due date quotes based on pertinent information concerning customer orders,

uncommitted finished goods quantities as well as customer priority and preference, represent the core ofAATP planning mechanisms. Consistently, operations and inventory management have to provide the

relevant information and data for the models and algorithms applied. Both the batch model and the real-

time planning mechanism, introduced in the previous section, clarify the information which has to be

provided by operations and inventory management software systems in order to successfully perform order

quantity and due date quoting. The relevant data can be divided into data on the availability of finished

goods as well as data regarding handling and shipping lead times and costs (see Sections 4.1 and 4.2). In

regard to the former, operations management has to provide an exhaustive master schedule, which must

specify exactly the type, quantity and date of completion for the products produced within the AATP

planning horizon. Inventory management must provide the real inventory on hand throughout the AATP

planning horizon and the previously committed quantities of finished goods. Furthermore, handling and

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transportation capacity as well as costs, depending on customer and warehouse locations, need to be on

hand. The latter are a pre-requisite for the assessment of alternative fulfillment strategies including

deliveries from diverse warehouse locations, when multi-location AATP is practiced. Apparently, a detailed

process analysis in regard to handling and shipping lead times and costs must precede the implementationof AATP.

Applying multi-location AATP also calls for manufacturing resource planning systems and inventory

management systems at the incorporated locations, which provide the relevant data on time and in a pre-

defined processable format.

Employing a real-time AATP further increases the demands on information provision. The relevant data

for performing the real-time planning mechanism has to be made available on a continuous basis. The

committed quantities of finished goods must instantly be updated after executing the planning mechanism

(see Section 4.2). Online access and fast transmission techniques for attaining satisfactory response times

and accurate order quantity and due date quotes must be employed.

The AATP performance depends significantly on the quality of the provided information.

Essential requirements are the availability and transmission of accurate and error-free data. Also,

delivery lead times and costs have to be determined on the basis of a detailed analysis of the

warehousing, handling and transportation activities in order to avoid rejection or delayed completion of

customer orders due to erroneous information used in the AATP planning mechanism. Besides that, a

precise lead time and cost analysis is indispensable for an accurate assessment of alternative fulfillment

strategies.

Clearly, supplying and processing operations and inventory management information is a crucial

factor for a successful application of AATP. However, AATP performance also depends strongly

on the capability of operations and inventory management to actually fulfill the orders, promised

on the basis of AATPs calculations. To a great extent, AATPs adherence to due dates is affected

by the consistency and stability of the master schedule as well as inventory and transportation

planning. Frequent amendments, especially of the master schedule, will ultimately diminish the AATP

performance due to discrepancies between the master schedule used for AATP and the implemented masterschedule.

Subsequent to the requirements specified for AATP based on finished goods, AATP based on supply

chain resources needs meticulous information regarding supply chain capacity requirements for each

product subject to the AATP planning mechanism being employed. In addition, AATP requires complete

information about supply chain resource availability within the AATP planning horizon. It becomes clear

that for AATP based on supply chain resources is an extensive integration of manufacturing resource,

inventory and transportation planning with the AATP module is essential.

Passive AATP on the basis of supply chain resources does not have direct impact on manufacturing

resource planning, apart from determining the accepted orders and their due dates. They may, however,

reserve supply chain capacity on the basis of the orders accepted. Evidently, the AATP performance is, to a

large extent, determined by the ability of production planning and scheduling to meet the due dates givenby the AATP schedule. This in turn is subject to the pre-disposition of the AATP model or algorithm

engaged to resolve AATP schedules and also, yet again on the quality of the information utilized in the

AATP planning mechanism.

When utilizing active AATP, a further amalgamation of the AATP with manufacturing resource

planning as well as inventory and transportation management is indispensable. Active AATP not only

determines due dates and reserves supply chain capacity, but also is, in fact, integrated into master

scheduling and material requirements planning. Therefore, active AATP on the basis of supply chain

resources is not just simply a stand-alone mechanism for allocating customer orders to finished goods

and supply chain resources on the basis of operations and inventory data; it is in fact a vital part of

production and inventory planning.

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6. Conclusions

The development of models and algorithms for AATP has to be based on well-founded classification of

alternative AATP types. On the basis of such classification as well as characterization of additional AATPfeatures, we presented a model for batch AATP and a planning mechanism for real-time AATP based on

finished goods inventory, taking alternative fulfillment strategies, e.g. AATP with partial deliveries, into

account. Both the model and the algorithm can be modified in order to meet company-specific demands

and therefore represent a good starting point for further development and implementation of methods

supporting AATP. The models and algorithms presented in this paper cover only two generic AATP types.

Consequently, further research in the field of AATP is required with regard to the other AATP types,

identified in Section 3. Yet, both the fundamentals of AATP, provided in Section 4 and the requirements

for operations management and inventory management, derived in Section 5 can give valuable input for

supplementary research and development.

Appendix A. Real-time AATP Algorithm

The real-time AATP algorithm generates order quantities and due dates for a single order i0, which is

assigned to a specific warehouse location h 1. In case of a shortage of finished goods at location h 1,

the algorithm generates three alternative fulfillment strategies depending upon finished product availability:

Partial deliveries from warehouse location h 1. Complete delivery from alternative warehouse locations, denoted by h 2; . . . ; H. Complete delivery of pre-defined substitute products, denoted by s 1; . . . ; S, from warehouse locations

h 1; . . . ; H.

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