614
Understanding the Development of Free E-Commerce/E-Business Software
FOSSD Capability Enabling Free, Open
ERP, and EC/EB Systems
The array of social, technological, and informa-
tional resources that enable a FOSS development
project like GNUe is substantial. However, they
differ in kind and form from the traditional enter-
prise resources that are provided to support pro-
prietary, closed source software systems. These
traditional software engineering resources are
money (budget), time (schedule), skilled (salaried)
development staff, project managers (adminis-
trative authority), quality assurance (QA) and
testing groups, documentation writers, computer
hardware and network maintainers, and others (cf.
Sommerville, 2004). Free software projects like
GNUe seem to get by with comparatively small
amounts of money, though subsidies of various
kinds and sources are present and necessary. They
also get by without explicit schedules, though
larger projects may announce target release dates,
as well as (partially) order which system functions
or features will be included in some upcoming
versions, for some target releases. Further, they
get by without a rule-making and decision-making
authority of corporate project managers or enter-
prise executives, who may or may not be adept at
empowering, coaching, or rewarding development
staff to achieve corporate software development
goals. Instead, in GNUe, participants rely on an

implicit but frequently recited regime of beliefs,
values, and norms that help organize coopera-
WLYHDFWLYLW\ DQG UDWLRQDOL]H FRQÀLFWPLWLJDWLRQ
(Elliott & Scacchi, 2003, 2005). The remaining
resources are provided within a free software
development effort via subsidies, sponsorship,
or volunteer effort.
Thus, the resources for free software develop-
ment efforts are different in kind, and in how they
are arrayed and brought to bear when compared
to a traditional software engineering effort. Free
software project resources are not mobilized, al-
located, or otherwise brought to bear in the man-
ner traditional to the development of proprietary,
closed source software systems. Hopefully, it
should be clear that the differences being high-
lighted are not based simply on a comparison of
functionality or features visible in the develop-
ment or use of open vs. close source software
products. As such, the resource-based capability
for developing free software packages for ERP
and EC/EB applications is different, though not
necessarily more or less costly.
DISCUSSION AND CONCLUSION
Many questions about free software development
remain u nanswered or unexamined by this st udy.
For example, it is unclear whether there must be
a critical mass of salaried software developers
whose job includes development or support of
GNUe software, and if so, how many software

developers this entails. Over the four years in the
study of GNUe, the number and composition of
core software developers has changed, partly in
response to their changing interests and work
situations. The project has not grown to the point
where commercialization of the GNUe software
has become an imperative or new venture start-
up opportunity, as has happened with OSS ERP
projects of Compiere, OpenMFG, and Openbravo.
Thus, it is unclear whether GNUe will become a
viable enterprise capable of hiring a professional
or full-time staff, as well as engaging in con-
tracted provision of installation, customization,
and support services that normally accompany
ERP and EC/EB software packages. Further
comparative study of other free software projects
and their approach for commercialization are
needed. However, it does seem clear that GNUe
has managed to sustain itself as a viable ongoing
enterprise that continues to develop and sustain
free ERP and EC/EB software packages, which
its developers use and deploy in their day jobs or
consulting practices.
615
Understanding the Development of Free E-Commerce/E-Business Software
Beyond this, three conclusions can be drawn
from the study, data, and analysis presented in this
UHSRUW)LUVWWKLVFKDSWHULGHQWL¿HVPDQ\W\SHV
of socio-technical resources and resource-based
capabilities for free EC/EB that may explain/pre-

dict (a) what’s involved, (b) how it works, or (c)
what conditions may shape the longer-term suc-
cess or failure of such efforts. In simple terms,
these resources include time, skill, effort, belief,
personal and corporate subsidies, and commu-
nity building on the part of those contributing
as developers and users of free EC/EB systems
and techniques. Of these, belief in the freedoms
that open source system development embraces,
including freedom of choice and freedom of ex-
pression (Elliott & Scacchi, 2003, 2005, 2006)
appears central. Such belief in turn enables and af-
fords the ongoing commitment, development, and
articulation of a web of social, technological, and
information resources that sustain a free software
project, without the traditional administrative and
¿QDQFLDOUHVRXUFHVIRXQGLQWUDGLWLRQDOVRIWZDUH
development enterprises. Developers and users
who believe in the promise and potential of free
ERP or EC/EB packages are willing to allocate
(or volunteer) their time and apply their skills to
make the effort of developing or using open source
systems a viable and successful course of action.
Thus, companies seeking to invest in or exploit
free EC/EB techniques or systems must account
for how it can most effectively cultivate a free
software culture, belief system, and community
of practice, as part of their strategic choice.
6HFRQG WKLV LV WKH ¿UVW VWXG\ WR HPSOR\ D
resource-based view of a FOSS development

project. The resource-based view of organiza-
tional capability and competitive advantage is the
dominant analytical lens employed in studies of
organizational strategy and strategic management
(cf. Acedo et al.,2006; Barney, 2001). Why should
people interested in FOSS development practices
be concerned or interested in such a strategic per-
spective? Many reasons might be cited in support,
but attention here can be drawn to determining
whether free software systems and development
methods offer sustained or differentiated ad-
vantages over traditional software engineering
approaches applied to the development of close
source, proprietary (non-free) software systems.
If there are advantages that can be traced to the
resource arrangements found in free software proj-
ects like GNUe, then these would be noteworthy
¿QGLQJVDVZHOODVDSRVVLEOHEDVLVIRUIXUWKHU
exploration and theorizing. Accordingly, in the
GNUe case, resources like personal computing
tools that help subsidize the development effort,
beliefs that provide a cultural basis for making
decisions about technical choices, trust and social
accountability, discretionary software develop-
ment work times, and the preferred use of software
informalisms instead of software engineering
IRUPDOLVPV OLNH ³UHTXLUHPHQWV VSHFL¿FDWLRQV´
DQG³SURMHFWPDQDJHPHQWSODQV´DOOGLIIHUHQWLDWH
the practice of free software development from
that advocated in traditional software engineering

textbooks (e.g., Sommerville, 2004).
Last, this study links free software with ERP
and EC/EB. No prior case studies of ERP or EC/
(%V\VWHPVKDYHLGHQWL¿HGRUDGGUHVVHGZKHWKHU
or how free software (or open source software)
methods might be used to develop or integrate EC/
EB software packages, at least beyond the use of
OSS Web servers or Web site content management
systems (Carbone & Stoddard, 2001). Thus, there
LVDQRSSRUWXQLW\IRU¿UPVWREHJLQFRQVLGHULQJ
whether these results merit timely consideration
or exploratory investments in free software or
OSS. For example, companies offering consumer
products or high value, information technology-
based products and services may begin to consider
whether free EC/EB capabilities that offer lower
purchase prices, lower total cost of ownership,
and higher quality (Scacchi, 2002b) represent
new market entry or new product differentiation
RSSRUWXQLWLHV6LPLODUO\FRPSDQLHVPD\¿QG
that free/open source software represents a new,
616
Understanding the Development of Free E-Commerce/E-Business Software
highly innovative approach to software product
or application system development that marries
the best capabilities from both private investment
and collective action (von Hippel & von Grogh,
2003; Olson, 1971).
REFERENCES
Acedo, F. J., Barroso, C., & Galan, J. L. (2006).

The resource-based theory: Dissemination and
main trends. Strategic Management J., 27(7),
621-636.
Ackerman, M., & Halverson, C. (2000, January).
Reexamining organizational memory, Commu-
nications ACM, 43(1), 59-64.
Barney, J. B. (2001). Resource-based theories of
competitive advantage: A ten-year retrospective
on the resource-based view. J. Management,
27(6), 643-650.
Benkler, Y. (2006). The wealth of networks. New
Haven, CT: Yale University Press.
Bergquist, M., & Ljungberg, J. (2001). The power
of gifts: Organizing social relationships in open
source communities. Information Systems J.,
11(4), 305-320.
Carbone, G., & Stoddard, D. (2001). Open source
enterprise solutions: Developing an e-business
strategy. New York: John Wiley and Sons, Inc.
Crowston, K., & Howison, J. (2006). Hierarchy
and centralization in free and open source software
team communications. Knowledge, Technology
and Policy, 18(4), 65-85, Winter.
Crowston, K., & Scozzi, B. (2002). Open source
software projects as virtual organizations: Com-
petency rallying for software development. IEE
Proceedings—Software, 149(2), 3-17.
Curran, T. A., & Ladd, A. (2000). SAP R/3 busi-
ness blueprint: Understanding enterprise supply
chain management (2

nd
ed.). Upper Saddle River,
NJ: Prentice-Hall.
CW360. (2002, June 12). JD Edwards pushes
modular ERP. ComputerWeekly.
Danziger, J. (1979). The skill bureaucracy and
intraorganizational control: The case of the data-
processing unit. Sociology of Work and Occupa-
tions, 21(3), 206-218.
DiBona, C., Ockman, S., & Stone, M. (1999). Open
sources: Voices from the open source revolution.
Sebastopol, California: O’Reilly Press.
Elliott, M., & Scacchi, W. (2003, November 21-
30). Free software developers as an occupational
FRPPXQLW\5HVROYLQJFRQÀLFWVDQGIRVWHULQJFRO-
laboration. In Proc. ACM Intern. Conf. Supporting
Group Work (Group’03), Sanibel Island, FL.
Elliott, M., & Scacchi, W. (2005). Free software
G H YHO R S P H QW  & R R S H U DW LR Q D QG F R Q À L F W L Q D Y L U W X D O 
organizational culture, in S. Koch (Ed.). Free/
open source software development (pp. 152-172).
Pittsburgh, PA: Idea Publishing.
Elliott, M., & Scacchi, W. (2006). Mobilization of
software developers: The free software movement
(submitted for publication).
Espinosa, J. A., Kraut, R. E., Slaughter, S. A.,
Lerch, J. F., Herbsleb, J. D., et al. (2002, De-
cember). Shared mental models, familiarity, and
coordination: A multi-method study of distributed
software teams. Intern. Conf. Information Sys-

tems, Barcelona, Spain (pp. 425-433).
Feller, J., & Fitzgerald, B. (2002). Understand-
ing open source software development. NY:
Addison-Wesley.
Fielding, R. (1999). Shared leadership in the
Apache project, Communications ACM, 42(4),
42-43, 1999.
Fogel, K., (1999). Supporting open source develop-
ment with CVS. Scottsdale, AZ: Coriolis Press.
617
Understanding the Development of Free E-Commerce/E-Business Software
Gallivan, M. (2001). Striking a balance between
trust and control in a virtual organization: A con-
tent analysis of open source software case studies.
Information Systems J., 11(4), 277-304.
German, D. (2003). The GNOME project: A case
study of open source, global software develop-
ment. Software Process—Improvement and
Practice, 8(4), 201-215.
Glaser, B., & Strauss, A. (1967). The discovery
of grounded theory: Strategies for qualitative
research. Chicago: Aldine Publishing.
Hann, I-H., Roberts, J., Slaughter, S. L., & Fielding,
R. (2002, May). Why do developers contribute to
open source projects? First evidence of economic
incentives. In Proc. 2
nd
Wo r k s h o p o n O p e n S o u r c e
Software Engineering, Orlando, FL.
Hars, A., & Ou, S. (2002). Working for free? Mo-

tivations for participating in open-source projects.
Intern. J. Electronic Commerce, 6(3), 25-39.
Hakken, D. (1999). Cyborgs@Cyberspace?
An ethnographer looks at the future. London:
Routledge.
Hertzum, M. (2001). The importance of trust in
software engineers’ assessment and choice of
information sources. Information and Organiza-
tion, 12(1), 1-18.
Hine, C.M. (2000). Virtual ethnography. Newbury
Park, California: Sage Publications. .
Hoopes, D. G., Madsen, T. L., & Walker, G. (2003).
Why is there a resource-based view? Toward a
theory of competitive heterogeneity. Strategic
Management J., 24(10), 889-902.
Jensen, C., & Scacchi, W. (2005). Process model-
ing across the Web information infrastructure.
Software Process—Improvement and Practice,
10(4), 255-272.
Jensen, C., & Scacchi, W. (2007). Role migration
and advancement processes in OSSD projects.
In Proceedings of the 29th Inter. Conference on
Software Engineering (to appear). Minneapolis,
MN: ACM Press.
Keller, G., & Teufel, T. (1998). SAP R/3 process
oriented implementation: Iterative process pro-
totyping (A. Weinland, Trans.). Harlow, England:
Addison Wesley Longman.
Kim, A.J. (2000). Community building on the
Web: Secret strategies of successful online com-

munities. Peachpit Press.
Kwansik, B., & Crowston, K. (2005). Introduction
to the special issue: Genres of digital documents.
Information, Technology and People, 18(2).
Lave, J., & Wenger, E. (1991). Situated learning:
Legitimate peripheral participation. Cambridge,
UK: Cambridge University Press.
Lerner, J., & Tirole, J. (2002). Some simple
economics of open source. Journal of Industrial
Economics, 52.
Lessig, L. (2005). Free culture: The nature and
future of creativity. New York: Penguin Press.
Madey, G., Freeh, V., & Tynan, R. (2005).
Modeling the F/OSS community: A quantative
investigation. In S. Koch (Ed.). Free/open source
software development (pp. 203-221). Hersey, PA:
Idea Group Publishing.
Marwell, G., & Oliver, P. (1993). The critical
mass in collective action: A micro-social theory.
Cambridge University Press.
Monge, P. R., Fulk, J., Kalman, M. E., Flanagin, A.
J., Parnassa, C., & Rumsey, S. (1998). Production
of col le ctive act ion in a llia nce-based i nterorga ni-
zational communication and information systems.
Organization Science, 9(3), 411-433.
Noll, J., & Scacchi, W. (1999, February). Support
-
ing software development in virtual enterprises.
Journal of Digital Information, 1(4).
618

Understanding the Development of Free E-Commerce/E-Business Software
Oliver, C. (1997). Sustainable competitive advan-
tage: Combining institutional and resource-based
views. Strategic Management J., 18(9), 697-713.
Olson, M. (1971). The logic of collective action.
Cambridge, MA: Harvard University Press.
Pavlicek, R., (2000). Embracing insanity: Open
source software development. Indianapolis, In-
diana: SAMS Publishing.
Samuelson, P. (1954). The pure theory of public
expenditure. Review of Economics and Statistics,
36, 387-390.
Scacchi, W. (2001). Redesigning contracted ser-
vice procurement for Internet-based electronic
commerce: A case study. J. Information Technol-
ogy and Management, 2(3), 313-334.
Scacchi, W. (2002a). Understanding the require
-
ments for developing open source software
systems, IEE Proceedings—Software, 149(2),
24-39.
Scacchi, W. (2002b). Is open source software
development faster, better, and cheaper than soft-
ware engineering? In Proceedings 2
nd
Workshop
on Open Source Software Engineering, Orlando,
FL.
Scacchi, W. (2004, January/February). Free/open
source software development practices in the game

community. IEEE Software, 21(1), 59-67.
Scacchi, W. (2005). Socio-technical interaction
networks in free/open source software develop-
ment processes. In S. T. Acuña & N. Juristo (Eds.).,
Software process modeling (pp. 1-27).New York:
Springer Science+Business Media Inc.
Scacchi, W., Jensen, C., Noll, J., & Elliott, M.
(2006). Multi-modal modeling: Analysis and
validation of open source software requirements
processes. Int. Journal of Information Technology
and Web Eng., 1(3), 49-63.
Sharman, S., Sugurmaran, V., & Rajagopalan,
B. (2002). A framework for creating hybrid-open
source software communities. Information Sys-
tems J., 12(1), 7-25.
Skok, W., & Legge, M. (2002). Evaluating enter-
prise resource planning (ERP) systems using an
interpretive approach. Knowledge and Process
Management, 9(2), 72-82.
Sommerville, I. (2004). Software engineering (7
th
ed.). New York: Addison-Wesley.
Strauss, A., & Corbin, J. (1980). Basics of quali-
tative research: Techniques and procedures for
developing grounded theory. Newbury Park,
CA: Sage.
Viller, S., & Sommerville, I. (2000). Ethnographi-
cally informed analysis for software engineers. In-
tern. J. Human-Computer Studies, 53, 169-196.
von Hippel, E., & von Krogh, G. (2003). Open

VRXUFHVRIWZDUHDQGWKH³SULYDWHFROOHFWLYH´LQ-
novation model: Issues for organization science.
Organization Science, 14(2), 209-223.
West, J., & O’Mahony, S. (2005). Contrasting
community building in sponsored and commu-
nity founded open source projects. In Proc. 38
th
Hawaii Intern. Conf. Systems Sciences, Waikola
Village, HI.
Williams, S. (2002). Free as in freedom: Richard
Stallman’s crusade for free software. Sebastopol,
California: O’Reilly Books.
ENDNOTES
1
The research described in this report was
supported by grants from the U.S. National
Science Foundation Industry/University
Research Cooperative CRITO Consor-
tium; the National Science Foundation
#0083075, #0205679, #0205724, #0350754,
and # 0534771; and the Defense Acquisition
University by contract N487650-27803. No
endorsement implied.
619
Understanding the Development of Free E-Commerce/E-Business Software
2
Compiere.com is a software product devel-
opment community that is building an open
source software ERP system that requires the
use of Oracle. It is not, however, free soft-

ZDUHDVLQ³IUHHGRP´VRIWZDUH:LOOLDPV
2002). Compiere.com, however, claims more
than 500K copies of its software have been
downloaded or installed, making it the most
widely deployed ERP system in the world,
whether as a proprietary or FOSS-based
offering.
3
End-user license agreements (EULAs), as-
sociated with probably all software, often
VHHNWRGHFODUH³IUHHGRPIURPOLDELOLW\´IURP
people who want to use licensed software
for intended or unintended purposes. But
liability freedom is not the focus here.
This work was previously published in Electronic Commerce: Concepts, Methodologies, Tools, and Applications, edited by A.
Becker, copyright 2008 by Information Science Reference (an imprint of IGI Global).
620
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 2.18
Balancing Accuracy of Promised
Ship Date and IT Costs
Young M. Lee
IBM T. J. Watson Research Center, USA
ABSTRACT
In an ideal e-business environment, when a
customer order is scheduled and a ship date is
computed, the availability should immediately be
reserved and not be available for future orders.
However, in reality the availability data that are
used for the scheduling the orders are not real

time availability (physical availability), but they
are availability information stored in an IT system
(system availability). The availability data in the
IT system (static view of availability) is typically
refreshed (synchronized with real time availabil-
ity) only periodically since it is very expensive
to update the database in real time. Due to this
potentially inaccurate view of the availability,
some orders cannot be shipped on the promised
ship date. Therefore, for certain customer orders,
products are shipped later than the promised ship
date resulting in customer dissatisfaction. There-
IRUH RQH RI NH\ GHFLVLRQV LQ RUGHU IXO¿OOPHQW
process is to properly balance IT system (e.g., IT
expense) and accuracy of promised ship date. In
this work, we study how availability fresh rate
(IT system) impacts customer service level. The
simulation model we develop helps making critical
business decision on refresh rate of availability,
and avoiding expensive IT investment.
INTRODUCTION
Being able to promise customers the desirable
GHOLYHU\GDWHDQGIXO¿OOLQJWKHRUGHUVDVSURPLVHG
are an important aspects of customer services.
With the recent surge and widespread use of e-
commerce, shoppers can now easily assess and
compare customer service quality in addition
to quality of goods and price among different
vendors. This creates a very competitive business
environment, thus making customer service a

critical factor for success and survival of many
companies. Competitive pressures are forcing
companies to constantly look for ways to improve
customer services by evaluating and redesigning
supply chain processes. Availability Management
Process (AMP), also called Available-to-Promise
(ATP) process, is a key supply chain process that
621
Balancing Accuracy of Promised Ship Date and IT Costs
impacts customer service since it determines
customer promised ship (or delivery) dates, the
accuracy of the promised ship date, order sched-
XOLQJGHOD\DQGRUGHUIXO¿OOPHQWUDWHDVZHOODV
inventory level.
The availability management involves generat-
ing availability outlook, scheduling customer or-
GHUVDJDLQVWWKHDYDLODELOLW\RXWORRNDQGIXO¿OOLQJ
the orders. Generation of Availability Outlook is a
push-side of the availability management process,
and it allocates availability into ATP (Available-
to-Promise) quantities based on various product
and demand characteristics and planning time
periods. Order Scheduling is a pull-side of avail-
ability management process, and it matches the
customer orders against the Availability Outlook,
determines when customer order can be shipped,
and communicates the promised ship date to
FXVWRPHUV 2UGHU IXO¿OOPHQW LV H[HFXWLQJ WKH
shipment of the order at the time of promised ship
date. Even if an order is scheduled for shipment

for a certain date based on the outlook of avail-
ability, the resources that are required to ship the
product on the promised ship date may not actu-
ally available when the ship date comes. A key
role for effective availability management process
is to coordinate and balance the push-side and
pull-side of ATP, and to have adequate Informa-
tion System (IS) capability so that desirable and
accurate ship date is promised to customer and
product is shipped on the promised date.
AMP or ATP process has been described in
several research papers. Ball, Chen, and Zhao
(2004) gave an overview of the push-side (Avail-
ability Planning) and pull-side (Availability
Promising) of ATP with examples from Toshiba,
Dell, and Maxtor Corporation. They stressed the
importance of coordinating the push and pull-
side of availability management for supply chain
performance by making good use of available
resources. Although ATP functions has been
available in several commercial ERP and Supply
Chain software such as SAP’s APO, i2’s Rhythm,
Oracle’s ATP Server and Manugistics’ SCPO
modules, and so forth for several years (see Ball et
al. (2004) for details), those ATP tools are mostly
fast search engines for availability database, and
they schedule customer orders without any so-
phisticated quantitative methods. Research on the
quantitative side of ATP is still at an early stage,
and there are only a limited number of analytic

models developed in supporting ATP.
For the push-side of ATP, Ervolina and Dietrich
(2000) developed an optimization model as the
resource allocation tool, and described how the
PRGHOLVXVHGIRUDFRPSOH[&RQ¿JXUHGWR2UGHU
(CTO) environment of the IBM Server business.
They also stress how the push-side (Availability
Promising) and pull-side (Availability Planning)
have to be work together for the overall availability
management performance.
For the pull-side of ATP, Chen, Zhao, and
Ball (2002) developed a Mixed-Integer Program-
ming (MIP) optimization model for a process
where order promising and fulfillment are
KDQGOHGLQDSUHGH¿QHGEDWFKLQJLQWHUYDO7KHLU
model determines the committed order quantity
for customer orders that arrive with requested
delivery dates by simultaneously considering
material availability, production capacity as well
as material compatibility constraints. They also
studied how the batching interval affects sup-
ply chain performance with different degree of
resource availability. Moses, Grand, Gruenwald,
and Pulat (2004) also developed a model that
computes optimal promised ship date considering
QRWRQO\DYDLODELOLW\EXWDOVRRWKHURUGHUVSHFL¿F
characteristics and existing commitments to the
previous scheduled orders. Pan and Shi (2004)
also developed a heuristics-based order promis-
ing model but with E-commerce environment in

mind. They modeled a process where customer
orders arrive via Internet and as earliest possible
shipment dates are computed in real-time and is
promised to customers.
All the previous work described above deal
with either push-side of ATP or pull-side of ATP
with an assumption that accurate inventory data
622
Balancing Accuracy of Promised Ship Date and IT Costs
are available in real time. However, in reality
the inventory data is less than perfect, and even
if the optimal ATP tools were in place, order
IXO¿OOPHQWSHUIRUPDQFHZRXOGEHOHVVWKDQRS-
timal. The optimality can be approached only if
there exists information technology (IT) in the
availability management process making avail-
able accurate inventory data in real time. In this
article, we describe an availability management
s i m u l a t i o n t o ol t h a t e s t i m a t e s t h e a c c u r a c y o f s h i p
date commitment at the presence of imperfect IT
environment, which results in inaccurate view of
available inventory.
Determination of promised ship date is based
on availability (inventory) information kept in a
computer system (system inventory), which is
assumed to be accurate. In actuality, the system
inventory and the actual inventory (physical
inventory) are synchronized only periodically
due to various reasons such as IT costs for the
data synchronization, inventory loss, transac-

WLRQHUURUDQGLQFRUUHFWSURGXFW LGHQWL¿FDWLRQ
The error between the system inventory and the
physical inventory could accumulate over time and
is not corrected until the refresh of availability
(synchronization of inventory), which takes place
only periodically (for example, once a day, or a
few times a day) since it is expensive to generate
new snapshot of availability that are consistent
throughout various corporate business systems
including ERP (Enterprise Resource Planning)
system. In fact, inventory inaccuracy has been
LGHQWL¿HGDVDOHDGLQJFDXVHIRURSHUDWLRQDOLQHI-
¿FLHQF\LQVXSSO\FKDLQPDQDJHPHQW$UHFHQW
study (DeHoratius & Raman, 2004) shows that
WKHYDOXHRIWKHLQYHQWRU\UHÀHFWHGE\WKHVHLQDF-
curate records amounts to 28% of the total value
of the on-hand inventory for a leading retailer
in the U.S.
There have been studies on impact of inventory
inaccuracy on supply chain performance, includ-
ing Iglehard and Morley (1972), Wayman (1995),
Krajewski, King, Ritzman, and Wong (1987) and
Brown Brown, Inman, and Calloway (2001). More
recently, Kang and Koh (2002) simulated the ef-
fect of inventory shrinkage (thus inaccuracy) in
an inventory replenishment system with an (s, S)
policy. Kang and Gershwin (2004) and Kök and
Shang (2004) developed methods to compensate
for the inventory inaccuracy in replenishment.
Fleisch and Tellkamp (2005) analyzed the im-

pact of various causes of inventory discrepancy
between the physical and the information system
inventory on the performance of a retail supply
chain based on a simulation model. Our work also
studies the impact of inventory inaccuracy, but
on accuracy on ship date commitment through a
discrete-event simulation modeling approach.
Discrete-event simulation has been around
for many years in simulating supply chain man-
agement (SCM) processes to evaluate its effec-
tiveness. McClelland (1992) used simulation to
study the effect of MPS method, variability of
demand/supplier response on customer services,
order cycle, and inventory. Hieta (1998) analyzed
the effect of alternative product structures, alter-
native inventory and production control methods
on inventory and customer service performance.
Bagchi, Buckley, Ettl, and Lin (1998) evaluated the
design and operation of SCM using simulation and
optimization, analyzed SCM issues such as site
location, replenishment policies, manufacturing
policies, transportation policies, stocking levels,
lead time, and customer services. Yee (2002)
analyzed the impact of automobile model and
option mix on primary supply chain performance
such as customer wait time, condition mismatch
and part usage. Lee, Cheng, and Leung (2004)
simulated the impact of RFID on supply chain
performance. However, there has not been any
simulation modeling work that analyzes the impact

of IT system on the supply chain performance.
Development of simulation model for supply chain
such as availability management process can be
time-consuming. We hope that the simulation-
modeling framework we describe in this article
can be easily adapted to simulate various avail-
ability management situations in many business
623
Balancing Accuracy of Promised Ship Date and IT Costs
environments. The simulation framework has
been used in IBM for several years, and has been
playing a critical role in making strategic busi-
ness decisions that impacted customer services
DQGSUR¿WDELOLW\LQ,%0
The rest of chapter is organized as follows.
In the next section, we describe the availability
management process. In the following section, we
describe how ship date promising is simulated in
various availability refresh frequency. Then, we
describe simulation experiments done for IBM’s
server business, its impacts and results. Finally,
we provide conclusion and remarks.
AVAILABILITY MANAGEMENT
PROCESS
The availability management typically consists
of three main tasks: (1) generating availability
outlook, (2) scheduling customer orders against
WKH DYDLODELOLW\ RXWORRN DQG  IXO¿OOLQJ WKH
orders. The process described here is based on
IBM’s hardware businesses. For certain business,

customer orders arrive without any advance no-
WLFHUHTXHVWLQJDVHDUO\SRVVLEOHIXO¿OOPHQWRI
the orders, usually in a few days. For some other
businesses, on the other hand, customers place
orders in advance of their actual needs, often a
few months in advance. Typically, this kind of
customer places orders as early as 3 months before
the requested delivery (due) dates, and early de-
livery and payment are not allowed. Many buyers
in this environment purchase products based on
DFDUHIXO¿QDQFLDOSODQQLQJDQGWKH\W\SLFDOO\
know when they want to receive the products and
make payment.
Generation of availability outlook, is a push-
side of the availability management process, and
it pre-allocates ATP quantities, and prepares
searchable availability database for promising
future customer orders. For certain business,
availability outlook is generated by daily buckets,
and the availability planning horizon goes out a
few weeks into the future. For some other busi-
nesses, the availability outlook is allocated by
weekly buckets, and the availability is planned in
a much longer horizon, often a quarter (3 months)
into the future. ATP quantity is called Availability
Outlook for this reason. The availability outlook
is typically generated based on product type,
demand classes, supply classes, and outlook time
EXFNHWV7KHSURGXFWW\SHFDQEH¿QLVKHGJRRGV
(FG) level for Make-to-Stock (MTS) business

or components (Comp) level for Make-to-Order
072RU&RQ¿JXUHGWR2UGHU&72EXVLQHVV
Demand classes can be geographic sales locations,
sales channels, customer priority, sensitivity to
GHOLYHU\GDWHVSUR¿WDELOLW\DQGGHPDQGTXDQWLW\
Supply classes can be a degree of constraints and
value of products. Availability is pre-allocated into
Availability Outlook buckets based on the dimen-
sion described earlier, and rolled-forward daily
or weekly. The availability outlook is determined
EDVHGRQWKHDYDLODELOLW\RIFRPSRQHQWV¿QLVKHG
goods, WIP (work-in-process), MPS (master
production schedule), supplier commitment, and
SURGXFWLRQ FDSDFLW\ÀH[LELOLW\ :KHQ FXVWRPHU
orders arrive, the availability outlook is searched
in various ways according to scheduling polices
to determine the ship (delivery) date, which is
then promised to customers.
Customer order scheduling is a pull-side of
availability management, and it reacts to cus-
tomer orders and determines ship date for the
orders. Customer orders arrive with various
information such as product type, the demand
class, customer class, and due date. The order
scheduler then searches through the availability
RXWORRNGDWDEDVHDQGLGHQWL¿HVWKHDYDLODELOLW\
that meets the characteristics. The scheduling can
also be done by an ATP engine that uses certain
algorithm to optimize the scheduling consider-
ing various resources, policies, and constraints.

7KHVFKHGXOHUWKHQUHVHUYHVVSHFL¿FDYDLODELOLW\
against each order, and decrements the availability
according to the purchase quantity of the order.
The ship date of the order is determined from