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z3091427
Date Received
Title
First Name
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Mr
Joel
Judge
Class No.
Course
Assignment No.
Due Date
03-261
Information Technology for Managers
Assignment 2
8 October 2003
Class Facilitator
No. Pages (incl Appendices)
No. Words (excl Appendices)
Michelle Salmona
16
2100
Comments
Subtracted all in-text references (Smith 1999, etc..) to roughly estimate word length. I note that there is supposed to
be a separation between literature based findings and student comment. Given that I have limited experience with
DSS I thought it inappropriate to present my own views without referencing support from expert sources. As such, it
is difficult to differentiate between my comments and the literature as I made a concerted effort to support all of my
views with referenced articles. Given that academic papers should generally not be written in the first person, I found
it difficult to meet the requirement for student comment to be clear. I hope that this does not detract from my
assignment.
Declaration
I hereby declare that the work contained in this assignment is my own, and not transcribed, paraphrased, or otherwise
copied from other sources except where this is clearly acknowledged. (Note that the University provides for severe
penalties in the case of plagiarism. See http://www.student.unsw.edu.au/studentguide/misconduct.htm.)
Signed (if submitting electronically, enter your full name in place of signing)
Date
Joel Judge
08 October, 2003
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GBAT9115 - Information Technology for Managers - 03-261
Assignment 2
Decision Support Systems Overview & Assessment of the
Australian Quarantine & Inspection Service
Ballast Water Decision Support System
Student: Joel Judge (z3091427)
Due Date: 8 October 2003
Assignment Length: 2100
Class Facilitator: Michelle Salmona
1
Table of Contents
Page
Introduction
3-4
Part 1: Overview of DSS
4-8
1.1 The Components of a Decision-Support System
4-7
1.2 DSS Challenges and Limitations
7-8
Part 2: AQIS BWDSS
8-12
2.1 BWDSS Background
8
2.2 Components of the BWDSS
9-10
2.3 BWDSS Operating Environment
10-11
2.4 BWDSS Assessment
12
Conclusion
12
List of References
13-15
Figure 1: BWDSS – System Users and DSS
10
Figure 2: BWDSS Risk Assessment
11
2
Introduction:
With the ever-increasing processing power of computers and corresponding decrease
in computer costs, more and more businesses and government organisations are using
decision support systems (DSS) (Waalewijin & Arons 1997, p.1). Shim et al (2002)
define DSS as, “…computer technology solutions that can be used to support complex
decision making and problem solving”, while Turban et al (2002, p.445) offer the
following, “…a computer based information system that combines models and data in
an attempt to solve semi-structured problems with extensive user involvement.” In
today’s information hungry world, it is critical for companies to collect, analyse and
make decisions based on enormous amounts of information. (Vahidov & Kersten
2003, pp.1-2). The effective and efficient application of strategic information is
crucial for business success. Given the overwhelming nature of much of this
information, it is becoming increasingly important for organisations to develop
computer systems, which can store vast quantities of information and present this
information in ways that assists management in making key strategic business
decisions (Hamid et al 2002, p.143). The ability to make key decisions based on
extensive raw data, multiple information sources, and in a climate of ‘information
overload’, is a major challenge for many organisations. The development of DSS in
countless fields of business, applied science, engineering, medicine, etc…is helping
organisations to meet this challenge.
This paper will consider the components of DSS and provide a number of examples of
how DSS are used to assist in the decision making process. In examining DSS theory
and practice, some of the benefits and challenges of such technology will be
presented. The paper will also specifically examine the development and use of the
Australian Quarantine and Inspection Service (AQIS), Ballast Water Decision
3
Support System (BWDSS). The BWDSS will be assessed against the DSS
components identified, and the benefits and limitations of the system will be
discussed.
Part 1: Overview of DSS
1.1 The Components of a Decision-Support System:
Turban et al (2002, pp.447-448) outline ten characteristics that ‘ideal’ DSS should
have. These characteristics are:
1. Support for decision makers at all levels – semistructured and unstructured
situations/problems – combining human judgement and objective information
2. Support for multiple independent decisions
3. Support for all phases of decision making process
4. Adaptability over time to meet changing needs and environment
5. Easy to build and use models
6. Promotes learning through useful and thought provoking presentation of
information
7. Usually uses quantitative models
8. Equipped with knowledge management component which helps to solve very
complex situations
9. Can be used via the internet
10. Allows sensitivity analysis – permitting user to enter their own information best-case and worst-case scenarios. Small changes in the data may lead to
quite different recommendations.
(Turben et al 2002, pp.447-448)
At the most basic level, effective DSS must be able to assist in the decision-making
process at three clearly defined stages. A DSS needs to be able to gather relevant
information, define and analyse potential alternative courses of action, and assist with
the selection and implementation of actions (Clarke & Lambert 2000, p.85). Clarke
and Lambert (2000) define these three stages as intelligence, design and choice. They
also make the important observation, that these three stages are “interwoven” and
4
“inseparable”. This being the case DSS must be able to provide assistance with
decision-making at each of these stages for such systems to be accurate and useful.
Zapatero (1996, p.19) makes the point that there are a number of “general
characteristics” that DSS should have including; “ease of use and training, effective
response time, ability to be modified, ability to perform automatic calculations,
generation of reports, graphs etc…” The need for DSS ease of use cannot be
overemphasised. This is particularly true in business environments in which
management may not possess an expert understanding of computer systems. A fear of
the technology or lack of understanding as to its application, may lead to a DSS being
either shelved, or not being fully utilized. Likewise, management will be less likely
to rely on DSS findings and or recommendations if the system is overly complicated
and therefore difficult to comprehend. (Bharati & Chaudhury 2003).
In many business environments the speed at which issues/information can be
considered and appropriate decisions made is imperative. For example, in stock
market portfolio management, time is of the essence. Any undue delay in buying or
selling shares can lead to considerable losses and unacceptable business risks. (Tseng
& Gmytrasiewicz c2003). DSS can assist in gathering information from various
sources, which can be used to help make investment recommendations. In today’s
highly competitive business world, the need for quick, yet well considered business
decisions, based on accurate date and utilizing robust complex models is important.
With the advent of the Internet and the World Wide Web (WWW) and their
increasingly commercial application (Turben et al 2002, pp.463-465) web based DSS
are becoming ever more important. Much of the growth in DSS today is in the area of
web based systems. (Gregg et al 2002; Bharati & Chaudhury 2003; Carlsson &
5
Turban 2002; Khoo & Forgionne 2002; Bhargava & Power c2002). DSS are
becoming more adaptive and are now being used not only to support management
decisions but also to provide greater support to customers and clients. It is in the area
of customer support that web based DSS are coming to the fore. Web-based DSS…
“allow individual customers to design their own products by choosing from a menu of
attributes, components, prices, and delivery options.” (Bharati & Chaudhury 2003,
p.1). Such DSS are readily available at any number of online stores and retailers.
Similarly, companies are now in a position to monitor online customer purchasing
preferences and habits according to a wide range of factors; including geographic
location, age, gender, income, occupation, martial status, etc…Such information can
be used by DSS to assist companies in developing and marketing new products to
meet specific customer needs, requirements and preferences (Lee & Park 2003,
p.619).
When considering published research on DSS it is clear that such systems are being
used in private and public organisations for countless purposes. While it is not
possible to go into great detail here, below are listed just a few of the ways in which
DSS are being used to assist in the decision making process:
· Assisting with management decisions in libraries e.g. DECIDE – A DSS for
European libraries – (Clarke & Lambert 2000);
· Marketing DSS – Assisting marketers to make better and more informed
decisions based on “more recent, more comprehensive and more reliable”
information (Bruggen et al 2001; Waalewjin & Arons 1997; Cassie 1997). A
specific subset of DSS called Geographical information systems (GIS) are
being used to assist retailers by providing information on customer location
and purchasing habits. –(Nasirin & Birks 2002);
· Executive Information Systems (ESS) – DSS used to assist management with
strategic decision making, allowing executive analysis and facilitating
communication (Turban et al 2002: O’Donnell 2001);
6
· Insurance DSS – Helping to determine risk, insurance premiums, policy
options etc… Insurance agents/customers use Online Analytical Processing
(OLAP) to assist in determining most appropriate policy selection (Cho &
Ngai 2000);
·
Military DSS – Used for strategic military planning and combat scenarios, use
information such as terrain, enemy strength (equipment and personnel),
weather etc. e.g. The Integrated Marine Multi-Agent Command and Control
System (IMMACCS) (Pohl et al, 1999); and
· Clinical and Medical DSS – Helping doctors and patients to make informed
decisions about treatment options and assisting with clinical research (Faunt &
Leworthy 1998; Brooks 1998; Ben-Tovin 1998)
The above is by no means an exhaustive list, but gives an indication of the infinite
ways in which DSS can be used. Wherever people are confronted with semistructured
or unstructured problems, DSS can assist in the decision-making process.
1.2 DSS Challenges and Limitations:
One of the greatest challenges for the continued growth of DSS is user acceptance and
support for their implementation. There is ample evidence indicating that many DSS
are not used effectively and their findings and recommendations are often not adopted
(Lawrence et al 2002, p.382). Research supports the view that DSS are more likely to
be used and their recommendations accepted if users play a key role in the
development of the DSS (Igabaria & Guimares quoted in Eom 1999, p214).
Lack of flexibility and adaptability can also be a problem for DSS. DSS must be able
to meet new challenges and the ability to make adjustments to DSS is important.
Often systems are designed too narrowly and are incapable of providing the decision
making support required.
7
Often it is difficult for DSS models to truly reflect real-world situations. If DSS
models are not designed with great care their usefulness to decision-makers will be
greatly compromised (garbage in garbage out, or worse – fact out!!)
Liang and Hung identify a number of potential problems in developing DSS
including, “problems in defining DSS requirements, user resistance to computers, lack
of user and management support, difficulty in building models, inappropriate
hardware and software, integration problems and lack of appropriate benchmarks.”
(Liang & Hung 1997, pp.308-309).
Part 2: Analysis of the AQIS Ballast Water Decision Support System:
2.1 BWDSS Background:
AQIS is responsible for protecting Australia from the possible introduction of exotic
pests and diseases, which may be harmful to Australia’s animal, plant and human
health status. As part of this mission, AQIS is responsible for protecting Australian
coastal waters from the discharge of ballast water from ships of foreign origin.
Ballast water derived from a foreign port and discharged in Australian waters can
contain harmful aquatic pests and diseases (CSIRO 2003; Colgan & Foden 1999;
AQIS 1999). To protect against this threat, it is now mandatory for all ships arriving
in Australia to comply with ballast water management procedures (AQIS c2001, p.2).
Due to the complexity involved in assessing the ‘biological risk’ of international
vessels ballast water, AQIS in consultation with key stakeholders, developed the
BWDSS. The BWDSS “undertakes a biological risk assessment that predicts the
likelihood of entry of harmful aquatic organisms and pathogens on a tank by tank
basis on uptake and discharge information entered by the vessels Master or agent”
(AQIS c2002, p.2).
8
2.2 Components of the BWDSS:
The BWDSS has been designed as an effective ballast water management tool for
AQIS and the shipping industry. The BWDSS has a number of key components as
follows:
1. Trigger mechanism provides AQIS advance notification of a ships arrival and
the need to run the BWDSS;
2. Provides a quantitative risk assessment methodology – based on ‘target
species’
3. Provides a qualitative risk assessment methodology – based on management of
the vessel and its ballast water
4. Database containing static and dynamic information which is used by the
BWDSS
5. A decision mechanism – associates the risk with an associated action,
maintains audit trail of the decision process
6. Communication link – AQIS and Vessel
(AQIS 1999, pp. 3-4)
As depicted in figure 1, the BWDSS provides decision-making support to a number of
stakeholders including: shipmasters, AQIS Port Staff and AQIS Canberra Staff. The
BWDSS allows a number of voyage scenarios to be run, assisting the ship’s captain to
make appropriate decisions regarding ballast water management procedures prior to
the vessels arrival in Australian waters. The BWDSS also allows AQIS staff to
produce reports from the BWDSS database. These reports assist AQIS management
in formulating appropriate ballast water management policies and allow further
refinement of ballast water risk assessments for foreign ports (Colgan & Foden 1999).
9
BWDSS – System Users and DSS
Functions
Test Scenario
Utilise Risk Assessment
Enter BWRF
Information
Ship's Master/
Agent
View Risk
Assessment
Record
Compliance
AQIS Port Staff
Produce Reports
Analsye Data
AQIS Ballast
Water Program
Maintain
Reference Data
BWDSS
Administrator
(Figure 1) Reproduced with the permission of the AQIS Seaports Program
2.3 BWDSS Operating Environment:
The BWDSS uses a structured email form to receive ballast water information from
shipmasters. This form can be sent via an Internet connection or Inmarsat-C (satellite
communication). Information required by the DSS includes:
· Ballast Water Uptake location – Longitude and Latitude
· Date of uptake
· Time of uptake - optional
· Details of uptake tanks
· Strainer details – used to limit uptake of large organisms
10
· Intended discharge Port
· Intended Discharge date
· Partial or full discharge – tanks to be discharged (AQIS, July 2001).
Based on marine biological information about both uptake Port and discharge Port,
taking into account seasonal factors and ballast water management procedures, the
BWDSS provides a recommendation on what ballast water management procedure
may be required. In high-risk cases the ballast water would need to be discharged
outside Australian waters.
Figure 2 shows the relationship between the quantitative and qualitative risk
assessment of the BWDSS. These separate risk assessments are combined to provide
an overall ballast water risk assessment, which is used to help the ship’s captain in
deciding which measures need to be taken to mitigate the ballast water risk.
BWDSS Risk Assessment
Qualitative Risk Assessment
Quantitative Risk Assessment
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Ÿ
Biological Risk using target species
Ecological factors
Economic factors
Port comparisons
Vessel Information
Hull fouling risk
verification of exchange
reporting history
ballast water management plans
ship safety and humanitarian concerns
health risks
compliance levels
impact of penalties - risk versus reward
Combination of
Risks
Risk Level
Audit Trail
Decision
Allow Vessel in
Verification
Reject Entry
Other Actions
(Figure 2) Reproduced with the permission of the AQIS Seaports Program
11
2.4 BWDSS Assessment:
The BWDSS, while only in operation since 2001, is proving to be a useful ballast
water management decision support tool. The system, while requiring a degree of
training, is quite simple to use and provides sound recommendations in a timely
manner. Measured against the DSS components identified by Turben et al (2002) and
Zapatero (1996), the BWDSS fares well. The system can be modified to take into
account changes in port information, provides immediate and long term reporting,
assists with strategic decision-making and is cost effective. The system has been well
received by the shipping industry and AQIS alike.
Conclusion:
DSS are powerful tools, which can help with the decision-making process in countless
fields and business environments. In today’s highly competitive world, the
appropriate utilization of information is critical to the decision-making process and
ultimate business success or failure. DSS are forever changing the way in which
decisions are made at all levels. As computer-processing power increases and staff IT
skills and understanding improve, DSS will become even more important
management tools. Through continued research and further DSS development, people
in all walks of life and endeavours will continue to benefit from the growth of DSS
applications.
12
List of References
AQIS 2001, Guide for Agents/Masters completing a submission to the Ballast Water
Decision Support System (BWDSS) using Internet email / Inmarsat –C, [online]
Available: www.aqis.gov.au/shipping [2003, August 20].
AQIS c2001, Australian Ballast Water Management Requirements Agriculture
Fisheries and Forestry Australia, Canberra.
AQIS 1999, Ballast Water Decision Support System: Draft Outline: For Discussion at
Stakeholder Meeting, (Internal document, not publicly available).
Ben-Tovim, D.I. 1998, ‘Clinical Decision Making in a Mental Health Environment’,
1998 Decision Support Systems Conference, Compact Disk, Australia.
Bharati, P., Chaudhury, A. 2003, ‘An empirical investigation of decision-making
satisfaction in web-based decision support systems’, Decision Support Systems, 1051,
pp.1-11, [online] Available: www.elevier.com/locate/dsw [2003, September 15].
Bhargava, H, K., Power, D, J. c2002, ‘Decision Support Systems and Web
Technologies: A Status Report’, [online] Available:
http://www.smeal.psu.edu/faculty/hob2/Pub/Papers/DSS/dssstatusreport.pdf [2003,
August 22].
Brooks, B. 1998, ‘Enabling Decision Makers: The Royal Perth Hospital Experience’,
1998 Decision Support Systems Conference, Compact Disk, Australia.
Bruggen, G.H., Smidts, A., Wierenga, B. 2001, ‘The powerful triangle of marketing
data, managerial judgement, and marketing management support systems’, European
Journal of Marketing, vol.35, no.7/8, pp. 796-814, [online] Available:
http://www.emerald-library.com/ft [2003, August 22].
Carlsson, C., Turban, E. 2002, ‘DSS: directions for the next decade’, Decision
Support Systems, 33, pp.105-110, [online] Available: www.elevier.com/locate/dsw
[2003, September 15].
Cassie, C. ‘Marketing decision support systems’, Industrial Management & Data
Systems, no. 8, pp. 293-296, MCB University Press, United Kingdom.
Clarke, Z., Lambert, S. 2000, ‘Management information and decision support libraries
in Europe: a concerted action’, Performance Measurement and Metrics, vol. 1, no. 2,
pp. 77-98.
Cho, V., Ngai, E. 2000, Intelligent Decision Support System with Embedded OLAP
Technology for the Insurance Industry, Hong Kong Polytechnic University, Hong
Kong (SAR), China.
Colgan, K., Foden, D. 1999, Ballast Water Decision Support System (DSS): Business
Requirements, Version 2.3, 08 September, AQIS, Commercial-in-Confidence.
(Internal document, not publicly available).
CSIRO 2003, Media Release: Australian, US scientists set invasive species
benchmark, [online] Available:
13
http://www.marine.csiro.au/PressReleasesfolder/03releases/14jul03.htm [2003,
August 22].
Eom, S, B. 1999, ‘Decision support systems research: current state and trends’,
Industrial Management & Data Systems, no.5, pp. 213-220, [online] Available:
http://www.emarald-library.com [2003, September 10].
Faunt, J., Leworthy, R. 1998, ‘Clinical Research Using Decision Support Systems:
Early Experiences’, 1998 Decision Support Systems Conference, Compact Disc,
Australia.
Gregg, D, G., Goul, M., Philippakis, A. 2002, ‘Distributing decision support systems
on the WWW: the verification of a DSS metadata model’, Decision Support Systems,
32, pp.1-11, [online] Available: www.elevier.com/locate/dsw [2003, September 15].
Khoo, B., Forgionne, G. c2002, Web-based Decision Making Support Systems:
Management Information Systems, California State Polytechnic University, CA USA.
Lawrence, M., Goodwin, P., Fildes, R. 2002, ‘Influence of user participation on DSS
use and decision accuracy’, Omega: The International Journal of Management
Science, vol. 30, pp. 381-392, [online] Available: www.elevier.com/locate/dsw [2003,
September 15].
Lee, J.H., Park, C, S. 2003, ‘Agent and data mining based decision support system
and its adaptation to a new customer-centric electronic commerce’, Expert Systems
with Applications, vol. 25, pp.619-635, [online] Available:
www.elevier.com/locate/eswa [2003, September 15].
Liang, T., Hung S. 1997, ‘DSS and EIS applications in Taiwan’, Information
Technology & People, vol.10, no. 4, pp. 303-315, [online] http://www.emeraldlibrary.com [2003, August 22].
Nasirin, S., Birks, D.F. 2002, ‘DSS implementation in the UK retail organisations: a
GIS perspective’, Information and Management, no. 40, pp. 325-336 [online]
Available: www.elevier.com/locate/dsw [2003, September 15].
O’Donnell, T. 2001 Effective Decision Support Requires a Dynamic Strategy,
[online] Available:
http://www.ncr.com/repository/brochures/services/Decision_Support_Strategy.pdf
[2003, August 22].
Pohl, J.G., Wood, A.A., Pohl, K.J., Chapman, A.J. 1999 IMMACCS: A Military
Decision-Support System, [online], Available:
http://www.cadrc.calpoly.edu/pdf/immaccs_2_military.pdf [2003, August 22].
Shim, J.P., Warkentin, M., Courtney, J. F., Power, J., Sharda, R., Carlsson, C. 2002,
‘Past, present, and future of decision support technology’, Decision Support Systems,
vol. 33, pp. 111-126, [online] Available: www.elevier.com/locate/dsw [2003,
September 15].
Tseng, C, Gmytrasiewicz. c2002, ‘Real Time Decision Support System for Portfolio
Management, [online], Available: http://www.cs.uic.edu/~piotr/papers/hicss02portfolio.pdf [2003, October 07].
14
Turban, E., McLean, E., Wetherbe, J., Bolloju, N., Davidson, R. 2002, Information
Technology For Management: Transforming Business in the Digital Age, 3rd ed, John
Wiley & Sons Inc, New York USA.
Vahidov, R., Kersten, G. E. 2003, ‘Decision station: situating decision support
systems’, Decision Support Systems, xx, pp.1-21, [online] Available:
www.elevier.com/locate/dsw [2003, September 15].
Waalewjin, P, Arons, H. 1997, Are Decision Support Systems helping Marketers?,
[online] Available:
http://www.few.eur.nl/few/people/deswaanarons/publ/waalewijn_de_swaan_arons_e
mc97.pdf [2003, October 07].
Zapatero, E, G. 1996, ‘A quality assessment instrument for multi-criteria decision
support software’, Benchmarking for Quality Management & Technology, vol. 3, no.
4, pp. 17-27, MCB University Press.
15