Understanding Financial Modeling
Sam Choon Yin (2003)
The use of spreadsheets in financial modeling
has grown rapidly since the late 1979. Thanks to the introduction of the Visicalc spreadsheet program in 1979 by a Harvard
Business School student in the United States, more sophisticated computer software like Lotus 1-2-3 and Excel have been developed
to cater to the needs of both individuals and organizations. This development is not surprising given the usefulness of spreadsheets
in financial modeling as compared to carrying out the tasks manually using hand calculators. It is the intention of this essay
to highlight the uses of the spreadsheets in financial modeling and some of the problems associated with the usage. Since
financial modeling requires the use of microcomputers, it is essential that one gain an idea on the evolution and uses of
computers. I therefore include a short section in the essay on computers.
It may be useful to begin by defining some terms, starting off with the word ‘model’. In
some books on financial modeling, the term model is often defined as the ‘representation of a particular reality’
(Pfaff, 1990, p. 2), but ‘built on a scale that can be more easily manipulated’ (Beaman et al, 1997, p. 3). The
latter indicates that the models built are meant for manipulation either deliberately or not deliberately therefore rendering
it both useful and useless. Allowing the models’ variables to change is deemed appropriate if one is interested to perform
scenario analysis or sensitivity analysis. The objective in these analysis is to allow the users to substitute different values
into the variable inputs and observe how the outputs would alter under different scenarios. Using the spreadsheets to perform
the task is much simpler and time saving as compared to doing the same task manually. However, manipulation makes the models
subjective. The users may have the urge and tendency to substitute the values into the variable inputs to make the outputs
‘look good’. The users might not seriously question the reasonableness of the inputs (that is, using the data
without a proper reasoning behind them). The other problem is that with manipulation of data, the use of financial models
may be more dominantly used but adopted unwisely. The users may be so reliance on the use of the models for decision making
that the decisions are made solely based on the outputs from the models without sufficient consideration on other variables
left out of the models.
The term ‘finance’
deals with the issues on the sources of funds and the uses of funds. The role and responsibility of the finance managers are
to ensure that sufficient funds are obtained and available for the firms. Typically, a firm can obtain funds from three sources;
the issuance of shares (for listed companies), borrowing (say through the issuance of bonds, or borrowing from financial institutions)
and retained earnings (which is the internal source of funds). With the funds, the next question to address is ‘what
is the most effective way to allocate the firm’s scarce funds to yield the best returns for the firms’. The issue
on funds allocation requires the financial managers to acquire techniques like the net present value calculations and the
internal rate of return computations for the purpose of evaluating the various projects that the firms can choose from.
Combining the terms finance
and model, it is now appropriate to provide the definition of financial models. According to Pfaff (1990), financial modeling
is a ‘mathematical construct which can be used to help better understand, control, examine or manage the financial sector
of a firm, or the financial affairs of an individual or institution’. (p. 19). The definition is self-explanatory. Basically,
financial models assist the planners to understand how reality works. Through techniques like sensitivity analysis, the planners
can focus on those variables that are more important in explaining the behavior of the reality, and put less focus on the
less important factors. With the models, which provide the planned outcome, the planners could use the models as a basis of
comparison between the actual outcomes and the planned outcomes. With this knowledge, the planners can take correction actions
to correct any deviations that may arise.
The other definition of financial
modeling can be obtained from Beaman et al (1997). Beaman et al (1997) defines a financial model as ‘a computer-generated
model representing the whole or parts of an organization’ (p. 3). They then noted that a financial model ‘can
encompass a few aspects of a company’s financial situation, or all of the thousands of activities of a major corporation’
(p. 3). It is interesting to note that the financial model in Beaman et al’s sense does not incorporate only the activities
of the financial sector in the company but extends the focus to a larger section of the organization. The authors recognize
that the term ‘financial models’ is too narrow since the models are often used as a planning tool not only relating
to the financial aspects of the firm but the physical, operational and human aspects as well. Hence they refer to ‘‘corporate
modeling’ as possibly a better description of their nature’ (p. 3). I agree with them. It is widely accepted that
the main objective of the firm is to maximize its profits. The attainment of this objective would in turn maximize the share
price of the listed firms and the values of the shareholders. While it is not my intention to downgrade the importance of
the financial sector, the achievement of the objective does require collaboration between the various functional areas of
the organization, not just the finance functional area. Success in the marketing function, operations function, the research
function and the human resource function for example is equally important. It is not surprising therefore for some to suggest
cross- functional decision making in the organization to highlight the fact that success of any organization requires the
co-operations and interactions between the various functional areas (see for example Schroeder, 2000). This is easy to say,
but difficult to achieve in practice. One reason is that responsibilities of the functional areas are conflicting with one
another (see Sam, 2002).
Despite this deficiency, the
term ‘financial modeling’ is nevertheless used in this essay as part of the norm. However, it is important to
recognize that the success of using computer-systems to aid decision-making encompasses the co-operations from the various
functional areas in the organization. Bill Gates (1999) uses the term ‘digital nervous system’ to describe the
essentiality to unite all the systems and processes using information technology, and release the information speedily to
allow the companies to achieve quantum leaps in growth, profits and efficiency. Like the human body, a company too needs a
‘nervous system’ – an internal communication mechanism – to coordinate its actions and provide the
necessary information and data to the staffs at the ‘speed of thought’.
of financial models
There are several ways one can categorize the financial models, which basically make use of the computer system to
aid in the decision-making process. Herbert Simon, Nobel Laureate in Economics in 1978 for ‘pioneering the research
into the decision-making process within economic organizations’ (http://www.nobel.se/economics), identified two types
of decisions. The first type, ‘programmed decisions’ represents those decisions that are repetitive and
routine in nature (Simon, 1977). These decisions are usually made on a regular basis to assist in the decision-making. The
models built can be used again and again over time, with the specifications remaining more or less unchanged. It is the responsibility
of the modeler to update the variables inputs so that a new set of outputs can be obtained. The second type is ‘non-programmed
decisions’. These decisions are unique and it is usually not known when one is required to make the decisions. Because
of the non-predictability and uniqueness of these decisions, they should be treated separately. Models could be tailor-made
to suit the nature of the problem addressed. Beaman et al (1997) called such models ‘ad hoc models’ (p. 9).
Financial models can be broadly categorized into, first, simulation models. Also known as ‘what if ‘models,
the models aim to simulate the effects of different scenarios on the outcomes so that the planners become aware of the different
outcomes obtained base on alternative values of inputs. With the information, the planner can foresee to some extent, the
likely outcome of the model under different circumstances. Different strategies can then be placed to minimize the negative
effects of the outcome or maximize the opportunities present. The technique on sensitivity analysis falls under the ambit
of ‘what-if models’. Instead of making changes to a set of values in the case of scenario analysis, the sensitivity
analysis involves the alternation of only one variable input at one time. This allows the planner to assess the change on
the outcome of the model when one variable input changes. Doing so allows the planner to assess the degree and relative importance
of the respective inputs. Noting which of the inputs have a greater impact on the outcome permits the planner to place more
concentration on these variables.
The second type of financial
models belongs to the group known as the optimization models. The objective of these models is to maximize (or minimize) the
objective function subject to some constraints that exist in achieving the objective. For example, the decision to locate
the optimal amount of goods X and Y to produce could be for the purpose of maximizing the revenue collection for the firm.
The attainability of the objective lies on the production constraints that the operations have to face. For instance, scarcity
of resources like labor and certain inputs may pose a limitation on the number of units of X and Y the operations can produce
on a typical day. It is the interest of the models to select the optimal points of the decision variables that maximizes the
objective function, while at the same time, satisfying all the constraints encountered.
The third type of financial
models can be collectively named as forecasting models. Forecasting models can be of two types: time series forecasting models
and causal forecasting models. The former involves forecasting a variable using only the variable itself. It is important
that historical data of sufficient size is available for proper usage of the models. Using techniques like moving average,
weighted moving average and exponential smoothing technique, short-term forecast can be derived for the variable concerned.
Relative easy to construct, the task can be performed on most of the spreadsheets in the market. An example is the Excel spreadsheet.
Causal forecasting technique on the other hand involves the estimation of the regression equations. With historical data on
at least two variables, it is the interest of the forecaster to develop the relationship between the dependent variable (the
variable whose behavior is explained by the independent variable(s)) and the independent variable(s) (the variable(s) that
explains the behavior of the dependent variable). Once the relationship is derived, short, medium and long-term forecasts
can be obtained by substituting the relevant estimated figures of the independent variables into the regression equation.
Causal forecasting can be performed on Excel for both the simple regression and multiple regression cases.
It is useful to note that
the forecasting models can be used in collaboration with the ‘what-if’ models. For example, in financial planning,
the sales forecasts can first be estimated using the forecasting models. Given the sales forecast, the planner could assess
the need of external financing to finance and support the growth in sales. ‘What-if’ or sensitivity analysis can
then be performed on the spreadsheets (such as altering the retention ratio, tax rates and/or cost-to sales ratio) to assess
the magnitude of the external financing needed (if any) under different scenarios.
In some books like Beaman et al (1997, p. 95) and Shim and Siegel (1989, p. 190), financial models are also categorized
as deterministic and probabilistic. Deterministic models are those models where the inputs are imputed with certainty. They
do not include any random variables. Historical data on sales are usually deterministic in nature. Probabilistic models on
the other hand contain inputs that are uncertain in terms of their occurrences. The variables concerned contain probability
distributions where one can attach probabilities to each of the values reported. Forecasted values on sales for example are
probabilistic where each of the probabilities of occurrence is usually less than 1.
The other distinction in the financial models lies on the users of the models. Individuals who wish to develop their
own financial plans base on their goals, income and preferences can develop financial models to assist in their planning.
Or, financial models can be used and developed by individuals working for a corporation to assist the organization in its
planning and decision-making.
of financial models
The ultimate aim of an
organization is to maximize profits. This is usually an acceptable objective for the firm from the society’s point of
view, although the society requires that the objective is attained via ethical means. To assist in the attainment of the objective,
it is useful for the firm to set up reasonably sound financial models to incorporate the inputs from the various functional
areas in the organization. For example, the sales projection of the firm should be in line with the industry standards and
economic conditions prevailing during that time. It must be acceptable from the marketing’s perspective. Constructing
supporting models pertaining to long-term financial planning, capital investment, budgeting and tax planning for instance
is appropriate. It is important that the planners are well verse with the concerned concepts in the accounting and finance
Financial models help to present the reality in a more manageable and neat sense, providing us with
the essential materials to understand how the reality works. Central locations like mean, mode and median and other descriptive
statistical measures can be obtained with ease using spreadsheets. The use of easy-to-read graphs and charts also helps to
provide a graphical representation on the data involved. As noted earlier, financial models are manipulative. Alterations
of certain variables help the planners to assess the degree of importance of certain variables (so that more attention can
be placed on them), and assess the effects on the changes of the outcomes of the models. As a controlling tool, financial
models provide the planned (or forecasted) scenario, which could be compared with the actual outcome. The intention is to
assess the areas which have gone wrong (if any) and take correction actions to rectify the problems, and prevent similar incidents
from taking place again. Last but not the least important, construction of financial models provide enjoyments to the people.
It is indeed challenging for one to build up the models, making them work out the way they wanted, and ultimately helping
one to perform his or her tasks in a more efficient and effective manner. The satisfaction derived from knowing that the models
work is priceless to the modelers.
In general, financial models
provide support for decision-making. Using the computers, financial models provide aid to people in ‘identifying problems,
consider alternatives, evaluate uncertainty and make decisions’ (Beaman et al, p. 5), as part of the larger concept
of decision support system (DSS). Coined by MIT professors, G.Anthony Gorry and Michael S.Scott Morton, the decision support
system uses the computer to support the management in decision-making (see Gorry and Morton, 1971). Depending on the extensiveness
of the problem addressed, the demand for more computer power to solve the problem increases as the level of complexity of
the problem increases. For example, the decision support system needed could be less sophisticated if one is only interested
to retrieve information from some sources. But, should the individual be interested in the preparation of reports from multiple
files, the estimation of decision consequences, the proposition of decision and making of decisions, the type of decision
support system required becomes increasingly demanding and complex. More functions on the spreadsheets for instance may be
required to perform the task(s) (Alter, 1976; see McLeod, 1995, chapter 14 for more details on the DSS).
Limitations of the financial models
While the use of financial
models have been increasing substantially over the years particular since the introduction of more advanced and easy-to-use
computer software, it is essential for one to recognize the problems associated with the use of financial modeling in decision-making.
It is the interest of this sub-section to discuss some of these problems.
First, it is not possible
for models to incorporate all the variables affecting the outcome into the analysis. Failure to take all the variables into
consideration renders the models ineffective. Although one may say that the degree of ineffectiveness depends on the importance
of the excluded variables, the fact that the outcomes could be altered with any of excluded variables changing implies that
the models cannot be used to predict certain events with certainty. An example is illustrated by the Tacoma Narrows Bridge
built in the state of Washington. Shortly after it was built in 1940, the bridge collapsed. The designers of the bridge later
found that the cause of the collapse was due to their ignorance for not taking into consideration the aerodynamic characteristics
of the bridge, which they deemed as unimportant. As it turned out, the analysis was very important indeed (Pfaff, 1990, p.
5). Given that they are numerous variables to be taken into consideration, it is a challenge to the modeler to decide which
of the variables are important, and which are less. In fact, financial modelers face a dilemma in terms of the size of the
models to be constructed. It is not easy to decide on the right size of the models. While a big model is comprehensive, it
may become too complicated and costly to handle. Updating of the model may become clumsy, time consuming and costly given
the numerous variables to consider. Models that are too small pose a problem too. Excluding too many variables for example
may render the model ineffective with low predictive power. It is therefore not surprising for Pfaff (1990) to conclude that
model building is more of an art than a science (p. 5). In the financial and economics contexts, some variables are not included
in the financial models simply because they are not quantifiable. This highlights a problem with the financial models, which
is over-reliance on quantifiable variables. Important information like quality, culture, and customers’ satisfaction
are not easy to be quantified, and therefore are often excluded in the construction of the financial models. They are nevertheless
important variables to consider.
Second, the users of the
financial models may end up relying too heavily on the financial models in making the final decisions. This problem is particularly
serious in cases where the models considered are too simple. As noted, ignorance of important but non-quantifiable variables
may render the models ineffective in predicting the outcomes, particularly if there is high probability for the neglected
variables to alter in the near future. In many financial models, even quantifiable variables are excluded from the models.
It is therefore essential that the modelers and decision-makers are aware of these deficiencies of using financial models.
One should not follow blindly on the predictions made by the models. Instead, careful thoughts and knowledge on the subjective
internal and external environments are crucial to supplement the financial models in decision-making.
Third, financial models
tend to over-rely on accounting data as a source of input. Accounting data are constructed on an accrual basis. Cash flows
are ignored. For example, salary payments for the month of December are likely to be made in January the next year, but recorded
in the financial statements in December (when the financial statements are prepared). As a result, the profit and loss recorded
in the financial statements does not reflect the cash flow positions of the firm. The proper method in the evaluation of projects
therefore is the use of expected cash flows generated from them, rather than the use of accounting information. Despite these
problems, the use of accounting data in financial modeling is still common.
Fourth, the use of accounting
data forces the modelers to deal with average costs and average revenues in the decision-making. This tends to mitigate the
effectiveness of the financial models for decision-making is better made with marginal costs and revenues instead. For example,
in deciding whether to produce another unit of the output, the decision maker is required to assess the additional revenue
the unit contributes to the organization and the additional cost incurred in the production of that unit. It makes economic
sense to produce the additional unit if the marginal revenue exceeds the marginal cost of production. Otherwise (when the
marginal cost exceeds the marginal revenue), the additional unit should not be produced from the economic point of view. The
profit maximizing level of output is reached when the marginal revenue is equivalent to the marginal cost of production. Accounting
data does not allow the user to carry out marginal analysis.
Fifth, while the use of
spreadsheets has been extensive over the years and more and more functions have also been added into the software, problems
exist. One of the problems faced is the difficulty of transferring the models from one user to another. The transfers may
be required when there is a change in the modeler. Taking over the spreadsheet and updating them could lie in the hands of
another person. Wastage of time and repetition of work may be resulted. To minimize repetition of work and time, it is essential
that the person taking over is capable of interpreting the models in the spreadsheets. To facilitate the transition, it is
the responsibility of the organization to provide training to the staffs involved on the tools used. Recruitment of new staffs should also be screened properly. The other essential task is to inculcate the
culture of applying the spreadsheet design principles to the modelers (Beaman et al, 1997, chapter 2). These are simply
principles to adopt and practice in the development of spreadsheets for ease of updating, transferring and interpretation.
For example, proper specification of formulas in the cells in Excel, careful use of range names, auditing and protecting the
worksheets are some guiding principles to adopt to assist the model builder to construct models that are error-free, user-friendly
and reliable in support of managerial decision-making (see Beaman et al, 1997, chapter 2 for more details on the principles).
Basically, not everyone is capable of churning out useful financial models using the spreadsheets. Without proper training
and planning, the spreadsheet model errors may be numerous leading to mistakes made into the cells, and therefore inaccuracies
in the prediction of the intended outcomes.
Computers: evolution and their uses
The first electronic computing
machine was completed in 1945. Known as the Electronic Numerical Integrator and Computer (ENIAC), its emergence replaced the
mechanical calculating machines using gears and wheels. Although it was able to perform computations quickly (it did 333 multiplications
or 5,000 additions per second), it did not assist decision-making in an intelligent way.[i] Users needed to be trained to use the technology. It was also costly to make, costing approximately
USD500,000 in 1946 dollars (about USD 5 million to USD 10 million today or SGD 8.5 million to SGD 17 million). Today, it costs
less than SGD1,000 for one to purchase a desktop computer! Large in size, the ENIAC was 8 feet tall, 80 feet long and weighing
about 30 tons.
With the introduction of transistors, electronic computers became more reliable. In the late 1970s,
the integrated circuits (or chips) allowed computers to be built in smaller sizes. The first personal computer (the term ‘personal
computer’ was used as early as in 1974) – Apple II – was sold to the public in 1977 by Steve Wozniak and
Steve Jobs (Apple Computer, Inc.). The International Business Machines Corporation (IBM) sold its first personal computers
to the public in 1981. The growth of software development and other peripherals soon followed in the 1980s. Of particular
significance was the establishment of Microsoft (founded by Bill Gates and Paul Allen) – the largest software company
in the world – in 1975.
A typical computer system
contains four main components commonly known as the computer hardware. The central processing unit (CPU) contains the
processor, the heart of the computer system. A tiny computer chip, the processor is the sub-component of the computer where
all the operations like calculations, comparisons and messages sending and receiving are carried out. The processor itself
stores very little data. It acts more like the retriever of information from a data storage device called the memory. The
most common type of the memory is the RAM (random access memory), which can move data at high speed within the computer’s
circuits. RAM contains another chip that store data and are retrievable easily by the processor. A computer memory chip (with
a size smaller than a finger nail) can store as much as 64 megabytes of data equivalent to the texts of more than 50 books
(Basse, 1997, p. 7). The second component of the computer system is the storage device. Storage of memory in the semiconductor
chips in RAM is expensive. Cheaper ways of storing mass amount of data and information are available. Data can be stored in
the hard disk, which has emerged as the ‘most efficient solution for storing large amounts of data at low, and declining
cost’, (McKendrick et al, 2000, p. 17). Besides the hard disk drive, data storage can be done on floppy disks. The floppy
disks (which have decreased in size over the years from 8 inches to 3.5 inches) are capable of storing 2.88 megabytes of information
each. The other alternative is to store the data in the compact disks (CDs), which can hold up to 650 megabytes. The third
component of a computer system is the input devices, which include the keyboard, joystick and mouse. The main function of
the input device is to give instructions and provide data to the microprocessor. The last component, the output device, contains
the monitor/screen (that displays the output of the program being run) and the printer (to provide hard copies of the output
in a format that is controllable by the users).[ii]
Besides the hardware, the
computer software is essential contributing significantly to the overall usefulness of the computer system. Computer software
is a ‘set of instructions that tells the computer what to do in order to accomplish a task or an activity’ (Pfaff,
1990, p. 14). Examples of the computer software are the computer operating system and the spreadsheets. The former is like
the conductor of an orchestra, helping us to understand what is in the computer, and aiding in the running of the programs
stored. Understanding how the operating systems works enable the users to enjoy the benefits of the computer on a fuller extent.
The latter (spreadsheets) are programs written by the respective providers like Lotus 1-2-3 and Excel to assist the users
in performing certain tasks like graphing, tabulating, and statistical analysis in a simpler manner. Formulas can be imputed
in the spreadsheets to tell the computers to perform the calculations intended. Information can be altered and saved for later
used, and they are transferable.
The rise of computers has
posed numerous challenges, both good and bad, to the people around the world. The use of computers at home has been growing
tremendously (a computer is found in every three households in the United States). More organizations are relying on the computers
to perform spreadsheet calculations, report writing, performance of day-to-day operations, and to aid decision-making. Labor
productivity has increased with the use of computers. Things that are done manually can now be completed using the computer
at a fraction of the time. In manufacturing organizations, the use of computer has helped to facilitate the operational and
production processes. For example, time and monetary savings are enjoyed with the use of computer-aided design (CAD) during
the product design stage. Instead of building prototypes, designers can use the CAD software to design the products at different
geometrical dimensions on the computer itself. The information can be saved and sent to the clients for approval without the
need to build actual models for consideration. It has been recognized that the use of information technology in general, and
computers in particular will extend beyond a particular functional area to an integrated form where processes and systems
are united across the organization to facilitate planning and release of information at a faster speed to support decision-making
Despite the pros of using
computers, one should acknowledge the downside of the computer revolution. Sara Baase (1997) wrote an excellent book on the
legal and social issues regarding the use of computers. Some of the social and legal issues highlighted in Baase (1997) include
the following. First, rising unemployment has been resulted from the increasing use of information technology in the
working place. Citing examples like reduction in the number of tellers in banks, telephone operators, and electric meter readers,
Baase (1997) acknowledged that some jobs have become more redundant with the growth of the information technology. However,
the situation may not be as bad as one perceives. This is so as the rise of the information technology sector has increased
job opportunities, albeit of different kinds, that had not existed in the early 1980s. Examples include computer programmers,
computer engineers and Internet-related jobs. Second, interactions via the computers and machines have led to lower customer
contact between the service providers and customers. More transactions are being carried out with the machines (like Internet
banking and the Automated Teller Machines, ATMs), creating frustrations to some consumers who desire face-to-face interactions
with the service providers. Third, crimes involving the computers like hacking, theft (say from the ATMs), tax evasion, money
laundering and vandalism have imposed losses on the individuals and the society in general. Monetary losses and physical injuries
may be resulted from thefts at the ATMs. Closure and interruptions to essential services may be resulted from hacking and
vandalism on computer-related systems and products, while tax evasion may lead to loss of tax revenue to government therefore
reducing the government’s capability to finance its expenditures on essential services (like public and merit goods)
to its citizens. The ease of money laundering via the computers could have stimulated more criminal activities from taking
place given the easiness for one to remit the earnings to another country. Fourth, the increasing use of computers may lead
to loss of privacy. The filing of taxes via the Internet for instance, involves revealing information like income, identity
card numbers and other confidential information from the taxpayers. The government has huge databases on its residents. The
issues of who should have access to the information and how should it be protected from abuse and errors are important issues
to consider and address. Consider another example. The purchase of items over the Internet results in the purchasers’
information being recorded in the computer databases of the sellers (for example, the credit card numbers and the purchasers’
addresses). It is possible (and not uncommon) for others to have access to the database leading to exploitation and abuses
on the users of the computer services.[iii]
In this essay, I have discussed
several issues pertaining to financial modeling, organized in the following manner. The essay begins with a discussion on
the meanings of the term financial modeling - an area of study which uses the computer system to aid decision-making in an
organization to achieve its objective of profit maximization. The essay then identifies the different types of financial models
exist in the literature followed by a discussion on the uses of the financial models. In that section, I highlighted some
of the main uses of the financial models aimed to recognize the areas of contributions that financial models can provide to
the organizations. Then, to provide a balance view on the use of financial models, the essay recognizes and discusses some
of the limitations exist in using financial models in decision-making. Suggestions to soften the negative effects of these
limitations are provided whenever appropriate. The last section of the essay provides a brief introduction to computers containing
discussions on the revolution of computers, the components of the computer systems, and some pros and cons of using computers. The understanding of the computer system is deemed appropriate and relevant in our
context given the extensive use of computers in financial modeling.
Alter, Steven, L. (1976) ‘How Effective Managers Use Information Systems’, Harvard Business Review
54 (November-December), pp. 97-104.
Baase, Sara (1997) A Gift of Fire: Social, Legal, and Ethical Issues in Computing. Prentice Hall, Inc. (United
Beaman, Ian; Ratnatunga, Janek; Krueger, Peter; and Mudalige, Nihal (1997) ‘Financial Modeling’.
Third edition. Quill Press (Australia).
Capron, H.L. (2000) Computers: Tools for an Information Age. Sixth edition. Prentice Hall, Inc (United States).
Gates, Bill (1999) Business @ the Speed of Thought: Using a Digital Nervous System. Warner Books (United States).
Gorry, Anthony, G. and Morton, Michael S. Scott (1971) ‘A Framework for Management Information Systems’,
Sloan Management Review 13, pp. 55-70.
McKendrick, David.G.; Doner, Richard, F. and Haggard, Stephan (2000) From Silicon Valley to Singapore: Location
and Competitive Advantage in the Hard Disk Drive Industry. Stanford University Press (United States).
McLeod, Raymond, Jr. (1995) Management Information Systems. Sixth edition. Prentice Hall, Inc. (United States).
Pfaff, Philip (1990) ‘Financial Modeling’. Allyn and Bacon (United States).
Sam, Choon Yin (2002) ‘Operations function: Can it work independently?’. Unpublished paper.
Schroeder, Roger, G. (2000) Operations Management: Contemporary Concepts and
Cases. Irwin McGraw-Hill (United States).
Simon, Herbert, A. (1977) ‘The New Science of Management Decision’. Revised edition. Englewood Cliffs
Shim, Jae, K. and Siegel, Joel, G. (1989) Encyclopedic Dictionary of Accounting and Finance. Prentice Hall,
Inc. (United States).
[i] See Baase (1997, chapter 1) for an interesting introduction on the history and growth
[ii] For a fuller discussion on the components of the computer system, read (Capron, 2000).
[iii] There are other interesting issues addressed by Baase (1997), which will not be elaborated
in this essay. I strongly recommend the book to anyone who is interested to know more about the legal and social issues pertaining
to the use of computers.