Handbook on Decision Support Systems 1 || Spreadsheet-Based Decision Support Systems

CHAPTER 14 Spreadsheet-Based Decision Support Systems

Michelle M.H. Şeref 1 and Ravindra K. Ahuja2

1 Information Sciences and Operations Management Department, Warrington College of Business, University of Florida, Gainesville, FL, USA 2 Industrial & Systems Engineering Department, University of Florida, USA

With the increasing use of information technology and availability of high-quality data, business organizations have a greater need for analyzing data and using this analysis to guide their decisions. An information system based on decision models is called a decision support system (DSS). A DSS uses the data residing in spreadsheets and/or databases, models it, processes or analyzes it using problem-specific methodologies, and assists the user in the decision-making process through a graphical user interface. In this chapter, we discuss the usefulness and capabilities of spreadsheet software for developing a DSS. Spreadsheets are a great option for developing many DSS applications since they are available with almost any operational system and have many features that are relatively easy to learn and can be implemented for a large variety of problems.

Keywords: Spreadsheet-based decision support systems; Excel; Visual basic for applica-tions; Graphical user interface; Procedures; Re-solve options

1 Introduction

The ability to extract data from external sources and embed analytical decision models within larger systems are two of the most valuable skills required of entering today’s information technology (IT)-dominated workplace. Model-based information systems, called decision support systems (DSS), use data residing in a spreadsheet or a database, model the data, process or analyze it using problem-specific methodologies, and assist the user in the decision-making process through a graphical user interface (GUI). A DSS may be developed in various environ-ments which support data storage, data analysis, solution method development, and graphical user interface. In this chapter, we discuss the usefulness and cap-abilities of spreadsheet software for developing a DSS.

In an article by Geoffrion and Krishnan (2001) two well-known researchers in operations research, state: “The digital economy is creating abundant opportuni-ties for operations research (OR) applications. … Because OR is well matched to the needs of the digital economy in certain ways and because certain enabling conditions are coming to pass, prospects are good for OR to team with related analytical technologies and join information technology as a vital engine of

278 Michelle M.H. Şeref and Ravindra K. Ahuja

further development for the digital economy.” Intelligent information systems or decision support systems will indeed play a vital role in the digital economy in the years to come and spreadsheet-based decision support systems will become quite common for moderately sized business applications due to the widespread popu-larity of spreadsheets in business and managerial environments.

In this chapter, we define a spreadsheet-based DSS and discuss the usefulness of spreadsheets as a DSS development environment. The chapter is organized as follows. Section 2 defines in detail the components of a spreadsheet-based DSS. Section 3 discusses spreadsheet features, including a brief overview of the history of spreadsheet software as well as specific features for the common spreadsheet software, Microsoft Excel and Visual basic for applications (VBA) for Excel. In Section 4, we present a six-step process for developing spreadsheet-based DSS applications, and in Section 5, we illustrate this process through the development of a portfolio management and optimization DSS. In Section 6, we briefly discuss alternatives to spreadsheets for DSS environments. We conclude with Section 7.

We would like to inform the reader about the book we have written on this topic, Developing Spreadsheet-Based Decision Support Systems: Using Excel and VBA for Excel (Seref et al. 2007). This book provides a comprehensive discussion of how to develop a spreadsheet-based DSS. It includes several chapters describing Microsoft Excel features and several chapters describing how to use VBA for Excel. It then illustrates how to use these spreadsheet and programming tools to develop a complete DSS application. This book is intended to be a text-book for a DSS development course in industrial engineering (IE), operations research/management sciences (ORMS), and business curriculums. For more details on spreadsheet-based decision support systems, we encourage you to read this reference.

2 Defining Spreadsheet-Based Decision Support Systems

We define a spreadsheet-based decision support system as a decision support system developed in a spreadsheet environment with the enhancement of a pro-gramming language and user interface developer. A decision support system gives its users access to a variety of data sources, modeling techniques, and stored domain knowledge via an easy to use GUI. A DSS can refer to data residing in the spreadsheets, prepare a mathematical model using this data, solve it or analyze it using problem-specific methodologies implemented via spreadsheet functions or a programming language, and assist the user in the decision-making process through a graphical user interface. Let us define the general components of a DSS and then discuss how spreadsheets are indeed a valid choice for DSS development.

A decision support system (DSS) is a model- or knowledge-based system intended to support managerial decision making in semistructured or unstructured

Spreadsheet-Based Decision Support Systems 279

situations (Turban and Aronson 2001). A DSS is not meant to replace a decision maker, but to extend his/her decision making capabilities. It uses data, provides a clear user interface, and can incorporate the decision maker’s own insights.

A DSS application contains five components: database, model base, knowledge base, GUI, and user (see Figure 1). The database stores the data, the model and knowledge bases store the collections of models and knowledge, respectively, and the GUI allows the user to interact with the database, model base and knowledge base. The database and knowledge base can be found in a basic information system. The knowledge base may contain simple search results for analyzing the data in the database. For example, the knowledge base may contain the number of employees in a company database who have worked at the company for over 10 years. A decision support system is an intelligent information system because of the addition of the model base. The model base includes models used to perform optimization, simulation, or other algorithms for advanced calculations and analysis. These models allow the decision support system not only to supply information to the user but aid the user in making a decision.

Spreadsheet software provides all of the components necessary for a DSS. In the database component, spreadsheets can easily store relatively large amounts of data in rows and columns on multiple worksheets. This data can be organized using sorting features or various formats. In the model base component, spread-sheet software can perform calculations using spreadsheet functions or a program-ming language. We will discuss in more detail in the next section the specific model base features available in common spreadsheet software. In the knowledge base component, spreadsheets can again be used to store basic knowledge or

Figure 1. A schematic view of a decision support system


a programming language can be used to perform more-advanced knowledge base analysis. In the GUI component, most spreadsheet software offer a variety of GUI options from basic formatting and drawing tools to more advanced GUI features such as user forms and control tools. Thus, a user is able to access and analyze data, perform problem solving, and interact with a GUI through a spreadsheet-based DSS.

3 Spreadsheet Features for DSS Development

Several spreadsheet software are available for DSS development. We give a brief overview of these and list specific features of the common spreadsheet software, Microsoft Excel. We also discuss specific features of Excel’s programming lan-guage, visual basic for applications (VBA).

3.1 Spreadsheet Software

The first electronic spreadsheet software, VisiCalc, was introduced in 1978 for the Apple Computer (Power 2006). Features of this initial program included data entry and basic accounting calculations. By 1983, Lotus 1-2-3 was introduced for the IBM PC, using Intel. This software improved on previous offering by adding charting and database features. Then, Microsoft Excel was introduced (for Apple in 1985 and for Windows in 1987). At that time, Excel provided an improved user interface compared to its predecessors and began adding other features. The software gradually improved to include what-if analysis features (such as goal seek). More GUI features were then added. These included different data entry options (such as the validation tool) and basic form features presented in simple toolbars which placed controls directly on the spreadsheet without any advanced coding. Next, more-advanced analysis tools were developed such as what’s best and Frontline System’s solver. Later, other tools, such as simulation tools like Crystal Ball and @RISK, were made available. Now, programming editors are available for most spreadsheet softwares to provide more-advanced features and GUI development options.

Over the past few years, several platforms that allow for the integration of basic spreadsheet features with more-advanced programming capabilities have become available. The most common such platform is Microsoft Excel. Excel, which is the most widely used spreadsheet package among managers and engineers, allows data storage and model building. Excel also has many built-in programs as well as many add-on programs available that allow the optimization and simulation of various models built in Excel. Excel also has a macro programming language, visual basic for applications (VBA), which allows building of GUIs and manipulation of Excel objects. Thus, Excel provides a platform in which fairly sophisticated DSS applications can be built.


Microsoft Excel spreadsheets have become one of the most popular software packages in the business world, so much so that business schools and engineering schools have developed several popular Excel-based spreadsheet modeling courses. Educators of management science claim “spreadsheets [to be] the pri-mary delivery vehicle for quantitative modeling techniques” (Ragsdale 2001). A spreadsheet application has functionality for storing and organizing data, performing various calculations, and using additional packages, called add-ins, for more-advanced problem solving and analysis. These software packages are usually quick to learn and provide both basic and advanced features for a variety of applications.

3.2 Excel Features

Microsoft Excel has several features available for data storage and analysis. In fact, “spreadsheet users often do not use many of the commonly available spread-sheet features” (Chan and Storey 1996). We describe Excel features in two main categories: basic functionality and extended functionality features.

Excel basic functionality includes referencing and names, functions and formulas, charts, and pivot tables. Figure 2 shows an example of some of these basic features. Referencing and naming cells are key features for a spreadsheet; they allow the user to develop an entire worksheet of calculations that may reference one or more cells containing data or preceding calculation results. To perform calculations in Excel, the user can choose from a large set of predefined functions or create their own formulas. The predefined functions include simple calculations (such as sum, average, min, and max) as well as functions in statistics, finance, referencing, text, logic, and other mathematics categories. Excel charts are very useful for displaying results to the user. There are several types of charts available including user-defined charting options. Pivot tables provide

Figure 2. Microsoft Excel basic features


advanced sorting and analysis features for large amounts of data (refer to Figure 3 for an example). They are a very useful tool for finding information as well as displaying select results. They can also be connected to charts, providing pivoting features with a graphical display (see Figure 4).

Excel extended functionality includes statistical analysis, mathematical pro-gramming and optimization using the solver, simulation, and querying large data. Statistical analysis can be performed using tools such as descriptive statistics and histograms in the data analysis tool set (refer to Figure 5 for an example of descriptive statistics output).

The solver is one of Excel’s most popular extended functionality features. The solver, created by Frontline Systems, allows one to solve linear and integer mathe-matical programming problems. The solver has an interface in which we define decision variables, constraints, and objective (refer to Figure 6 for an example). There is also a new version of the solver, called the premium solver, which provides nonlinear solution options. Simulation can be performed in Excel using special functions and formulas. When VBA is used, simulation can be enhanced to include automatic run execution and animation. Although spreadsheets have some limit in terms of data storage, Excel has the ability to interface with database software, namely Microsoft Access, to query large data. This enhances Excel’s database features by creating collaboration between data stored in a database and analysis available through Excel’s spreadsheet features.

The basic and extended functionality features in Excel are very appropriate for building a decision support system. The ability to model a problem and solve or simulate it adds the model base component of the DSS that we are building. It is

Figure 3. Microsoft Excel pivot table


important to be familiar with the capabilities of Excel or other spreadsheet software so that one knows what they can offer when developing a decision support system.

Figure 4. Microsoft Excel pivot chart

Figure 5. Microsoft Excel Data Analysis statistical tools


3.3 VBA Features

Visual basic for applications (VBA) is a programming language that is included with the Microsoft Excel software. It can be used to code standard or advanced procedures as with any other programming language; however, it is primarily designed to manipulate Excel objects. For example, one may select a cell object and change its formatting properties or copy its data values using VBA. The envir-onment in which VBA coding is written is shown in Figure 7. This display can be opened from Excel. It includes the code window, where code is written, the project explorer, where spreadsheets, user forms, or modules are selected, and the properties window, where object properties are set.

Figure 6. Microsoft Excel Solver


Figure 7. The VBA for Excel editor

Figure 8. Recorded macro with VBA object manipulation


VBA has several features, one of which is recording and running macros. Macros capture actions performed in Excel and automatically create the corresponding VBA code. This code can then be run later to perform the actions recorded in Excel again. The structure of the VBA programming language is object-based. That is, an Excel object is usually named and then manipulated using properties (to change certain formatting features of the object) or methods (to perform certain actions with the object). A set of examples of such object manipulation is shown in Figure 8; this code was created by recording a macro. The VBA programming language also includes variables, procedures, programming structures, and arrays. These are typical features of any programming language. The VBA language is straightforward to learn, especially but not necessarily for those with experience in other programming languages.

Additional features of VBA involve creating a user interface. This includes build-ing user forms, working with several different form controls, using navigational

Figure 9. Creating a user form in VBA


functions, and designing a clear and professional application. In Figure 9, a user form is created in the visual basic environment using a control toolbox. This form would use VBA code to record user input values. VBA can also enhance the modeling, simulation, and query features of Excel. These are key features in developing a DSS; that is, one can prompt the user for input by displaying a form, automate a simulation or perform optimization using VBA. This allows for advanced features to be implemented without the DSS user having to do more than press a button.

VBA for Excel is an easy to understand programming language. Even if they have not programmed before, a user should be able to program several types of applications after learning these features. VBA code allows a DSS developer to create dynamic applications that can receive user input to form the model base component of the DSS. VBA is beneficial as it places all of the complicated spreadsheet calculations and any other analysis in the background of a user-friendly system.

4 Developing Spreadsheet-Based Decision Support Systems

4.1 Development Approach

We now discuss how to develop a DSS application that integrates data, models, methodologies, and user interfaces in a spreadsheet environment. After learning how to work in the Excel spreadsheet environment and how to program in VBA to manipulate Excel objects and perform advanced calculations and analysis, one can then combine these tools to develop a complete spreadsheet-based DSS appli-cation. Before entering formulas into Excel or coding procedures in VBA, it is necessary to plan the overall layout for the DSS and give some thought to the design and implementation of the application.

We propose six basic steps for developing a DSS; these steps have been our guidelines in developing decision support systems. We do not claim that it is ne-cessary to follow these, but rather consider them helpful guidelines when devel-oping a DSS application (Şeref et al. 2006).

1. Application overview and model development: Create a layout of the entire application, designing the flow from the user input to the model calculations to the output, and outlining the model details.

2. Worksheets: Determine how many worksheets the programmer requires to best handle input, calculations, and output.

3. User interface: Outline what interface a programmer requires to receive input from the user and navigate him or her through the application.

4. Procedures: Outline what programming procedures are required to receive input, perform calculations, and display output.


5. Re-solve options: Decide what re-solve options the user will be given. 6. Testing and final packaging: Ensure that the application is working

properly and has a clear and professional appearance.

By following these steps, one can ensure that the DSS is designed in an efficient manner to meet the user’s needs. In the spreadsheet environment, all input can be sufficiently collected and stored, simple calculations as well as advanced problem solving can be performed, and output can be displayed clearly to the user. Spreadsheets are thus a friendly environment for both the DSS developer as well as the end user.

4.2 A Portfolio Management and Optimization Spreadsheet DSS

Using the DSS development process described above with the features available in Excel and VBA, several spreadsheet-based DSS applications can be efficiently developed. Some possible DSS applications that a manager may want to use may include a facility or warehouse layout DSS, an inventory management DSS, a forecasting DSS, a staff or production scheduling DSS, a reliability analysis DSS, or a queuing simulation DSS. Other business analysis applications may include an option pricing DSS, a stock investment DSS, a retirement planning DSS, or a capital budgeting DSS. (We refer the reader to Şeref et al. 2006 for detailed examples of these DSS applications.)

Below we describe a portfolio management and optimization DSS developed in Excel using VBA. We will describe the DSS based on the development process proposed in the previous section.

Application overview and model development: In this application, we allow the user to create and/or edit their portfolios as well as optimize their investments. The user begins by creating their portfolio; to do this, the user can choose from a list of stocks that are stored in the DSS. Listed by name and category, these stocks can be compared to each other in terms of their annual return and change in market price. Once the user has created their portfolios, they can return to edit them at any time. The user can then choose to optimize their portfolio investments. To do this, the user specifies a minimum desired total return to achieve on their portfolio, a maximum amount to invest per stock, and the total cash they have available for investing. The DSS then solves a model specific to the user’s portfolio that minimizes the total risk. The final optimal investment amount for each stock in the portfolio is then displayed to the user. The user can then resolve the model using various parameters (such as more investment cash or a higher desired return) or can modify their portfolio (by adding or removing stocks) and re-run the optimization.

Model: The underlying model for optimizing the portfolio investment plan minimizes risk as the objective. We define the risk of the portfolio as the sum of


the product of the variance and the square of the investment over all stocks plus the total covariance factor for all stocks in the portfolio. The model is subject to certain constraints including: the cash invested must be less than or equal to the cash available for investing; the return on the portfolio must be greater than or equal to the minimum return desired for the entire portfolio; and the amount invested per stock must be less than or equal to the maximum amount to be invested in each stock. Here, the return on the portfolio is defined as the sum of the product of the investment amount and the expected return over all stocks. We use the premium solver to solve this nonlinear programming problem.

User inputs: Portfolio (selected stocks), total cash for investing, minimum return desired, and maximum amount to invest per stock.

DSS data: Stock information (including quarterly price and annual return), covariance between all stocks, and quarterly price variance and mean as well as annual return mean calculations.

Outputs: Returns earned, cash used, portfolio return per stock, and optimal amount to invest per stock.

Worksheets: We use nine worksheets in this application: the welcome sheet, four data sheets, two information sheets, a model sheet, and an output sheet.

Welcome sheet: A description of the application is given along with relevant assumptions to the optimization model. The user presses the ‘Start’ button to begin.

Figure 10. Portfolio DSS: Welcome sheet


First data sheet: “All Stocks” This sheet contains the complete list of stocks available for this application. The sheet lists each stock’s name and category, as well as its quarterly price for five quarters (including the current price) and the annual return for five years.

Second data sheet: “Covariance” This worksheet may actually be considered part of the knowledge base of the DSS. This sheet stores the covariance values between all pairs of stocks. This data will be used in preparing the optimization model to calculate the total covariance factor for the portfolio.

Figure 12. Portfolio DSS: Second data sheet

Figure 11. Portfolio DSS: First data sheet


Third data sheet: “Portfolio” This sheet records the information from the “All Stocks” sheet for the stocks in the user’s portfolios. We will also use this sheet later to perform some calculations for the optimization. These calculations are for the mean price over five quarters, the variance in price for each quarter, the sum of the variances, and the sum of the annual returns.

Figure 15. Portfolio DSS: Fourth data sheet

Figure 13. Portfolio DSS: Third data sheet

Figure 14. Portfolio DSS: Calculations on the third data sheet


Fourth data sheet: “Chart Data” This contains data copied from the “All Stocks” data sheet for a set of stocks selected by the user. The copied data is used to produce charts that are shown on one of the information sheets. If the user has selected to plot the change in market price, then we copy the quarterly prices for the selected stocks from the “All Stocks” sheet and paste them on the first table shown. If the user has selected to plot the annual returns, then we copy the annual returns from the “All Stocks” sheet and paste them on the second table shown. Two graphs on the “Compare Stocks” information sheet refer to these tables as their source data.

First user input sheet: “Create/Edit Portfolio” In this sheet, the user can create or modify their portfolio by performing one of the two following actions: selecting several stocks from the “All Stocks” list and clicking the “Add” arrow button to add these stocks to their portfolios; or selecting several stocks from the “Portfolio” list and clicking the “Remove” arrow button to remove these stocks from their portfolios. This is the first sheet that the user visits after clicking the “Start” button on the “Welcome” sheet. If the user is not sure which stocks are best to add or remove from their portfolios, they can click the “Compare All Stocks” button to proceed to the second information sheet: the “Compare Stocks” sheet. Otherwise, if they feel their portfolio is complete, they can click the “Save Portfolio and Return to Main Menu” button to return to the Main Menu. The “Exit” button allows the user to exit the application.

Figure 16. Portfolio DSS: First information sheet


Second user input sheet: “Compare Stocks” This sheet allows the user to select several stocks from the “All Stocks” list and plot either their annual returns or the change in market price. The user selects one of these options from the combo box on the worksheet and then presses the “Show Plot” button. The corresponding chart then appears. We create these charts using the data from the fourth data sheet. The user can then return to the “Create/Edit Portfolio” sheet by clicking the “Return to Create/Edit Portfolio” button. The user can also exit the application at this point by clicking the “Exit” button.

Model sheet: The model sheet lists all of the stocks in the portfolio with adjacent cells for the investment amount. These are the changing cells whose values are determined when optimization is performed. These values, solved as percentages, are converted into dollar amounts in the next column. We refer to the dollar amounts to check the constraint that each stock receives less than or equal to the maximum investment amount per stock. The code then calculates the expected return and variance and displays these figures in the next two columns. We use these values in the third column; and the sum of this column is the risk value. This risk value, in the “Minimum Risk” cell at the top of the sheet, is the objective function cell. The code procedures also calculate the formulas for the forth column: the covariance factors. These factors are calculated for all pairs of stocks in the portfolio using the stock in each row as the pivot. The total covariance factor is the sum of the values in this column. The “Min Return” and “Unit Constraint” columns capture the other two constraints. We then use the premium

Figure 17. Portfolio DSS: Second information sheet


solver with the objective function as the “Minimum Risk” cell and the constraints as the minimum return, maximum stock investment, and unit constraint cells. The solver’s nonlinear algorithm is used to find a solution, if one exists.

Figure 19. Portfolio DSS: Output sheet

Figure 18. Portfolio DSS: Model sheet


Output sheet: “Investment Report” This sheet displays a graph of the invest-ment amounts for each stock in the user’s portfolio as well as the corresponding values in an adjacent table. The minimized risk, or variance, of the portfolio is also provided. The return and desired return are displayed, as well as the cash used and the total cash available. The “Modify Input” and “Modify Portfolio” buttons correspond to two re-solve options, which we discuss later. The “View Model” button allows the user to view the model sheet, and the “Exit” button allows them to exit the application.

User interface: We use two user forms, one control on a worksheet, and several navigational and functional buttons for the user interface for this DSS.

First form: “Main Menu” The first form that the user sees is the “Main Menu”. It provides them with three navigational options: create a new portfolio, edit their current portfolio, or optimize their portfolio investment plan. If the user selects to create a new portfolio, then they are taken to the “Create/Edit Portfolio” sheet, which is cleared of past data. If the user selects to edit their current portfolio, then

Figure 20. Portfolio DSS: First form

Figure 21. Portfolio DSS: Second form


they are taken to the same sheet, except no clearing is done. If the user selects to optimize their portfolio investment plan, then an optimization procedure is run and the user is taken to the output sheet.

Second form: “Minimize Risk” When the user selects “Optimize Portfolio Invest-ment Plan” from the “Main Menu” form, they will see the optimization input form: “Minimize Risk”. This form prompts the user for three optimization inputs: the total cash available to make the investment, the minimum desired return on the portfolio, and the maximum amount they can invest per stock.

Worksheet user control: “Compare Stocks Combo Box” This control is on the “Compare Stocks” worksheet. It is a combo box that lists the two plot options: “Change in Market Price” and “Annual Return”. Once the user makes a selection, they can use the “Show Plot” functional button to display the plot for the selected stocks.

Procedures: We use several procedures in this DSS application. Some of these navigate the user through the various worksheets and forms while others are used to manipulate the user input, provide data analysis, run the optimization, and produce the output. We will exempt an in-depth discussion of each procedure. (For more details, refer to Şeref et al. 2006.)

Re-solve options: This application consists of two re-solve options, which are associated with the “Modify Input” and “Modify Portfolio” buttons on the output sheet. If the user clicks “Modify Input,” then the optimization input form is displayed again. The user can modify their input values, press “OK,” and the optimization will be re-performed. If the user selects the “Modify Portfolio” button, they will return to the “Create/Edit Portfolio” sheet. They can then modify their portfolio and click the “Save Portfolio and Return to Main Menu” button to return and select the optimization button again. The optimization will then be re-performed.

Thus, using Excel and VBA, a complete portfolio management and optimi-zation DSS can be developed. This spreadsheet-based DSS allows the user to view

Figure 22. Portfolio DSS: Worksheet user control


several stocks and compare stock data, select a portfolio, and find the optimal investment plan which minimizes investment risk while achieving a minimum return value.

5 Alternatives to Spreadsheets

As expressed in this chapter, we feel that spreadsheets are a very capable environment for developing a DSS. Spreadsheet software are available on most operating systems and allow for efficient data storage, data analysis, problem solving, and user interface. These tools allow for complete DSS development for most applications.

However, for some applications, a DSS developer may prefer another envir-onment. One alternative to the spreadsheet environment for DSS development is a strict database environment. Database software allows for much larger amounts of data storage than spreadsheets. Most database software, such as Microsoft Access and Oracle, also include programming languages for problem solving as well as GUI features such as forms for receiving input and reports and charts for displaying output.

Another alternative to spreadsheets for DSS development is Web-interface software. Such software includes programming languages such as VB.Net and ASP.Net. This software allows the DSS developer to create an interface that is available online. Thus users can access data and perform analysis or problem solving through a webpage. This makes the DSS more available as it can be used on almost any computer with Internet access.

We again advocate spreadsheets as a good choice for developing most DSS applications. Spreadsheet software is easy to find, learn, and use. However, there are alternatives available that allow a DSS to be developed for applications with requirements for larger data storage, online access, or other features that exceed spreadsheet capabilities.

6 Conclusion

Spreadsheets are an excellent option for developing many DSS applications since they are available for almost any operational system and have many features that are relatively easy to learn and can be implemented for a large variety of problems. With the increasing demand for IT-based systems and quick decision making, spreadsheet-based DSS are an important and necessary tool for any industry. Thus, students and professionals should learn the basic process for developing a DSS. Spreadsheets are an attractive environment for developing a DSS as they can be easily found and quickly learned. Once the tools of spreadsheet software and a programming language are learnt, an efficient and user-friendly DSS application can be created for almost any situation.


Acknowledgements

We thank the referees for their insightful comments and suggestions which led to an improved presentation of the chapter. The second author gratefully acknow-ledges the support of NSF Grant # 4506177 given for the course curriculum development.

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Handbook on Decision Support Systems 1 || Spreadsheet-Based Decision Support Systems