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Decision support systems for forest management: a financial analysis for South Carolina's state forests.


Decision support systems (DSS), also known as forest resource information systems, focus on providing forest managers information to make better decisions. DSS in forestry organizations have evolved from the integration of geographic information systems and database management systems with common forestry applications. These types of systems are becoming widely used within forestry organizations as the planning and documentation of activities become ever more critical due to forest certification activities and increased public scrutiny. While methods to define the cost of these types of technologies are relatively straightforward, defining the benefits associated with system implementation is more difficult. A benefit/cost analysis of a DSS for South Carolina's state forests is presented. This analysis derives the majority of the benefits from improvements in business process, not the effects of individual applications or functions. Process alternatives currently available to forest managers and the benefits and cost of these alternatives are identified. While results presented apply specifically to South Carolina's state forests system, the alternatives and methodology have broad implications to medium and large forest landowners.


From their beginning, geographic information systems (GIS) have been widely adopted by forestry organizations. Initially used to improve the efficiency of map creation and editing, GIS have evolved and are now being used for many different functions within forestry organizations. Fueling this evolution has been the flexibility and perceived value-added by GIS. This has led to an integration of GIS with other common forestry applications such as forest inventory software, database management systems (DBMS), growth and yield models, and forest planning models. These integrations, commonly called decision support systems (DSS) or forest resource information systems (FRIS), have shifted the focus from efficiency to providing forest managers information to make better decisions. This fundamental change in how technologies are used forces managers to reevaluate how the systems are valued within the organization.

Valuing GIS has never been a straightforward process as it produces many intangible benefits. Formal economic evaluations of GIS are rare and many systems are justified based on their perceived value-added alone (Worral 1994). While methods to define cost are relatively straightforward and described in the literature, adequate methodologies to quantify benefits are generally agreed to be much more elusive (Devine and Field 1986, Prisley and Mead 1987, Hall et al. 2000).

Benefits of GIS are commonly broken into three categories: efficiency, effectiveness, and intangible (Devine and Field 1986, Prisley and Mead 1987). Efficiency benefits, which are cost reductions related to time savings, are commonly utilized in analyses as these benefits are relatively straightforward and easy to quantify (Smith and Tomlinson 1992). However, effectiveness benefits, which are benefits due to better decisions, are much more difficult to quantify. Because of this difficulty, these benefits are often not quantified and simply listed as intangible.


Integrations with other systems have made the difficult task of valuing GIS technologies even more difficult. When systems are integrated, attributing the benefits to individual applications or functions within the system becomes increasingly difficult. As this occurs, it becomes more reasonable to look at the cross-functional benefits of the integrated system to an organization, thereby evaluating the integrated system in the context of the business process it is trying to improve (Gartner, Inc. 2003). For this analysis, business process relates to forest management practices on South Carolina's state forests. However, business process can be more generally defined as a "collection of activities or tasks that create outputs of value" (Grover and Malhotra 1997).

Our discussion centers on a benefit/cost analysis of DSS for South Carolina's state forests. This analysis derives the majority of the benefits from improvements in business process, not the effects of individual applications or functions. We identify process alternatives currently available to forest managers and the benefits and costs of these alternatives. While results presented apply specifically to South Carolina's state forests system, the alternatives and methodology have broad implications to medium and large forest landowners who might want to apply these technologies.

South Carolina's state forests

The South Carolina Forestry Commission (SCFC) is the state agency responsible for the protection, enhancing, and nurturing of the state's forestlands (SCFC 2003a). It is also responsible for the management of a state forests system. This system originated in 1939 when the SCFC acquired its two largest forests, Manchester State Forest (MSF) and Sand Hills State Forest (SHSF). Since inception, the state forests have been managed for multiple uses, including wildlife habitat, recreation, environmental education, and the production of sustainable forest products. Currently the state forests system includes five forests encompassing approximately 90,000 acres. The forests are spread across the state with forest in each of the states physiographic regions. Each forest is unique and demands varying management techniques based on the individual forest's characteristics.

Operations on South Carolina's state forests have been funded primarily through forest products sales. Historically about 75 percent of the operating budget has been from receipts. Declining state funding for the agency has resulted in increasing pressure on the state forests to become 100 percent self supporting. This is complicated by increased recreational use on the state forests, the large amount of acreage in reserve due to endangered species and longleaf pine straw enhancement, and the self-imposed management restrictions such as green-up constraints.

To date, inventories on state forests have been conducted by periodically remeasuring continuous forest inventory (CFI) plots. While this inventory method provides good growth and mortality data, it does not provide the necessary stand level information needed in the current management environment. Also, forest managers have been skeptical of the accuracy of the inventory data due to variations in the inventory methodology over time and a lack of standardization for re-measurement intervals.

Forest planning on state forests has historically been based on current budget needs with harvest areas chosen based on individual stand parameters and the local knowledge of forest managers. Allowable harvest is calculated periodically to ensure that harvest levels do not exceed growth. This reactionary approach to budgeting is evident while looking at the forest products revenues generated by the state forests over the past 10 years (Fig. 1) and has also impacted the regulation of harvest on state forests. Harvest levels have been erratic with allowable harvest levels being exceeded some years and not met in others. Harvesting levels on the MSF are a good example of this phenomenon (Fig. 2).

In 1995, the SCFC begin using GIS technologies to capture and track spatial data on the state forests. However, success has been limited due to variability in the GIS technologies employed, limited expertise, and non-centralized systems. To date, GIS has been used exclusively to automate traditional mapping tasks with minimal utilization during decision making processes.

Overall, the lack of information about forests inventory levels, combined with the reactionary forest planning process and limited use of technologies like GIS, has led to the sub-optimization of revenues from the state forests. This is especially true in the current business environment. Forest managers are now accountable not only for sustainable harvests but also for improving recreational opportunities and environmental considerations, including endangered species and air/water quality. Long-range plans for the state forests call for effective use of technology to enhance the management strategies on state forests, including the centralization of data, implementation of a stand level inventory program, improved use of GIS, the incorporation of growth and yield models, and spatial forest planning (SCFC 2003b). The financial analysis presented here represents the first step toward these goals.



The methodology used to analyze DSS evaluates change due to improvements in business process, instead of effects of individual applications. The business process being evaluated is the management of growth and removals from South Carolina's state forests. At the beginning of the analysis, a needs assessment was conducted and then potential vendors were evaluated to determine alternatives and costs. Benefits were then estimated and divided into three categories: effectiveness, efficiency, and intangible. Minimum, maximum, and expected levels of effectiveness and efficiency were calculated to determine sensitivity of the analysis to estimated benefits and, finally, benefit/cost ratios were calculated.

Needs assessment

Prior to beginning the financial analysis, a needs assessment was conducted for the SCFC. This was done by surveying state forests personnel, interviewing SCFC leaders, and reviewing the long-range plan. Results generally indicated a need to update forest management processes on the state forests to better reflect the current environment in which they operate and a desire to expand the technologies suggested for utilization on state forests into other agency programs. Summary results of the needs assessment are shown in Table 1.

Evaluation of vendors

Following the needs assessment, vendors were identified who provide forestry consulting, software programming, and/or forestry specific DSS subscription services. All vendors were briefed on the business needs of SCFC and asked to propose their solution for the agency. Solutions were reviewed, categorized, and alternatives formulated. Alternatives were broken into three broad classes: forest inventory, forest planning, and DSS (Table 2). Classes of alternatives were treated independently while alternatives within a class were mutually exclusive. To determine cost for the analysis, vendors were contacted again and asked to provide itemized estimates for alternatives within their areas of expertise.

Three estimates were obtained for each alternative with two exceptions. Only two companies offered subscription DSS services tailored to forest management. Both offered web-enabled DSS using an Application Service Provider (ASP) model with prices based on the number of acres enrolled in their system, but only one of the companies provided an integrated graphic user interface (GUI) to access spatial data, which greatly increased the functionality of this particular system for use on the state forests and significantly enhanced the potential for expansion into other SCFC programs. Thus, only one cost estimate was used for this alternative. Current web-based GIS software does not provide full editing capabilities via web-based GIS. One vendor opted not to provide an estimate for this alternative because of the software limitations. Thus, only two cost estimates were used for this alternative.

Financial analysis

All costs were categorized into one of the following: personnel, hardware, software, consulting services, or miscellaneous (Devine and Field 1986, Hall et al. 2000). Cash flows were annualized, averaged (if necessary), and the present value calculated for each alternative. Benefits derived from forest inventory and forest planning alternatives are assumed equal within their respective classes and as such were not derived independently. Alternatives within these classes were ranked based on present value (PV) and the highest ranking alternative from each class used in the benefit/cost analysis.

Benefits for this analysis were derived by comparing the incremental changes in state forests revenues or costs due to business process improvements related to the implementation of a combination of alternatives. Benefits derived from process improvements were divided into three categories: effectiveness, efficiency, and intangible (Devine and Field 1986, Prisley and Mead 1987).

Effectiveness benefits are defined as the gain attributed to making better decisions. In this analysis, effectiveness is related to the incremental change in attainable harvest through increased knowledge of forest conditions due to improved forest inventory, optimization by forest planning model, and better data availability due to the implementation of a DSS (e.g., current and more accurate stand maps and improved querying and reporting functions). The difference between average harvest and allowable harvest represents opportunities for increased revenues (Fig. 3). However, not all of this additional volume is available for harvest due to mandatory and voluntary harvest restrictions (like best management practices and green-up constraints). Therefore the actual opportunity for increased revenues is something less than the increment between allowable harvest and average harvest.

Literature suggests that spatial constraints on a forest planning problem in southern pine forests may result in reductions in attainable harvest volumes of approximately 25 percent (Boston and Bettinger 2001, Walters and Cox 2001). Reductions in harvest volumes on South Carolina's state forest may be more dramatic than this due to the high occurrence of endangered species and other constraining factors. To account for the impact of these expected volume reductions on the analysis, three levels of attainable harvest (in terms of percent of strategic harvest that is attainable) were estimated: maximum (75 percent), minimum (50 percent), and expected (60 percent). It is assumed that strategic harvest is equal to current allowable cut and is constant through all periods. Thus the effectiveness benefit becomes the increment between some level of attainable harvest and the average level of harvest, hereafter referred to as the additional harvestable volume (Fig. 4).

Efficiency benefits are defined as the cost reductions derived from time savings due to better processes. The analysis focused on two major areas: harvest planning and reporting. Time studies of the current planning and reporting processes were conducted and these times were then compared to minimum, maximum, and expected time to complete the same task within the new system and the incremental change valued.

Other benefits are expected due to the implementation of a new system, but were difficult to quantify. These benefits and costs were not quantified and were listed as intangible (Table 3). Lastly, a sensitivity analysis of estimated benefits was performed using minimum, maximum, and expected levels of effectiveness and efficiency.

A desire to utilize the technologies implemented by the state forests in other SCFC program areas was noted during the needs assessment. To quantify the potential expansion capabilities of the various DSS alternatives, costs of expansion were estimated. The PV of these costs was calculated and subtracted from the net present value (NPV) of the DSS alternatives to derive the value of the option.

A real discount rate of 2 percent was used for all financial analyses. This is based on the 5-year South Carolina Bond yield rate of 4 percent minus 2 percent to make the rate inflation free (The Bond Market Association 2003). The average annual inflation rate of 2 percent was calculated from the consumer price index (CPI) from the past 2 years (Bureau of Labor Statistics 2003a). Stumpage prices were projected based on the real rate of change calculated using the 10 previous years average stumpage prices for South Carolina (Timber Mart--South 1993-2003) and 10 previous years lumber and wood products Producer Price Index data (Bureau of Labor Statistics 2003b). Due to the volatility of technology, a 6-year planning horizon was used for all analyses.



Estimated average inventory costs ranged from $36,000 to $78,000 per year. Consultant estimates ranged from $35,000 to $100,000 per year. Alternative 12, SCFC hires technician to conduct annual inventory, had the lowest PV with a cost of $148,000 for the 6-year planning period (Table 4). Alternative 12 was selected for use in the benefit/cost analysis.

Forest planning

Initial development of the forest planning model cost ranged from $10,000 to $42,000 with consultant estimates ranging from $10,000 to $40,000. Estimates to rerun the model annually ranged from $1,500 to $17,000 with consultant estimates ranging from $1,500 to $4,000 per year. Alternative F2, consulting firm contracted to build and rerun forest planning model, had the lowest PV with a cost of $69,000 for the 6-year planning period (Table 4). Alternative F2 was selected for use in the benefit/cost analysis.


Decision support system

First year average cost for DSS ranged from $140,000 to $265,000 with alternative 1, subscription DSS, having the lowest initial average cost and alternative 3, contract out construction of DSS with a desktop GIS application, having the highest initial average cost. Average costs for years 2 through 6 ranged from $86,000 to $132,000 per year. Alternative 1 had the lowest average annual costs while alternatives 2 and 4 shared the highest average annual costs. The proportion of cost by category varied for all DSS alternatives (Table 5). Note that the high proportion of personnel cost in alternative 2 is due to additional staffing to compensate for the lack of functionality in current web-based GIS software. Alternative 1 had the lowest PV with a cost of $537,000 for the 6-year planning period (Table 4).

Benefit/cost analysis of process improvements

The PV of the estimated effectiveness benefits ranged from $744,000 to $3,056,000 with the PV of the average expected effectiveness benefit being $1,716,000. PVs of estimated efficiency benefits ranged from $10,000 to $38,000 with the average expected efficiency benefit being $30,000. Expected efficiency benefits are relatively low with the majority of the benefits being derived from forest planning improvements. Relatively little efficiency was gained in reporting due to current usage of GIS technologies to automate reporting and mapping processes.

Benefit/cost ratios ranged from 0.56 to 4.11 with DSS alternatives 1 and 3 being the only alternatives within their class to produce a benefit/cost ratio greater than 1 for minimum levels of efficiency and effectiveness benefits. All DSS alternatives produced a benefit/cost ratio greater than 1 for expected and maximum levels of benefits. Alternative 1, subscription DSS, ranked the highest of all possible process combinations with benefit/cost ratios ranging from 1.31 to 4.11 and an expected benefit/cost ratio of 2.46 (Table 6). Rank of process alternatives was not sensitive to discount rate or to the level of efficiency and effectiveness benefits.

Cost of expansion

The value of the option to expand DSS into other SCFC program areas ranged from $211,000 to $816,000 with the PV of expansion costs ranging from $44,000 to $498,000 (Table 7). Alternative 3, desktop application, having the second lowest expected NPV and a moderate cost of expansion, ranked highest among the alternatives when considering expansion. Alternative 1, subscription DSS, having the lowest expected NPV, ranked third due to the high cost of expansion related to this system.


The results of this analysis suggest that the implementation of improved forest management processes including the utilization of DSS technologies is a worthwhile investment. All alternatives evaluated produced acceptable benefit/cost ratios at expected levels of efficiency and effectiveness benefits. Implementation of the improved business processes will result in regulated harvest and more predictable cash flows on South Carolina's state forests. This is critical as state forests move to become 100 percent self sufficient. Additionally, utilization of DSS can provide forest managers with better information ensuring sound management practices and increased values to the residents of South Carolina.

In this analysis, it was more cost effective for the SCFC to hire additional personnel to conduct the inventory. Not only was the cost of the inventory lower but as additional personnel time will not be utilized completely during inventory this additional time can be used for other value-added functions within the state forests system. It was more cost effective for the SCFC to contract out the development and annual rerun of the forest planning model. This may produce additional benefits as it utilizes professionals with higher levels of expertise during model development and it allows SCFC personnel more time to analyze the models output. However, these potential benefits are difficult to quantify and may be outweighed by the ability to make modifications or rerun the model at will if it were maintained in house. These potential benefits and cost were not utilized within the analysis.

Results are somewhat ambiguous for DSS. The subscription DSS and desktop application alternatives stand out in this class with each having its strengths and weaknesses. While subscription DSS yields the highest benefit/cost ratio for the state forests business process, the desktop application has a clear advantage when evaluating expansion potential. When choosing between these two alternatives, managers must focus on three factors; initial cost, available infrastructure, and expansion strategy.

Subscription DSS is a good choice for organizations whose business needs match the application provided. They offer low initial cost, low annual cost, and full support for clients with minimum technical expertise. It is interesting to note that the costs of ASP services evaluated are lower than the personnel costs and software maintenance fees needed to maintain a similar system in house.


The ASP applications evaluated were developed to generally match the business model of most forestry operations and are especially suited to forest management in the southern United States. However, the flexibility of these applications is relatively limited and the cost of modifications high. This is reflected in the low value of the option to expand this alternative to other SCFC programs.

A custom-built DSS featuring a desktop GIS interface is a good choice for organizations whose business needs do not match an ASP's model or who wish to utilize the DSS capabilities in areas of the organization other than traditional forest management. It is also a good choice for organizations with a mature information technology infrastructure. However, this type of system is limited by high start-up cost and the organization must be willing to expend the necessary resources to acquire the software and expertise to maintain the system if this is not already in place.

While the web-based DSS alternative was less than optimal in this analysis, it should be noted that new web-based GIS software packages are beginning to enter the market. As these mature, this should decrease the development cost of these types of applications. This will make the development of a web-based DSS more attractive to organizations, especially since the cost of deployment and expansion is relatively low for this type of application.

It is clear that in-house development is not desirable for the SCFC. In general, outsourcing development may be preferred to in-house development of DSS for organizations without well-established information technology infrastructures. This is due to the high cost related to information technology personnel (i.e., programmers and database administrators) needed to develop the system as well as the delay of benefits caused by the longer development time. Organizational decisions to pursue in-house development should be weighed carefully and all options evaluated prior to proceeding.


The methodology presented in this article is an effective way to evaluate the benefits and costs associated with DSS implementation for forest management purposes. While not all benefits can be quantified by the process approach, it is much less subjective than previous methods of benefits determination for DSS. Additionally, evaluating DSS in the context of business processes links the value of these systems closer to the business strategy of the organization, which may aid in the justification of expenditures on these types of technology.

Although many of the benefits presented can be attributed to improved forest inventory and planning methods, one must realize that DSS is a critical enabler of improved processes on state forests. Harvesting constraints like endangered species and green-up intervals require a spatial approach to forest planning and thus create a definite need for GIS capabilities (Bettinger and Sessions 2003). The process improvements being utilized on South Carolina's state forests would not be feasible without the utilization of DSS technologies. Implementing new inventory and forest planning methods while leveraging technologies like DSS can provide land managers better information about the forests, decreasing the chance of suboptimal management practices being implemented.

Literature cited

Bettinger, P. and J. Sessions. 2003. Spatial forest planning: To adopt or not to adopt? J. of Forestry 101(2):24-29.

The Bond Market Association. 2003. Municipal bond prices. The Bond Market Assoc. Accessed September 2, 2003.

Boston, K. and P. Bettinger. 2001. The economic impact of green-up constraints in the southeastern United States. Forest Ecol. Manage. 145:191-202.

Bureau of Labor Statistics. 2003a. Consumer Price Index (all goods). U.S. Bureau of Labor Statistics, Washington, DC. Accessed September 2, 2003.

______. 2003b. Producer Price Index (lumber and wood products). U.S. Bureau of Labor Statistics, Washington, DC. Accessed September 2, 2003.

Devine, H.A. and R.C. Field. 1986. The gist of GIS. J. of Forestry 84(8):17-22.

Gartner, Inc. 2003. Going beyond IT ROI--Estimating the business value of process integration solutions. Accessed June 14, 2003.

Grover, V. and M.K. Malhotra. 1997. Business process reengineering: A tutorial on the concept, evolution, method, technology, and application. J. Oper. Manage. 15(3): 193-213.

Hall, J.P., T.J. Kim, and M.I. Darter. 2000. Cost-benefit analysis of geographic information system implementation: Illinois Dept. of Transportation. Transport. Res. Rec. 1719:219-226.

Prisley, S.P. and R.A. Mead. 1987. Cost-benefit analysis for geographic information systems. In: Proc. of 2nd Inter. Conf., Exhibits, and Workshops on Geographic Information Systems. American Congress on Surveying and Mapping, American Soc. for Photogrammetry and Remote Sensing, Bethesda, MD. pp. 29-37.

South Carolina Forestry Commission (SCFC). 2003a. South Carolina Forestry Commission: History and mission. SCFC, Columbia, SC. Accessed May 15, 2003.

______. 2003b. Long range plan: South Carolina Forestry Commission's state forests. SCFC, Columbia, SC. Accessed May 15, 2003.

Smith, D.A. and R.F. Tomlinson. 1992. Assessing costs and benefits of geographical information systems: Methodological and implementation issues. Inter. J. Geographical Info. Sci. 6(3):247-256.

Timber Mart--South. 1993-2003. Norris Foundation, Univ. of Georgia, Athens, GA.

Walters, K.R. and E.S. Cox. 2001. An empirical evaluation of spatial restrictions in industrial harvest scheduling: The SFI planning problem. Southern J. of Applied Forestry 25(2):60-68.

Worral, L. 1994. Justifying investment in GIS: A local government perspective. Inter. J. Geographical Info. Sci. 8(6):545-565.

Scott L. Phillips

Thomas J. Straka*

Christopher J. Post

Timothy O. Adams

The authors are, respectively, Forest Analyst, South Carolina Forestry Commission, Columbia, SC (; Professor and Assistant Professor, Dept. of Forestry and Natural Resources, Clemson Univ., Clemson, SC (;; and Development Forester, South Carolina Forestry Commission ( This paper was received for publication in February 2005. Article No. 10008.

*Forest Products Society Member.
Table 1. -- Summary results of SCFC needs assessment.

Forest inventory
 1. Stand level inventory completed every 5 years.
 2. Should minimize cost to state forests.
 3. Shall be of sufficient accuracy to meet forest planning objectives.
Forest planning
 1. Forest planning model should optimize net present value subject to
 management constraints.
 2. Model shall ensure sustainable harvests.
 3. Model shall provide harvest volume targets.
 4. Model shall account for spatial aspects and distribution of
 management activities.
 1. Shall be accessible to management and field personnel.
 2. Shall include a comprehensive database management system (DBMS)
 tailored to forest management activities. DBMS should be
 standardized and centrally located.
 3. Should be user friendly allowing employees with minimum technical
 raining to use the system efficiently.
 4. Shall include a functional GIS, allowing creation and editing of
 spatial data and maintaining topological relationships of the
 spatial data.
 5. Shall possess full reporting capabilities.
 6. Shall provide for activity planning.
 7. Shall be capable of supporting forest planning and forest
 certification efforts.
 8. Should have capability of being expanded into other SCFC programs.

Table 2. -- Alternatives evaluated for SCFC.

Class Alternative

Forest inventory 11. SCFC utilizes Forest Inventory Analysis (FIA) crew
 to conduct annual inventories on state forest.
 12. SCFC hires technician to conduct annual inventory.
 Consulting firm contracted to conduct first year
 of inventory.
 13. SCFC hires technician to conduct remainder of
 annual inventories.
 14. Consulting firm contracted to conduct inventory on
 an annual basis.
Forest planning F1. SCFC purchases forest planning software and builds
 forest planning model.
 F2. Consulting firm contracted to build and rerun
 forest planning model.
DSS 1. Subscription DSS.
 2. Contract out construction of a web-based DSS
 3. Contract out construction of DSS with a desktop
 GIS application.
 4. In-house development of DSS.

Table 3. -- Intangible benefits and costs associated with implementation
of DSS on South Carolina's state forests (Prisley and Mead 1987).

Improved cooperation with other agencies
Improved public image
Improved employee pride and satisfaction
Keeping pace with technology
Better public service
Better availability of data for making decisions

Employee dissatisfaction with technology
Resistance to change

Table 4. -- Present value of costs and ranking of inventory, forest
planning, and DSS alternatives for South Carolina's state forests.

 value of
Rank Alternative cost

Inventory (S)
 1 12 -- Inventory tech. 148,000
 2 13 -- Contract first year/inventory tech. 198,000
 3 11 -- SCFC inventory crew 201,000
 4 14 -- Contract inventory 435,000
Forest planning
 1 F2 -- Consultant develops model 69,000
 2 F1 -- SCFC develops model 102,000
 1 1 -- Subscription DSS 537,000
 2 3 -- Desktop application 689,000
 3 4 -- In-house development 807,000
 4 2 -- Web-based application 860,000

Table 5. -- Percent costs by category of DSS alternatives evaluated for
South Carolina's state forests.

 Alternative Alternative Alternative Alternative
Category 1 2 3 4

Personnel 28 41 22 54
Hardware 6 4 5 4
Software 7 19 44 37
Consultant services 50 32 25 0
Miscellaneous 9 4 4 5

Table 6. -- Rank and expected benefit/cost ratios of DSS alternatives
for South Carolina's state forests.

Rank DSS alternative Expected B/C ratio

1 1 -- Subscription DSS 2.46
2 3 -- Desktop application 2.05
3 4 -- In-house development 1.72
4 2 -- Web-based application 1.37

Table 7. -- Rank and expected value of option to expand DSS alternatives
into SCFC program areas outside of the state forests system.

 PV of
 NPV expansion Value of
Rank DSS alternative optimum costs option

1 3 -- Desktop application 950,000 134,000 816,000
2 2 -- Web-based application 779,000 44,000 734,000
3 1 -- Subscription DSS 1,102,000 498,000 604,000
4 4 -- In-house development 394,000 182,000 211,000
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Author:Phillips, Scott L.; Straka, Thomas J.; Post, Christopher J.; Adams, Timothy O.
Publication:Forest Products Journal
Article Type:Author abstract
Geographic Code:1U5SC
Date:May 1, 2006
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