Applications of Decision Support System

DSS can theoretically be built in any knowledge domain.

One example is the clinical decision support system for medical diagnosis. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based.

DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Due to DSS all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decision. For example, one of the DSS applications is the management and development of complex anti-terrorism systems. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package, developed through financial support of USAID during the 80s and 90s, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. Precision agriculture seeks to tailor decisions to particular portions of farm fields. There are, however, many constraints to the successful adoption on DSS in agriculture.

DSS are also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context the consideration of single or multiple management objectives related to the provision of goods and services that traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems.

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.

DSS have been used for risk assessment to interpret monitoring data from large engineering structures such as dams, towers, cathedrals, or masonry buildings. For instance, Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the Ridracoli Dam (Italy), is still operational 24/7/365. It has been installed on several dams in Italy and abroad (e.g., Itaipu Dam in Brazil), and on monuments under the name of Kaleidos. Mistral is a registered trade mark of CESI. GIS have been successfully used since the ‘90s in conjunction to DSS, to show on a map real-time risk evaluation based on monitoring data gathered in the area of the Val Pola disaster (Italy).

• DSS tends to be aimed at the less well structured, underspecified problem that upper level managers typically face;
• DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;
• DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an interactive mode; and
• DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the decision-making approach of the user.

Typically, business planners will build a DSS system according to their needs and use it to evaluate specific operations, including

• A large stock of inventory, where DSS applications can provide guidance on establishing supply chain movement that works for a business.
• A sales process, where DSS software is a “crystal ball” that helps managers theorize how changes will affect results.
• Other specialized processes related to a field or industry.

DSS can help manage inventory

DSS can come in handy by evaluating stock held in a facility, or any other type of business asset that can be moved around or otherwise optimized. This is often one way a business can profit from “itemizing” its assets with DSS.

DSS can aid sales optimization and sales projections

Decision support technology can also be a tool that analyzes sales data and makes predictions, or monitors existing patterns. Whether it’s big picture decision support tools, active or passive solutions, or any other kind of DSS tool, planners often tackle sales numbers using a variety of decision support resources.

Utilize DSS to optimize industry-specific systems

There are other uses for this powerful software option, to make good projections on the future for a business or to get an overall bird’s-eye view of events that determine a company’s progress. This can come in handy in difficult situations where a lot of financial projection may be necessary when determining expenditures and revenues.