SECTION A Read the case study carefully and answer the questions that follow. Choosing the Right DSS System for Your Needs The most appropriate DSS depends upon organizational maturity, complexity and, to a certain extent, size. In small organizations, hybrid systems may suffice. If the organization is new to analytics, historical DSS systems would be a good place to start, while those involved in activities such as trading and commodities may benefit more from a predictive decision support system example. Without a doubt, the greatest benefit lies with selecting a prescriptive analytics derived decision management system that models the business and provides the ability to determine the most advantageous decision based on certain criteria, such as revenue and profitability. While entailing a greater investment in resources and money, such a solution has a greater probability of exceeding expectations and achieving a greater ROI. Additionally, it takes the guesswork out of decision-making, and because the model replicates the business, this type of decision support system example is more likely to offer feasible and rational solutions. Common Day-to-Day Decision Support System Examples Decision support systems operate at many levels, and there are many examples in common day-to-day use. For example, GPS route planning determines the fastest and best route between two points by analysing and comparing multiple possible options. Many GPS systems also include traffic avoidance capabilities that monitor traffic conditions in real time, allowing motorists to avoid congestion. Farmers use crop-planning tools to determine the best time to plant, fertilize and reap. Medical diagnosis software that allows medical personnel to diagnose illnesses is another example. Most systems share a common attribute in that decisions are repetitive and based on known data. However, they aren't infallible and may make incorrect or irrational decisions, something many early GPS users discovered. Decision Support System Examples That Use Historical Data Historical data analysis, used in every facet of business and life, is well-developed and mature. Although such information is not always directly actionable, it's an important part of DSS because it reports past performance and highlights areas that need attention. Some examples include: Descriptive analytics: Metrics such as sales results, inventory turnover and revenue growth. Diagnostic analytics: Diagnostic information that digs a bit deeper to reveal results and explains reasons for past performance as measured by descriptive analytics. Business intelligence (BI): Although largely based on historical data, BI solutions allow users to develop and run queries that are used to guide and support decision-making. ERP dashboards: User-configurable dashboards that allow managers to monitor a variety of performance indicators. The Systems Development Life Cycle (SDLC) has a sequence of steps or phases it passes through between the time the system is conceived and the time it is phased out. The phases of the SDLC include investigation, analysis, design, development, implementation, maintenance and retirement. Discuss the Investigation, Analysis and Implementation phases in detail