Enabling the Organization – Decision Making
Ø Reasons for the growth of decision-making information systems:
§ People need to analyze large amounts of information.
§ People must make decisions quickly.
§ People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions.
§ People must protect the corporate asset of organizational information.
ØModel – a simplified representation or abstraction of reality.
Ø IT systems in an enterprise.
Transaction Processing Systems(TPS):
Ø Moving up through the organizational pyramid users move from requiring transactional information to analytical information.
Ø Transaction processing system - the basic business system that serves the operational level (analysts) in an organization.
Ø Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information.
Ø Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making.
Decision Support Systems(DSS):
Ø Models information to support managers and business professionals during the decision-making process.
Ø Three quantitative models used by DSSs include:
1. Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model.
Eg: What will happen to the supply chain if a tsunami in Sabah reduces holding inventory from 30% to 10%?
2. What-if analysis – checks the impact of a change in an assumption on the proposed solution.
Eg: Repeatedly changing revenue in small increments to determine it effects on other variables.
What-if analysis
3. Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output.
Eg: Determine how many customers must purchase a new product to increase gross profits to $5 million.
Goal-seeking analysis
Interaction between a TPS and a DSS
Executive Information Systems:
Ø
Ø Most EISs offering the following capabilities:
§ Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information.
Eg: Data for different sales representatives can be rolled up to an office level. Then state level, then a regional sales level.
§ Drill-down – enables users to get details, and details of details, of information.
Eg: From regional sales data then drill down to each sales representatives at each office.
§ Slice-and-dice – looks at information from different perspectives.
Eg: One slice of information could display all product sales during a given promotion, another slice could display a single product’s sales for all promotions.
Interaction between a TPS and an EIS
Ø Digital dashboard – integrates information from multiple components and presents it in a unified display
Ø Intelligent system – various commercial applications of artificial intelligence
Ø Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn.
§ Advantages: can check info on competitor
The ultimate goal of AI is the ability to build a system that can mimic human intelligence.
Ø Four most common categories of AI include:
- Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Eg: Playing Chess.
- Neural Network – attempts to emulate the way the human brain works. Eg: Finance industry uses neural network to review loan applications and create patterns or profiles of applications that fall into two categories – approved or denied:
- Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem. Eg: Business executives use genetic algorithm to help them decide which combination of projects a firm should invest.
- Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users:
• Multi-agent systems.
• Agent-based modeling.
Eg: Shopping bot: Software that will search several retailers’ websites and provide a comparison of each retailers’ offering including prive and availability.
- Data-mining software includes many forms of AI such as neural networks and expert systems.
- Common forms of data-mining analysis capabilities include:
- Cluster analysis.
- Association detection.
- Statistical analysis.
- Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible.
- CRM systems depend on cluster analysis to segment customer information and identify behavioral traits.
- Eg: Consumer goods by content, brand loyalty or similarity.
- Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information.
- Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services.
Eg: Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.
- Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis.
- Forecast – predictions made on the basis of time-series information.
- Time-series information – time-stamped information collected at a particular frequency.
Eg: Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods.
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