9.1. Define the system organization use to make decision making and gain competitive advantages.
9.2. Describe the three quantitative models typically used by decision support system.
9.3. Describe the relationship between digital dashboard and executives information systems.
9.4. List and describe four types of artificial intelligence systems.
9.5. Describe three types of data-mining analysis capabilities.
Decision Making
- Reasons for the growth of decision-making information systems are:
- People need to analyse large amount of information.
- People must make decision quickly in order to compete with competitors.
- People must apply sophisticated analysis techniques, such as modelling and forecasting to make a good decisions.
- People must protect the corporate asset (information regarding the company) of organizational information.
- IT systems in an enterprise.
- Executives Information Systems (EIS - usually use by the executives)
- Decision Support Systems (DSS - usually use by the managers)
- Transaction Processing Systems (TPS - usually use by the operational/analyst members)
- Need transactional information.
- Used by operational level members.
- The trend of the TPS is moving up through the organizational pyramid. Users move from requiring transactional information to analytical information.
- Meaning that, system use online transaction process (OLTP) then will be processed to online analytical processing (OLAP).
- Transaction Processing System is the basic business system that serves the operational level (analysts) in an organization.
- Online Transactional Processing (OLTP) is the capturing of transaction and event information using technology to process the information according to defined business rules, store the information and update existing information to reflect the new information.
- Online Analytical Processing (OLAP) is the manipulation of information to create business intelligence in support of strategic decision making.
- system that help analyst, manger and executives levels to make a decision.
- usually used by the managerial level.
- DSS is a model information that use to support managers and business professionals during the decision making process.
- DSS also using OLAP which provides assistance in evaluating and choosing among different courses of action.
- DSS enables high -level managers to examine and manipulate large amounts of detailed data from different internal and external resources.
- For example, doctors may enters symptoms into a decision support system so it can help diagnose and treat patient.
- DSS is a quantitative models because it generates report in order to help a decision making effort.
- Sensitivity analysis
- is the study of the impact on other variables when one variables is changed repeatedly.
- Sensitivity analysis is useful when users are uncertain about the assumptions made in estimating the value of certain key variables.
- for example, repeatedly changing revenue in small increments to determine its effect on other variable would help the manager understand the impact of various revenue levels on other decisions factors.
- checks the impact of a change in a variable or assumption on the model.
- For example, "what will happen to the supply chain if a hurricane in South Carolina reduces holding inventory from 30% to 10%?
- By using this analysis the user would be able to observe and evaluate any changes that occurred to the values in the model such as profit.
- Find the inputs necessary to achieve a goal such as a desired level of output.
- it is a reversed of what if analysis and sensitivity analysis.
- Goal-seeking analysis sets a target value (a goal) for a variable and then repeatedly changes other variable until the target value is achieved.
- For example, goal-seeking analysis could determine how many customer must purchased a new product to increase gross profits to $5 million.
- finds the optimum value for a target variable by repeatedly changing other variables, subject to specified constraints.
- For example, by changing revenue and costs variables managers can calculate the highest potential profits.
- Usually used by the executives level
- EIS is a specialized DSS that supports senior-level executives and unstructured, long term, non-routine decision requiring judgement, evaluation and insight.
- these decision do not have a right or wrong answer, only efficient and effective answers.
- Granularity refers to the level of detail in the model or the decision making process.
- the greater the granularity, the deeper the level of detail or fineness of data.
- EIS requires data from external sources to support unstructured decisions.
- EIS use visualization to deliver specific key information to top managers at a glance, which little or no interaction with the system.
- Consolidation
- involves the aggregation of information and features simple rolls-up to complex groupings of interrelated information.
- For example, data for different sales representatives can be rolled up to an office level, state level, regional sales level.
- It enables users to get details and details of details of information.
- For example, from regional sales data then drill down to each sales representatives at each office.
- looks at information from different perspectives.
- for example, one slice of information could display all products sales during a given promotion, another slice could display a single product's sales for all promotions.
- Slicing and dicing is often performed along a time axis to analyse trends and find time-based patterns in the information.
- Digital dashboard is one of the EIS tools which tracks key performance indicators (KPIs) and critical success factors (CSFs) by compiling information from multiple sources and tailoring it to meet users needs.
- Digital dashboards integrates information from multiple components and present it in a unified display.
- for example, car dashboard which we can see all the information about the car systems.
Artificial Intelligence (AI)
- Artificial intelligence simulates human thinking and behaviour, such as the ability to reasons and learn.
- Its ultimate goal is to build a system that can mimic human intelligence.
- The advantages of AI is it can check the information on competitor.
- Intelligence system is a various commercial application of AI.
The categories of AI are :-
- Expert system
- are computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems.
- expert system are the most common form of AI in the business arena because they fill the gap when human experts are difficult to find or retain or are too expensive.
- for example, the best known system play chess and assists in medical diagnosis.
- Attempt to emulate the way the human brain works.
- For example, finance industry uses neural network to review loan application and create patterns or profiles f application that fall into two categories which is approved or denied.
- under neural network there is a Fuzzy logic.
- Fuzzy logic is a mathematical method of handling imprecise or subjective information.
- For example, washing machine has been set up to determine themselves how much water to use and how long to wash.
- an artificial intelligence system that mimics the evolutionary, survival - of - the - fittest process to generate increasingly better solution to a problem.
- For example, business executives use genetic algorithm to help them decide which combination of projects a firm should invest.
- A special-purposed knowledge-based information system that accomplishes task on behalf of its users.
- there are two kind of intelligent agent which are multi-agent system and agent-based modelling.
- for example, shopping bot: software that will search several retailers websites and provide a comparison of each retailer's offering including price and availability such as Trivago and Sky scanner for airlines.
- Data-mining is a software includes many forms of AI such as neural networks and experts systems.
- Cluster Analysis
- is a techniques 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 group are far apart as possible.
- CRM systems depend on cluster analysis to segment customer information and identity behavioural traits.
- For example, consumers goods by content, brand, loyalty or similarity.
- reveals the degree to which variables are related and the nature and the frequency of these relationship in the information.
- Market basket analysis - analyse such items as web sites and checkout scanner information to detect consumer's buying behaviour and predict future behaviour by identifying affinities among customers choices of products and services.
- For example, Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.
- Perform such functions as information correlations, distributions, calculations and variance analysis.
- Forecast is a predictions made on the basis of times-series information
- Times-series information is a time-stamped information collected at a particular frequency.
- For example, kraft use statistical analysis to assure consistent flavour, color, aroma, texture and appearance for all of its line of foods.
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