9.1 Define the systems organizations use to make decisions and gain competitive advantages.
9.2 Describe the three quantitative models typically used by decision support systems.
9.3 Describe the relationship between digital dashboards and executive information systems.
9.4 List and describe four types of artificial intelligence systems.
9.5 Describe three types of data-mining analysis capabilities.
1.DECISION MAKING
i)Reasons for the growth of decision-making information systems.
⇨People need to analyze large amounts of the information to compete with competitors.
⇨People must make decisions quickly if do manually it takes times.
⇨People must apply sophisticated analysis techniques to make good decisions if use manually it cannot be predict because did not complete.
⇨People must protect the corporate asset of organizational information such as security and password.
ii)Model -a simplified representation or abstraction of reality .
⇒IT systems in an enterprise.
→Different level use by different department.
2)Transaction Processing Systems
➞Moving up through the organizational pyramid users move from requiring transactional information to analytical information .
➠Transaction processing systems fine information want coarse information. Need coarse information but can be understand.
i)Transaction processing system- the basic business system that serves the operational level (analysts) in an organization for daily to daily activities eg: Excel and accounts payable system.
ii)Online transaction processing (OLTP) -the capturing of transaction and event information using technology to process the information ,store information and update existing information to reflect the new information.
iii)Online analytical processing (OLAP)-the manipulation of information to create business intelligence in support of strategic decision making.
3)Decision support systems
➤Use by managers level and get the information from transaction processing systems.If transaction processing system did not key in all data ,decision support systems cannot make decision.
➤3 quantitative models used by DSSs include :
i)Sensitivity analysis -the study of the impact that changes in one (or more )parts of the model have on other parts of the model in future .Eg: Price of petrol change every month ,changes price of petrol will give impact to other.
ii)What-if analysis -checks the impact of a change in an assumption on the proposed solution .Eg: What will happen customers buy many product?Will increase revenue .If many customers did not much order products ?Will decrease revenue.
iii)Goal-seeking analysis -finds the iputs necessary to achieve a goal such as desired level of output.Eg:How to achieve target to get RM8 million in this month ?
4)Executive information systems (EIS)- more good and complex than decision support systems .It also help senior level executive .If decision support systems cannot make decisions EIS will do.
➤Most EISs offering the following capabilities :
i)Consolidation -involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information .Eg :if KFC want to get profit or revenue of state of Malacca ,they can get profit for every branch in state of Malacca and combine the information.
ii)Drill-down :Find information more details eg : can know every employees sales.
iii)Slice-and-dice : Looks at information from different perspectives same like cube. Can know total every sales ,even have all information you can choose what you want to see.
➤Digital dashboard -integrates information from multiple components and presents it in a unified display.From different component that have all information . Every executive will get digital dashboard .
5)Artificial Intelligence (AI)
➤i)Intelligent system -various commercial applications of artificial intelligence(use everyday to make decisions .
➤ii)Artificial intelligence (AI) -simulates human intelligence such as the ability to reason and learn. Advantages : can check info on competitors .
➜Ultimate goal of AI is the ability to build a system that can mimic human intelligence .Eg: They build robot to do all work.
➜4 most common categories of AI include :
a)Expert system -computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems.
b)Neural network -attempts to emulate the way the human brain works.Eg: Finance industry uses neural network to review loan applications whether approved or denied.
*Fuzzy logic -a mathematical method of handling imprecise or subjective information.
c)Genetic algorithm -an artificial intelligent system that mimics the evolutionary ,survival-of -the-fittest process to generate increasingly better solutions to a problem.
d)Intelligent agent- special-purposed knowledge -based information system that accomplishes specific tasks on behalf of its users.
*Multi-agent systems
*Agent-based modelling
6)Data Mining
➢Data mining software includes many forms of AI such as neural networks and expert systems.
➣Common forms of data-mining analysis capabilities include :
a)Cluster analysis -a technique used to divide an information set into mutually exclusive groupus.Eg:Tesco cluster the things if baby things they put all baby brand (Pureen,Johnson) into baby places.
b)Association detection -reveals the degree to which variables are related and nature and frequency of these relationships.
*Market basket analysis-can detect customers behavior .Eg:When Hari Raya ,Tesco will put the things of most customers demand when Hari Raya in front of entrance.
c)Statistical analysis - performs such functions as information correlations ,distribution ,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:expired date)
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