Chapter 2 - Tarkowski PDF

Title Chapter 2 - Tarkowski
Course Management Information Systems
Institution DePaul University
Pages 8
File Size 67.7 KB
File Type PDF
Total Downloads 26
Total Views 123

Summary

Tarkowski...


Description

Chapter 2 Wednesday, June 12, 2019

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10:06 PM

Project ○ A temporary activity a company undertakes to a create a unique product, service result § Not day to day Metrics ○ Measurements that evaluate results to determine whether a project is meeting goals Critical success factors (CSF) ○ The crucial steps that companies make to perform to achieve their goals and objectives and implement strategies § Create high-quality § Retain competitive advantages § Reduce product costs § Increase customer satisfaction § Hire and retain the best professionals Key performance indicators (KPI) ○ The quantifiable metrics a company uses to evaluate progress toward critical success factors § Turnover rates of employees § Number of product returns § Number of new customers § Average customer spending □ How quality of our products or how customers value comp. advanta ○ Not good to have too many because you will look at numbers a lot ○ External KPI § Market share □ The portion of the market that a firm captures ○ Internal KPI § Return on investment (ROI) □ Indicates the earning power of a project Model ○ A simplified representation or abstraction of reality

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Models help managers to □ Calculate risks □ Understand uncertainty □ Change variables □ Manipulate time to make decisions § Operational □ Makes structured decisions using a transaction processing systems § Managerial □ Semi-structured and DSS's § Strategic □ Un-structured and use exec info Operational support systems ○ Transaction processing system (TPS) § Basic business system that serves the operational level and assists in maki structured decisions ○ Online transaction processing (OLTP) § Capturing of transaction and event information using technology to proce store and update ○ Source document § The original transaction record □ Receipt or email Managerial support systems ○ Online analytical processing (OLAP) § Manipulation of information to create business intelligence in support of strategic decision making ○ Decision support system (DSS) § Models information to support managers and business professionals durin the decision-making process □ Excel § 4 quantitative models used by DSSs include □ What-if analysis □ Sensitivity analysis □ Goal-seeking analysis □ Optimization analysis Strategic support systems ○ Executive information system (EIS) § A specialized DSS that supports senior level executives within the organiza □ Granularity §

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® Level of detail they see □ Visualization ® Creating a graph or infographic to show what's happening □ Digital dashboard ○ Most EISs offering the following capabilities § Consolidation § Drill-down □ Want more info about sales in a region, drill down until they found t information § Slice and dice § Pivot Making organizational business decisions ○ Managerial decision-making challenges § Analyze large amounts of information § Apply sophisticated analysis techniques § Make decisions quickly Decision making process 1. Problem identification 2. Data collection 3. Solution generation 4. Solution test 5. Solution selection 6. Solution implementation Common company structure ○ Strategic § Unstructured decisions □ Occurs in situations in which no procedures or rules exist to guide decision makers toward the correct choice § Managers develop overall business strategies, goals and objectives as part the company's strategic plan ○ Managerial § Semi-structured decisions □ Occur in situations in which a few established processes help to evaluate potential solutions, but not enough to lead to a definite recommended decision § Employees are continuously evaluating company operations to hone the fi abilities to identify, adapt to and leverage change ○ Operational St t d d i i

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Structured decisions □ Situations where established processes offer potential solutions § Employees can develop, control and maintain core business activities requ to run day to day operations Machine learning ○ A type of artificial intelligence that enables computers to both understand conce in the environment, and also to learn §

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