Information Systems in Organizations Decision Making February 24, 2016 1 Mintzberg s five 5 organizational parts 2 Types of Organizations An organization is an administrative and functional structure where people work toward a specific goal. Understanding the organization s IT needs requires a good understanding of its administrative and functional structure. Hierarchical Flat Matrix Holacratic Other Source: http://www.forbes.com/sites/jacobmorgan/2015/07/06/the-5-types-of-organizationalstructures-part-1-the-hierarchy/#788b78c43853 Source: https://www.ics.uci.edu/~corps/phaseii/mintzberg-structurein5s-mgmtsci.pdf 3 1
IS & Hierarchical Organizational structure. 4 IS & Hierarchical Organizational structure. 5 Administrative Information Systems Transaction Processing Systems (TPS) Basic business system that serves the operational level (including analysts) in organizations Capture & process data generated during day to day activities Office Automation Systems (OAS) Systems designed to help office in doing their job. Decision Support Systems (DSS) Systems designed to support middle managers and business professionals during the decision making process Executive Information Systems (EIS) or Executive Support Systems (ESS) Specialized DSS that help senior level executives make decisions. GDSS: computer based systems that facilitate solving of unstructured problems by set of decision makers 6 2
Organization & IS: another view Office Office Top Management Office Middle Management Knowledge Lower Management Office Types of Information Systems: - Transaction Processing Systems - Office Automation Systems - Knowledge Worker Systems - Management Information Systems - Decision Support Systems - Executive Information Systems Questions Operational 7 Decision Making process Simon s decision-making process model Intelligence Design Choice (Implementation) Simon, H. (1955), A Behavioral Model of Rational Choice, Quarterly Journal of Economics, vol. 69, 99 188 Drucker Peter (1954), The Practice of Management, Harper & Brothers, New York Newell, A., and Simon, H. A. (1972). Human problem solving Englewood Cliffs, Prentice-Hall, New Jersey. 8 Intelligence Phase Scan the environment for a problem. Determine if problem is real, important enough, solvable Determine if problem within their scope of influence? Scan Environment for problem to be solved or decision to be made Problem? Yes Problem within scope of influence? No END Data source No END Organizational IS & external data Fully define the problem by gathering more information. Yes Gather more information about the problem Internal & External data 9 3
Design Phase Develop a model of the problem. Determine type of model. Verify model. Develop and analyze potential solutions. Develop a model of problem to be solved Verify that the model is accurate Develop potential solutions 10 Choice Phase Evaluate solutions and select the solution to implement. More detailed analysis of selected solution might be needed. Verify initial conditions. Analyze proposed solution against real-world constraints. Questions 11 12 4
DSS structure Systems designed to help middle managers make decisions Major components Data management subsystem Internal and external data sources Analysis subsystem Typically mathematical in nature User interface How the people interact with the DSS Data visualization is the key Text Graphs Charts * OLAP: OnLine Analytical Processing User Interface Analysis - Sensitivity Analysis -What-if Analysis - Goal-seeking Analysis -Data-driven tools -> Data mining -> OLAP* Data Management - Transactional Data - Data warehouse - Business partners data - Economic data 13 DSS Analysis Tools Simulation is used to examine proposed solutions and their impact Sensitivity analysis Determine how changes in one part of the model influence other parts of the model What if analysis Manipulate variables to see what would happen in given scenarios Goal seeking analysis Work backward from desired outcome Determine monthly payment given various interest rates. Works backward from a given monthly payment 14to determine various loans that would give that payment. Executive Information Systems Specialized DSS that supports senior level executives within the organization Most EISs offer the following capabilities: Consolidation involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information Drill-down enables users to get details, and details of details, of information Slice-and-dice looks at information from different perspectives Digital dashboards are common features 15 5
Artificial Intelligence (AI) systems Common categories of AI systems: 1. Expert system computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems 2. Neural Network attempts to emulate the way the human brain works Analyses large quantities of info to establish patterns and characteristics in situations where logic or rules are unknown Uses Fuzzy logic a mathematical method of handling imprecise or subjective information 3. Genetic algorithm an artificial intelligent system that mimics the evolutionary, survival of the fittest process to generate increasingly better solutions to a problem 4. Intelligent agent special purposed knowledge based information system that accomplishes specific tasks on behalf of its users PBS Report on AI in today s world 16 Expert Systems Artificial Intelligence systems that codify human expertise in a computer system Main goal is to transfer knowledge from one person to another Wide range of subject areas Medical diagnosis Computer purchasing Finance Accounting (http://raw.rutgers.edu/miklosvasarhelyi/resume%20articles/chapters%2 0IN%20BOOKS/C10.%20expert%20systems%20app%20in%20act.pdf ) Knowledge engineer elicits the expertise from the expert and encodes it in the expert system System engineer: IT professional who develops AI inference engine and other components of the Expert System. 17 Expert Systems Components Knowledge base: database of the expertise, often in IF THEN rules. Inference engine: derives recommendations from knowledge base and problem specific data User interface: controls the dialog between the user and the system Explanation system: Explain the how and why of recommendations Knowledge Engineer Encoded expertise Domain Expert Expertise Knowledge base User User Interface Inference Engine Explanation System Example of rules IF family is albatross AND color is white THEN System bird is laysan albatross. Engineer IF family is albatross AND color is dark THEN bird is black footed albatross - Knowledge engineer codify the human expert s expertise into the systems knowledge base. - System engineer is the IT professional who develop the user interface, the inference engine, and the explanation system. 18 6
Expert Systems: Pros and Cons Pros More reliable than humans, incorporating expertise from many sources. Able to deduce rules that are not apparent, even to experts Capable of being extended, as the system is applied and knowledge grows Built in high-level computer languages (called Shell e.g. CLIPS, JESS, DROOLS) requiring few IT skills 19 Expert Systems: Pros and Cons Cons Often difficult to know a priori if experts who devise the rules really do know all they claim to. Experts may sabotage the system with false information, or withhold information, particularly if their jobs are threatened. Rule following is not the best approach for all situations. Built in high-level computer languages (and Shells e.g. CLIPS, JESS, DROOLS) that require few IT skills 20 Summary Questions 1) Compare Middle management and lower management tasks in terms of their structure. Notes 2) Compare Middle management and lower management tasks in terms of their repetitiveness. 3) What is the difference between GDSS and DSS in terms of their target users? 4) What is the difference between Decision Support Systems (DSS) and Executive Information Systems (EIS) in terms of their target users. 6) Give an example of TPS. 7) (a) What are the major components in a DSS? (b) What is the function of each? 8) What is an Expert System? What are the main components of an Expert system? What is a knowledge engineer? 21 7
Mintzberg s 5 organizational parts Operating core Those who perform the basic work related directly to the production of products and services. Strategic apex Charged with ensuring that the organization serve its mission in an effective way, and also that it serve the needs of those people who control or otherwise have power over the organization. Middle line Managers who stand in a direct line relationship between the strategic apex and the operating core. Techostructure The analysts who serve the organization by affecting the work of others. They may design it, plan it, change it, or train the people who do it, but they do not do it themselves. Examples: engineers, accountants, planners, researchers, etc. Support staff The specialists who provide support to the organization outside of its operating workflow. Examples: food service, busing, legal consel, etc. References Drucker Peter (1954), The Practice of Management, Harper & Brothers, New York Hendry, J. (1997). Mintzberg s theory of organization structure: a critical assessment and extension. Retrieved from http://johnhendry.co.uk/wp/wp- content/uploads/2013/07/mintzbergs-theory-of-organization- Structure.pdf on June 3, 2015. Herbert S. (1955), A Behavioral Model of Rational Choice, Quarterly Journal of Economics, vol. 69, 99 188 Mintzberg, H. (1980). Structure in 5 s: A synthesis of the research on organization design. Management Science, 26(3), 322-341. Available at: https://www.ics.uci.edu/~corps/phaseii/mintzberg-structurein5s- MgmtSci.pdf Newell, A., and Simon, H. A. (1972). Human problem solving Englewood Cliffs, Prentice-Hall, New Jersey. 8