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Useful Tools in MIS

Useful Tools in MIS. Rev: June, 2012 Euiho (David) Suh , Ph.D. POSTECH Strategic Management of Information and Technology Laboratory (POSMIT: http://posmit.postech.ac.kr) Dept. of Industrial & Management Engineering POSTECH. Contents. SWOT Analysis (1/2). SWOT Analysis. Weaknesses.

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Useful Tools in MIS

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  1. Useful Tools in MIS Rev: June, 2012 Euiho (David) Suh, Ph.D. POSTECH Strategic Management of Information and Technology Laboratory (POSMIT: http://posmit.postech.ac.kr) Dept. of Industrial & Management EngineeringPOSTECH

  2. Contents

  3. SWOT Analysis (1/2) SWOT Analysis Weaknesses Strengths Opportunities Threats • What is SWOT analysis? • Development of the idea of matching the organization’s internal factors with external environmental circumstances • How to use SWOT analysis? • TOWS Matrix • To develop strategies that take into account the SWOT profile, a matrix of these factors can be constructed • The SWOT matrix, can be changed into what is known as the TOWS Matrix

  4. SWOT Analysis (2/2) • Supporting of the foundation • Brilliant students • Staffs of superior ability • High quality facilities for research • POVIS system • Hard-studying campus environment • Hard to attract students and faculty • Lack of Globalization • Poor External Advertisement • Small scale of Alumni Association • Lack of Leadership • Globalization and knowledge society • Increasing expectation of high quality human resource • Increasing attention to specialized graduate school (ex. Steel graduate course) • S-O strategies • Caring system for better human source • W-O strategies • Advertise POSTECH through external cooperation • Produce high quality human resource through a select few education. • W-T strategies • Increasing the number of foreign exchange students • Provide privilege to top notch students • Increasing the number of students evading science and engineering department • Competitive universities’ advance. • Increasing competition in receiving large-scale project. • S-T strategies • Advertise POSTECH by showing POSTECH has better research outcomes than other competitive universities • Foundation of a branch school abroad • Example • SWOT Analysis for POSTECH

  5. BCG Matrix & GE/Mckinsey Matrix(1/3) • What is BCG Matrix? • Well-known portfolio management tool used in product life cycle theory • How to use BCG Matrix? • Plot business units or products into the matrix by assessing their relative market share and market growth values ※ Limitations of BCG Matrix • The link between market share and profitability is questionable since increasing market share can be very expensive • The approach may overemphasize high growth, since it ignores the potential of declining markets • The model considers market growth rate to be a given. In practice the firm may be able to grow the market

  6. BCG Matrix & GE/Mckinsey Matrix (2/3) Competitive Strength • Invest aggressively • Invest selectively • Harvest or divest High Low Medium Low Market Attractiveness Medium High • What is GE/Mckinsey Matrix? • Model to perform a business portfolio analysis on the Strategic Business units of a corporation • GE/Mckinseymatrix attempt to improve upon the BCG Matrix • Market (Industry) attractiveness replaces market growth as the dimension of industry attractiveness • Competitive strength replaces market share as the dimension by which the competitive position of each SBU is assessed • GE/McKinsey Matrix works with a 3 x 3 grid, while the BCG Matrix has only 2 x 2.This also allows for more sophistication ※ Limitations of GE/Mckinsey Matrix • Core competencies are not represented • Interactions between Strategic Business Units are not considered

  7. BCG Matrix & GE/Mckinsey Matrix (3/3) LGE’s market share Relative Market Share= Rival’s market share Market Growth MC High HE HA AC Low Bubble size: LGE’s relative sales account 10 1 0.1 Relative Market Share • Example • BCG Matrix of LG Electronics

  8. Value Chain (1/3) Firm Infrastructure Human Resource Management Support activities Those that are involved in the creation, sale and transfer of products (including after-sales service) Technology Development Procurement Outbound Logistics Operations Service Inbound Logistics Marketing Those that merely support the primary activities Primary Activities • What is Value Chain? • A tool for systematically examining the activities of a firm and how they interact with one another and affect each other’s cost and performance • A tool to gain a competitive advantage by performing these activities better or at a lower cost than competitors • A tool to represent the main activities in the business and their relationships in terms of how they add value so as to satisfy the customer and obtain resources from suppliers

  9. Value Chain (2/3) Firm Infrastructure Human Resource Management Support activities Technology Development Procurement Outbound Logistics Operations Service Inbound Logistics Marketing Primary Activities • How to use Value Chain? • Internal Analysis for the firm • Analysis all the activities according to the description below

  10. Value Chain (3/3) • Example • Value chain of a generic airport company

  11. P5CFM (1/3) • Threat of Potential Entrants • Bargaining Power of Buyers • Bargaining Power of Suppliers • Degree of • Existing Rivalry • Threat of Substitutes • What is P5CFM (Porter’s Five-Force Model)? • A tool to know about difference forces that impact on a company’s ability to compete • A tool to diagnose the principal competitive pressures in a market • A tool to assess how strong and important each force is

  12. P5CFM (2/3) • How to use P5CFM? • External Analysis for the firm • Analysis all the activities according to the description below • Threat of Potential Entrants • Bargaining Power of Buyers • Bargaining Power of Suppliers • Degree of • Existing Rivalry • Threat of Substitutes

  13. P5CFM (3/3) • Example • A lubricants industry analysis

  14. BSC (1/2) • What is BSC (BalancedScoreCard)? • A strategic performance management tool that is used extensively in business and organizations worldwide • To align business activities to the vision and strategy of the organization • To improve internal and external communications • To monitor organization performance against strategic goals • Set of measures that gives top managers a fast but comprehensive view of the business • How to use BSC? • Build up goals and measure in terms of the four perspectives

  15. BSC (2/2) Financial Perspective Customer Perspective Internal Business Perspective Learning and Growth Perspective • Example • ECI ’s Balanced Business Scorecard

  16. Knowledge Map (1/3) • What is Knowledge Map? • A diagrammatic representation of corporate knowledge, having nodes as knowledge and links as the relationships between knowledge, and knowledge specification or profile • Two components • Diagram: graphical representation of knowledge • Node: rectangular object denoting knowledge captured from business processes • Linkage: arrow between nodes implying relationships among knowledge • Specification: descriptive representation of knowledge • Advantages • Formalization of all knowledge inventories within an organization • Perception of relationships between knowledge • Efficient navigation of knowledge inventory • Promotion of socialization/externalization of knowledge by connecting domain experts with knowledge explorers • The figure left depicts a conceptual model ofknowledge map

  17. Knowledge Map (2/3) Defining organizational knowledge To provide a uniform, text-based intermediate representation of the knowledge types specific to a development effort that is comprehensible by either humans or machines Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • How to use Knowledge Map? • Procedures of building the knowledge map

  18. Knowledge Map (2/3) Defining organizational knowledge • Business process is analyzed using a process map technique • Composed of process, flow, event, and external object Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • How to use Knowledge Map? • Procedures of building the knowledge map • An example of a process map of issuing a membership card

  19. Knowledge Map (2/3) Defining organizational knowledge • Three types of the extracted knowledge: • Prerequisite knowledge before process execution • Used knowledge during execution • Produced knowledge after execution • Techniques available: • Interviewing • Document analysis • System analysis • Knowledge workshop • Brainstorming, etc. Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • How to use Knowledge Map? • Procedures of building the knowledge map

  20. Knowledge Map (2/3) Defining organizational knowledge Process map analysis Knowledge extraction • Supports connecting people with information and connecting people with people by providing • Informational attributes • keywords, description, importance • People-finder attributes • an expert or author Knowledge profiling Knowledge linking Knowledge map validation • How to use Knowledge Map? • Procedures of building the knowledge map

  21. Knowledge Map (2/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking • Knowledge link is represented as an arrow in a knowledge map • Link denotes pre- and post-relationship between knowledge Knowledge map validation • How to use Knowledge Map? • Procedures of building the knowledge map • An example of knowledge link

  22. Knowledge Map (2/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • A structured walkthrough is conducted with domain experts, business managers, and knowledge map producer • How to use Knowledge Map? • Procedures of building the knowledge map

  23. Knowledge Map (3/3) Defining organizational knowledge • The rolling mill reduces a hot slab into a coil of specified thickness • To specify the knowledge requirement, analyze input sources, and develop basic taxonomy • Five categories of segment knowledge • Mechanical • Electrical • Instrumental • Information system • Control Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • Example • P steel company

  24. Knowledge Map (3/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • Example • P steel company

  25. Knowledge Map (3/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • Example • P steel company

  26. Knowledge Map (3/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • Example • P steel company

  27. Knowledge Map (3/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • Example • P steel company

  28. Knowledge Map (3/3) Defining organizational knowledge Process map analysis Knowledge extraction Knowledge profiling Knowledge linking Knowledge map validation • A structured walkthrough is conducted with domain experts • Example • P steel company

  29. Decision Tree (1/3) • What is Decision Tree? • A decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility • To display an algorithm, to help identify a strategy most likely to reach a goal • To be used in operations research, specifically in decision analysis • Advantages • Are simple to understand and interpret • People are able to understand decision tree models after a brief explanation • Have value even with little hard data • Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes • Use a white box model • If a given result is provided by a model, the explanation for the result is easily replicated by simple math • Can be combined with other decision techniques • may use Net Present Value calculations, PERT 3-point estimations and a linear distribution of expected outcomes • Disadvantages • For data including categorical variables with different number of levels, information gain in decision trees are biased in favor of those attributes with more levels

  30. Decision Tree (2/3) • How to use Decision Tree? • 3 types of nodes • Decision nodes • Represented by squares • Chance nodes • Represented by circles • End nodes • Represented by triangles • Process • Interview decision makers and construct a preliminary tree • Present tree and show how various concerns are captured • Solicit a list of new concerns • Revise tree • Estimating Probabilities from Data, Experts & Literature • Estimating Costs • Analysis of Trees • Folding back • Replace a node with its expectation • Continue until the decision node

  31. Decision Tree (3/3) SatisfactionLevel Payoff Sunny (0.4) 4×0.4 = 1.6 4 1.6 + 4.8 = 6.2 Screen golf 8×0.6 = 4.8 8 Rainy (0.6) Start Sunny (0.4) 10 10×0.4 = 4.0 Filed golf 4.0 + 1.2 = 5.2 2 2×0.6 = 1.2 Rainy (0.6) • Example • Screen golf or field golf? • Chance of rain tomorrow: 60%

  32. What-if Analysis (1/2) Identify the relationship Analyze effect of change Understanding the relationship between the variables selected to be changed and the variables which will be affected by the selected variables’ change Changing the selective variables at various levels and (mathematically or strategically) predicting the effect of the change to the variables we are observing Make strategyagainst the change With the results from the previous step, design strategies to make the negative effect minimum and the positive effect maximum when the expected change occurs • What is What-if Analysis? • Observing how changes to selected variables affect other variables • Examples of what if analysis • What if air traffic was shut down due to another volcano? What would this do to our supply chain? • What if we offered our client a new discount model? Would they buy more products in the future? • What if we were able to reduce our expenses by 5%? How much flexibility would we gain? • What if every employee reduced their business travel by just one trip per year? • What if we changed our fixed phone plans to variable ones? Would we be able to save cost? • Sensitivity analysis: A special case of what-if analysis. Typically, the value of only one variable is changed repeatedly, and the resulting changes on other variables are observed • How to use What-if Analysis

  33. What-if Analysis (2/2) • Simple example of What-if Analysis • Lead time: 3 days / Demand per day: 10 items / Safety Stock: 30 items (3 days × 10)Re-order Quantity: 100 / Initial Stock: 100Thus, we need to re-order on 7th, 17th, 27th…day (re-order interval: 10 days). • What if demand per day increased to 20 items? What would this do to our inventory control?  The safety stock would be 60 items (3 days x 20) and re-order quantity would be 200 items or, re-order interval would be shortened to 5 days with re-order quantity as 100 items

  34. Delphi Method (1/2) Define the problem Give everyone the problem Identifying the problem(s) in various forms from a questionnaire to a broad and open question Collate the response Recruiting experts to the Delphi group, sending the problem(s) to everyone in the group and asking them to respond Give everyone the collation Taking the responses that experts send back and collating these into a single anonymous list or sets of lists Repeat as necessary Sending the collation back out to everyone with request to score each item on a given scale (typically 1 to 5) and may allow them to add further items or comments Act on the findings Repeating the rounding until a certain stopping condition meets(Number of iterations, a specific level of agreement) Analyzing the findings and putting plans in place to deal with future risks and opportunities in the project • What is Delphi Method? • Communication technique based on a structured process for collecting and synthesizing knowledge from a group of experts by means of a series of questionnaires accompanied by controlled opinion feedback • Key characteristics: • Structuring of information flow • Regular feedback • Anonymity of the participants • How to use Delphi Method

  35. Delphi Method (2/2) • Example • Choosing the next strategy for the company with 5 experts (Stopping Condition: 3 rounds) • 1st round (Questionnaire & Scoring result) • 2ndround (Questionnaire & Scoring result)

  36. Delphi Method (2/2) • Example • Choosing the next strategy for the company with 5 experts (Stopping Condition: 3 rounds) • 3rdround (Questionnaire & Scoring result)  The company choose “Organizing a task force team” strategy

  37. ERD (1/5) Entity 1 Entity 2 • Attribute 1-1 • Attribute 1-2 • Attribute 1-3 • Attribute 2-1 • Attribute 2-2 • Attribute 2-3 • What is ERD (Entity Relationship Diagram)? • A detailed, logical representation of the entities, associations and data elements for an organization or business • How to use ERD? • Data entities • An entity is a business object that represents a group, or category of data • Person, place, object, event or concept about which data is to be maintained • Attributes • An attribute is a sub-group of information within an entity • Named property or characteristic of an entity • Relationship models • Association between the instances of one or more entity types • Mandatory Relationships • Optional Relationships • Many-to-Many Relationships • One-to-Many Relationships • One-to-One Relationships • Recursive Relationships

  38. ERD (2/5) Instructor Student Instructor Student • Attribute I-1 • Attribute I-2 • Attribute I-3 • Attribute St-1 • Attribute St-2 • Attribute St-3 • Attribute I-1 • Attribute I-2 • Attribute I-3 • Attribute St-1 • Attribute St-2 • Attribute St-3 Department Student Department Student • Attribute D-1 • Attribute D-2 • Attribute D-3 • Attribute St-1 • Attribute St-2 • Attribute St-3 • Attribute D-1 • Attribute D-2 • Attribute D-3 • Attribute St-1 • Attribute St-2 • Attribute St-3 • How to use ERD (continued)? • Relationship models: Mandatory, many-to-many • Relationship models: Optional, many-to-many

  39. ERD (3/5) Instructor Skill Instructor Skill • Attribute I-1 • Attribute I-2 • Attribute I-3 • Attribute Sk-1 • Attribute Sk-2 • Attribute Sk-3 • Attribute I-1 • Attribute I-2 • Attribute I-3 • Attribute Sk-1 • Attribute Sk-2 • Attribute Sk-3 Product Vendor Product Vendor • Attribute P-1 • Attribute P-2 • Attribute P-3 • Attribute V-1 • Attribute V-2 • Attribute V-3 • Attribute P-1 • Attribute P-2 • Attribute P-3 • Attribute V-1 • Attribute V-2 • Attribute V-3 • How to use ERD(continued)? • Relationship models: Optional/mandatory, many-to-many • Relationship models: Optional/mandatory, one-to-many

  40. ERD (4/5) Automobile Engine Automobile Engine • Attribute A-1 • Attribute A-2 • Attribute A-3 • Attribute En-1 • Attribute En-2 • Attribute En-3 • Attribute A-1 • Attribute A-2 • Attribute A-3 • Attribute En-1 • Attribute En-2 • Attribute En-3 EMPLOYEE Employee supervises • Attribute Em-1 • Attribute Em-2 • Attribute Em-3 is supervised by • How to use ERD(continued)? • Relationship models: Mandatory, one-to-one • Relationship models: Recursive

  41. ERD (5/5) Example

  42. DFD (1/2) Function File/Database Input/Output Flow • What is DFD (Data Flow Diagram)? • Graphical representation of the "flow" of data through an information system, modeling its process aspects • Preliminary step used to create an overview of the system which can later be elaborated • The visualization of data processing (structured design) • How to use DFD? • Draw diagrams to show… • What kinds of data will be input to and output from the system • Where the data will come from and go to • Where the data will be stored • Notations

  43. DFD (2/2) • Example • General Model Of Publisher's Present Ordering System

  44. Statistical Hypothesis Testing (1/3) • What is Statistical Hypothesis Testing? • Method of making decisions using experimental data • Procedure for deciding if a null hypothesis should be accepted or rejected in favor of an alternate hypothesis • How to use Statistical Hypothesis Testing? • Hypothesis • H0: θ∈ϴ0vs. H1: θ∈ϴ1where ϴ0andϴ1are partition of possible parameter values H0: null hypothesis, H1: alternative hypothesis • H0: θ≥k vs. H1: θ< k; one-sided hypothesesH0: θ= k vs. H1: θ≠k; two-sided hypotheses • The threshold value c is called a critical valueSetting a critical value is equivalent to dividing the range of the test statistic X into{x:x < c}: acceptance region, {x:x ≥ c}: rejection region • Consequences of a decision • Type I error probability: α(θ) = P(Reject H0| H0) = Pθ(X ≥ c),Type Ⅱ error probability: β(θ) = P(Accept H0| H1) = Pθ(X < c), • Traditional approach is keeping the type I error probabilityunder a pre-specified levelα(θ) = P(Reject H0| H0) ≤ α, for some 0 < α < 1

  45. Statistical Hypothesis Testing (2/3) • Two-sided test α/2 α/2 α k -k k Do not reject H0 Reject H0 Do not reject H0 Reject H0 Reject H0 • How to use Statistical Hypothesis Testing?(continued) • Critical region • One-sided test • p-value • The probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true • p-value = P(observed value | H0 is true )

  46. Statistical Hypothesis Testing (3/3) • critical region (α=0.05) n(0, 1) p=0.0217 0 1.645 2.02 Reject H0 Do not reject H0 • Example • A random sample of 100 recorded deaths in the United States during the past year showed an average life span of 71.8 years. Assuming a population standard deviation of 8.9 years, does this seem to indicate that the mean life span today is greater than 70 years? Use a 0.05 level of significance. • Solution • H0: μ ≤ 70 years • H1: μ >70 years • α = 0.05 • Critical region: z > 1.645=z0.05, where • Computations: =71.8 years, σ=8.9 years, and = 2.02 • Decision: Reject H0and conclude that the mean life span today is greater than 70 years • P-value : P=P(Z>2.02) = 0.0217 < 0.05

  47. Regression Analysis (1/4) • The unknown parameters, denoted as β, which may represent a scalar or a vector • The independent variables, X • The dependent variable, Y Y = f(X, β) Y = • What is Regression Analysis? • Techniques for modeling and analyzing the relationship between dependent variables and independent variables • Estimating the conditional expectation of the dependent variable given the independent variables • Used for prediction and forecasting, understanding related independent variables, and exploring the forms of the relationships • How to use Regression Analysis? • Regression models • Usually formalized as E(Y|X) = f(X,β) • Simple linear regression model • and : parameters of the model • : error term (random variable with mean of zero)

  48. Regression Analysis (2/4) E(y)= • : y intercept of the regression line • : slope of the regression line • ŷ: estimated value of y for a given x value • : observed value of the dependent variable for the ith observation • ŷ: estimated value of the dependent variable for the ith observation min • How to use Regression Analysis? (Cont’d) • Simple linear regression Equation • :y intercept of the regression line • : slope of the regression line • : expected value of y for a given x value • Estimated simple linear regression equation • Least squared criterion • Slope for the estimated regression equation: • Y-Intercept for the estimated regression equation:

  49. Regression Analysis (3/4) • How to use Regression Analysis? (Cont’d) • R squared: Coefficient of determination • - Regression sum of squares • - Total sum of squares • - Error sum of squares • since • : Proportionate reduction of total variation associated with the use of the predictor variable X • When all observation fall on the fitted regression line, then and • When the fitted regression line is horizontal so that and , then and 0

  50. Regression Analysis (4/4) • Example • Reed Auto periodically has a special week-long sale. As part of the advertising campaign Reed runs one or more television commercials during the weekend preceding the sale. Data from a sample of 5 previous sales are shown in the below box • Slope for the Estimated Regression Equation: • y-Intercept for the Estimated Regression Equation: • Estimated Regression Equation:  If the company puts 5 TV ads, it is expected for the company to sell about 35 cars

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