BUSN 6251 Notes

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Trinity Western University *

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6251

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Management

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Nov 24, 2024

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10

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BUSN 6251: Decision Analysis and Modeling Module 1: Introduction and Course Overview SAR approach to problem solving: Situation - Analysis - Recommendations. We present the situation, analyze, or build a model of the alternatives, and then use the results of the model to make recommendations. Big data is often described in terms of the four Vs: volume, variety, velocity, veracity. Video Business Analytics Defined Descriptive Involves gathering and describing data Managers call this reporting Predictive Use data from the past to predict the future May predict that a certain type of customer will respond in a particular fashion to a particular product advertisement Prescriptive Suggest a course of action o We are most confident when we know very little o Business analysis Transferable academic skills for BA Financial management HR Marketing Supply chain Professional tradecraft skills Data analysis Modeling tools Decision analysis tools Must have skills certification bodies Communication Technical Analytical Problem solving Decision-making Managerial Negotiation and persuasion Must have skills from practitioners Contextual modeling Communicating details and concepts Curiosity Negotiation Mentoring and coaching
Communicating risks Leverage core facilitation skills in every meeting Change management Getting to WHY Get visual BA is the smartest one on the team, but also has the least content knowledge Decision Making Part I o Picking out of the alternatives is producing recommendations for decision making Module 2: Spreadsheet Engineering & Error Reduction Spreadsheet engineering is all about how to design spreadsheets to reduce the frequency of errors. "culture eats strategy for breakfast" but "structure eats culture for lunch." Videos When Spreadsheets go wrong Types of Excel errors o #NUM! o #DIV/0! o #NULL! o Dates Module 2A: Spreadsheet Engineering Part 1 Hardcoding data in spreadsheet In spreadsheets, separate the following o Title of spread sheet, dates and comments o Data block o Decisions o Formulas o Charts and analysis Format the spreadsheet for printing Make fonts and styles consistent Share cell notes Module 2B: Spreadsheet Engineering Part 2 Three issues with spreadsheets
o Data o Format o Formulas ISO 8601 codified standard o Date format = YYYY-MM-DD o All dates MUST spell out the month No hard coding constants in formulas Everything after the “=” sign MUST be a cell reference Press <CRTL>~ to show formulas Use style sheets for consistent formatting Use sperate sheets to group similar kinds of information Assumptions, calculations and results should be on sperate sheets o Link them together Check formatting o Use error traps with IF statements or validation o Green error in cell means check error warning Reduce the amount of new formulas or things into a spreadsheet, by putting in $ signs, copying formulas, etc. Use blocks or modularize your spreadsheets Never hardcode numbers in formula The Tao of Excel Tao o Simplicity o Traceability o Consistency o Adaptability o Ease of use Document must be printable Leverage standard templates Formate the spreadsheet o Title and description of workbook is located in upper left corner o Name spreadsheeted tabs o Remove gridlines o Always save workbook with A1 as the active cell o Print format must be printer ready o Clear cell labels o Format all work as numbers o Never hide columns or rows, group them instead o Create hyper link for footnotes Use formula whenever you can Separate the data
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Always add a comment when external source involved Module 3: Data Analysis—Descriptive Statistics Once we collect the raw data, we have to summarize so we better understand it. It is a three- step process. o Step 1: Chart it. o Step 2: Find the centre or middle of the data. o Step 3: Find the dispersion of the data. Video o Berinato, S. (2019). Storytelling with data: A Good Charts Workbook tool. o to tell stories with data, follow the simple process that all good stories use: setup, conflict, and resolution. o The fundamental goal of data literacy is to understand the data and what it is trying to tell us. To do this, our first step should always be to plot the data. o Three elements of story Setup Conflict Something caused the data to change Resolution Is the aftermath of the conflict o Interactive and Visual Review of Statistics o When approaching a question in interview, apply the SAR Situation Analysis Recommendation o Descriptive Statistics Part 1 o Dunning-kruger effect Cognitive bias where people mistakenly assess their cognitive ability greater that what it is Allusion of superiority o Six types of data analysis Descriptive statistics Exploratory Inferential Predictive Causal Mechanistic o Correlation does not imply causation o Descriptive statistics
Step 1 Plot data first Step 2 Mean Median Step 3 Find measures of dispersion o Range o Variance o Standard deviation Module 4: Forecasting—Predicting the Future NADA Module 5: Modelling—Framing Decision Analysis Problems You will go through the entire client modelling process—SAR: Situation - Analysis - Recommendations. o You will start with a very basic description of the Situation. o In the first stage of the Analysis, you will build a basic model or prototype. o In the second stage of the Analysis, you will add improvements or refinements until you have the best model possible given the data you have. o In the third stage of the Analysis, you will create a series of charts and tables, and then complete a full sensitivity analysis of the critical decision variables. o You will then use your analysis to create a series of Recommendations to the client. o We will work through creating this model and all the post-solution or sensitivity analysis that you will need to create the Recommendations. Note: This is a complicated model, so we will break it down into multiple parts. Part 1: Assume Bob retires at 65 years of age. Here are some check numbers for you to validate your model. o Check data at end of year 66 Bob’s fund = $532,136 o Check data at end of year 75 Bob’s fund = $28,177 o Age that Bob’s fund goes negative = 76, and fund balance = $36,548 Analyzing the model: We need to: o Find the worst case and best case scenarios, using the uncontrollable variables. o From these two scenarios, find the range and Bob's 95% confidence interval. o Use a Tornado Chart free Excel add-in to find the sensitivity of both the controllable and uncontrollable variables. o Create three single data tables, one for each decision variable.
o Plot each of Bob's three controllable or decision variables and use their respective slopes to inform your recommendations. o Produce a two-way data table to inform your final recommendations. The WHY? o We need to help Bob determine when he should retire The WHAT? o We will build model and create a video presentation for Bob The HOW? o Using excel The GOAL? o Create movie o 4 min 30 seconds – max 5 min 30 seconds o Must have slides Slide 1 Title slide Name Date Whatever info you want First impressions are important Slide 2 Must be assumptions and data Summarize assumptions you made Insights into data used that drive the model Last Slide Summary and recommendations to Bob Module 6: Simulation—A Framework for Analyzing Risk Risk is the potential loss of unintentional outcome o Uncertainty of the future Risk analysis o Step one Estimate the risk The risk value = probability of event x cost that it happens o Step 2 Identify threats Human Operational Reputational
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Procedural Project Financial Technical Natural Political o Step 3 Manage risk Avoid risk Control or mitigate risk Share or transfer the risk Accept the risk Threats and opportunities o Interest rates o Foreign exchange rates o Supply of service or product o Demand o Economy o Weather o Stock market We need risk analysis o Because single point estimates and forecasts are dangerous Risk analysis approach o Scenario analysis o Sensitivity analysis o Monte-carlo simulation Determine probability and impact Assignment 6 Discounted cashflow o NPV Triangular and normal distribution charts Resource Allocation—Optimization Models Optimization models o Mathematical models o Designed to help decide how to allocate scarce resources to activities Op models have three parts o Objectives
goals o Decision variables Excel will change o Constraints Module 8: Decision Trees—Multi-Period Decisions Decision trees are very useful when we have multi-stage decisions. Decision trees are graphical tools to help us structure multi-stage decisions Part A video Payoff table is the expected outcome of a decision Module 9: AHP & DEA—Group Decision-Making Tools In a group decision-making mode, you would start with a brainstorming session or divergent thinking process to come up with creative alternatives Research has shown that groups come up with more creative ideas or alternatives than individuals. Once you have the ideas or alternatives generated, you then need a process for selecting and recommending the best alternative the Analytic Hierarchy Process (AHP) is a structured technique for organizing and analyzing complex group decisions, based on mathematics and minimizing group psychology. Rather than prescribing a "correct" decision, the AHP finds the decision that best suits the goal Data Envelopment Analysis (DEA) is a non-parametric (assumes no statistical distribution) method to empirically measure the relative efficiency of decision-making units (DMUs). People are more inclined to work in a team if there is a history of previous success with that team Team has mutual accountability People become possessive about initial ideas, and are more closed off to new ideas Few people generally dominate conversations, which doesn’t bring out the true point of view of the group. Louder is not generally better AHP captures human judgement o Relative assessment on a scale of 1-9
AHP Identify goal Identify criteria Identifty alternatives Calculate results & determine relative importance Data Envelope Analysis (DEA) Determines the most efficient Relative ranking How does it work o Identify the inputs o Identify the outputs o Outcome of DEA model Module 11: Dashboards & Visualizations Why are visualizations so important? COMMUNICATION to provide real-time visualizations of a variety of key performance indicators (KPIs) on their journey in monitoring and improving their performance. Dashboards consolidate a series of visuals. These allow you to quickly see what you are doing right and where you need to improve They can be helpful anytime you want to: o Provide a quick overview of a topic. o Explain a complex process. o Display research findings or survey data. o Compare and contrast multiple options. o Raise awareness about an issue or cause. Marketers use infographics to build brand awareness and boost engagement. A data dashboard is an information management tool that visually tracks, analyzes, and displays key performance indicators (KPIs), metrics, and key data points to monitor the health of a business, department, or specific process. Data-driven dashboards replace traditional reports, and if connected to live data they will be real-time snapshots of a company's financial health.
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Module 12: Data and Decisions—A Management Consultant Perspective The first step in any consulting or analyst assignment is a thorough understanding of the situation. The second step in any consulting or analyst assignment is locating the correct data. Once we locate and explore the data, we then have a two-step process. Videos