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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docx
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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
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