Stakeholder Presentation Final Project

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Question Description

The stakeholder presentation serves as the bridge between analytics and business decision making. The presentation provides you with the framework to integrate the course content through a real life example.

Choose an organization that you are passionate about. Consider how this organization is performing. Are there areas for improvement? Even the most well-run organizationsexperience problems.

Now imagine for a moment that your team has been recently hired as analytic consultants for this organization. Brainstorm and come up with 2-3 ideas that that organization may be facing currently. It is your teams job to investigate why things are happening. For example: Why are sales declining or why do our products sell more during poor weather?

Choose the most appealing idea, and use the template below to answer that question for your organization.

Using the provided template, analyze the problem and present your findings. You are expected to integrate relevant models and concepts from assigned readings in your analysis, along with using logic and insights/skills from previous classes and personal experiences. The presentation should be at least 15 minutes and should not exceed 2 minutes. Your team should use PowerPoint slides to support your presentation.

You should provide sufficient information to capture all the components of the Business Analytics Process. A presentation must include a statement of and background description of the problem; identification of data sources; synopsis of data preparation methodologies used and detailed discussion of the data preparation steps taken; description of and reasoning for the modeling techniques used in analysis; appropriate and detailed visualization of results; explanation of conclusions, recommendations, and predictions drawn from each of the three types of business analytics: descriptive, predictive and prescriptive.

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(Business) Analytics is the use of:  data,  information technology,  statistical analysis,  quantitative methods, and  mathematical or computer-based models to help managers gain improved insight about their business operations and make better, factbased decisions.  Pricing ◦ setting prices for consumer and industrial goods, government contracts, and maintenance contracts  Customer segmentation ◦ identifying and targeting key customer groups in retail, insurance, and credit card industries  Merchandising ◦ determining brands to buy, quantities, and allocations  Location ◦ finding the best location for bank branches and ATMs, or where to service industrial equipment  Social Media ◦ understand trends and customer perceptions; assist marketing managers and product designers      Business intelligence Information Systems Statistics Operations research/Management science Decision support systems  Benefits ◦ …reduced costs, better risk management, faster decisions, better productivity and enhanced bottom-line performance such as profitability and customer satisfaction.  Challenges ◦ …lack of understanding of how to use analytics, competing business priorities, insufficient analytical skills, difficulty in getting good data and sharing information, and not understanding the benefits versus perceived costs of analytics studies.    Descriptive analytics: the use of data to understand past and current business performance and make informed decisions Predictive analytics: predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. Prescriptive analytics: identify the best alternatives to minimize or maximize some objective            Database queries and analysis Dashboards to report key performance measures Data visualization Statistical methods Spreadsheets and predictive models Scenario and “what-if” analyses Simulation Forecasting Data and text mining Optimization Social media, web, and text analytics    Most department stores clear seasonal inventory by reducing prices. Key question: When to reduce the price and by how much to maximize revenue? Potential applications of analytics:  Descriptive analytics: examine historical data for similar products (prices, units sold, advertising, …)  Predictive analytics: predict sales based on price  Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue  IBM Cognos Express ◦ An integrated business intelligence and planning solution designed to meet the needs of midsize companies, provides reporting, analysis, dashboard, scorecard, planning, budgeting and forecasting capabilities.  SAS Analytics ◦ Predictive modeling and data mining, visualization, forecasting, optimization and model management, statistical analysis, text analytics, and more.  Tableau Software ◦ Simple drag and drop tools for visualizing data from spreadsheets and other databases.   Data: numerical or textual facts and figures that are collected through some type of measurement process. Information: result of analyzing data; that is, extracting meaning from data to support evaluation and decision making.         Annual reports Accounting audits Financial profitability analysis Economic trends Marketing research Operations management performance Human resource measurements Web behavior  page views, visitor’s country, time of view, length of time, origin and destination paths, products they searched for and viewed, products purchased, what reviews they read, and many others.  Data set - a collection of data. ◦ Examples: Marketing survey responses, a table of historical stock prices, and a collection of measurements of dimensions of a manufactured item.  Database - a collection of related files containing records on people, places, or things. ◦ A database file is usually organized in a two-dimensional table, where the columns correspond to each individual element of data (called fields, or attributes), and the rows represent records of related data elements. Records Entities Fields or Attributes   Big data to refer to massive amounts of business data from a wide variety of sources, much of which is available in real time, and much of which is uncertain or unpredictable. IBM calls these characteristics volume, variety, velocity, and veracity. “The effective use of big data has the potential to transform economies, delivering a new wave of productivity growth and consumer surplus. Using big data will become a key basis of competition for existing companies, and will create new competitors who are able to attract employees that have the critical skills for a big data world.” - McKinsey Global Institute, 2011    Metric - a unit of measurement that provides a way to objectively quantify performance. Measurement - the act of obtaining data associated with a metric. Measures - numerical values associated with a metric.  Discrete metric - one that is derived from counting something. ◦ For example, a delivery is either on time or not; an order is complete or incomplete; or an invoice can have one, two, three, or any number of errors. Some discrete metrics would be the proportion of on-time deliveries; the number of incomplete orders each day, and the number of errors per invoice.  Continuous metrics are based on a continuous scale of measurement. ◦ Any metrics involving dollars, length, time, volume, or weight, for example, are continuous.     Categorical (nominal) data - sorted into categories according to specified characteristics. Ordinal data - can be ordered or ranked according to some relationship to one another. Interval data - ordinal but have constant differences between observations and have arbitrary zero points. Ratio data - continuous and have a natural zero.    Reliability - data are accurate and consistent. Validity - data correctly measures what it is supposed to measure. Examples: ◦ A tire pressure gage that consistently reads several pounds of pressure below the true value is not reliable, although it is valid because it does measure tire pressure. ◦ The number of calls to a customer service desk might be counted correctly each day (and thus is a reliable measure) but not valid if it is used to assess customer dissatisfaction, as many calls may be simple queries. ◦ A survey question that asks a customer to rate the quality of the food in a restaurant may be neither reliable (because different customers may have conflicting perceptions) nor valid (if the intent is to measure customer satisfaction, as satisfaction generally includes other elements of service besides food).  Model - an abstraction or representation of a real system, idea, or object.  Captures the most important features  Can be a written or verbal description, a visual representation, a mathematical formula, or a spreadsheet. The sales of a new product, such as a first-generation iPad or 3D television, often follow a common pattern. 1. Verbal description: The rate of sales starts small as early adopters begin to evaluate a new product and then begins to grow at an increasing rate over time as positive customer feedback spreads. Eventually, the market begins to become saturated and the rate of sales begins to decrease. 2. Visual model: A sketch of sales as an S-shaped curve over time 3. Mathematical model: S = aebect where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants.  Influence diagram - a visual representation of a descriptive model that shows how the elements of the model influence, or relate to, others.  An influence diagram is a useful approach for conceptualizing the structure of a model and can assist in building a mathematical or spreadsheet model. Basic Expanded    total cost = fixed cost + variable cost variable cost = unit variable cost × quantity produced (1.2) total cost = fixed cost + variable cost = fixed cost + unit variable cost × quantity produced (1.3) (1.1) Mathematical model:      TC = Total Cost F = Fixed cost V = Variable unit cost Q = Quantity produced TC = F +VQ (1.4)   Decision model - a logical or mathematical representation of a problem or business situation that can be used to understand, analyze, or facilitate making a decision. Inputs: ◦ Data, which are assumed to be constant for purposes of the model. ◦ Uncontrollable variables, which are quantities that can change but cannot be directly controlled by the decision maker. ◦ Decision variables, which are controllable and can be selected at the discretion of the decision maker. TC(manufacturing) = $50,000 + $125*Q TC(outsourcing) = $175*Q Breakeven Point: TC(manufacturing) = TC(outsourcing) $50,000 + $125 × Q = $175 × Q $50,000 = 50 × Q Q = 1,000 General Formula F + VQ = CQ Q = F/(C - V)  (1.5) In the grocery industry, managers typically need to know how best to use pricing, coupons and advertising strategies to influence sales. Grocers often study the relationship of sales volume to these strategies by conducting controlled experiments to identify the relationship between them and sales volumes. That is, they implement different combinations of pricing, coupons, and advertising, observe the sales that result, and use analytics to develop a predictive model of sales as a function of these decision strategies. Sales = 500 – 0.05(price) + 30(coupons) + 0.08(advertising) + 0.25(price)(advertising) If the price is $6.99, no coupons are offered, and no advertising is done (the experiment corresponding to week 1), the model estimates sales as Sales = 500 - 0.05 × $6.99 + 30 × 0 + 0.08 × 0 + 0.25 × $6.99 × 0 = 500 units  Assumptions are made to ◦ simplify a model and make it more tractable; that is, able to be easily analyzed or solved. ◦ better characterize historical data or past observations.   The task of the modeler is to select or build an appropriate model that best represents the behavior of the real situation. Example: economic theory tells us that demand for a product is negatively related to its price. Thus, as prices increase, demand falls, and vice versa (modeled by price elasticity — the ratio of the percentage change in demand to the percentage change in price). As price increases, demand falls. Assumes price elasticity is constant (constant ratio of % change in demand to % change in price)    Uncertainty is imperfect knowledge of what will happen in the future. Risk is associated with the consequences of what actually happens. “To try to eliminate risk in business enterprise is futile. Risk is inherent in the commitment of present resources to future expectations. Indeed, economic progress can be defined as the ability to take greater risks. The attempt to eliminate risks, even the attempt to minimize them, can only make them irrational and unbearable. It can only result in the greatest risk of all: rigidity.” – Peter Drucker   Prescriptive decision models help decision makers identify the best solution. Optimization - finding values of decision variables that minimize (or maximize) something such as cost (or profit).  Objective function - the equation that minimizes (or maximizes) the quantity of interest.  Constraints - limitations or restrictions.  Optimal solution - values of the decision variables at the minimum (or maximum) point.    A firm wishes to determine the best pricing for one of its products in order to maximize revenue. Analysts determined the following model: Sales = -2.9485(price) + 3240.9 Total revenue = (price)(sales) = price × (-2.9485 × price + 3240.9) = 22.9485 × price2 + 3240.9 × price Identify the price that maximizes total revenue, subject to any constraints that might exist.   Deterministic model – all model input information is known with certainty. Stochastic model – some model input information is uncertain. ◦ For instance, suppose that customer demand is an important element of some model. We can make the assumption that the demand is known with certainty; say, 5,000 units per month (deterministic). On the other hand, suppose we have evidence to indicate that demand is uncertain, with an average value of 5,000 units per month, but which typically varies between 3,200 and 6,800 units (stochastic). 1. Recognizing a problem 2. Defining the problem 3. Structuring the problem 4. Analyzing the problem 5. Interpreting results and making a decision 6. Implementing the solution Problems exist when there is a gap between what is happening and what we think should be happening.  For example, costs are too high compared with competitors.   Clearly defining the problem is not a trivial task. Complexity increases when the following occur: - large number of courses of action - the problem belongs to a group and not an individual - competing objectives - external groups are affected - problem owner and problem solver are not the same person - time limitations exist    Stating goals and objectives Characterizing the possible decisions Identifying any constraints or restrictions   Analytics plays a major role. Analysis involves some sort of experimentation or solution process, such as evaluating different scenarios, analyzing risks associated with various decision alternatives, finding a solution that meets certain goals, or determining an optimal solution.   Models cannot capture every detail of the real problem Managers must understand the limitations of models and their underlying assumptions and often incorporate judgment into making a decision.   Translate the results of the model back to the real world. Requires providing adequate resources, motivating employees, eliminating resistance to change, modifying organizational policies, and developing trust. www.puzzlOR.com  Maintained by an analytics manager at ARAMARK.  Each month a new puzzle is posted.  Many puzzles can be solved using techniques you will learn in this book.  The puzzles are fun challenges.  A good one to start with is SurvivOR (June 2010).  Have fun!    Many commercial software packages can be used for Business Analytics. Spreadsheet software, such as Microsoft Excel, is widely available and used across all areas of business. Spreadsheets provide a flexible modeling environment for manipulating data and developing and solving models.   Mac versions of Excel do not have the full functionality that Windows versions have – particularly statistical features which are important to this book. The Excel add-in that we use in later chapters, Analytic Solver Platform, only runs on Windows. Thus, if you use a Mac, you should either run Bootcamp with Windows or use a third-party software product such as Parallels or VMWare.           Opening, saving, and printing files Using workbooks and worksheets Moving around a spreadsheet Selecting cells and ranges Inserting/deleting rows and columns Entering and editing text, data, and formulas Formatting data (number, currency, decimal) Working with text strings Formatting data and text Modifying the appearance of a spreadsheet    Tabs - Home, Insert, Page Layout, Formulas, … Groups - Font, Alignment, Number, Styles, … Buttons and Menus - Buttons appear as small icons. - Menus of additional choices are indicated by small triangles.   Common mathematical operators are used. For example: a− bP5 c + would be entered into Excel as: d =a− b*P^5 + c/d  Cell references can be relative or absolute. Using a dollar sign before a row and/or column label creates an absolute reference. ◦ Relative references: A2, C5, D10 ◦ Absolute references: $A$2, $C5, D$10    Using a $ sign before a row label (for example, B$4) keeps the reference fixed to row 4 but allows the column reference to change if the formula is copied to another cell. Using a $ sign before a column label (for example, $B4) keeps the reference to column B fixed but allows the row reference to change. Using a $ sign before both the row and column labels (for example, $B$4) keeps the reference to cell B4 fixed no matter where the formula is copied. Two models for predicting demand as a function of price Linear D = a – bP Formula in cell B8: =$B$4-$B$5*$A8 Nonlinear D = cP-d Formula in cell E8: =$E$4*D8^-$E$5 Note how the absolute addresses are used so that as these formulas are copied down, the demand is computed correctly. Formulas in cells can be copied in many ways.  Use the Copy button in the Home tab, then use the Paste button  Use Ctrl-C, then Ctrl-V  Drag the bottom right corner of a cell (the fill handle) across a row or column       Split Screen Paste Special Column and Row Widths Displaying Formulas in Worksheets Displaying Grid Lines and Column Headers for Printing Filling a Range with a Series of Numbers       =MIN(range) =MAX(range) =SUM(range) =AVERAGE(range) =COUNT(range) =COUNTIF(range,criteria) ◦ Excel has other useful COUNT-type functions: COUNTA counts the number of nonblank cells in a range, and COUNTBLANK counts the number of blank cells in a range. In addition, COUNTIFS(range1, criterion1, range2, criterion2,… range_n, criterion_n) finds the number of cells within multiple ranges that meet specific criteria for each range. =MIN(F4:F97) =MAX(F4:F97) =SUM(G4:G97) =AVERAGE(H4:H97) =COUNT(B4:B97) =COUNTIF(D4:D97,”=O-Ring”) =COUNTIF(H4:H97,”<30”) =COUNTIFS(D4:D97,"O-Ring",A4:A97,"Spacetime Technologies")   SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS can be used to embed IF logic within mathematical functions. For instance, the syntax of SUMIF is ◦ SUMIF(range, criterion, [sum range]). "Sum range" is an optional argument that allows you to add cells in a different range.  Example: In the Purchase Orders database, to find the total cost of all airframe fasteners, use =SUMIF(D4:D97,"Airframe fasteners", G4:G97)   Net Present Value (or discounted cash flow) measures the worth of a stream of cash flows, taking into account the time value of money. Excel function: =NPV(rate,value1,value2,…) ◦ F is the cash flow ($) ◦ Rate (i) is the discount rate ◦ value1, value2,…are equally-spaced payments or income values ◦ t is a time period Cell B8: =NPV(B6, C4:H4) – B5   Click the Insert function button fx. You may type in a description or search. Example for COUNTIF function    =IF(condition, value if true, value if false) – a returns one value if the condition is true and another if the condition is false, =AND(condition1, condition2, …) – returns TRUE if all conditions are true and FALSE if not, =OR(condition1, condition2, …) – returns TRUE if any condition is true and FALSE if not.     =IF(condition, value if true, value if false) Conditions may include the following: = equal <> not equal to > greater than >= greater than or equal to < less than <= less than or equal to You may nest up to 7 IF functions, replacing the value if false with another IF function Example: =IF(A8 =2,(IF(B3 =5,”YES”,“ ”)),15)  Suppose that orders with quantities of at least 10,000 units are classified as Large. ◦ Cell K4: =IF(F4>=10000, “Large”, “Small”)  Suppose that large orders with a total cost of at least $25,000 are considered critical. ◦ Cell L4: =IF(AND(K4=“Large”, G4>=25000),“Critical”,“”)  These functions are ...
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The NBA Company
Business Analytics

Introduction








The National Basketball Association is the men’s professional basketball league in
North America.
The league is composed of 30 teams.
NBA is considered to be the premier men’s professional basketball in the word.
NBA generates more than $7 billion per season.
The company faces a number of challenges with regards to its daily operations.

There is need to establish lasting solution to these challenges.

Findings: Challenges of NBA
• Declining entertainment value of NBA Basketball games.
• Revenue gab between big teams and small teams in the league.
• Lack of competitive balance.

Declining Entertainment Value
• The NBA suffered through its most lackluster season in recent past.
• The practice of resting healthy players to regain their energy and reduce
their injury chances has reduced its value.

• Fans have express dissatisfaction and have expr...

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