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Business PPT on Business Analytics For Beginners
1.
Slide - 1 Business
Analytics: Methods, Models, and Decisions Third Edition, Global Edition Chapter 1 Introduction to Business Analytics Copyright © 2021 Pearson Education Ltd.
2.
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Pearson Education Ltd. Slide - 2 Business Analytics (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, fact-based decisions.
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Pearson Education Ltd. Slide - 3 Examples of Applications • 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
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Pearson Education Ltd. Slide - 4 Examples of Applications (continued) • Supply Chain Design – determining the best sourcing and transportation options and finding the best delivery routes • Staffing – ensuring appropriate staffing levels and capabilities, and hiring the right people • Health care – scheduling operating rooms to improve utilization, improving patient flow and waiting times, purchasing supplies, and predicting health risk factors
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Pearson Education Ltd. Slide - 5 Impacts of Analytics • 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.
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Pearson Education Ltd. Slide - 6 Evolution of Business Analytics • Analytic Foundations – Business Intelligence (BI) – Information Systems (IS) – Statistics – Operations Research/Management Science (OR/MS)
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Pearson Education Ltd. Slide - 7 Evolution of Business Analytics • Modern Business Analytic – Data mining – Simulation and risk analysis – Decision Support Systems (DSS) – Visualization
8.
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Pearson Education Ltd. Slide - 8 A Visual Perspective of Business Analytics
9.
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Pearson Education Ltd. Slide - 9 Software Support and Spreadsheet Technology • Commercial software – IBM Cognos Express – SAS Analytics – Tableau • Spreadsheets – Widely used – Effective for manipulating data and developing and solving models – Support powerful commercial add-ons – Facilitate communication of results
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Pearson Education Ltd. Slide - 10 Software Used in This Book • Primary Spreadsheet Software – Excel 2016 for Windows – Excel 2016 for Mac • Optional Software – StatCrunch (Technology Help sections) – Analytic Solver (online supplements)
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Pearson Education Ltd. Slide - 11 Descriptive, Predictive, and Prescriptive Analytics • 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
12.
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Pearson Education Ltd. Slide - 12 Example 1.1: Retail Markdown Decisions • 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 in retail: – Descriptive analytics: examine historical data for similar – Predictive analytics: predict sales based on price – Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue products (prices, units sold, advertising …)
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Pearson Education Ltd. Slide - 13 Data for Business Analytics • Data: numbers or textual data 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
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Pearson Education Ltd. Slide - 14 Examples of Data Sources and Uses • 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
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Pearson Education Ltd. Slide - 15 Big Data • Big data refers to massive amounts of business data (volume) from a wide variety of sources (variety), much of which is available in real time (velocity), and much of which is uncertain or unpredictable (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
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Pearson Education Ltd. Slide - 16 Data Reliability and Validity • Reliability - data are accurate and consistent. • Validity - data correctly measures what it is supposed to measure.
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Pearson Education Ltd. Slide - 17 Data Reliability and Validity 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.
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Pearson Education Ltd. Slide - 18 Data Reliability and Validity Examples 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.
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Pearson Education Ltd. Slide - 19 Data Reliability and Validity Examples 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).
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Pearson Education Ltd. Slide - 20 Models in Business Analytics • 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.
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Pearson Education Ltd. Slide - 21 Example 1.2: Three Forms of a Model 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.
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Pearson Education Ltd. Slide - 22 Example 1.2 Continued 2. Visual model: A sketch of sales as an S-shaped curve over time
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Pearson Education Ltd. Slide - 23 Example 1.2 Continued 3. Mathematical model: where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants
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Pearson Education Ltd. Slide - 24 Decision Models • 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 – assumed to be constant – Uncontrollable inputs – quantities that can change but cannot be controlled – Decision options – controllable and selected at the discretion of the decision maker
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Pearson Education Ltd. Slide - 25 Nature of Decision Models
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Pearson Education Ltd. Slide - 26 Descriptive Models • Descriptive models explain behavior and allow users to evaluate potential decisions by asking “what-if?” questions.
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Pearson Education Ltd. Slide - 27 Example 1.3: Gasoline Usage Model • G = gallons of fuel consumed per month • m = miles driven per day to and from work or school • d = number of driving days per month • f = fuel economy in miles per gallon (mpg) • a = additional miles for leisure and household activities per month
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Pearson Education Ltd. Slide - 28 Example 1.3 Continued • Use dimensions for logical consistency: (m miles/day) x (d days/month) = m x d miles/month • Total miles driven per month = m x d + a • Gallons consumed per month = (m x d + a miles/month)/(f miles/gallon) = (m x d + a)/f gallons/month
29.
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Pearson Education Ltd. Slide - 29 Example 1.4: An Outsourcing Decision Model • Production cost: $125/unit plus fixed cost of $50,000 • Outsourcing cost: $175/unit • Q = production volume • TC (manufacturing) = $50,000 + $125 x Q • TC (outsourcing) = $175 x Q • Breakeven Point: TC (manufacturing) = TC (outsourcing) $50,000 + $125 x Q = $175 x Q $50,000 = 50 x Q Q = 1,000 • If Q < 1,000, outsourcing is cheaper.
30.
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Pearson Education Ltd. Slide - 30 Example 1.4 Continued
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Pearson Education Ltd. Slide - 31 Predictive Models • Predictive models focus on what will happen in the future. • Many predictive models are developed by analyzing historical data and assuming that the past is representative of the future.
32.
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Pearson Education Ltd. Slide - 32 Example 1.5: A Sales-Promotion Decision Model 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.
33.
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Pearson Education Ltd. Slide - 33 Example 1.5 Model 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 Model: Total Sales = 1105.55 + 56.18 x Price + 123.88 x Coupon + 5.25 x Advertising Total Sales = 1105.55 + 56.18 x 6.99 + 123.88 x 0 + 5.25 x 0 = 1,498.25 units
34.
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Pearson Education Ltd. Slide - 34 Prescriptive Models • Prescriptive models help decision makers identify the best solution to a decision problem. • 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 – Optimal solution - values of the decision variables at the minimum (or maximum) point
35.
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Pearson Education Ltd. Slide - 35 Example 1.6: A Prescriptive Pricing Model • 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 x Price + 3,240.9 Total Revenue = Price x Sales = Price x (-2.9485 x Price + 3,240.9) = -2.9485 x Price2 + 3,240.9 x Price • Identify the price that maximizes total revenue.
36.
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Pearson Education Ltd. Slide - 36 Model Assumptions • 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.
37.
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Pearson Education Ltd. Slide - 37 Model Assumptions - 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). • A key assumption in developing a model is the type of relationship between demand and price.
38.
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Pearson Education Ltd. Slide - 38 Example 1.7: A Linear Demand Prediction Model As price increases, demand falls. A simple model is: D = a – bP where D is the demand, P is the unit price, a is a constant that estimates the demand when the price is zero, and b is the slope of the demand function.
39.
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Pearson Education Ltd. Slide - 39 Example 1.8: A Nonlinear Demand Prediction Model Assumes price elasticity is constant (constant ratio of % change in demand to % change in price): D = cP-d where c is the demand when the price is 0 and d > 0 is the price elasticity.
40.
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Pearson Education Ltd. Slide - 40 Uncertainty and Risk • 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
41.
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Pearson Education Ltd. Slide - 41 Problem Solving with Analytics 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
42.
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Pearson Education Ltd. Slide - 42 Recognizing a Problem 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.
43.
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Pearson Education Ltd. Slide - 43 Defining the Problem • 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
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Pearson Education Ltd. Slide - 44 Structuring the Problem • Stating goals and objectives • Characterizing the possible decisions • Identifying any constraints or restrictions
45.
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Pearson Education Ltd. Slide - 45 Analyzing the Problem • 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.
46.
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Pearson Education Ltd. Slide - 46 Interpreting Results and Making a Decision • 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.
47.
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Pearson Education Ltd. Slide - 47 Implementing the Solution • 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.
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