2DA3 Decision Making with Analytics Lecture 1.2 PDF

2DA3 Decision Making with Analytics LECTURE 1.2: INTRODUCTION TO BUSINESS ANALYTICS INSTRUCTOR: ZAHRA MASHAYEKHI 1 Chapter 1 Decision Making and Analytics 2 ILOs and/or outline At the end of this sessio...

2DA3 Decision Making with Analytics LECTURE 1.2: INTRODUCTION TO BUSINESS ANALYTICS INSTRUCTOR: ZAHRA MASHAYEKHI 1 Chapter 1 Decision Making and Analytics 2 ILOs and/or outline At the end of this session, you will be able to: oDefine decision making and distinguish strategic, tactical, and operational decisions, oDefine Business Analytics, oCategorize Analytical Methods and Models as o Descriptive, predictive, prescriptive 3 Decision Making 4 Decision Making Managers’ responsibility: To make strategic, tactical, or operational decisions. Strategic decisions: ◦ Involve higher-level issues concerned with the overall direction of the organization. ◦ Define the organization’s overall goals and aspirations for the future. 5 Decision Making Tactical decisions: ◦ Concern how the organization should achieve the goals and objectives set by its strategy. ◦ Are usually the responsibility of midlevel management. Operational decisions: ◦ Affect how the firm is run from day to day. ◦ Are the domain of operations managers, who are the closest to the customer. 6 Decision Making Decision Making Pyramid Example: Amazon decides to open retail store 7 Decision Making Decision making can be defined as the following process: 1. Identify and define the problem. 2. Determine the criteria that will be used to evaluate alternative solutions. 3. Determine the set of alternative solutions. 4. Evaluate the alternatives. 5. Choose an alternative. 8 Decision Making Common approaches to making decisions include: ◦ Tradition. ◦ Intuition. ◦ Rules of thumb. ◦ Using the relevant data available. 9 Business Analytics Defined 10 Business Analytics Defined ◦ What makes decision making difficult? ◦ Uncertainty ◦ Enormous number of alternatives that we cannot evaluate them all ◦ Business analytics: ◦ Scientific process of transforming data into insight for making better decisions. ◦ It is used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making. 11 Business Analytics Defined Tools of business analytics can aid decision making by: ◦ Creating insights from data. ◦ Improving our ability to more accurately forecast for planning. ◦ Helping us quantify risk. ◦ Yielding better alternatives through analysis and optimization. 12 A Categorization of Analytical Methods and Models DES CR IPTI VE A N A LYTICS P R E DIC TIV E A N A LYTICS P R ESCR IPTIV E A N A LYTICS 13 A Categorization of Analytical Methods and Models Descriptive Analytics: Descriptive analytics: Encompasses the set of techniques that describes what has happened in the past; examples include: ◦ Data queries. ◦ Reports. ◦ Descriptive statistics. ◦ Data visualization (including data dashboards). ◦ Data-mining techniques. ◦ Basic what-if spreadsheet models. 14 A Categorization of Analytical Methods and Models Descriptive Analytics (cont.): Data mining: The use of analytical techniques for better understanding of patterns and relationships that exist in large data sets. Examples of data-mining techniques include: ◦ Cluster analysis. ◦ Sentiment analysis. 15 A Categorization of Analytical Methods and Models Predictive Analytics: Predictive analytics: Consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another. Survey data and past purchase behavior may be used to help predict the market share of a new product. 16 A Categorization of Analytical Methods and Models Predictive Analytics (cont.): Techniques used in Predictive Analytics include: ◦ Linear regression. ◦ Time series analysis. ◦ Data mining uses past data to learn the relationship between an outcome variable of interest and a set of input variables. ◦ Simulation involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision. 17 A Categorization of Analytical Methods and Models Prescriptive Analytics: Prescriptive Analytics: Indicates a best course of action to take: ◦ The output of a prescriptive model is a best decision. ◦ A forecast or prediction, when combined with a rule, becomes a prescriptive model. ◦ Prescriptive models that rely on a rule or set of rules are often referred to as rule-based models. 18 A Categorization of Analytical Methods and Models Prescriptive Analytics (cont.): Optimization models: Models that give the best decision subject to constraints of the situation. Model Field Purpose Portfolio models Finance Use historical investment return data to determine the mix of investments that yield the highest expected return while controlling or limiting exposure to risk. Supply network Operations Provide the cost-minimizing plant and distribution center locations design models subject to meeting the customer service requirements. Price-markdown models Retailing Use historical data to yield revenue-maximizing discount levels and the timing of discount offers when goods have not sold as planned. 19 A Categorization of Analytical Methods and Models Prescriptive Analytics (cont.): ◦ Simulation optimization: Combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings. ◦ Decision analysis: ◦ Used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. ◦ Employs utility theory, which assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors. 20 Big Data Big data: Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. IBM describes the phenomenon of big data through the four Vs (as shown in Figure 1.1): ◦ Volume. ◦ Velocity. ◦ Variety. ◦ Veracity. 21  Big Data Figure 1.1: The 4 Vs of Big Data Source: IBM 22 Business Analytics in Practice 23 Business Analytics in Practice Figure 1.2: The Spectrum of Business Analytics Predictive and prescriptive analytics are sometimes referred to as advanced analytics. Source: Adapted from SAS 24 Business Analytics in Practice Financial Analytics: Use of analytical models to: ◦ Forecast financial performance. ◦ Assess the risk of investment portfolios and projects. ◦ Construct financial instruments. ◦ Construct optimal portfolios of investments. ◦ Allocate assets. Simulation is also often used to assess risk in the financial sector. 25 Business Analytics in Practice Human Resource (HR) Analytics: New area of application for analytics. The HR function is charged with ensuring that the organization: ◦ Has the mix of skill sets necessary to meet its needs. ◦ Is hiring the highest-quality talent and providing an environment that retains it. ◦ Achieves its organizational diversity goals. 26 Business Analytics in Practice Marketing Analytics: o A better understanding of consumer behavior through the use of scanner data and data generated from social media has led to an increased interest in marketing analytics. ◦ A better understanding of consumer behavior through marketing analytics leads to: ◦ Better use of advertising budgets. ◦ More effective pricing strategies. ◦ Improved forecasting of demand. ◦ Improved product-line management. ◦ Increased customer satisfaction and loyalty. 27 Business Analytics in Practice Health Care Analytics: ◦ Descriptive, predictive, and prescriptive analytics are used to improve: ◦ Patient, staff, and facility scheduling. ◦ Patient flow. ◦ Purchasing. ◦ Inventory control. ◦ Use of prescriptive analytics for diagnosis and treatment may prove to be the most important application of analytics in health care. 28 Business Analytics in Practice Supply-Chain Analytics: ◦ The core service of companies such as UPS and FedEx is the efficient delivery of goods, and analytics has long been used to achieve efficiency. ◦ The optimal sorting of goods, vehicle and staff scheduling, and vehicle routing are all key to profitability for logistics companies such as UPS and FedEx. ◦ Companies can benefit from better inventory and processing control and more efficient supply chains. ◦ Use of analytics to increase the resiliency of supply chain. 29 Business Analytics in Practice Web Analytics: ◦ Web analytics is the analysis of online activity, which includes, but is not limited to, visits to web sites and social media sites such as Facebook and LinkedIn. ◦ Leading companies apply descriptive and advanced analytics to data collected in online experiments to determine the best way to: ◦ Configure web sites. ◦ Position ads. ◦ Utilize social networks for the promotion of products and services. 30 Glossary Strategic decision: A decision that involves higher-level issues and that is concerned with the overall direction of the organization, defining the overall goals and aspirations for the organization’s future. Tactical decision: A decision concerned with how the organization should achieve the goals and objectives set by its strategy. Operational decisions: A decision concerned with how the organization is run from day to day. Business analytics: The scientific process of transforming data into insight for making better decisions. Descriptive analytics: Analytical tools that describe what has happened. Predictive analytics: Techniques that use models constructed from past data to predict the future or to ascertain the impact of one variable on another. Prescriptive analytics: Techniques that analyze input data and yield a best course of action Data mining: The use of analytical techniques for better understanding patterns and relationships that exist in large data sets Simulation The use of probability and statistics to construct a computer model to study the impact of uncertainty on the decision at hand. 31 Glossary Rule-based model: A prescriptive model that is based on a rule or set of rules. Big data: Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. Optimization models: A mathematical model that gives the best decision, subject to the situation’s constraints. Simulation optimization: The use of probability and statistics to model uncertainty, combined with optimization techniques, to find good decisions in highly complex and highly uncertain settings. Decision analysis: A technique used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. Utility theory: The study of the total worth or relative desirability of a particular outcome that reflects the decision maker’s attitude toward a collection of factors such as profit, loss, and risk. 32