Analytics in Strategic Decision Making Brazil Executive Seminar, April 2014

1. Analytics in Strategic Decision MakingBrazil Executive Seminar, April 2014 PRESENTED BY DR. FAIZUL HUQ OHIO UNIVERSITY

2. AGENDA • Introduction to Business Analytics. • Example of Analytical Tool Implementation for Supply Chain Sustainability • How to make Analytics work for Strategic Success • Practice Quiz

3. Introduction to Analytics What is Analytics? The extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions. The most recent step in increasingly sophisticated and effective approaches to providing technical assistance to decision-making.

4. Analytics Chronology

5. Related Methodologies

6. Hierarchy of Analytics Sophistication Analytics Competitors Analytical Companies Competitive Advantage Analytical Aspirations Localized Analytics Analytically Impaired Sophistication

8. Who Uses Analytics (continued)

9. Netflix • Started in 1997 by an angry Blockbuster customer • Looked like another dot.com flop • Online ordering • Snail mail delivery • Competing against giant with $3 Billion in Revenues • Grew from $5 Million 1999 revenue to $1 Billion 2006 revenue

10. Netflix- Competing Through Analytics • Deliver a personalized web page for each customer • Cinematch Movie Recommendation Engine • Developed by a mathematician • $1 Million prize for 10% improvement by an outsider • Discovering and focusing on most profitable customers

11. Harrah’s Entertainment – Competing Through Analytics • No override of revenue management system • Measuring customer loyalty and targeting service levels accordingly • Optimize range and configuration of games • Provide options at bottlenecks

12. Characteristics of Analytical Executives

13. In God we trust; All others bring data. Barry Beracha, CEO of Earthgrains

14. Do we think this is true? Or do we know? Gary Loveman, CEO of Caesar’s Entertainment

15. Characteristics of Analytical Executives

16. Tools and Techniques in Analytics

17. Individual versus group decisions Subjective probability assessments Interactions among decisions Objective probability measurements Restrictions on decisions Factors in Decision Making Multiple (antagonistic) goals and/or objectives

18. Factors in Decision Making • Potential deferment of some decisions • Possibility of obtaining additional information • One-time versus repetitive decisions

19. Example • Receive an additional $500, or • Watch him flip a coin and receive an additional $1000 if heads but receive nothing additional if it is tails. • Take the sure thing? • Gamble?

20. Example • Return $500 immediately, or • Watch him flip a coin and return nothing if heads but return $1000 if it is tails. • Take the sure thing? • Gamble?

21. Decision Tree Analysis $1,500 Take Sure Thing $2,000 Heads: P=0.5 Gamble Tails: P=0.5 EV = .5($2000)+.5($1000)=$1500 $1,000

22. Decision Analysis Permits the analysis of the consistency of decision making Assists supervision in evaluating the decision making of subordinates Neither necessary nor sufficient to produce good results but may increase the frequency of obtaining good results Allows for consistent and unbiased comparisons of options

23. Good Decision – Bad Outcome • To Jeanne Calment (age 90) until her death • To buy her apartment in Arles “for life” • And Jeanne celebrated her 120th birthday • Raffray’s widow and children are obligated to continue payments

24. Decision Models Always involve a degree of abstraction and simplification of the actual decision environment Do not create information, but rather concentrate and focus information

25. The Basic Proposal of Decision Analysis Something is to be gained from making the learning-adaptive processes of management more nearly like those of science.

26. Modeling and Decision Making Problem Recognition Enrich Model Simplify Model Problem Definition Correct Model Model Solution And Validation Implement Model Evaluate Model Effectiveness Model Formulation Data Gathering and Processing Termination

27. DHL Application of Analytics for Sustainability The degradation of the environment has led many governments and customers to pressure businesses to make their operations more environmentally friendly. The case illustrates an effective example of corporate social responsibility. Specifically, it demonstrates how a small increase in a supply chain budget can drastically reduce carbon dioxide emissions in the transportation of LCD TVs from their manufacturing bases to a distribution centre. source: Ivey/NUS Cases

28. DHL Sustainability Case Cont… Issues: • Environmental Sustainability; Linear Programming; Logistics; Optimization Analysis; Spreadsheet Modeling; Corporate Social Responsibility; China Disciplines: • Management Science,  Operations Management,  International Industries: • Transportation and Warehousing Setting: • China, Large, 2011 Please look at the Excel Spreadsheet Handout with the Data

29. Making Analytics Help in Strategic Advantage Get Buy In from Top to Bottom of the Company That Will Help Build an Operational Infrastructure for the Application of Analytics Employ the Workforce Necessary to Effectively and Successfully Implement Analytics for Business Decision Making

30. In order for a company to be successful in their use of Business Analytics(BA) it must expend the most effort to truly implement BA in their organizational decision making

31. Get Buy In from Top to Bottom of the Company That Will Help Build an Operational Infrastructure for the Application of Analytics The successful use of BA requires building Operational Infrastructures within its Supply chain and Intra-organizational entities that can continuously support the most effective use of BA.

32. Employ the Workforce Necessary to Effectively and Successfully Implement Analytics for Business Decision Making For successful use of BA in organizational decision making the personnel must be in place who are dedicated to and capable of effectively employ BA for making decisions.

33. Make Analytics a Common Practice in the Company

34. Permeate the Company’s Decision Making Process With the Use of Business Analytics • Expand BA practices where feasible • Companies with reliance on BA are more successful • Low reliance on BA for Decision making leads to less effective implementation of BA • Make BA second nature within the company • Shift BA use from occasional to routine leading to greater effectiveness

35. Reliance on BA and Effectiveness Source: SAS Institute/Bloomberg Research Services

36. Reliance on Analytics Source: SAS Institute/Bloomberg Research Services

37. Make Decision Making an Integrated Process Across The Company • Integrate Analytics organization wide • Do not isolate BA to a single department function • Avoid siloing of data and functionalities • Breakdown Silos • Create cross-divisional data teams • Create transparency through data sharing and process cooperation across the company

38. Cross Divisional Integration Source: SAS Institute/Bloomberg Research Services

39. Make the Use of Analytics Strategically and Mission Focused • By bringing Analytics to task a company is twice as likely to be successful then not • Majority of companies using BA employ Analytics heavily in Finance • Majority of the successful companies use Analytics in Marketing and Sales, SCM, and Product Development • Start with one or two BA initiatives because employing Analytics can be complex.

40. Summary Data of Analytics use in Functional areas Source: SAS Institute/Bloomberg Research Services

41. Be More Aggressive in Acquiring/Learning Sophisticated and Specific analytical Tools • Use Business reporting, KPI, and Dashboards • Employ sophisticated Forecasting tool • Undertake Data and Text Mining • Use Simulations and Scenario development • Employ Web Analytics

42. Use of Analytics Tools by Companies Source: SAS Institute/Bloomberg Research Services

43. Build Operational Infrastructure Acquire and Implement the Appropriate Technology Needed for Data Driven Analytics Activities Formalize the Data Management Process

44. Data Access Summary Source: SAS Institute/Bloomberg Research Services

45. Articulate a Strategy for Managing and Accessing Data • Improve the Quality, Integrity, and Consistency of Data • Increase Accessibility of Data that leads to Positive and Effective BA efforts • Make Business Information Readily Available to those who need it • Make the Source of Information and Data Central • Facilitate Data and Information Sharing across the Company

46. Acquire and Implement the Appropriate Technology needed for Data Driven Analytics Activities • Much of BA is fairly low-tech. Such as Standard Electronic Spreadsheets • Additionally acquire Software and other Technology that is more Complex and Capable than Standard Spreadsheets • Integrate Software for Data Mining, Forecasting, and Predictive Analysis into Business Processes • Make the Technology for Accessing the Data needed for Analysis • Standardize the Technology for Accessing, Integrating, and Analyzing information from all functional areas.

47. Use of Technology for Analytics in Firms Source: SAS Institute/Bloomberg Research Services

48. Formalize the Data Management Process • Put in place Appropriate Data Management Processes • Put in place general Data-Governance Rules and Policies • Articulate Defined Data Stewardship • Identify Master Data Definitions

49. Survey Information of Data Management Process Source: SAS Institute/Bloomberg Research Services

50. Employ the Workforce Necessary Establish Clear Lines of Communication Thus Creating Transparency in The Decision Making Process Hire Talented Analysts and Also Develop Home Grown Ones