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Module_1___Analytical_Thinking___Problem_Solving.ppt.pptx
- 5. Making Decisions with Data • Quantify and interpret the variation you observe • Determine which method to use and when
- 7. Use a control chart for time- . ordered data
- 9. common cause variation (inherent o th e proces) typica l typica l
- 10. Toimproveanyproces,younedtounderstand ... •theproces •thevariationintheproces •thecausesofvariation
- 13. CommonProblem- SolvingMethodologies PDSA (PDCA) DMAIC (Six Sigma) A3 (Toyota) 8D (Eight Disciplines) Plan Define Clarify the problem Form team and collect data Measure Break down the problem Describe the problem Set the target/goal Contain the problem (interim) Analyze Analyze root causes Analyze root causes Develop countermeasures Identify corrective actions Do Improve Implement countermeasures Implement corrective actions Study (Check) Control Evaluate results Implement preventive actions Act Standardize success Verify and congratulate team
- 14. Identify Problem/ Opportunity Identify Potential Causes Evaluate and Confirm Causes Identify Potential Solutions Evaluate and Select Solutions Implement and Confirm Solutions Standardize , Monitor, and Control Process AnalyticalProblemSolvin g P r o b l e m Cause Solution S u s t a i n
- 15. Identify Problem/ Opportunity Identify Potential Causes Evaluate and Confirm Causes Identify Potential Solutions Evaluate and Select Solutions Implement and Confirm Solutions Standardize , Monitor, and Control Process AnalyticalProblemSolvin g Proble m Cause Solution S u s t a i n
- 16. Identify Problem/ Opportunity Identify Potential Causes Evaluate and Confirm Causes Identify Potential Solutions Evaluate and Select Solutions Implement and Confirm Solutions Standardize , Monitor, and Control Process AnalyticalProblemSolvin g P r o b l e m Cause Solution S u s t a i n
- 18. Criteria Easier Problems Harder Problems Time to solve Hours, days Months, years Complexity Low, one-dimensional, problem is well defined High, multidimensional, problem is ill defined Scope Small, limited to one process or one process step Large, problem spans multiple processes or organizational boundaries Is there data? Relevant, high-quality data are readily available No relevant data, difficult to measure, need to collect or compile Cause Single, identifiable Multiple, hard to identify, interconnected Success Measured, quantifiable Immeasurable, fuzzy Solution Simple, straightforward Complex, multi-phased, multi-dimensional
- 20. High Low Low High Frequency o f Problem s Medium ComplexityofProble ms Youcanusestatistic al thinking to solve mos t problems.
- 23. Identify Problem/ Opportunity Identify Potential Causes Evaluate and Confirm Causes Identify Potential Solutions Evaluate and Select Solutions Implement and Confirm Solutions Standardize , Monitor, and Control Process AnalyticalProblemSolvin g Proble m Cause Solution Sustai n
- 24. Poorly Defined Problem Can't Identify Root Causes Can't Solve Problem Won’t Collect Right Data
- 25. Problem Statement • what • when • where • how much (how many) Problem Statement
- 26. Problem Statement The yields for black anodized parts are extremely low. initial statement
- 27. Problem Statement Currently, the black anodizing process has very low daily yields, usually below 40%, and is averaging 19% yield. This results in high scrap and rework costs. Also, Components Inc.'s largest customer is threatening to find another supplier if quality and on- time delivery are not substantially improved. In the past six months, scrap and rework costs have totaled approximately $450,000 and on-time delivery is below 60%. revised statement
- 29. Project Goal Statement Improve the black anodize process yield from 19% to a minimum of 90% by July xxxx (a six- month timeframe).
- 30. DEFINING THE PROCESS Segment IV
- 31. People Materials Environment Methods Machines Measurements Product or Service Input s Transformation Output s Process or System
- 32. The Cook, Rice, Water, Salt, Kitchen Temp, Method Used, Stove, Pot, Cooking Temp White Rice - Tender, Fluffy, Delicious High-Level(Macro)ProcesMap Make boiled white rice. Inputs ProcesSteps Outputs
- 33. To understand the process 1.Observe the process in action. 2.Interview people involved in the process. 3.Interview internal suppliers and internal customers. 4.Review operating procedures or manuals.
- 34. People Materials Environment Methods Machines Measurements Product or Service Input s Output s SIPOC ma p High- Level(Macro)Proces Ma p Process or System Transformation
- 35. WHAT IS DATA? Segment V
- 36. What is Data? • Data is the building block of modern economy • Data can be in various formats • Data can be subjective • Data can be derived • Data can be deceptive
- 37. Data • Data: facts and figures from which conclusions can be drawn • Data set: the data that are collected for a particular study – Elements: may be people, objects, events, or otherentries • Variable: any characteristic of an element 7
- 38. Data Types • Quantitative data – Numbers and things that can be measured objectively • Qualitative data – Characters and descriptions that could have subjective measurement
- 39. Quantitative Data • Discrete – Is a count e.g. number of students – Whole numbers • Continuous – Is a linear measurement e.g. height, temperature – Multiple levels of granularity
- 40. Qualitative Data • Binary – Two mutually exclusive categories • Nominal – Categories with no value or rank e.g. colors of candy in a bag of M&M • Ordinal – Implicit or natural order e.g. High, Medium Low
- 41. Categoric al unorderedcategories •defective/non- defective •pas/fail •typeofdefect Nomina l Ordina l ordered categorie s •severityratings •sizecategories •satisfaction Continuou s numericaldata/coun ts •size •time •temperature
- 42. Quantitative and Qualitative Variables • Measurement: A way to assign a value of a variable to the element • Quantitative: the possible measurements of the values of a variable are numbers that represent quantities • Qualitative: the possible measurements fall into several categories 2
- 43. Cross-Sectional Data • Cross-sectional data: Data collected at the same or approximately the same point in time • Time series data: data collected over different time periods 3
- 44. Data Sources, Data Warehousing, and Big Data • Existing sources: data already gathered by public or private sources – – – – Internet Library US Government Data collection agency • Experimental and observational studies: data we collect ourselves for a specific purpose – – Response variable: variable of interest Factors: other variables related to response variable 4
- 45. Transactional Data, Data Warehousing, and Big Data • Companies hope to use past behavior and other information to predict customer responses • Data warehousing: a process of centralized data management and retrieval – Its objective is the creation and maintenance of a central repository for all of an organization’s data • Big data: massive amounts of data – – Often collected in real time in different forms Sometimes needing quick analysis
- 46. BUSINESS ANALYTICS AND BIG DATA Segment VI
- 47. Business Analytics • The use of traditional and newly developed statistical methods, advances in IS, and techniques from management science to explore and investigate past performance 4 7
- 48. Business Analytics Terms • Descriptive Analytics • Predictive Analytics • Prescriptive Analytics 4 8
- 49. Descriptive Analytics • Descriptive analytics is the interpretation of historical data to better understand a problem – Useful in discovering historical trends – Identifying problems with data – Providing preliminary relationship amongst variables
- 50. Applications of Predictive Analytics • Anomaly (outlier) detection • Association learning • Classification • Cluster detection • Prediction • Factor detection 5 0
- 51. Predictive Analytics Techniques • Supervised learning – – – – Linear regression Logistic regression Neural networks Decision trees (classification trees and regression trees) • Unsupervised learning – – – Cluster analysis Factor analysis Association rules 5 1
- 52. Prescriptive Analytics 5 2 Techniques that combine external and internal constraints with results from descriptive or predictive analytics to recommend an optimal course of action