際際滷

際際滷Share a Scribd company logo

JSM 2012 statistical engineering study case barcelona spain

Igor Barahona
Igor Barahona

This document discusses a case study on applied statistics in business management in Spain. It aims to demonstrate how statistical engineering can help companies improve decision-making. The researchers identified 4 key drivers that may impact a company's use of analytical tools: data-based competitive advantage, management support for data analysis, systematic thinking, and communication outside the company. They surveyed 255 Spanish companies to analyze how these drivers relate to the companies' level of adoption of analytical tools. The researchers applied concepts of statistical engineering by integrating multiple statistical tools to extract relevant information from the dataset. This included designing a questionnaire, analyzing the results using 7 statistical tools, and concluding that different statistical methods can be complementary.

1 of 32
Downloaded 22 times
APPLIED STATISTICS ON BUSINESS MANAGEMENT AT SPAIN. A CASE OF STATISTICAL ENGINEERING Igor BARAHONA & Alex RIBA igorbarahona@hotmail.com 1
匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 2
匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 3
MOTIVATION The big motivation for this research is: To help Companies to improve their decision making by promoting the use of statistical tools To understand how a set of 4 key-drivers are related with better analytic performance in companies. To demonstrate how the Statistical Engineering is a powerful approach for the successful integration of several statistical tools. To provide another documented case of Statistical Engineering, that was made using data from the real world.
匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 5
WHAT IS IT DONE? Porter (1998) 1 We are introducing 4 key-drivers for the expansion of the Level of Adoption of Analytical Tools (LAAT) McDonough III. (2000) Checkland 1. Data-Based Competitive Advantage. (DB-CA) (1999) 2. Management Support on Data Analysis. (MS-DA) 3. Systematic Thinking. (SYS) Davenport & 4. Communication Outside the Company. (COM-OUT) Harris (2010) We are proposing a 5-level scale to measure the 2 LAAT at companies. 1. Statistical Ignorance. Davenport. & 2. Local Focus. Harris (2007) 3. Statistical Aspirations. 4. Statistical Engineering. 5. Statistics as Competitive Advantage. We are applying the concepts of Statistical 3 Engineering to extract relevant information from the dataset Statistical Deming (2000) Thinking Hoerl & Snee 1. The starting point is a survey with 255 responses. (2010) 2. One questionnaire with 21 items was designed. 3. Seven statistical tools were used and integrated. Statistical Engineering 4. With this is clear that different statistical tools are complementary rather than exclusive. Statistical Methods and Tools
匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 7
This scale was used for classifying Statistics as each surveyed company 5competitive advantage 4 Statistical Engineering DM ASBM is an 3 Statistical aspirations DM ASBM impact decision making at important toolkit for maintaining the leadership at the market 2 Local focus DM The beginning of the ASBM as competitive strategic, tactical and operational levels GOALS Maintaining the DM 1 Statistics ignorance ASBM supports only specific tasks and local impact advantage. GOALS strengthening the interaction between leadership through creating new and better GOALS three levels ways to analyse DM (strategic, tactical Maintain and data Based on past GOALS and operational) improving the experience, Improving emerging system by judgements and interaction between Systems working on the Key Systems under uncertainty functional areas at Innovation and Drivers Analysis of all types company leadership in the GOALS to understand current and future market, but as Having data of Systems Systems results. Consolidate well in the quality and improve Predictions and Local systems, e.g. the Business creation metrics datasets forecasts of all types, Return over intelligent systems. and indicators. measurement of Systems Investment (ROI), intangibles as brand To make from the Statistical Process equity and human ASBM a competitive None Control (SCP) capital. advantage
From literature review we It was sent to 6460 companies at THE ROADMAP defined 4 key-drivers Barcelona, Spain. 255 responses received A questionnaire was designed based on the key-drivers The Statistical Engineering concept is applied on the data analysis How to best utilize statistical concepts, methods and tools and integrate them with information technology to Conclusions and generate improved discussion results Hoerl & Snee (2010) 9
THE FLOWCHART This is a 5 steps methodology and is BASED ON STATISTICAL ENGINEERING concepts, as is shown in the following figure. DATA BASED. COMPETITIVE ADVANTAGE Understanding Flowchart 1 projects scope MANAGEMENT Survey design and Operational Definition (DO) * 2 collect data SUPPORT ON Principal Component Analysis DATA ANALYSIS APPLIED STATISTICS ON BUSINESS Applying the scale Bar Chart 3 at companies Box plots MANAGEMENT (ASBM) Relationships Factorial Analysis 4 SYSTEMATIC between THINKING Correspondence Analysis companies Relationships Correlation matrix between key- 5 COMMUNICATION drivers Logistic regression OUTSIDE COMPANY Final conclusions. 10
GETTING DATA FROM THE REAL WORLD This is the questionnaire s structure There are 5 sections and 21 ITEMS in the questionnaire, as it is shown in the following table: number of section ITEMS General information about the company 4 Data Based Competitive Advantage 5 Management Support Data Analysis 6 Systemacic Thinking 5 Comunication outside the company 1 Total 21 5-level Likert scale was used on the 17 ITEMS https://www.surveymonkey.com/s/ASBM 11
DATASET APPEARANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
QUESTIONNAIRE DESIGN In order to support our conceptual model, the 17 items were clustered on the first 4 factors using the loadings as classification criteria Rotated Component Matrixa Component MS-DA DB-CA SYS COM-OUT DB-CA. Data-Based Competitive 1 2 3 4 Advantage DB-CA2 .766 DB-CA3 .851 DB-CA4 .707 DB-CA5 .570 .614 MS-DA. Management Support MS-DA1 .837 on Data Analysis MS-DA2 .753 MS-DA3 .635 .523 DB-CA1 .595 .584 SYS. Systemic Vision of the MS-DA4 .831 business MS-DA5 .644 .400 SYS1 .433 .595 SYS2 .754 COM-OUT. Communication SYS3 .739 Outside company. (clients and SYS4 .630 .528 suppliers) COM-OUT .904 MS-DA6 .561 SYS5 .430 .519 .534 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. PCA gave us a quantitative foundation to support our conceptual model 13
APPLYING THE SCALE Cum. Cum. 7% L1 Level Freq Percent 25% L2 Freq Percent 1 65 25.5 65 25.5 20% L3 L4 2 38 14.9 103 40.4 L5 3 83 32.6 186 72.9 4 52 20.4 238 93.3 5 17 6.7 255 100.0 15% 33% 83 65 52 38 17 L1 L2 L3 L4 L5 Communication outside the Companies at Level 3 are the company is highest Key- biggest group Drivers
FACTOR ANALYSIS The 255 responses were discomposed and represented at the 2 biggest factors 15
FACTOR ANALYSIS 16
FACTOR ANALYSIS Level 1 is close from Micro Size. Level 4 is close from Middle Size 17
FACTOR ANALYSIS Services Companies are more suitable to be analytical oriented Products Companies are more related with level 1 and Micro size 18
FACTOR ANALYSIS Middle size companies are closer to better and different strategies. There is a group for Micro-size, Products, Level 1 and No Competitive Advantage 19
CORRELATION MATRIZ ANALYSIS C.M allows us to understand and quantify relationships between the Key Drivers Pearson Correlation Coefficients DBCA MSDA SYS COMOUT DBCA. Data Based Competitive Advantage 1.000 0.70243 0.69484 0.05246 MSDA. Management support data analysis 1.000 0.64852 -0.03397 SYS. Systematic Thinking 1.000 0.30036 COMOUT. Communication Outside Company 1.000 DB. COMPETITIVE ADVANTAGE 0.695 0.702 SYSTEMATIC MANAGEMENT THINKING SUPPORT. DA 0.648 0.300 COMMUNICATION OUTSIDE COMPANY 20
LOGISTIC REGRESSION To predict of a set of 255 Spanish companies, either a company has analytics aspirations or not. (Level=>4) Level 4 is the starting point of the use of data and statistics as a distinctive competence in the industry RESPONSE VARIABLE: Y =0 If the company does not has analytical aspirations. (Level<4) Y =1 If the company has analytical aspirations. (Level>=4) PREDICTORS NO ANALYTICAL ANALYTICAL ASPIRATIONS. (LEVEL ASPIRATIONS TOTAL 1 , 2AND 3) (LEVEL 4 AND 5) G1 Understanding the benefits of Statistics 186 69 255 G2 Statistics builds the Comp. Adv 73% 27% 100% G3 There is one mission and vision The predictors were taken from the G4 Communication with clients and suppliers questionnaire ITEMS 21
PROPORTIONAL ODDS THE MODEL 錚 P 錚 Ln錚 錚 = 硫 0 + 硫 i G1 + 硫 j G2 + 硫 k G3 + 硫 l G4 +竜 (ijkl ) 錚 1 P 錚 Logistic Regression Table Odds 95% CI have p-values less than 0.05, Predictor Coef SE Coef Z P Ratio Lower Upper indicating that there is Constant -17.8045 3.13596 -5.68 0.000 DB_CA1 1.65439 0.313537 5.28 0.000 5.23 2.83 9.67 sufficient evidence that the DB_CA3 0.723906 0.271505 2.67 0.008 2.06 1.21 3.51 coefficients are not zero using SYS2 1.12321 0.273354 4.11 0.000 3.07 1.80 5.25 an alfa level of 95% COM_OUT 1.54055 0.382019 4.03 0.000 4.67 2.21 9.87 Goodness-of-Fit Tests The goodness-of-tests, with Method Chi-Square DF P p-value equal to 1.000. Pearson 105.652 111 0.625 Indicate that there is Deviance 72.350 111 0.998 insufficient evidence to Hosmer-Lemeshow 4.405 8 0.819 claim that the model does not fit the data adequately. 1. UNDERSTANDING THE BENEFITS OF APPLIED STATISTICS BUSINESS. Coefficients for 2. BUILDING A COMPETITIVE ADVANTAGE BY DATA ANALYSIS. these variables 3. ESTABLISHING A MISSION AND VISION STATEMENTS FOR THE COMPANY are not cero. 4. STIMULATING COMMUNICATION OUTSIDE COMPANY. 22
匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 23
STATISTICAL ENGINEERING DISCUSSION A set of 7 statistical tools were applied in this research Different statistical tools can be successfully integrated, in order to extract relevant information from a unique problem With this, It was demonstrated that several statistical tools can be complementary rather than exclusive Hoerl & Snee The three previous points are (2010) the core philosophy of the Statistical Engineering. 24
匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 25
REFERENCES Banks, D. "Is Industrial Statistics Out of Control?," Statistical Science, (8:4), 1993 , pp. 402-409 Checkland, P. (1999). "Systems Thinking, Systems Practice: Includes a 30-Year Retrospective". Wiley; 1 edition, New York USA Cronbach, L. J. (1951). "Coefficient alpha and the internal structure of tests" Psychometrika. 16, 297-334. Davenport, T, and Harris, J. (2010) "Analytics at Work: Smarter Decisions, Better Results". Harvard Business School Press , Boston USA "Davenport, T. Harris, J. (2007). ""Competing on analytics the new science of winning"". Harvard Business School Press , Boston USA" Deming, W.E. (2000). "Out of the Crisis". The MIT Press, Boston USA Ghemawat, P (2007). "Redefining Global Strategy: Crossing Borders in a World Where Differences Still Matter". Harvard Business School Press, Boston USA Hoerl, R.W and Snee R.D. "Closing the gap: Statistical Engineering can bridge statistical thinking with methods and tools" , May 2010, pp. 52-55, Quality Press Hoerl, R.W and Snee R.D. "Statistical Thinking and Methods in Quality Improvement: A Look to the Future", Quality Engineering, (22), 2010, pp 119-129 Hoerl, R.W and Snee R.D. (2001). " Statistical Thinking: Improving Business Performance". Duxbury Press; 1er edition, CA USA Poon, P., and C.Wagner. "Critical success factors revisited: success and failure cases of information systems for senior executives," Decision Support Systems, (30:4), 2001, pp. 393-418 Porter, M. (1998). "Competitive advantage : creating and sustaining superior performance". Free Press, New Yorw USA Roberts, H. "Applications in Business and Economic Statistics: Some Personal Views", Statistical Science, (5:4), 1990, pp. 399- 402 Stainberg, D.M., "The Future of industrial statistics: A panel discussion", Technometrics, (50:2), 2008, pp103-127 Wang, Y.R., Kon, B.H and Madnick, S.E, "Data Quality Requirements Analysis and Modeling", the Ninth International Conference of Data Engineering, Vienna, Austria, April 1993, pp 670-677 Wang, R.Y and Strong, D. M. "Beyond accuracy: What data quality means to data consumers", Journal of Management Information Systems, (12:4), Spring 1996, pp 5 Yeo,K. "Systems thinking and project management time to reunite," Int.J.Project Manage, (11:2), 1993, pp 111-117 26
THE END OF PRESENTATION 27
28
29
30
31
32

More Related Content

JSM 2012 statistical engineering study case barcelona spain

  • 1. APPLIED STATISTICS ON BUSINESS MANAGEMENT AT SPAIN. A CASE OF STATISTICAL ENGINEERING Igor BARAHONA & Alex RIBA igorbarahona@hotmail.com 1
  • 2. 匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 2
  • 3. 匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 3
  • 4. MOTIVATION The big motivation for this research is: To help Companies to improve their decision making by promoting the use of statistical tools To understand how a set of 4 key-drivers are related with better analytic performance in companies. To demonstrate how the Statistical Engineering is a powerful approach for the successful integration of several statistical tools. To provide another documented case of Statistical Engineering, that was made using data from the real world.
  • 5. 匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 5
  • 6. WHAT IS IT DONE? Porter (1998) 1 We are introducing 4 key-drivers for the expansion of the Level of Adoption of Analytical Tools (LAAT) McDonough III. (2000) Checkland 1. Data-Based Competitive Advantage. (DB-CA) (1999) 2. Management Support on Data Analysis. (MS-DA) 3. Systematic Thinking. (SYS) Davenport & 4. Communication Outside the Company. (COM-OUT) Harris (2010) We are proposing a 5-level scale to measure the 2 LAAT at companies. 1. Statistical Ignorance. Davenport. & 2. Local Focus. Harris (2007) 3. Statistical Aspirations. 4. Statistical Engineering. 5. Statistics as Competitive Advantage. We are applying the concepts of Statistical 3 Engineering to extract relevant information from the dataset Statistical Deming (2000) Thinking Hoerl & Snee 1. The starting point is a survey with 255 responses. (2010) 2. One questionnaire with 21 items was designed. 3. Seven statistical tools were used and integrated. Statistical Engineering 4. With this is clear that different statistical tools are complementary rather than exclusive. Statistical Methods and Tools
  • 7. 匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 7
  • 8. This scale was used for classifying Statistics as each surveyed company 5competitive advantage 4 Statistical Engineering DM ASBM is an 3 Statistical aspirations DM ASBM impact decision making at important toolkit for maintaining the leadership at the market 2 Local focus DM The beginning of the ASBM as competitive strategic, tactical and operational levels GOALS Maintaining the DM 1 Statistics ignorance ASBM supports only specific tasks and local impact advantage. GOALS strengthening the interaction between leadership through creating new and better GOALS three levels ways to analyse DM (strategic, tactical Maintain and data Based on past GOALS and operational) improving the experience, Improving emerging system by judgements and interaction between Systems working on the Key Systems under uncertainty functional areas at Innovation and Drivers Analysis of all types company leadership in the GOALS to understand current and future market, but as Having data of Systems Systems results. Consolidate well in the quality and improve Predictions and Local systems, e.g. the Business creation metrics datasets forecasts of all types, Return over intelligent systems. and indicators. measurement of Systems Investment (ROI), intangibles as brand To make from the Statistical Process equity and human ASBM a competitive None Control (SCP) capital. advantage
  • 9. From literature review we It was sent to 6460 companies at THE ROADMAP defined 4 key-drivers Barcelona, Spain. 255 responses received A questionnaire was designed based on the key-drivers The Statistical Engineering concept is applied on the data analysis How to best utilize statistical concepts, methods and tools and integrate them with information technology to Conclusions and generate improved discussion results Hoerl & Snee (2010) 9
  • 10. THE FLOWCHART This is a 5 steps methodology and is BASED ON STATISTICAL ENGINEERING concepts, as is shown in the following figure. DATA BASED. COMPETITIVE ADVANTAGE Understanding Flowchart 1 projects scope MANAGEMENT Survey design and Operational Definition (DO) * 2 collect data SUPPORT ON Principal Component Analysis DATA ANALYSIS APPLIED STATISTICS ON BUSINESS Applying the scale Bar Chart 3 at companies Box plots MANAGEMENT (ASBM) Relationships Factorial Analysis 4 SYSTEMATIC between THINKING Correspondence Analysis companies Relationships Correlation matrix between key- 5 COMMUNICATION drivers Logistic regression OUTSIDE COMPANY Final conclusions. 10
  • 11. GETTING DATA FROM THE REAL WORLD This is the questionnaire s structure There are 5 sections and 21 ITEMS in the questionnaire, as it is shown in the following table: number of section ITEMS General information about the company 4 Data Based Competitive Advantage 5 Management Support Data Analysis 6 Systemacic Thinking 5 Comunication outside the company 1 Total 21 5-level Likert scale was used on the 17 ITEMS https://www.surveymonkey.com/s/ASBM 11
  • 12. DATASET APPEARANCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
  • 13. QUESTIONNAIRE DESIGN In order to support our conceptual model, the 17 items were clustered on the first 4 factors using the loadings as classification criteria Rotated Component Matrixa Component MS-DA DB-CA SYS COM-OUT DB-CA. Data-Based Competitive 1 2 3 4 Advantage DB-CA2 .766 DB-CA3 .851 DB-CA4 .707 DB-CA5 .570 .614 MS-DA. Management Support MS-DA1 .837 on Data Analysis MS-DA2 .753 MS-DA3 .635 .523 DB-CA1 .595 .584 SYS. Systemic Vision of the MS-DA4 .831 business MS-DA5 .644 .400 SYS1 .433 .595 SYS2 .754 COM-OUT. Communication SYS3 .739 Outside company. (clients and SYS4 .630 .528 suppliers) COM-OUT .904 MS-DA6 .561 SYS5 .430 .519 .534 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. PCA gave us a quantitative foundation to support our conceptual model 13
  • 14. APPLYING THE SCALE Cum. Cum. 7% L1 Level Freq Percent 25% L2 Freq Percent 1 65 25.5 65 25.5 20% L3 L4 2 38 14.9 103 40.4 L5 3 83 32.6 186 72.9 4 52 20.4 238 93.3 5 17 6.7 255 100.0 15% 33% 83 65 52 38 17 L1 L2 L3 L4 L5 Communication outside the Companies at Level 3 are the company is highest Key- biggest group Drivers
  • 15. FACTOR ANALYSIS The 255 responses were discomposed and represented at the 2 biggest factors 15
  • 17. FACTOR ANALYSIS Level 1 is close from Micro Size. Level 4 is close from Middle Size 17
  • 18. FACTOR ANALYSIS Services Companies are more suitable to be analytical oriented Products Companies are more related with level 1 and Micro size 18
  • 19. FACTOR ANALYSIS Middle size companies are closer to better and different strategies. There is a group for Micro-size, Products, Level 1 and No Competitive Advantage 19
  • 20. CORRELATION MATRIZ ANALYSIS C.M allows us to understand and quantify relationships between the Key Drivers Pearson Correlation Coefficients DBCA MSDA SYS COMOUT DBCA. Data Based Competitive Advantage 1.000 0.70243 0.69484 0.05246 MSDA. Management support data analysis 1.000 0.64852 -0.03397 SYS. Systematic Thinking 1.000 0.30036 COMOUT. Communication Outside Company 1.000 DB. COMPETITIVE ADVANTAGE 0.695 0.702 SYSTEMATIC MANAGEMENT THINKING SUPPORT. DA 0.648 0.300 COMMUNICATION OUTSIDE COMPANY 20
  • 21. LOGISTIC REGRESSION To predict of a set of 255 Spanish companies, either a company has analytics aspirations or not. (Level=>4) Level 4 is the starting point of the use of data and statistics as a distinctive competence in the industry RESPONSE VARIABLE: Y =0 If the company does not has analytical aspirations. (Level<4) Y =1 If the company has analytical aspirations. (Level>=4) PREDICTORS NO ANALYTICAL ANALYTICAL ASPIRATIONS. (LEVEL ASPIRATIONS TOTAL 1 , 2AND 3) (LEVEL 4 AND 5) G1 Understanding the benefits of Statistics 186 69 255 G2 Statistics builds the Comp. Adv 73% 27% 100% G3 There is one mission and vision The predictors were taken from the G4 Communication with clients and suppliers questionnaire ITEMS 21
  • 22. PROPORTIONAL ODDS THE MODEL 錚 P 錚 Ln錚 錚 = 硫 0 + 硫 i G1 + 硫 j G2 + 硫 k G3 + 硫 l G4 +竜 (ijkl ) 錚 1 P 錚 Logistic Regression Table Odds 95% CI have p-values less than 0.05, Predictor Coef SE Coef Z P Ratio Lower Upper indicating that there is Constant -17.8045 3.13596 -5.68 0.000 DB_CA1 1.65439 0.313537 5.28 0.000 5.23 2.83 9.67 sufficient evidence that the DB_CA3 0.723906 0.271505 2.67 0.008 2.06 1.21 3.51 coefficients are not zero using SYS2 1.12321 0.273354 4.11 0.000 3.07 1.80 5.25 an alfa level of 95% COM_OUT 1.54055 0.382019 4.03 0.000 4.67 2.21 9.87 Goodness-of-Fit Tests The goodness-of-tests, with Method Chi-Square DF P p-value equal to 1.000. Pearson 105.652 111 0.625 Indicate that there is Deviance 72.350 111 0.998 insufficient evidence to Hosmer-Lemeshow 4.405 8 0.819 claim that the model does not fit the data adequately. 1. UNDERSTANDING THE BENEFITS OF APPLIED STATISTICS BUSINESS. Coefficients for 2. BUILDING A COMPETITIVE ADVANTAGE BY DATA ANALYSIS. these variables 3. ESTABLISHING A MISSION AND VISION STATEMENTS FOR THE COMPANY are not cero. 4. STIMULATING COMMUNICATION OUTSIDE COMPANY. 22
  • 23. 匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 23
  • 24. STATISTICAL ENGINEERING DISCUSSION A set of 7 statistical tools were applied in this research Different statistical tools can be successfully integrated, in order to extract relevant information from a unique problem With this, It was demonstrated that several statistical tools can be complementary rather than exclusive Hoerl & Snee The three previous points are (2010) the core philosophy of the Statistical Engineering. 24
  • 25. 匍鰻意檎或禽雨遺意鴛或鰻 Motivation What is it done? THE METHODOLOGY DISCUSSION REFERENCES 25
  • 26. REFERENCES Banks, D. "Is Industrial Statistics Out of Control?," Statistical Science, (8:4), 1993 , pp. 402-409 Checkland, P. (1999). "Systems Thinking, Systems Practice: Includes a 30-Year Retrospective". Wiley; 1 edition, New York USA Cronbach, L. J. (1951). "Coefficient alpha and the internal structure of tests" Psychometrika. 16, 297-334. Davenport, T, and Harris, J. (2010) "Analytics at Work: Smarter Decisions, Better Results". Harvard Business School Press , Boston USA "Davenport, T. Harris, J. (2007). ""Competing on analytics the new science of winning"". Harvard Business School Press , Boston USA" Deming, W.E. (2000). "Out of the Crisis". The MIT Press, Boston USA Ghemawat, P (2007). "Redefining Global Strategy: Crossing Borders in a World Where Differences Still Matter". Harvard Business School Press, Boston USA Hoerl, R.W and Snee R.D. "Closing the gap: Statistical Engineering can bridge statistical thinking with methods and tools" , May 2010, pp. 52-55, Quality Press Hoerl, R.W and Snee R.D. "Statistical Thinking and Methods in Quality Improvement: A Look to the Future", Quality Engineering, (22), 2010, pp 119-129 Hoerl, R.W and Snee R.D. (2001). " Statistical Thinking: Improving Business Performance". Duxbury Press; 1er edition, CA USA Poon, P., and C.Wagner. "Critical success factors revisited: success and failure cases of information systems for senior executives," Decision Support Systems, (30:4), 2001, pp. 393-418 Porter, M. (1998). "Competitive advantage : creating and sustaining superior performance". Free Press, New Yorw USA Roberts, H. "Applications in Business and Economic Statistics: Some Personal Views", Statistical Science, (5:4), 1990, pp. 399- 402 Stainberg, D.M., "The Future of industrial statistics: A panel discussion", Technometrics, (50:2), 2008, pp103-127 Wang, Y.R., Kon, B.H and Madnick, S.E, "Data Quality Requirements Analysis and Modeling", the Ninth International Conference of Data Engineering, Vienna, Austria, April 1993, pp 670-677 Wang, R.Y and Strong, D. M. "Beyond accuracy: What data quality means to data consumers", Journal of Management Information Systems, (12:4), Spring 1996, pp 5 Yeo,K. "Systems thinking and project management time to reunite," Int.J.Project Manage, (11:2), 1993, pp 111-117 26
  • 28. 28
  • 29. 29
  • 30. 30
  • 31. 31
  • 32. 32