Six Sigma  Six Sigma is a comprehensive and flexible system for achieving, sustaining, and maximizing business success by minimizing defects and variability in processes.  It relies heavily on the principles and tools of TQM.  It is driven by a close understanding of customer needs; the disciplined use of facts, data, and statistical analysis; and diligent attention to managing, improving, and reinventing business processes. © 2007 Pearson Education
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Lean Six Sigma – What is it? • Lean Six Sigma is a comprehensive and flexible system for achieving, sustaining and maximizing organizational success. • The Lean Six Sigma approach is driven by: Improve Processes Process Flow Teamwork Variation & Defects Speed • To insure an organizational transformation, Lean Six Sigma also focuses on the culture of an organization. Delight Customers Quality – Closely understanding customer needs – Disciplined use of facts, data & statistical analysis – Diligent attention to managing, improving & reinventing organizational processes Lean Six Sigma Data and Facts Source: What Is Lean Six Sigma, George, Rowlands & Kastle 2
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Six Sigma (1 of 2) • The Six Sigma Way authors, Peter Pande, Robert Neuman, and Roland Cavanagh, define Six Sigma • A comprehensive and flexible system for achieving, sustaining, and maximizing business success. Six Sigma is uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business processes.” Information Technology Project Management, Ninth Edition. © 2019 Cengage. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website for classroom use.
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Preprogrammed Voice Messages Incident Message Leader Activate DPS 1 Fire Temporal horn continuous-no voice message None 2 Tornado Tornado warning, take shelter Slow whoop Y 3 Active shooter Active Shooter, Police Responding, Stay in Place Attention Attention Y 4 General Alert WMU Alert, Check WMU homepage Attention Attention Y 5 This is a test This is a test of the emergency system Attention Attention Y 6 Test of fire alarm This is a test of the fire alarm Attention Attention Y 7 Test of active shooter This is a test of the active shooter alert 8 Test of tornado message This is a test of the tornado warning 9 Test of WMU Alert message This is a test of the WMU alert 10 All Clear test Testing has ended 11 All clear fire Fire emergency has ended 12 All clear general alert WMU alert has ended 13 All clear weather Tornado warning has ended 14 All clear active shooter Active shooter emergency has ended Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Attention Activat e Local Y Y Y Y Y Y Y Y Y Y Y Y Y
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2. Data analytics (DA). Significant impacts will result from advances in analysis, simulation, modeling, and interpretation to facilitate discovery of phenomena, to realize causality of events, to enable prediction, and to recommend action. Advances will allow, for example, 1. 2. 3. 4. 5. 6. 7. modeling of social networks and learning communities, reliable prediction of consumer behaviors and preferences, and the surfacing of communication patterns among unknown groups at a larger, global scale; extraction of meaning from textual data; more effective correlation of events; enhanced ability to extract knowledge from large-scale experimental and observational datasets; and extracting useful information from incomplete data. Potential research areas include, but are not limited to: 1. Development of new algorithms, programming languages, data structures, and data prediction tools; 2. Computational models and underlying math and statistical theory needed to capture important performance characteristics of computing over massive data sets; 3. Data-driven high fidelity modeling and simulations and/or reduced-order models enabling improved designs and/or processes for engineering industries, and direct interfacing with measurements and equipment; 4. Novel algorithmic techniques with the capability to scale to handle the largest, most complex data sets being created now and in the future; 5. Real-time processing techniques addressing the scale of continuously generated data sets, as well as real-time visualization and analysis tools that allow for more responsive and intuitive study of data; 6. Computational, mathematical and statistical techniques for modeling physical, engineering, social or other processes that produce massive data sets; 7. Novel applications of inverse methods to big data problems; 8. Mining techniques that involve novelty and anomaly detection, trend detection and/or taxonomy creation as well as predictive models, hypothesis generation and automated discovery, incl. fundamentally new statistical, math. and computational methods for identifying changes in massive datasets; 9. Development of data extraction techniques (e.g. natural language processing) to unlock vast amounts of info currently stored as unstructured data (e.g. text); 10. New scalable data visualization techniques and tools, which are able to illustrate the correlation of events in multidimensional data, synthesize information to provide new insights, and allow users to drill down for more refined information; 11. Techniques to integrate disparate data and translate data into knowledge to enable on-the-fly decision-making; 12. Development of usable state-of-the-art tools and theory in statistical inference and statistical learning for knowledge discovery from massive, complex, and dynamic data sets; and 13. Consideration to potential limitations, e.g., the number of possible passes over the data, energy conservation, new communication architectures, and their implications for solution accuracy.
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Activity 4: Data Governance Maturity Model Data Governance Level 1 Level 2 Level 3 Level 4 Level 5 Informal Developing Adopted and Implemented Managed and Repeatable Integrated and Optimized Attention to Data Governance is informal and incomplete. There is no formal governance process. Data Governance Program is forming with a framework for purpose, principles, structures and roles. Culture Limited awareness about the value of dependable data. General awareness of the data issues and needs for business decisions. Data Quality Limited awareness that data quality problems affect decision-making. Data cleanup is ad hoc. General awareness of data Data issues are captured quality importance. Data quality proactively through standard data procedures are being developed. validation methods. Data assets are identified and valuated. Expectations for data quality are actively monitored and remediation is automated. Data quality efforts are regular, coordinated and audited. Data are validated prior to entry into the source system wherever possible. Communication Information regarding data is limited through informal documentation or verbal means. Written policies, procedures, data Data standards and policies are standards and data dictionaries communicated through written may exist but communication and policies, procedures and data knowledge of it is limited. dictionaries. Data standards and policies are completely documented, widely communicated and enforced. All employees are trained and knowledgeable about data policies and standards and where to find this information. Roles & Responsibilities Roles and responsibilities for data management are informal and loosely defined. Expectations of data ownership and valuation of data are clearly defined. Roles, responsibilities for data governance are well established and the lines of accountability are clearly understood. Roles and responsibilities for data management are forming. Focus is on areas where data issues are apparent. Data Governance structures, roles and processes are implemented and fully operational. Data Governance structures, roles and processes are managed and empowered to resolve data issues. Data Governance Program functions with proven effectiveness. Data is viewed as a critical, There is active participation and shared asset. There is Data governance structures acceptance of the principles, widespread support, and participants are integral structures and roles required to participation and endorsement to the organization and implement a formal Data of the Data Governance critical across all functions. Governance Program. Program. Roles and responsibilities are well-defined and a chain of command exists for questions regarding data and processes. 49
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TEAMS WHITE OPAL Alpha Sigma Alpha Sigma Phi Epsilon SILVER QUARTZ Sigma Sigma Sigma Delta Chi Phi Lambda Phi Cardinal Key PURPLE AMETHYST Alpha Phi Omega Phi Kappa Tau Prim Roses EMERALD GREEN AMBER ORANGE Delta Zeta Tau Lambda Sigma Lambda Chi Alpha Alpha Gamma Rho MAP Phi Delta RUBY RED YELLOW TOPAZ Delta Phi Epsilon Sigma Kappa Tau Kappa Epsilon Phi Sigma Kappa Delta Sigma Pi Sigma Tau Gamma BLUE SAPPHIRE BLACK ONYX Alpha Gamma Delta Alpha Sigma Gamma Pi Kappa Phi ABC Beta Theta Pi Alpha Kappa Lambda
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