Motorola launched Six Sigma in the 1980s to reduce defects through statistical analysis of processes. General Electric widely adopted it in the 1990s. Six Sigma aims to reduce variation and defects through training employees in statistical process analysis and improvement methods. It uses the "DMAIC" structure of Define, Measure, Analyze, Improve, and Control to systematically solve problems and track processes. The goal is to reduce defects to 3.4 per million opportunities.
A term (Greek) used in statistics to represent standard deviation from mean value, an indicator of the degree of variation in a set of a process.
Sigma measures how far a given process deviates from perfection. Higher sigma capability, better performance
Six Sigma - A highly disciplined process that enables organizations deliver nearly perfect products and services.
The figure of six arrived statistically from current average maturity of most business enterprises
A philosophy and a goal: as perfect as practically possible.
A methodology and a symbol of quality
A term (Greek) used in statistics to represent standard deviation from mean value, an indicator of the degree of variation in a set of a process.
Sigma measures how far a given process deviates from perfection. Higher sigma capability, better performance
Six Sigma - A highly disciplined process that enables organizations deliver nearly perfect products and services.
The figure of six arrived statistically from current average maturity of most business enterprises
A philosophy and a goal: as perfect as practically possible.
A methodology and a symbol of quality
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
1. Development of
Six Sigma
Motorola launched the Six Sigma program in
the 1980s
General Electric initiated the implementation
of Six Sigma in the mid-1990s
Organizations in all industries have applied
Six Sigma in recent years
Six Sigma has replaced TQM and BPR as
the key strategy for quality improvement
2. Definitions
s – Standard Deviation, a measure of
variability
Six Sigma – A quality improvement philosophy
that focuses on eliminating defects through
reduction of variation in a process
Defect – A measurable outcome that is not
within acceptable (specification) limits
3. TQM Versus Six Sigma
TQM Six Sigma
A management
philosophy of quality
improvement
A philosophy that focuses
on defect reduction and
cost reduction
Encourages involvement
of all employees
Relies on a selected group
of highly-trained employees
Senior management
provides direct support
Senior management is held
accountable for results
4. Key Success Factors for
Six Sigma
Committed leadership from top management
Integration with existing initiatives, business
strategy, and performance measurement
Process thinking
Disciplined customer and market intelligence
gathering
A bottom-line orientation and continuous
reinforcement and rewards
Training
5. Six-Sigma Metrics –
Measuring Defect Rate
Defects per unit (DPU) = number of defects
discovered number of units produced
Defects per million opportunities (DPMO) =
number of defects discovered opportunities
for error 1,000,000
6. Estimating Defect Rate –
Process Capability Index (Cp)
USL/LSL : Upper & Lower Specification Limit
Cp = (USL –LSL) / (6s)
Example : Time to process a student loan
application (Standard = 26 working days)
Specification Limits : 20 to 32 working days
s : 2 working days
Cp = (32 – 20)/ (6*2) = 1.00 (Three Sigma)
7. Cp Index and DPMO
Cp Index DPMO
1 2,700
1.33 63
1.5 6.8
2 0.002
8. Estimating Process Capability
Index from A Sample - Cpk Index
XBAR : average outcome from a sample
S : standard deviation from a sample
Cpk = min { (USL-XBAR) / (3S),
(XBAR-LSL) / (3S) }
Example : XBAR = 25 days, S = 3 days
Cpk = min { (32-25)/(3*3), (25-20)/(3*3)}
= min {0.77, 0.55} = 0.55
9. Six-Sigma Quality (Cp =2 with
Mean Shifting from the Center)
Ensuring that process variation is half the design
tolerance (Cp = 2.0) while allowing the mean to shift as
much as 1.5 standard deviations.
12. DMAIC - Define
Identify customers and their priorities
Identify business objectives
Select a six sigma project team
Define the Critical-to-Quality (CTQ’s)
characteristics that the customers consider
to have the most impact on quality
13. DMAIC - Measure
Determine how to measure the processes
•Identify key internal processes that
influence CTQ’s
•Measure the defect rates currently
generated relative to those processes
14. DMAIC - Analyze
Determine the most likely causes of
defects.
•Identify key factors that are most
likely to create process variation.
15. DMAIC - Improve
Identify means to remove causes of the
defects.
• Confirm the key variables and quantify the
effects on CTQ’s
• Identify maximum acceptable ranges for the
key variables and a system to measure
deviations of the variable
• Modify the process to stay within the
acceptable ranges
16. DMAIC - Control
Determine how to maintain the
improvement
•Put tools in place to ensure that the key
variables remain within the maximum
acceptable ranges under the modified
process
17. Tools for Six-Sigma
and Quality Improvement
Elementary and advanced statistics
Product design and reliability analysis
Measurement
Process control & Process improvement
Implementation and teamwork
Customer survey and feedback
Lean thinking
18. Organization for Six Sigma
Project Champions – project selection
and management, knowledge sharing
Master Black Belts – instructors, coaches,
technical leaders
Black Belts – project team leaders and
team members
Green Belts – project team members,
temporary team members