Gain Problem Solving Skills & Become A Professional With Six Sigma Black Belt Certification.

About Six Sigma Black Belt

The Certified Six Sigma Black Belt (CSSBB) is a professional who can explain Six Sigma philosophies and principles, including supporting systems and tools. A Black Belt should demonstrate team leadership, understand team dynamics, and assign team member roles and responsibilities.

Black Belts have a thorough understanding of all aspects of the DMAIC model in accordance with the Six Sigma principles. They have basic knowledge of lean enterprise concepts, are able to identify non-value-added elements and activities, and are able to use specific tools.

 

Key Characteristics of a Six Sigma Black Belt

Black Belts should possess a mastery of process improvement and statistical analysis techniques. They should have strong people skills and act as effective leaders and mentors for project team members. Black Belts are also able to teach Six Sigma principles to project teams and leadership when needed. Consistency is key when it comes to the Black Belt role as holders must use their time wisely, and be good decision makers to ensure their project teams stay on task and meet deadlines while providing better quality to the management.

 

Roles & Responsibilities for a Six Sigma Black Belt

Total Quality Management:

TQM i.e Total Quality Management is a process of embedding quality awareness at every step of production or service while targeting the end customer. It is a management strategy to embed awareness of quality in all organizational processes. By pursuing the process of continuous improvement and never-ending improvement the companies can outdistance their competitors by enticing the customers with high quality products at a low price. TQM has culminated in Six Sigma, Which targets 99.99927% defect free manufacturing.

 

Within the last two decades, Total Quality Management (TQM) has evolved as a strategic approach in most of the manufacturing and service organizations to respond to the challenges posed by the competitive business world. Today TQM has become an important responsibility to a comprehensive management strategy that is built on the foundation of continuous improvement & organization-wide involvement, with a core focus on quality.

 

Six Sigma improvement drives the most effective roles in the field of quality engineering and management spectrum. It enables organizations to make substantial improvements in their bottom line by designing and monitoring everyday business activities in ways that minimize all types of wastes and NVA activities and maximize customer satisfaction. While all the quality improvement drives are useful in their own ways, they often fail to make breakthrough improvements in the bottom line and quality.

 

Voelkel, J.G. contents that Six Sigma blends correct management, financial and methodological elements to make improvement in process and products in ways that surpass other approaches. Mostly led by practitioners, Six Sigma has acquired a strong perspective stance with practices often being advocated as universally applicable. Six Sigma has a major impact on the quality management approach, while still based on the fundamental methods & tools of traditional quality management (Goh & Xie, 2004). A strategic initiative to boost profitability, increase market share, and improve customer satisfaction through statistical tools that can lead to breakthrough quantum gains in quality.

 

Mike Harry (2000). Park (1999) believes that Six Sigma is a new paradigm of management innovation for a company’s survival in the 21st century, which implies three things:

1.Statistical Measurement

2.Management Strategy

3.Quality Culture

 

Six Sigma Black Belt Training Course

Six Sigma Black Belt is the training for a business improvement strategy used to improve profitability, to drive out waste, to reduce quality costs & improve the effectiveness and efficiency of all operational processes that meet or exceed customers’ needs & expectations (Antony & Banuelas, 2001).

 

 Six Sigma Black Belt Certification

Six Sigma Black Belt Certification is also defined as a program aimed at the near-elimination of defects from every product, process, and transaction, Tomkins (1997). A  business improvement approach that seeks to find and eliminate causes of mistakes or defects in business processes by focusing on process outputs that are of critical importance to customers (Since 2004).

 

Six Sigma Black Belt Methodologies and Six Sigma Black Belt Tools

The DMAIC Six Sigma Technology-

The DMAIC methodology follows the phases: define, measure, analyze, improve, and control. Although PDCA could be used for process improvement, to give a new thrust; Six Sigma was introduced with a modified model i.e. DMAIC. The phase determines the objectives & the scope of the project collects information on the process and the customers and specifies the deliverables to customers (internal & external).

 

The Acronym is further defined below:

Define

  • Will be able to select data collection methods and collect voice of the customer data and use customer feedback to determine customer requirements.
  • Will understand the elements of a project charter (problem statement, scope, goals, etc.) and be able to use various tools to track the project progress.

 

Measure

  • Will be able to define and use process flow metrics and analysis tools to indicate the performance of a process.
  • Will be able to develop and implement data collection plans and use techniques in sampling, data capture, and processing tools.
  • Will be able to define and describe measurement system analysis tools.
  • Will apply basic probability concepts and understand various distributions.
  • Will be able to calculate statistical and process capability indices.

 

Analyze

  • Will be able to analyze the results of correlation and regression analyses.
  • Will be able to define multivariate tools.
  • Will be able to perform hypothesis tests for means, variances, and proportions, and analyze their results.
  • Will understand the components and concepts for ANOVA, chi-square, contingency tables, and nonparametric tests.
  • Will understand the elements and purpose of FMEA and use root cause analysis tools. • Will be able to identify and interpret the seven classic wastes.
  • Will be able to use gap analysis tools.

 

Improve

  • Will be able to define and apply the design of experiments (DOE) principles and distinguish among the various types of experiments.
  • Will be able to apply various lean tools and techniques to eliminate waste and reduce cycle time.
  • Will understand how to implement an improved process and how to analyze and interpret risk studies.

 

Control

  • Will be able to apply, use, and analyze the various statistical process control (SPC) techniques.
  • Will understand total productive maintenance (TPM) and visual factory concepts.
  • Will be able to develop control plans and use various tools to maintain and sustain improvements. Design For Six Sigma (DFSS) Framework and Methodologies
  • Will understand common DFSS and DFX methodologies, and elements of robust designs.

 

Benefits of Six Sigma Black Belt Certification

Over the course of Six Sigma Black Belt professionals here are some of the benefits and tools that will now be explained minimal expectations that would need to be looked after and covered under management :

 

Organization-wide Planning and Deployment

  • Will understand how to deploy Six Sigma within a project.
  • Will be able to implement tools and techniques to deploy strategic directions for initiatives.
  • Will understand the roles and responsibilities for Six Sigma projects and how each group influences project deployment and will be able to support communications about the project deployment.
  • Will be able to apply operational change management techniques within their defined scope or domain.

 

Organizational Process Management and Measures

  • Will be able to define various types of benchmarking.
  • Will be able to describe various types of performance measures, and select an appropriate financial measure for a given situation and calculate its result.

 

Team Management

  • Will understand the components and techniques used in managing teams, including time management, planning and decision-making tools, team formation, motivational techniques and factors that demotivate a team, and performance evaluation and reward.
  • Will be able to describe elements that can result in a team’s success.
  • Will be able to use appropriate techniques to overcome various group dynamics challenges.

Ethics Of Machine Learning

Ethics Of Machine Learning


With machine learning systems exploding in popularity, there’s a sense of hope as well as fear. Will the rise of machines usher in an Utopian era or would it lead to the machines taking over humanity? The possibility of creating thinking machines has also led to the questions being raised about the inherent biases in machine learning algorithms. Since machine learning systems use lots of data to train the system to get smarter, issues related to transparency, a potential for bias and accountability have become the latest buzzwords in AI and machine learning.

Machine learning has gone beyond text prediction and anti-lock braking mechanism. With the widespread use of sophisticated machines, it has become imperative to look at the ethics of machine learning and its impact on human life.

What Is Machine Learning Bias

Machine algorithmic decisions today affect every aspect of human life. Due to its ubiquity and reduced costs along with the academic, government, military and commercial interests, machine learning algorithms have been increasingly shaping our everyday reality. From finding the best results on the World Wide Web to its use in the criminal justice system, machine learning algorithms have become the fabric of our experience. This, however, poses an important question regarding the ethics of artificial intelligence.

Machine learning systems are not implicitly good or bad. The AI systems are only as good as the data we put into them. Bad data can contain ideological, gender and caste biases which in turn may affect the decision-making process of machine learning algorithms.

For example, banks have increasingly been using machine learning algorithms to make lending decisions such as assessing a person’s eligibility for home loan applications. The algorithms consider gender, age, marital status, education, employment status and the number of dependents among other factors. By comparing these data points with the data points of thousands of prior customers, the algorithms generate a risk score, based on which banks will decide whether to extend a loan to someone.

While the results thrown up by the machine may appear unbiased, faster and judgmental, it’s important to note that biases can creep up in unexpected ways. Since the algorithms look at historical information about previous applicants and their subsequent performance, the machine can reinforce what they have learned from real-world data and may reject certain categories based on caste or gender. This implicit bias often arises for minorities due to the limited input data.

Since the machine learning systems only know that is fed into it, it’s able to tackle any problem that can be cast in suitable numerical form. So, whether it’s property prices or a list of terrorism suspects, problems are solved mechanically with machine learning algorithms. However, the power to generate quick results is also an Achilles heel. With the input data, the algorithms also include all potential biases that lie behind its construction as data in the first instance.

Apart from data bias, machine learning algorithms also suffer from the creator’s bias. The algorithms are architected from the programmers who create it and data it uses. Since the development team selects the algorithms to use, how the algorithms are set up and the metrics and parameters to use, the creator’s biases are implicitly integrated in the algorithm.

So, when machine learning is used for recruiting, we train a neural network on a set of resumes. This means that the training set of data itself contains all the biases of the original decision makers, which in turn are absorbed by the machine learning algorithm. However, unlike with a human operator, it is harder to identify reasons for bias by machines.

For example, in 2017, Amazon fired its recruiting tool for identifying software engineers as the system had become discriminatory against women. In 2016, the criminal justice system created to assist judges during sentencing and used to predict the likelihood of re-offence was found to be biased against blacks.

Ethical Machine Learning

The fragility of current machine learning systems is in stark contrast to human intelligence which can learn things quickly in one context and apply it to others. Prominent tech leaders have been sounding the alarms of the dangers of AI systems for quite some time. As machines rapidly take on decision-making roles in practical matters, it’s inevitable for data scientists and engineers to engage in discussions about ethical ramifications of machine learning. But how do you put ethics in a machine? AI ethics is concerned with ensuring that the machine’s behavior towards humans, and other machines, is ethically acceptable.

One way to resolve the ethical dilemma is to avoid unethical outcomes by creating software that implicitly supports ethical behavior. Data scientists have the moral responsibility to train the machine learning data with unbiased data. This means allowing for greater transparency along with a system of checks and balances to ensure that fair ethical AI principles are used in developing the machine learning model.

One of the biggest challenges behind the implementation of ethical principles in machine learning is a lack of a concrete definition of “fairness”. A collaboration between social scientists and machine learning engineers is needed to get a clear understanding of fairness and establish guidelines for machine ethics. A clear framework and an understanding of the fundamental principles of ethics is the only way forward. It would allow AI researchers to convince the general public that ethical machines can improve their lives and ethicists to discover and establish the fundamental principles of machine ethics.

Source

https://plus.maths.org/content/what-can-we-use-machine-learning

https://medium.com/humane-ai/ethics-in-machine-learning-54a71a75875c

http://www.psy.vanderbilt.edu/courses/hon182/The_Nature_Importance_and_Difficulty_of_Machine_Ethics.pdf

https://ideas.darden.virginia.edu/the-rise-of-artificially-intelligent-agents-part-1

 

Ethics Of Machine Learning

Ethics Of Machine Learning


With machine learning systems exploding in popularity, there’s a sense of hope as well as fear. Will the rise of machines usher in an Utopian era or would it lead to the machines taking over humanity? The possibility of creating thinking machines has also led to the questions being raised about the inherent biases in machine learning algorithms. Since machine learning systems use lots of data to train the system to get smarter, issues related to transparency, a potential for bias and accountability have become the latest buzzwords in AI and machine learning.

Machine learning has gone beyond text prediction and anti-lock braking mechanism. With the widespread use of sophisticated machines, it has become imperative to look at the ethics of machine learning and its impact on human life.

What Is Machine Learning Bias

Machine algorithmic decisions today affect every aspect of human life. Due to its ubiquity and reduced costs along with the academic, government, military and commercial interests, machine learning algorithms have been increasingly shaping our everyday reality. From finding the best results on the World Wide Web to its use in the criminal justice system, machine learning algorithms have become the fabric of our experience. This, however, poses an important question regarding the ethics of artificial intelligence.

Machine learning systems are not implicitly good or bad. The AI systems are only as good as the data we put into them. Bad data can contain ideological, gender and caste biases which in turn may affect the decision-making process of machine learning algorithms.

For example, banks have increasingly been using machine learning algorithms to make lending decisions such as assessing a person’s eligibility for home loan applications. The algorithms consider gender, age, marital status, education, employment status and the number of dependents among other factors. By comparing these data points with the data points of thousands of prior customers, the algorithms generate a risk score, based on which banks will decide whether to extend a loan to someone.

While the results thrown up by the machine may appear unbiased, faster and judgmental, it’s important to note that biases can creep up in unexpected ways. Since the algorithms look at historical information about previous applicants and their subsequent performance, the machine can reinforce what they have learned from real-world data and may reject certain categories based on caste or gender. This implicit bias often arises for minorities due to the limited input data.

Since the machine learning systems only know that is fed into it, it’s able to tackle any problem that can be cast in suitable numerical form. So, whether it’s property prices or a list of terrorism suspects, problems are solved mechanically with machine learning algorithms. However, the power to generate quick results is also an Achilles heel. With the input data, the algorithms also include all potential biases that lie behind its construction as data in the first instance.

Apart from data bias, machine learning algorithms also suffer from the creator’s bias. The algorithms are architected from the programmers who create it and data it uses. Since the development team selects the algorithms to use, how the algorithms are set up and the metrics and parameters to use, the creator’s biases are implicitly integrated in the algorithm.

So, when machine learning is used for recruiting, we train a neural network on a set of resumes. This means that the training set of data itself contains all the biases of the original decision makers, which in turn are absorbed by the machine learning algorithm. However, unlike with a human operator, it is harder to identify reasons for bias by machines.

For example, in 2017, Amazon fired its recruiting tool for identifying software engineers as the system had become discriminatory against women. In 2016, the criminal justice system created to assist judges during sentencing and used to predict the likelihood of re-offence was found to be biased against blacks.

Ethical Machine Learning

The fragility of current machine learning systems is in stark contrast to human intelligence which can learn things quickly in one context and apply it to others. Prominent tech leaders have been sounding the alarms of the dangers of AI systems for quite some time. As machines rapidly take on decision-making roles in practical matters, it’s inevitable for data scientists and engineers to engage in discussions about ethical ramifications of machine learning. But how do you put ethics in a machine? AI ethics is concerned with ensuring that the machine’s behavior towards humans, and other machines, is ethically acceptable.

One way to resolve the ethical dilemma is to avoid unethical outcomes by creating software that implicitly supports ethical behavior. Data scientists have the moral responsibility to train the machine learning data with unbiased data. This means allowing for greater transparency along with a system of checks and balances to ensure that fair ethical AI principles are used in developing the machine learning model.

One of the biggest challenges behind the implementation of ethical principles in machine learning is a lack of a concrete definition of “fairness”. A collaboration between social scientists and machine learning engineers is needed to get a clear understanding of fairness and establish guidelines for machine ethics. A clear framework and an understanding of the fundamental principles of ethics is the only way forward. It would allow AI researchers to convince the general public that ethical machines can improve their lives and ethicists to discover and establish the fundamental principles of machine ethics.

Source

https://plus.maths.org/content/what-can-we-use-machine-learning

https://medium.com/humane-ai/ethics-in-machine-learning-54a71a75875c

http://www.psy.vanderbilt.edu/courses/hon182/The_Nature_Importance_and_Difficulty_of_Machine_Ethics.pdf

https://ideas.darden.virginia.edu/the-rise-of-artificially-intelligent-agents-part-1