Quality control methods and important parameters forming these methods.Quality control is important concept in quality management. Quality management is an approach used in industry to manage operations and producing best quality products with minimum producer cost.
Statistical process control is defined as and use of statistical technique to control a process or production method .It is used in manufacturing or production process to measure how consistently a product perform according to its design specification.
The seven traditional tools of quality – New management tools – Six-sigma: Concepts, methodology, applications
to manufacturing, service sector including IT – Bench marking– Reason to bench mark, Bench marking process –
FMEA – Stages, Types
QUALITY MANAGEMENT- SEVEN BASIC TOOLS OF QUALITY CONTROLHelanJenifer
QUALITY MANAGEMENT- SEVEN BASIC TOOLS OF QUALITY CONTROL
This helps to know about the basic tools to be used by a HR professional from a social work background to quality control easily
Quality Journey -Introduction to 7QC Tools2.0.pdfNileshJajoo2
7QC Tool - Quality Journey , Myth about Quality :- Cost of Quality
Check Sheet
Histogram
Pareto Chart
Cause and Effect Diagram
Control Charts
Scatter Diagram
Process Flow Diagram
EFFECT is “WHAT?” Happens
CAUSE is “WHY?” it Happens
EFFECT = RESULT OR OUTCOME
CAUSE = REASON(S) OR FACTOR(S) CONTRIBUTING TO THE EFFECT
Quality Definition :- Doing the right thing , right at first time and every time, meeting
customer’s & investor’s expectations .
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Presented by:
Tooba Rafique (04)
Mehwish Naz (08)
M Wasim Amir (20)
Kamran sajjad (38)
Nalila Rasheed (47)
M. Rizwan (53)
Nazia Aslam (61)
3.
4. DEFINITIONS OF QUALITY
4
Quality means fitness for use
- quality of design
- quality of conformance
Quality is inversely proportional to
variability.
5. QUALITY IMPROVEMENT
5
• Quality improvement is the reduction of
variability in processes and products.
Alternatively, quality improvement is also
seen as “waste reduction”.
6. SPC (Statistical Process Control)
• SPC is a powerful collection of problem solving
tools useful in achieving process stability and
including capability through the reduction of
variability.
7. Statistical Process Control (SPC)
• SPC is a methodology for charting the process and quickly determining when a process is
"out of control“.
• (E.G., A special cause variation is present because something unusual is occurring in
the process).
• The process is then investigated to determine the root cause of the "out of control"
condition.
• When the root cause of the problem is determined, a strategy is identified to correct it.
8. Primary Goal of SPC
• Minimize production costs.
• Attain a consistency of products and services that will meet production
specifications and customer expectations.
• Create opportunities for all members of the organization to contribute to quality
improvement.
• Help both management and employees make economically sound decision about
actions affecting the process.
9. The John Tukey (1977) introduced a technique known as Stem-and-
Leaf Display.
A stem-and-leaf display can help us to compare data.
Stem and leaf
10. Stem and leaf
◦A stem and leaf plot is a special table where each
data value is split into a stem the first digit or digits
and a leaf usually the last digit.
11. Constructing a stem and leaf diagram
• Decide on your stems these are the digits which go down the
left hand side of your diagram.
• Your leaf are the digits go down the right hand side of your
diagram.
12. THE HEIGHTS OF 11 FOURTH-GRADE BADMINTON
PLAYERS ARE (IN INCHES):
56
61
61
60
59
57
56
61
61
60
59
57
13. Constructing a stem and leaf diagram
• First, order your data from least to greatest.
• The ordered numbers are: 56, 57, 58, 58, 59, 59,
60, 61, 61, 61, 63
• Then, put data in a stem-and-leaf plot
14. THE ORDERED NUMBERS ARE: 56, 57, 58, 58, 59,
59, 60, 61, 61, 61, 63
• Each STEM
stands for the
first digit of
each number.
• Each LEAF
stands for the
second digit
of each
number
HEIGHT IN INCHES
Stem Leaves
5
6
6, 7, 8, 8, 9, 9
0, 1, 1, 1, 3
15. What’s GOOD about stem and leaf diagrams?
• Well, the major advantage over things like bar charts and histograms, is
that no information is lost the stem and leaf diagram keeps and allows
you to see each original piece of data. It is also quite an effective way of
ordering and displaying relatively small sets of data.
16. What’s BAD about stem and leaf diagrams?
Well, it’s quite time consuming, and impractical for large data sets.
Imagine how long it would take to sort over 300 pieces of data, and
how complicated the final diagram would look.
17. HISTOGRAM
• First introduced by Karl Pearson.
• A histogram is the most commonly used graph to show
frequency distributions. It looks very much like a bar chart.
• A diagram consisting of rectangles whose area is proportional
to the frequency of a variable and whose width is equal to the
class interval.
18. Histogram
• A histogram is used to graphically summarize and display the distribution of a
process data set.
• It can be constructed by segmenting the range of the data into equal-sized bins
(segments, groups, or classes).
• The vertical axis of the histogram is the frequency (the number of counts for each
bin), and the horizontal axis is labeled with the range of the response variable.
• The number of data points in each bin is determined and the histogram
constructed.
The user defines the bin size.
19. Histogram
• The histogram, graphically shows the process capability.
• It also suggests the shape of the population.
22. When to use a histogram
• When the data are numerical.
• When we want to see the shape of the data’s distribution, especially when determining whether the output
of a process is distributed approximately normally.
• When analyzing whether a process can meet the customer’s requirements.
• When we need to find central tendency in the data.
• When seeing whether a process change has occurred from one time period to another.
• When you wish to communicate the distribution of data quickly and easily to others.
23. Benefits
Provides surveillance and feedback for keeping processes in control
Signals when a problem with the process has occurred
Detects assignable causes of variation
Reduces need for inspection
Monitors process quality
Once a process is stable, provides process capability analysis with comparison to
the product tolerance
