This document summarizes a quality control study on the production of 3-pole MCCBs at Havells India Ltd. The study found that the current process has low Cp and Cpk values, indicating high variation and rejection rates. The objectives were to analyze the production process, identify causes for low Cp and Cpk, and make improvements. Data was collected and analyzed using tools like control charts, fishbone diagrams, and Pareto charts. Improvements like fixing tooling issues and developing new springs increased Cp values but further work is needed to meet Cpk specifications. Recommendations include improving incoming material quality and using more in-house parts. The internship provided hands-on experience with quality control management systems and
Process Capability Analysis in Single and Multiple Batch Manufacturing SystemsDr. Amarjeet Singh
Any process, manufacturing or service in operations
is subject to constant variation. The underlying principle of
variation is any process / rather all processes are subject to
changes occurring due to the magical 5 M’s that make the
basis of operations management namely: Man, Machines,
Materials, Methods and Money.
This paper discusses about establishment of a
capable process, by means of stabilizing the 5 M’s and
studying the variations which occur by going deeply into the
well known term used in operations: RCA : Root Cause
Analysis.
Process Capability Analysis in Single and Multiple Batch Manufacturing SystemsDr. Amarjeet Singh
Any process, manufacturing or service in operations
is subject to constant variation. The underlying principle of
variation is any process / rather all processes are subject to
changes occurring due to the magical 5 M’s that make the
basis of operations management namely: Man, Machines,
Materials, Methods and Money.
This paper discusses about establishment of a
capable process, by means of stabilizing the 5 M’s and
studying the variations which occur by going deeply into the
well known term used in operations: RCA : Root Cause
Analysis.
An empirical evaluation of cost-based federated SPARQL query Processing EnginesUmair Qudus
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engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation
engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources
selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the
accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the
effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this
challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query
engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using
LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful
for the development of future cost-based federated SPARQL query processing engines.
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Experiments with Apache Hive and Spark on the cloud platforms of three major vendors validate our approach by comparing to the current TPCx-BB metric.
Results show how systems who fail to meet SLAs under concurrency due to queuing or degraded performance negatively affect the new metric. On the other hand, elastic systems meet a higher percentage of SLAs and thus are rewarded in the new metric. Such systems have the ability to scale up and down compute workers according to the demands of a varying workload and can thus save dollar costs.
Understanding printed board assembly using simulation with design of experime...Kiran Hanjar
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To assess the feasibility of process flow logic and relative impact of changing line configurations
It is aimed to identify constraints or bottlenecks and development of improvement strategies accordingly
By using DOE, the factors that are affecting the system’s efficiency are identified
Finally to improve the system’s overall performance
Aplication of on line data analytics to a continuous process polybetene unitEmerson Exchange
This Emerson Exchange, 2013 presentation summarizes the 2013 field trail results achieved by applying on-line continuous data analytics to Lubrizol’s continuous polybutene process. Continuous data analytics may be used to provide an on-line prediction of quality parameters, and enable on-line detection of fault conditions. Information is provided on improvements made in the model used for quality parameter prediction, and how the field trail platform was integrated into the process unit. Presenters Qiwei Li, production engineer, Efren Hernandez and Robert Wojewodka, Lubrizol Corp., and Terry Blevins, principal technologist at Emerson, won best in conference in the process optimization track for this presentation.
Whether you are producing cooking oil, baby oil or motor oil, one matter is constant, if you are trying to implement a quality system; you are competing with other projects for finite corporate resources. One of the most effective ways to compete for those resources and demonstrate project success is to be able to quantitatively demonstrate Return On Investment (ROI).
Louis Halvorsen, CTO, Northwest Analytics, explains how to develop the ROI of quality systems and provide example case studies. He presents:
• How to choose the project justifications that best meet your commercial requirements and to improve your bottom line.
• What are common ROI targets and how may they be adapted to your plant requirements.
• How to quantify ROI.
Quality systems and operations typically deliver substantial cost benefits and rapid returns. Properly done, ROI modeling will not only enable you to obtain the necessary resources to develop your projects, but will also provide an ongoing basis to justify the quality management operations.
Mr. Halvorsen presents three case studies which illustrate how to set up data collection and analysis to successfully demonstrate ROI and provide for continuing reporting.
• An assembly process where management must determine which defects are most amenable to process improvement and will provide the greatest return for the effort.
• A food processor facing product giveaway by overfilling. How can the processor improve the filling performance and calculate the ROI of the effort?
• A high-speed process that makes multiple products has historically required long startup and change-over times to become stable enough for normal production. How can the producer reduce startup time and waste?
Applying SPC to your production process is one of the quickest ways to realize a return from your quality system. SPC-based process improvements reduce variation and make further improvements possible.
View recording at https://www1.gotomeeting.com/register/794616632
An empirical evaluation of cost-based federated SPARQL query Processing EnginesUmair Qudus
Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation
engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation
engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources
selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the
accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the
effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this
challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query
engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using
LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful
for the development of future cost-based federated SPARQL query processing engines.
An Update on my Gas Modelling Tools: Addition of New Shale Diagnostics, Gas S...Colin Jordan
In addition to other tools, I have coded a Gas Modelling & Analytics tools (for reservoir engineering) which now includes more/new Shale Decline Models, as well as introduction of non-linear permeability due to gas slippage, stress, or both (i.e coupled models).
4th SEALNET meeting, item 8: Training on internal quality control - Overview ...Soils FAO-GSP
Overview of internal quality control measures - Rob De Hayr, GLOSOLAN Vice-Chair
4th Asian Soil Laboratory Network (SEALNET) meeting (online), 30 June - 2 July 2020
Benchmarking Elastic Cloud Big Data Services under SLA ConstraintsNicolas Poggi
We introduce an extension for TPC benchmarks addressing the requirements of big data processing in cloud environments. We characterize it as the Elasticity Test and evaluate under TPCx-BB (BigBench). First, the Elasticity Test incorporates an approach to generate real-world query submissions patterns with distinct data scale factors based on major industrial cluster logs. Second, a new metric is introduced based on Service Level Agreements (SLAs) that takes the quality of service requirements of each query under consideration.
Experiments with Apache Hive and Spark on the cloud platforms of three major vendors validate our approach by comparing to the current TPCx-BB metric.
Results show how systems who fail to meet SLAs under concurrency due to queuing or degraded performance negatively affect the new metric. On the other hand, elastic systems meet a higher percentage of SLAs and thus are rewarded in the new metric. Such systems have the ability to scale up and down compute workers according to the demands of a varying workload and can thus save dollar costs.
Understanding printed board assembly using simulation with design of experime...Kiran Hanjar
Understanding PCB assembly using simulation with DOE approach
To assess the feasibility of process flow logic and relative impact of changing line configurations
It is aimed to identify constraints or bottlenecks and development of improvement strategies accordingly
By using DOE, the factors that are affecting the system’s efficiency are identified
Finally to improve the system’s overall performance
Aplication of on line data analytics to a continuous process polybetene unitEmerson Exchange
This Emerson Exchange, 2013 presentation summarizes the 2013 field trail results achieved by applying on-line continuous data analytics to Lubrizol’s continuous polybutene process. Continuous data analytics may be used to provide an on-line prediction of quality parameters, and enable on-line detection of fault conditions. Information is provided on improvements made in the model used for quality parameter prediction, and how the field trail platform was integrated into the process unit. Presenters Qiwei Li, production engineer, Efren Hernandez and Robert Wojewodka, Lubrizol Corp., and Terry Blevins, principal technologist at Emerson, won best in conference in the process optimization track for this presentation.
Whether you are producing cooking oil, baby oil or motor oil, one matter is constant, if you are trying to implement a quality system; you are competing with other projects for finite corporate resources. One of the most effective ways to compete for those resources and demonstrate project success is to be able to quantitatively demonstrate Return On Investment (ROI).
Louis Halvorsen, CTO, Northwest Analytics, explains how to develop the ROI of quality systems and provide example case studies. He presents:
• How to choose the project justifications that best meet your commercial requirements and to improve your bottom line.
• What are common ROI targets and how may they be adapted to your plant requirements.
• How to quantify ROI.
Quality systems and operations typically deliver substantial cost benefits and rapid returns. Properly done, ROI modeling will not only enable you to obtain the necessary resources to develop your projects, but will also provide an ongoing basis to justify the quality management operations.
Mr. Halvorsen presents three case studies which illustrate how to set up data collection and analysis to successfully demonstrate ROI and provide for continuing reporting.
• An assembly process where management must determine which defects are most amenable to process improvement and will provide the greatest return for the effort.
• A food processor facing product giveaway by overfilling. How can the processor improve the filling performance and calculate the ROI of the effort?
• A high-speed process that makes multiple products has historically required long startup and change-over times to become stable enough for normal production. How can the producer reduce startup time and waste?
Applying SPC to your production process is one of the quickest ways to realize a return from your quality system. SPC-based process improvements reduce variation and make further improvements possible.
View recording at https://www1.gotomeeting.com/register/794616632
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quality control STUDY ON 3 POLE MCCB MBA SIP report
1. QUALITY CONTROL
STUDY ON
3 – POLE MCCB
PRODUCTION
Under The Guidance Of
Mrs. Nisha Taank
Senior Manager ,QCD
Havells India Ltd, Sahibabad (U.P)
Presented by :
Akshay Nair (1527503)
Christ University Institute
of Management
2. PRODUCT
• MCCB –Moulded Case Circuit Breaker
• Project is limited to 3 pole MCCB
• Daily production of 200 to 250 No.s
3. PROBLEM STATEMENT
• The present state of product shows lower Cp and
Cpk (process capability) values in many of 3 pole
MCCB parameters which is less than the specified 1
and 1.33.
• By virtue of which company is facing high rejection
rate & low in customer specification.
4. PROJECT OBJECTIVE
• To study quality control measures by analysing the
3 pole MCCB production process.
• To analyse causes for low Cp and Cpk values.
• To address the issues and necessary improvement
required for increasing values of Cp and Cpk .
5. DATA COLLECTION
• After Understanding the production process , 30 random
samples of MCCB is picked up.
• DFSS data is collected for different parametres of MCCB
manuualy.
• Data is collected before & after improvements as well.
• Each time 30 random samples are selected.
6. CALCULATION OF CP & CPK
• The number of random samples taken
• Upper specification limit (USL)
• Lower specification limit(LSL)
• Cp = (USL – LSL) ÷ 6σ( Process Variation)
• Cpk = Minimum[(USL – X bar)/3σ;(X bar –LSL)/3σ]
7. RESULTS OF CP & CPK BEFORE
IMPROVEMENT
• DFSS ANALYSIS.xlsx
• It shows low Cp & Cpk Values much below the
specified 1 & 1.33.
• Many samples were showing high variations.
• Fishbone Diagram & Pareto Analysis used to find out
the diff causes for low Cp & Cpk.
8. FISHBONE DIAGRAM & PARETO
ANALYSIS
G frame TP
METHOD MAN
MATERIAL
Fitment not
proper
DFS cover
Unskilled Man Power
Problem in Trip Bar
Extra filing in DFS base
Trip bar filing
Delatch
Push button slot
dimension
MACHINE
NC
10. IMPROVEMENTS TAKEN FOR
INCREASING CP & CPK
Step 1 : Cover slot depth in the breakers was less
than the required
Action Taken: The Tool has been rectified with
specified slot.
Step 2: Another observation in Push to trip spring
Action Taken: New springs has been developed from
special springs and the results shown good result.
11. RESULTS AFTER IMPROVEMENT
• improved data.xlsx
• All the parametres show Cp >1.
• Cpk values also increased significantly but still short of
specified limit.
• Much more improvement is going on from R&D team to
improve Cpk.
12. QUALITY IMPROVEMENT THROUGH 7 QC
TOOLS
S.N
o
Quality Control
Tool
Existin
g
Purpose Suggestion
s
1 Control Charts P-
Chart
Being used at all the stages
and used to calculate the
defect rate of the components
as well as process and it is
being updated daily
Using
Excel
template
for
P-Chart
instead of
manual
filling of
data
2 Flow Charts Present Indicate the flow of process
3 Check Sheet Present It is used while collecting the
data from the production
process.
4 Pareto Chart Present When analyzing data about
12
13. 5 Fish Bone
Diagram(Cause
and Effect
Diagram)
Not
Existing
Chart which enables to
find out the root cause of
the problem by analysing
each and every possible
opportunity for defect
6 Histogram Not
Existing
When seeing whether a
process change has
occurred from one
time period to another.
7 Scatter Diagram Not
Existing
The scatter diagram
graphs pairs of numerical
data, with one variable on
each axis, to look for a
relationship between
them.
Can be used
to find the
correlation
between
Defect rate
and various
causes for
defects
Quality Improvement through 7 QC Tools
Contd..
14. RECOMMENDATIONS
• Since the defects observed are mainly component
defects there is a necessity for a proper inspection
of Incoming materials by Internal Quality Control
(IQC) Department.
• Raw material should be in proper parameters, it may
give law cost and with minimum error’s.
• More number of In-house parts are preferable over
vendor.
• Usage of Cpk in control charts to understand the
variation in the product parameters.
15. HAVELLS EXPERIENCE
• Bridge the gap between theoretical knowledge we possess with
the real time hands-on practical experience at Havells.
• Huge importance of “Quality Control Management system”.
• Patience to gather the right information, how to organize that
data and use the necessary tools to make the data speak for
itself.
• Importance of Customer Satisfaction, Customer Retention,
Customer Loyalty.
