IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Use of Seven Quality Tools to Improve Quality and Productivity in Industryijsrd.com
This document discusses the use of seven quality tools (7 QC tools) to improve quality in industry. It describes each of the 7 tools: check sheets, flow charts, histograms, Pareto charts, cause-and-effect diagrams, scatter diagrams, and control charts. These tools can be used to collect and analyze quality data to identify problems, determine root causes of issues, and monitor processes over time to ensure statistical control. The document asserts that continuous use of these simple quality control tools can improve product quality, enhance employee skills at problem-solving, and help create a quality culture within an organization.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Quality Improvement Of Fan Manufacturing Industry By Using Basic Seven Tools ...IJERA Editor
This document summarizes a case study conducted at Fecto Fan Company in Gujranwala, Pakistan to address quality issues in their fan manufacturing process. The researcher implemented the basic seven quality tools - flow chart, check sheet, histogram, Pareto chart, cause-and-effect diagram, scatter diagram, and control charts. These tools helped identify the major defects causing 80% of issues, determine their root causes, remove the defects, and ensure the manufacturing process is now under statistical control. Implementing quality tools through the DMAIC methodology improved the process and reduced defects.
Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
Today’s competitive environment has, lower manufacturing cost, more productivity in less time, high-quality product, defect-free operation are required to follow to every foundryman. For the improvement of products quality, there are diff-diff quality tools used in various review papers. Here I am going to review these papers and identify the different way of uses of those tools in manufacturing industries to increase the quality of the product. There are so many defects in the manufacturing process and these defects directly affect productivity, profitability and quality level of organization. This study is aimed to review the research work made by several researchers and attempt to get a technical solution for the various defects and to improve the entire process of the manufacturing
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control is largely used in industries for monitoring the process parameters. It is a standard method for visualizing and controlling processes on the basis of measurements of randomly selected samples. The decisions about what needs to be improved, the possible methods to improve it, and the steps to take after getting results from the charts are all made by humans and based on wisdom and experience. The statistical process control described in this paper gives the details about the SPC, its advantages and limitation, applications and information regarding the control charts. Keywords: Statistical Process Control, Control chart, 5M’s, Capability Indices.
This document discusses quality risk management process for aseptic processes. It begins by defining an aseptic process as the manipulation of sterile components in a controlled environment to produce a sterile product. Aseptic processes carry a high risk of contamination, so quality risk management is essential. The document then discusses quality risk management and its uses, including determining the scope of audits, evaluating changes, and identifying critical process parameters. Finally, the document lists several quality management tools like check sheets, control charts, Pareto charts, and histograms that can be used in quality risk management.
Use of Seven Quality Tools to Improve Quality and Productivity in Industryijsrd.com
This document discusses the use of seven quality tools (7 QC tools) to improve quality in industry. It describes each of the 7 tools: check sheets, flow charts, histograms, Pareto charts, cause-and-effect diagrams, scatter diagrams, and control charts. These tools can be used to collect and analyze quality data to identify problems, determine root causes of issues, and monitor processes over time to ensure statistical control. The document asserts that continuous use of these simple quality control tools can improve product quality, enhance employee skills at problem-solving, and help create a quality culture within an organization.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Quality Improvement Of Fan Manufacturing Industry By Using Basic Seven Tools ...IJERA Editor
This document summarizes a case study conducted at Fecto Fan Company in Gujranwala, Pakistan to address quality issues in their fan manufacturing process. The researcher implemented the basic seven quality tools - flow chart, check sheet, histogram, Pareto chart, cause-and-effect diagram, scatter diagram, and control charts. These tools helped identify the major defects causing 80% of issues, determine their root causes, remove the defects, and ensure the manufacturing process is now under statistical control. Implementing quality tools through the DMAIC methodology improved the process and reduced defects.
Statistical Process Control (SPC) is a method of using statistical analysis to monitor and control a process. SPC helps determine whether a process is stable or unpredictable by comparing data to control limits on charts. There are control charts for variables (data that can be measured numerically) and attributes (data classified into categories). The document discusses types of control charts like p charts for proportions and u charts for defects per unit. It also covers process capability indices, which measure how well a process produces outputs within specifications. The goal of SPC is to detect non-routine variations and make processes as consistent as possible through continuous improvement.
Today’s competitive environment has, lower manufacturing cost, more productivity in less time, high-quality product, defect-free operation are required to follow to every foundryman. For the improvement of products quality, there are diff-diff quality tools used in various review papers. Here I am going to review these papers and identify the different way of uses of those tools in manufacturing industries to increase the quality of the product. There are so many defects in the manufacturing process and these defects directly affect productivity, profitability and quality level of organization. This study is aimed to review the research work made by several researchers and attempt to get a technical solution for the various defects and to improve the entire process of the manufacturing
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control is largely used in industries for monitoring the process parameters. It is a standard method for visualizing and controlling processes on the basis of measurements of randomly selected samples. The decisions about what needs to be improved, the possible methods to improve it, and the steps to take after getting results from the charts are all made by humans and based on wisdom and experience. The statistical process control described in this paper gives the details about the SPC, its advantages and limitation, applications and information regarding the control charts. Keywords: Statistical Process Control, Control chart, 5M’s, Capability Indices.
This document discusses quality risk management process for aseptic processes. It begins by defining an aseptic process as the manipulation of sterile components in a controlled environment to produce a sterile product. Aseptic processes carry a high risk of contamination, so quality risk management is essential. The document then discusses quality risk management and its uses, including determining the scope of audits, evaluating changes, and identifying critical process parameters. Finally, the document lists several quality management tools like check sheets, control charts, Pareto charts, and histograms that can be used in quality risk management.
7 QC Tools are simple statistical tools used for problem solving. Nilesh Arora presented basics of 7 QC Tool training and details about Pareto Diagram.
The document discusses process optimization of groove diameter turning. It identifies the problem of parts being rejected due to low groove diameter. Various tools are used to analyze the process including a process flow diagram, process capability chart, response analysis table, fishbone diagram and cause analysis table. Experiments are conducted to study the effect of various factors like feed rate, hydraulic temperature, stem hardness, spindle RPM and dwell time on the groove diameter. The analysis aims to identify the key factors contributing to the defect and potential actions for improvement.
Statistical Quality Control involves using statistical techniques to control quality by inspecting products and processes to determine if they meet quality standards. W. Edward Deming advocated for this approach to reduce variation and achieve consistency. There are three main categories of statistical quality control: descriptive statistics, acceptance sampling, and statistical process control (SPC). SPC involves measuring quality characteristics over time and charting the results to identify variations and determine whether a process is stable and in control. Control charts are a key tool in SPC, as they graph data over time and can be used to differentiate between common cause variation and special cause variation.
The document discusses various quality control tools used to identify issues, analyze causes, and monitor processes. It provides descriptions and examples of seven key QC tools: Pareto diagram, cause-and-effect diagram, histogram, scatter diagram, check sheet, control chart, and graph/flow chart. These tools can help objectively assess situations, identify problem areas, determine relationships between factors, and maintain process stability. The document emphasizes that collecting data and practicing the use of these tools is important for effectively solving problems and improving processes.
Control charts are used to monitor process variables over time in various industries and organizations. They tell us when a process is out of control by showing data points outside the control limits. When this occurs, those closest to the process must find and eliminate the special cause of variation to prevent it from happening again. Control charts have basic components like a centerline and upper and lower control limits. They are constructed by selecting a process, collecting data, calculating statistics and control limits, and plotting the results over time. Control charts come in two types - variables charts for continuous measurements and attributes charts for counting items. Common and special causes can lead to variations monitored by these charts.
This presentation provides an overview of control charts, including what they are, their purposes and advantages, different types of control charts, and how to construct and interpret them. Control charts graphically display process data over time to determine whether a manufacturing or business process is in a state of statistical control. The presentation discusses variable and attribute control charts, and specific charts like X-bar and R-bar charts. It provides examples of how to calculate control limits and plot data on a chart, and how to interpret results to determine if a process is capable or needs improvement. A case study example analyzing wait time data from a hotel management company is also reviewed.
Control charts are graphs used to monitor quality during manufacturing. They allow issues to be identified and addressed early to maintain consistent product quality. Key aspects of control charts include:
- Plotting statistics like the mean or range of sample measurements over time
- Using statistical limits to identify processes that are in or out of control
- Interpreting patterns in the charts to determine if corrective action is needed
Control charts enable manufacturers to efficiently produce uniform products by catching problems early and avoiding unnecessary adjustments to processes that are performing normally.
This document provides an overview of quality management systems definitions for pharmaceutical and medical device industries. It discusses key FDA regulations like 21 CFR Part 211 and guidance documents that inform quality management systems definitions. These definitions generally cover establishing a quality control unit and documenting quality processes around areas like document control, training, auditing, corrective actions and risk management. The document also lists several quality management tools commonly used, such as check sheets, control charts, Pareto charts, scatter plots and Ishikawa diagrams.
The document discusses the application of statistical tools to enhance productivity and quality control in industries. It explains key concepts like process control, process capability indices, acceptance sampling plans, and their use in quality management. Statistical process control techniques like control charts are used to monitor processes and make data-driven decisions about product and process quality. Acceptance sampling balances protecting consumers from defects and encouraging quality production.
IRJET- A Survey: Quality Control Tool in Auto Parts IndustryIRJET Journal
This document summarizes a survey on quality control tools used in the auto parts industry. It discusses that quality control using statistical methods is important for reducing costs and improving quality in modern industries. The seven basic quality control tools are described, including cause-and-effect diagrams, check sheets, control charts, histograms, Pareto charts, scatter plots, and stratification. An example problem of door noise in trucks is presented and how quality tools like Ishikawa diagrams and histograms could help reduce the noise by 7%. The document concludes that quality control tools are effective for improving productivity and quality in automobile manufacturing.
The graphical analysis for maintenace management methodPeterpanPan3
This article proposes a new graphical tool called the Graphical Analysis for Maintenance Management (GAMM) method to support maintenance management decision making. The GAMM method visualizes equipment dependability data like the number and duration of corrective and preventive interventions over time. It helps identify opportunities to improve operations and maintenance by analyzing patterns in the timing and impacts of interventions. The GAMM method estimates reliability using nonparametric estimators and graphs cumulative intervention rates over time based on historical maintenance data.
Statistical Process Control & Control ChartShekhar Verma
Statistical Process Control (SPC) uses control charts to monitor processes over time and identify sources of variation. Control charts graph key data metrics and establish control limits to determine whether the process is in a state of statistical control or if special causes are present. The benefits of SPC include early detection of quality issues, reduced waste, and improved process cycle times and customer satisfaction through a diminished likelihood of rework.
This chapter discusses statistical quality control and control charts. It covers the following key points:
1. Statistical process control uses tools like control charts to reduce variability and identify assignable causes of variation.
2. Control charts monitor a process over time and detect when the process moves out of the state of statistical control.
3. There are variables and attributes control charts. Variables charts like X-bar and R charts are for continuous data, while attributes charts like P and U charts are for discrete data.
4. Rational subgrouping aims to maximize differences between subgroups while minimizing within-subgroup differences to better detect assignable causes.
This document provides an introduction to statistical process control (SPC). It defines SPC as a strategy that uses statistical techniques to evaluate processes, identify variability, and find opportunities for improvement. The goal of SPC is to make high-quality products the first time by reducing variability, rather than reworking defective products. It focuses on monitoring process behavior rather than just final product quality. SPC distinguishes between common cause variability that is always present and special cause variability that can be addressed to improve the process. It emphasizes identifying and addressing special causes first before adjusting process means. Control charts are used to monitor processes and determine if they are in control or need adjustment.
Design and Manufacturing of Receiving GaugeIRJET Journal
This document summarizes the design and manufacturing of a receiving gauge. The receiving gauge is used to inspect parts for dimensional accuracy by checking dimensions precisely according to standards. It was designed based on the specifications and tolerances of the part being inspected. The receiving gauge provides accurate and precise inspection in a time-saving and cost-effective manner compared to a coordinate measuring machine. It is suitable for use in mass production environments. The receiving gauge design is presented, including diagrams of the gauge and how it is used to inspect parts. Advantages such as reduced inspection time and cost are highlighted.
This document discusses statistical process control (SPC) techniques for managing quality. It covers various SPC methods including error detection, error prevention, and process control systems. The benefits of SPC include controlling processes, predicting behavior, avoiding waste, and achieving defect prevention. Key SPC tools include data collection, summarization using charts, histograms, and control charts to monitor processes and detect issues. The document also discusses process capability, measurement of variation, and using frequency distributions and histograms to analyze process capability.
This document discusses statistical process control and control charts. It defines the goals of control charts as collecting and visually presenting data to see when trends or out-of-control points occur. Process control charts graph sample data over time and show the process average and upper and lower control limits. Attribute control charts indicate whether points are in or out of tolerance, while variables charts measure attributes like length, weight or temperature over time. Examples are provided to illustrate p-charts, R-charts and X-bar charts using hotel luggage delivery time data.
Este documento define y diferencia varios conceptos clave relacionados con la tecnología e informática, incluyendo tecnología, nuevas tecnologías, TIC (Tecnologías de la Información y Comunicación) e informática. Explica que las TIC se refieren al uso de dispositivos electrónicos, aplicaciones e Internet como herramientas en todas las áreas educativas, mientras que la informática implica un conocimiento más profundo de los procesos informáticos y desarrollo de aplicaciones. Las TIC deben usarse de forma transversal
El documento habla sobre diferentes tipos de cableado y conectividad de redes como coaxial, par trenzado y fibra óptica. También describe diferentes topologías de red como bus, anillo y estrella. Además, explica brevemente sobre routers, switches y otros dispositivos de red.
Las coordenadas polares son un sistema de coordenadas que representa cada punto en un plano mediante su distancia (r) al origen y el ángulo (θ) que forma con el eje x positivo. Permiten describir de forma simple curvas circulares y fenómenos relacionados con distancias y ángulos. Algunas aplicaciones incluyen modelar movimientos orbitales, navegación, y calcular límites y integrales donde la región de integración involucra circunferencias u otras curvas definidas por ecuaciones polares.
7 QC Tools are simple statistical tools used for problem solving. Nilesh Arora presented basics of 7 QC Tool training and details about Pareto Diagram.
The document discusses process optimization of groove diameter turning. It identifies the problem of parts being rejected due to low groove diameter. Various tools are used to analyze the process including a process flow diagram, process capability chart, response analysis table, fishbone diagram and cause analysis table. Experiments are conducted to study the effect of various factors like feed rate, hydraulic temperature, stem hardness, spindle RPM and dwell time on the groove diameter. The analysis aims to identify the key factors contributing to the defect and potential actions for improvement.
Statistical Quality Control involves using statistical techniques to control quality by inspecting products and processes to determine if they meet quality standards. W. Edward Deming advocated for this approach to reduce variation and achieve consistency. There are three main categories of statistical quality control: descriptive statistics, acceptance sampling, and statistical process control (SPC). SPC involves measuring quality characteristics over time and charting the results to identify variations and determine whether a process is stable and in control. Control charts are a key tool in SPC, as they graph data over time and can be used to differentiate between common cause variation and special cause variation.
The document discusses various quality control tools used to identify issues, analyze causes, and monitor processes. It provides descriptions and examples of seven key QC tools: Pareto diagram, cause-and-effect diagram, histogram, scatter diagram, check sheet, control chart, and graph/flow chart. These tools can help objectively assess situations, identify problem areas, determine relationships between factors, and maintain process stability. The document emphasizes that collecting data and practicing the use of these tools is important for effectively solving problems and improving processes.
Control charts are used to monitor process variables over time in various industries and organizations. They tell us when a process is out of control by showing data points outside the control limits. When this occurs, those closest to the process must find and eliminate the special cause of variation to prevent it from happening again. Control charts have basic components like a centerline and upper and lower control limits. They are constructed by selecting a process, collecting data, calculating statistics and control limits, and plotting the results over time. Control charts come in two types - variables charts for continuous measurements and attributes charts for counting items. Common and special causes can lead to variations monitored by these charts.
This presentation provides an overview of control charts, including what they are, their purposes and advantages, different types of control charts, and how to construct and interpret them. Control charts graphically display process data over time to determine whether a manufacturing or business process is in a state of statistical control. The presentation discusses variable and attribute control charts, and specific charts like X-bar and R-bar charts. It provides examples of how to calculate control limits and plot data on a chart, and how to interpret results to determine if a process is capable or needs improvement. A case study example analyzing wait time data from a hotel management company is also reviewed.
Control charts are graphs used to monitor quality during manufacturing. They allow issues to be identified and addressed early to maintain consistent product quality. Key aspects of control charts include:
- Plotting statistics like the mean or range of sample measurements over time
- Using statistical limits to identify processes that are in or out of control
- Interpreting patterns in the charts to determine if corrective action is needed
Control charts enable manufacturers to efficiently produce uniform products by catching problems early and avoiding unnecessary adjustments to processes that are performing normally.
This document provides an overview of quality management systems definitions for pharmaceutical and medical device industries. It discusses key FDA regulations like 21 CFR Part 211 and guidance documents that inform quality management systems definitions. These definitions generally cover establishing a quality control unit and documenting quality processes around areas like document control, training, auditing, corrective actions and risk management. The document also lists several quality management tools commonly used, such as check sheets, control charts, Pareto charts, scatter plots and Ishikawa diagrams.
The document discusses the application of statistical tools to enhance productivity and quality control in industries. It explains key concepts like process control, process capability indices, acceptance sampling plans, and their use in quality management. Statistical process control techniques like control charts are used to monitor processes and make data-driven decisions about product and process quality. Acceptance sampling balances protecting consumers from defects and encouraging quality production.
IRJET- A Survey: Quality Control Tool in Auto Parts IndustryIRJET Journal
This document summarizes a survey on quality control tools used in the auto parts industry. It discusses that quality control using statistical methods is important for reducing costs and improving quality in modern industries. The seven basic quality control tools are described, including cause-and-effect diagrams, check sheets, control charts, histograms, Pareto charts, scatter plots, and stratification. An example problem of door noise in trucks is presented and how quality tools like Ishikawa diagrams and histograms could help reduce the noise by 7%. The document concludes that quality control tools are effective for improving productivity and quality in automobile manufacturing.
The graphical analysis for maintenace management methodPeterpanPan3
This article proposes a new graphical tool called the Graphical Analysis for Maintenance Management (GAMM) method to support maintenance management decision making. The GAMM method visualizes equipment dependability data like the number and duration of corrective and preventive interventions over time. It helps identify opportunities to improve operations and maintenance by analyzing patterns in the timing and impacts of interventions. The GAMM method estimates reliability using nonparametric estimators and graphs cumulative intervention rates over time based on historical maintenance data.
Statistical Process Control & Control ChartShekhar Verma
Statistical Process Control (SPC) uses control charts to monitor processes over time and identify sources of variation. Control charts graph key data metrics and establish control limits to determine whether the process is in a state of statistical control or if special causes are present. The benefits of SPC include early detection of quality issues, reduced waste, and improved process cycle times and customer satisfaction through a diminished likelihood of rework.
This chapter discusses statistical quality control and control charts. It covers the following key points:
1. Statistical process control uses tools like control charts to reduce variability and identify assignable causes of variation.
2. Control charts monitor a process over time and detect when the process moves out of the state of statistical control.
3. There are variables and attributes control charts. Variables charts like X-bar and R charts are for continuous data, while attributes charts like P and U charts are for discrete data.
4. Rational subgrouping aims to maximize differences between subgroups while minimizing within-subgroup differences to better detect assignable causes.
This document provides an introduction to statistical process control (SPC). It defines SPC as a strategy that uses statistical techniques to evaluate processes, identify variability, and find opportunities for improvement. The goal of SPC is to make high-quality products the first time by reducing variability, rather than reworking defective products. It focuses on monitoring process behavior rather than just final product quality. SPC distinguishes between common cause variability that is always present and special cause variability that can be addressed to improve the process. It emphasizes identifying and addressing special causes first before adjusting process means. Control charts are used to monitor processes and determine if they are in control or need adjustment.
Design and Manufacturing of Receiving GaugeIRJET Journal
This document summarizes the design and manufacturing of a receiving gauge. The receiving gauge is used to inspect parts for dimensional accuracy by checking dimensions precisely according to standards. It was designed based on the specifications and tolerances of the part being inspected. The receiving gauge provides accurate and precise inspection in a time-saving and cost-effective manner compared to a coordinate measuring machine. It is suitable for use in mass production environments. The receiving gauge design is presented, including diagrams of the gauge and how it is used to inspect parts. Advantages such as reduced inspection time and cost are highlighted.
This document discusses statistical process control (SPC) techniques for managing quality. It covers various SPC methods including error detection, error prevention, and process control systems. The benefits of SPC include controlling processes, predicting behavior, avoiding waste, and achieving defect prevention. Key SPC tools include data collection, summarization using charts, histograms, and control charts to monitor processes and detect issues. The document also discusses process capability, measurement of variation, and using frequency distributions and histograms to analyze process capability.
This document discusses statistical process control and control charts. It defines the goals of control charts as collecting and visually presenting data to see when trends or out-of-control points occur. Process control charts graph sample data over time and show the process average and upper and lower control limits. Attribute control charts indicate whether points are in or out of tolerance, while variables charts measure attributes like length, weight or temperature over time. Examples are provided to illustrate p-charts, R-charts and X-bar charts using hotel luggage delivery time data.
Este documento define y diferencia varios conceptos clave relacionados con la tecnología e informática, incluyendo tecnología, nuevas tecnologías, TIC (Tecnologías de la Información y Comunicación) e informática. Explica que las TIC se refieren al uso de dispositivos electrónicos, aplicaciones e Internet como herramientas en todas las áreas educativas, mientras que la informática implica un conocimiento más profundo de los procesos informáticos y desarrollo de aplicaciones. Las TIC deben usarse de forma transversal
El documento habla sobre diferentes tipos de cableado y conectividad de redes como coaxial, par trenzado y fibra óptica. También describe diferentes topologías de red como bus, anillo y estrella. Además, explica brevemente sobre routers, switches y otros dispositivos de red.
Las coordenadas polares son un sistema de coordenadas que representa cada punto en un plano mediante su distancia (r) al origen y el ángulo (θ) que forma con el eje x positivo. Permiten describir de forma simple curvas circulares y fenómenos relacionados con distancias y ángulos. Algunas aplicaciones incluyen modelar movimientos orbitales, navegación, y calcular límites y integrales donde la región de integración involucra circunferencias u otras curvas definidas por ecuaciones polares.
El documento describe la topología de red en anillo, incluyendo su definición, características, elementos, resistencia a fallas y facilidad de crecimiento. Una red en anillo conecta estaciones en un círculo cerrado de tal forma que la información circula unidireccionalmente alrededor del anillo. Si se rompe la conexión, la red completa se cae, pero una topología de anillo doble agrega redundancia al incluir un segundo anillo.
Este documento presenta los conceptos básicos relacionados con la formulación de proyectos. Define términos como planificación, programa, presupuesto y proyecto. Explica que la planificación implica establecer objetivos y elegir medios para alcanzarlos, considerando la situación actual y factores internos y externos. Un proyecto se caracteriza por tener un comienzo y fin claros, objetivos específicos, ser único e implicar tiempo, recursos y costos. La formulación de proyectos implica coordinar estratégicamente los distintos aspect
El documento describe diferentes tipos de curvas matemáticas, incluyendo curvas elementales, simples, planas, diferenciables, cerradas y suaves. Explica cómo calcular la longitud de arco de una curva, ya sea mediante la suma de segmentos rectos pequeños que aproximan la curva o mediante integrales definidas en el caso de curvas paramétricas o polares.
“ÚNete: para poner fin a la violencia contra las mujeres” SerBolivianoEs
En febrero de 2008, el Secretario General de las Naciones Unidas, Ban Ki-moon lanzó la campaña global:
“ÚNete: para poner fin a la violencia contra las mujeres” respondiendo a un consenso internacional propicio para eliminar la violencia contra las mujeres y las niñas.
La campaña es multianual, hasta el 2015, y reconoce que eliminar la violencia contra las mujeres es clave para el logro de los Objetivos de Desarrollo del Milenio.
This document discusses the application of seven quality control tools - check sheet, histogram, Pareto chart, fishbone diagram, control chart, flowchart, and scatter diagram - to analyze defects in a construction industry case study. Data was collected using a check sheet and showed cracks on the sides were the most common defect. A histogram and Pareto chart further analyzed the defect data, identifying cracks and dimensional errors as primary issues. A fishbone diagram explored potential causes of cracks. Control charts monitored dimensional errors over time. The flowchart mapped the production process, and a scatter diagram found no correlation between cracks and temperature. In conclusion, these seven tools helped identify, analyze, and improve quality issues in the construction industry case study.
Statistical quality control applied industrial and manufacturing operations. Case study regarding the use of these tools. Description of statistical tools used in quality control and inspection.
IMPLEMENTATION OF STATISTICAL PROCESS CONTROL TOOL IN AN AUTOMOBILE MANUFACTU...Angela Williams
This document discusses the implementation of statistical process control (SPC) tools in an automotive manufacturing unit to reduce defects and costs. Only two main SPC techniques, cause and effect diagrams and control charts, were implemented. The work focuses on defects in a clamp coining tool manufacturing process. Data was collected on rejection rates from January to May 2015, showing a rising trend. Cause and effect analysis using the four M's (man, machine, material, method) identified several root causes. After implementing SPC tools to address the causes, the rejection rate decreased from 9.1% to 5%, reducing costs.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Implementation of quality improvement tools in brass industry to improve qual...Alexander Decker
This document discusses the implementation of quality improvement tools in the brass industry to improve quality and enhance productivity. It begins by introducing the significance of quality and increased productivity at affordable costs. It then describes the methodology used, which involved selecting products for analysis, collecting data, performing defect analysis with Pareto charts, developing control charts, and analyzing causes of defects with cause-and-effect diagrams. For brass strips and GMCS strips, the analysis revealed high initial rejection rates of 15% and 21%, which were then reduced to 8-10% and 12-14% respectively by addressing root causes identified with the quality tools. The results showed improved quality, increased savings, enhanced productivity and reduced waste.
This presentation discusses quality control tools including check sheets, flow charts, histograms, cause and effect diagrams, Pareto charts, scatter diagrams, and control charts. It provides examples and guidelines for when and how to use each tool, as well as their benefits. The seven tools are effective for problem solving, process measurement and continual improvement in quality control.
The document describes 7 quality control tools: 1) Flow chart, 2) Check sheet, 3) Histogram, 4) Pareto chart, 5) Cause and effect diagram, 6) Scatter plot, and 7) Control chart. It provides examples and brief explanations of each tool. Flow charts help communicate and analyze processes. Check sheets gather data on problems. Histograms show data distribution and outliers. Pareto charts rank issues to prioritize improvements. Cause and effect diagrams explore causes of outcomes. Scatter plots show correlations. Control charts have limits and plot process data over time.
IRJET- Mathematical Analysis of Performance of a Vibratory Bowl Feeder for Fe...IRJET Journal
This document analyzes the performance of a vibratory bowl feeder for feeding buttons of different sizes in a garment factory. It describes how experiments were conducted using the design of experiments methodology to develop a mathematical model relating the feed rate output to input parameters of part size, population, and frequency. The model was found to be statistically significant based on ANOVA analysis. The optimal model can predict the feeder's performance behavior based on the input parameters.
Seven tools of quality control.slideshareraiaryan448
7 tools of quality control help identify potential problem root cause and then target them for improvements and process optimization. These are widely used in all kind of manufacturing industries along with service industry as well.
After World War II, Japan adopted quality as an economic strategy and selected seven statistical tools to analyze quality problems and drive continuous improvement. The seven tools - Pareto charts, cause-and-effect diagrams, histograms, control charts, scatter plots, check sheets, and flow charts - can identify up to 95% of issues. Each tool has a specific purpose, such as prioritizing problems with Pareto charts or identifying relationships between variables with scatter plots. Using these tools, Japanese companies were able to dramatically improve quality and economic performance.
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This document discusses statistical process control (SPC), which uses statistical methods to monitor and control processes to improve quality. SPC aims to ensure processes operate efficiently and produce specification-conforming products with less waste. Key SPC tools include control charts, histograms, cause-and-effect diagrams and check sheets. Control charts in particular plot process data over time to identify changes or variability. SPC provides benefits like reduced waste, lower costs, improved customer satisfaction and early problem detection and prevention.
This document discusses various quality control tools used for data collection and analysis including check sheets, Pareto charts, flow charts, cause and effect diagrams, histograms, scatter diagrams, and control charts. It provides examples of how each tool can be used to identify issues, determine root causes, and monitor processes for quality improvement in areas like manufacturing and customer service. The document emphasizes using graphical representations to more easily recognize patterns in data and determine appropriate actions.
The document discusses project quality management. It defines quality and project quality management, which ensures projects satisfy needs. Quality is planned through quality planning and quality standards. Quality assurance evaluates performance while quality control monitors results. Tools like control charts, Pareto charts and sampling are used. Statistical concepts like six sigma and ISO standards help manage quality.
The document discusses 7 quality control tools used to identify, analyze, and resolve problems in a systematic manner. The tools include check sheets, histograms, Pareto charts, cause-and-effect diagrams, scatter plots, defect concentration diagrams, and control charts. These simple but powerful tools can help solve day-to-day work problems and identify solutions by collecting and analyzing process data.
The document discusses 7 quality control tools used to identify, analyze, and resolve problems in a systematic manner. The tools include check sheets, histograms, Pareto charts, cause-and-effect diagrams, scatter plots, defect concentration diagrams, and control charts. These simple but powerful tools can help solve day-to-day work problems and identify solutions by collecting and analyzing process data.
Seven Basic Quality Control Tools أدوات ضبط الجودة السبعةMohamed Khaled
The 7 QC tools are fundamental instruments to improve the process and product quality. They are used to examine the production process.
► The seven basic tools are:
1- Check sheet
2- Pareto analysis
3- Cause and Effect Diagram
4- Scatter plot
5- Histogram
6- Flowchart
7- Control charts
-------------------------------------------------------------------------------------
#7_Basic_Quality_Control_Tools #Check_sheet #Pareto_analysis #Fishbone #Scatter_plot #Histogram #Flowchart #Control_charts #CFturbo #Pump_simulation_using_ANSYS #Water_Hammer #أدوات_ضبط_الجودة_السبعة #نموذج_التحقق #مخطط_باريتو #مخطط_السبب_والأثر #مخطط_التشتت #مدرج_تكراري #خرائط_التدفق #خرائط_ضبط_الجودة
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1. Samadhan D. Bhosale, S.C.Shilwant, S.R. Patil / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.832-837
832 | P a g e
Quality improvement in manufacturing processes using SQC tools
Samadhan D. Bhosale1
, S.C.Shilwant2
, S.R. Patil3
1
PG Student (Department of Mechanical Engineering), Sinhgad Academy of Engineering,
Pune University, India.
2
Professor, (Department of Mechanical Engineering), Sinhgad Academy of Engineering,
Pune University, India.
3
Assistant Professor, (Department of Mechanical Engineering), Sinhgad Academy of Engineering,
Pune University, India.
ABSTRACT
The aim of this paper is to apply the
quality tools to find out the root causes of the
quality problems related to manufacturing of
mechanical seal. The modes of defects on
production line are investigated through direct
observation on the production line and statistical
tools like Check sheets, Histogram, Pareto
analysis, Cause and Effect diagram etc are used
in enhancing the process by continuous
monitoring through inspection of the samples.
The work shows utility of quality tools to find the
root causes of the problems and eliminate them.
A case study has been carried out in ‘EKK Eagle
Products India Pvt. Ltd’ company that
specializes in manufacturing of mechanical seal.
Keywords- Control Charts, Cause and effect
diagram, Histogram, Pareto Analysis, Mechanical
Seal, Statistical Quality control.
1. INTRODUCTION
Quality concepts have changed over time.
Quality of product means the product must be
within the acceptable limits. Edwards Deming,
explained chain reaction in his book "out of the
crisis" published in 1986. The benefits from quality
and process improvements to organization are:
a. Improved Quality
b. Less rework, fewer mistakes and hence
cost decreased
c. Capture the market with better quality and
lower price
d. Improved Business
e. Improved productivity [1]
The most common process of continuous
improvement is the PDCA Cycle shown in “Fig.1”
which was first developed by Walter Shewhart in
the 1920s and promoted by quality preceptors Dr
Edwards Deming. PDCA-cycle consists of four
consecutive steps, as follows:
• Plan – Perception for improvement
• Do – Implementation of the changes that is
decided in the Plan.
• Check – a) Control and measurement of processes
and products in accordance with changes in previous
steps
b) Policy, goals and requirements
c) Report on results
d) Decision making
• Act – a) Adoption of the changes or run through
PDCA-cycle again
b) Repetition of cycle for continuous improvement.
Figure 1. PDCA cycle
PDCA-cycle is required in process
improvement. When the process improvement starts
with careful planning results in corrective and
preventive actions supported by appropriate quality
assurance tools. Application of seven basic quality
tools in correlation with four steps of PDCA-cycle is
shown in Table 1. [2]
As shown in Table 1, for problem
identification Flow chart, Cause-and-Effect
diagram, Check sheet, Pareto diagram, Histogram
and Control charts can be used. For problem
analysis Cause-and-Effect diagram, Check sheet,
Pareto diagram, Scatter plot and Control charts tools
can be used. When the team is developing a solution
for analyzed problem Flow chart and Scatter plot
can be useful. For evaluation of results, Check sheet,
Pareto diagram, Histogram, Scatter plot and Control
charts can be used.
2. Samadhan D. Bhosale, S.C.Shilwant, S.R. Patil / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.832-837
833 | P a g e
Table 1. Seven basic quality tools (7QC tools) in
correlation with PDCA-cycle steps
Seven basic
quality tools
(7QC tools)
Steps of PDCA-cycle
Plan
Plan,
Check
Plan, Act Check
Proble
m
identifi
cation
Process
analysis
Solutions
develop
ment
Result
evaluation
Flow chart √ √
Cause-and-
effect
diagram
√ √
Check sheet √ √ √
Pareto
diagram
√ √ √
Histogram √ √
Scatter plot √ √ √
Control
charts
√ √ √
.
Receiving Inspection Stock parts Part Supply Assembly
Final Inspection Packing In-Process Inspection Appearance Check
Figure 2. Process flow chart
A case study has been carried out at EKK
Eagle Products India Pvt. Ltd to monitor the process
of manufacturing mechanical seals. Process flow
chart at EKK Eagle Products India Pvt. Ltd
producing mechanical seals is as shown in “Fig 2.”
The data was collected for improving the quality of
mechanical seals and reducing the defects
2. THE SQC TOOLS
Quality tools can be used in all phases of
production process, from the start of product
development up to product marketing and customer
support. The 7 QC Tools are simple statistical tools
used for problem solving. These tools were
developed by the Quality preceptors such as Deming
and Juran. Ishikawa has stated that these 7 tools can
be used to solve 95 percent of all problems. The
following are the 7 QC Tools:
1) Histogram
2) Check Sheet
3) Pareto Diagram
4) Brainstorming
5) Cause & Effect Diagram
6) Control Charts
7) Scatter Diagram
2.1. Histogram
A histogram is one of the basic quality
tools. It is used to graphically summarize and
display the distribution and variation of a process
data set. A frequency distribution shows how often
each different value in a set of data occurs. The
main purpose of a histogram is to determine the
shape of data set. We can present the same
information in a table; but the graphic presentation
shows relationship. Based on the frequency of
distributed values, one can come to a conclusion
whether the values falls within the specified values
and gives a normal distribution curve out of it. [3]
This can be achieved by Histogram. It is a useful
tool for breaking out process data into regions for
determining frequencies of certain events or
categories of data.
Typical applications of histograms in root cause
analysis include:
1) Presenting data to determine which causes
dominate
2) Understanding the distribution of
occurrences of different problems, causes,
consequences, etc.
Table 2 Shows Seal Ring height data of mechanical
seal. It shows the cell boundaries of Seal Ring
height and frequency of them. “Fig.3” shows
histogram for Seal Ring height data of mechanical
seal
Table 2. Cell boundaries of Seal Ring height data
Class
Cell Boundaries
(mm)
Frequency
1 1.00 1.05 0
2 1.05 1.10 0
3 1.10 1.15 0
4 1.15 1.20 0
5 1.20 1.25 5
6 1.25 1.30 26
7 1.30 1.35 18
8 1.35 1.40 1
3. Samadhan D. Bhosale, S.C.Shilwant, S.R. Patil / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.832-837
834 | P a g e
Figure 3. Histogram of seal ring height data.
2.2. Check Sheet.
Data collection is important because it is
the starting point for statistical analysis. The
function of a check sheet is to present information in
an efficient, graphical format. Check sheets help to
organize data by category. They show frequency of
each particular value and their information. Check
sheets are tools that make the data collection process
easier. Their power is greatly enhanced when they
are used in conjunction with other quality tools such
as histograms and Pareto analysis. The check sheet
shown in Table 3 was created by tallying each type
of mode of defect of mechanical seal during a
specified time. It shows the type of defects and how
many of each type occurred during that period.
2.3. Pareto analysis
It can be described as the 80/20 rule
applied to quality-control. The 80/20 rule was
formalized by economist Pareto after studying the
distribution of wealth. He observed that about 80%
of wealth was held by about 20% of the population.
Joseph Juran applied this principle to quality-
control. Pareto Analysis states that 80% of quality
problems in the end product or service are caused by
20% of the problems in the production or service
processes. In practice it is beneficial to separate “the
vital few” problems from “the trivial many”.
2.3.1 Pareto Chart
A Pareto Chart is simply a frequency
distribution (or Histogram) of attribute data. The
Pareto Chart is named after Italian economist
Pareto; A Pareto Chart is a series of bars whose
heights reflect the frequency or impact of problems.
The bars are arranged in descending order of height
from left to right. This means the categories
represented by the tall bars on the left are relatively
more significant than those on the right. This bar
chart is used to separate the “vital few” from the
“trivial many”. These charts are based on the Pareto
analysis which are useful for the user to focus
attention on a few important factors in a process.
Pareto chart shown in “Fig.4” was
constructed based upon the data collected by check
sheet shown in Table 3 for various modes of defects
with their respective frequency and percentage of
mechanical seal. The figure reveals that the
mechanical seal has vital few modes of defects and
represents around 80 % of total cumulative
percentage of non-conformities.
Figure 4. Pareto chart for mode of defects
Table 3. Check sheet for various modes of defects
with their respective frequency and percentage
2.4 Brainstorming
Brainstorming is a tool used by teams to
bring out the ideas of each individual and present
them in an orderly fashion to the rest of the team.
The key ingredient is to provide an environment free
of criticism for creative and unrestricted exploration
of options or solutions.
Sr.
no
.
Mode of
defect
Produ
ction
volum
e
(No.s)
No. of
defecti
ve
compo
nents
(No.s)
% of
defect
Cumula
tive qty
(No.s)
Cumula
tive %
1
Seal
Ring
pore
93823
528 35.22 528 35.22
2
latex
coating 277 18.48 805 53.70
3
MR
chipping 232 15.48 1037 69.18
4 US leak 225 15.01 1262 84.19
5
Projectio
n 122 8.14 1384 92.32
6
stationar
y leak 48 3.20 1432 95.53
7
Drop
sample 35 2.33 1467 97.86
8 load 7 0.47 1474 98.33
9 other 25 1.67 1499 100.00
Total 93823 1499
100.0
0 1499 100.00
4. Samadhan D. Bhosale, S.C.Shilwant, S.R. Patil / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.832-837
835 | P a g e
Some of the specific benefits of Brainstorming are
that it
1. Encourages creativity. It expands thinking to
include all aspects of a problem or a solution with
wide range of options.
2. Rapidly produces a large number of ideas by
encouraging people to offer creative and justified
ideas.
3. Equalizes involvement by all team members. It
provides a nonjudgmental environment that
encourages everyone to offer ideas. All ideas are
recorded.
The recommended sequence for conducting
Brainstorming and some suggestions for conducting
the session effectively are provided below:
Review the rules: describe how the session will
be conducted.
Set a time limit: assign a timekeeper and data
recorder.
State the topic: Write down and publicize it.
Ensure that everyone understands it.
Collect everyone’s ideas: After allowing a few
minutes for the participants to think about the
question, ask them to give their ideas.
Record ideas: Display the ideas where everyone
can see them. Having the words visible to
everyone at the same time avoids
misinterpretation and duplication.
Clarify each idea: After all ideas have been
presented, to ensure that all members have the
same understanding of it.
Eliminate duplications: Repeated ideas are to be
eliminated.
2.5 Cause & Effect Diagram
Process improvement involves taking
action on the causes of variation. A team typically
uses Cause-and-Effect (C&E) diagrams to identify
and isolate causes of a problem. The late Dr. Kauro
Ishikawa, Japanese quality expert, developed the
technique and hence it is called as Ishikawa
diagram. [4] The Diagram is a tool to show
systematic relationship between a result or a
symptom or an effect and its possible causes. It is an
effective tool to systematically generate ideas about
causes for problems and to present them in a
structured form.
2.5.1 Structure
The symptom or result or effect for which
one wants to find causes is put in the dark box on
the right. The lighter boxes at the end of the large
bones are main groups in which the ideas are
classified. Usually four to six such groups are
identified.
Figure 5. Cause & effect diagram
In a typical manufacturing problem, the
groups may consist of five Ms - Men, Machines,
Materials, Method and Measurement. The six M
Money may be added if it is relevant. In some cases
Environment is one of the main groups. Important
subgroups in each of these main groups are
represented on the middle bones and these branch
off further into subsidiary causes represented as
small bones. The arrows indicate the direction of the
path from the cause to the effect. [5]
Brainstorming technique is therefore very
useful in identifying maximum number of causes.
The cause & effect diagrams shown in “Fig.5” were
constructed by quality team and through
brainstorming sessions involving all employees
taking part in the related production and test
activities
Cause and effect diagram shown in Figure
5 shows major factors that can cause latex coating
damage of cartridge of mechanical seal. The
diagram was constructed by quality improvement
team through brainstorming sessions involving all
operators taking part in the related production and
test activities.
2.6 Control Chart
Control charts are the one of the most
important and effective statistical tools for
determining the process stability and variability. [4]
These charts contain the upper control limit and
lower control limit .Control charts are statistical
tools used to analyze and understand process
variables, to determine a process’s capability to
perform with respect to those variables and to
5. Samadhan D. Bhosale, S.C.Shilwant, S.R. Patil / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.832-837
836 | P a g e
monitor the effect of those variables on the
difference between customer needs and process
performance.
Every process has variation that can be
classified as the result of either common causes or
special causes. Common causes of variation are
inherent in a process while variation created by
special causes is not normally present in the process.
Control charts are used to identify and distinguish
between those two different causes of variation. One
goal of using a Control Chart is to achieve and
maintain process stability.
We get benefit from using a Control Chart when we
1. Monitor process variation over time.
2. Distinguish between special cause and
common cause variation.
3. Assess the effectiveness of changes to
improve a process.
4. Communicate how a process performed
during a specific period.
“Fig.6” shows X and R chart for Seal ring height of
mechanical seal. The figure illustrates natural
process limits controlled within specification limits
(1.00 to 1.40 mm)
Machine Name Finish Grinding M/c
Operation Seal Ring Grinding
Characteristic Seal Ring Height
Specification/ Tolerance 1.2 ± 0.2
Hour
Sample
X1 1.31 1.32 1.32 1.31 1.32 1.28 1.34 1.35
X2 1.32 1.35 1.34 1.33 1.27 1.30 1.32 1.32
X3 1.30 1.32 1.35 1.32 1.3 1.32 1.34 1.34
X4 1.29 1.31 1.34 1.32 1.28 1.29 1.31 1.30
X5 1.31 1.34 1.33 1.32 1.28 1.3 1.34 1.36
X 1.306 1.328 1.336 1.320 1.290 1.298 1.330 1.334
R 0.03 0.04 0.03 0.02 0.05 0.04 0.03 0.05
X 1.318 UCL X 1.337 LCL X 1.289
1.36
1.34
1.32
1.30
1.28
1.26
1.24
1.22
1.20
1 2 3 4 5 6 7 8
R 0.036 UCL ( R ) 0.0766 LCL ( R ) 0
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1 2 3 4 5 6 7 8
Sub Groups
X & R Control Chart
1 2 3 4 5 6 7 8
Average
Height
Range
Figure 6. Control Chart
2.7 Scatter Diagram
It is also called as scatter plot or X–Y
graph. A scatter diagram is a tool for analyzing
relationships between two variables. One variable is
plotted on the horizontal axis and called independent
variable; the other is plotted on the vertical axis and
called as dependent variable. The pattern of their
intersecting points can graphically show relationship
patterns. Scatter diagrams are used to evaluate cause
and effect relationships. The assumption is that the
independent variable is causing a change in the
dependent variable.
3. RESULTS AND DISCUSSION
Before implementation of SPC tools,
production process at EKK Eagle products India
Pvt. Ltd. was not in normal condition. After the
techniques were built into the process, the
management and operators had understood the cost
wastage due to rejection of work piece.
As seen from these studies, statistical process
control and statistical quality control were effective
in improving a process, irrespective of its
application, and are key success factors to stay in
business and the achievement of customer
satisfaction and business excellence.
1. A histogram is a picture of the data
distribution that includes its spread and shape.
This can provide clues about the variation that
exists in the work performed. Histogram for
Seal Ring height is normal and bell shaped. It
shows Cp is greater than 1.
2. Check sheets help to organize rejection data by
category.
3. Pareto analysis states that 80% of rejection is
due to a) Seal Ring pore
b) Latex coating damage
c) Mating Ring Chip
d) US leak
4. Cause and effect diagram identify and isolate
causes of problems.
5. Using control charts and by making changes in
processes, we can make processes stable.
4. CONCLUSION
It has been shown that with use of basic
quality tools, organization can monitor, control and
improve their processes. The seven basic quality
tools in general have demonstrated a great capacity
in the improvement of manufacturing and service
industries across the world. There are basically five
reasons behind this:
1. They are proven techniques for improving
productivity;
2. They are effective in minimization of defects;
3. They prevent unnecessary process adjustments;
4. They provide diagnostic information;
5. They provide information about process
capability to meet customer requirements.
REFERENCES
[1] Mohamed Aichouni, “On the use of the
basic quality tools for the improvement of
the construction industry: A case study of a
ready mixed concrete production process”,
International Journal of Civil &
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[5] Kamalesh Panthi, Syed M. Ahmed,
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