MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY FOR HIGH QUALITY TRAINING CAPSTONE PROJECT INDUSTRIAL MANAGEMENT AN EVALUATION OF T
Rationale
This study emphasizes the critical role of control charts in enhancing product quality and production efficiency within manufacturing plants In a competitive business landscape, it is essential for manufacturers to meet quality standards and optimize production processes to maintain their market position Neglecting these aspects can result in higher production costs, lower customer satisfaction, and a decline in market share.
Control charts are essential tools in statistical process control (SPC) that enable manufacturers to systematically monitor and manage production processes They help visualize process data over time, identify variability patterns, and facilitate informed decision-making to ensure process stability and enhance product quality By effectively utilizing control charts, manufacturers can detect and rectify process deviations, decrease defects, minimize rework, and optimize production for greater efficiency and productivity.
Many manufacturing plants encounter difficulties in implementing control charts, despite their advantages Key challenges include insufficient knowledge and training on effective usage, limited resources for investing in Statistical Process Control (SPC), and employee resistance to change Thus, it is crucial to explore successful applications of control charts in manufacturing and identify potential barriers to their implementation.
This study focuses on implementing control charts at Bosch Long Thanh to enhance production processes, minimize scrap rates, improve product quality, and increase customer confidence, ultimately leading to higher profitability By applying quality control methods and utilizing control charts, the study aims to effectively monitor process variability.
This study aims to highlight the advantages of using control charts to enhance product quality and production efficiency at Bosch Long Thanh It also offers recommendations for their effective implementation, thereby contributing to overall operational improvements.
2 the development and improvement of production processes in the manufacturing industry.
Objective
This Bachelor's thesis focuses on analyzing the current state of Statistical Process Control (SPC) management at Bosch Vietnam Co., Ltd and proposing effective solutions to enhance its implementation The research aims to identify key areas for improvement and develop strategies to optimize SPC practices within the company.
Survey the current state of SPC implementation at Bosch Vietnam Co., Ltd
Assess the current SPC situation at Bosch Vietnam Co., Ltd and the challenges the company is facing in implementing SPC
Propose solutions to improve the effectiveness of SPC practices at Bosch Vietnam Co., Ltd., reducing production errors and costs, thereby increasing production efficiency and the company's competitiveness
Evaluate the potential benefits and impacts of the proposed solutions on the company's production process and product quality
This article aims to enhance the understanding of the practical application of Statistical Process Control (SPC) within the manufacturing sector, with a specific focus on Bosch Vietnam Co., Ltd It offers valuable insights and recommendations that can be effectively implemented by other organizations in the industry.
This Bachelor's thesis seeks to deliver an in-depth analysis of Statistical Process Control (SPC) practices at Bosch Vietnam Co., Ltd., along with actionable recommendations aimed at enhancing the company's production processes and product quality.
Scope and object
Space scope: MSE1 Department of Bosch Vietnam Co., Ltd Long Thanh Industrial Park, Tam An Commune, Long Thanh District, Dong Nai Province, Vietnam
Time scope: The study was conducted over a period of 6 months, from December
- Object: The Element production in MSE1 department at the Bosch Vietnam Co.,
Research methodology
The author employed both qualitative and quantitative research methodologies in the study
Qualitative research techniques were employed to explore the challenges faced in implementing control charts within the MSE1 department By utilizing focus group interviews with operators, engineers, and production experts, valuable insights were gathered regarding existing issues and potential solutions This information provided a clearer understanding of the current operational status of the production line and offered suggestions for effectively integrating control charts into the facility.
Quantitative research methods were utilized to collect data samples necessary for implementing control charts in production The author conducted a statistical analysis of the collected data before suggesting any improvement recommendations.
Structure of the report
The expected structure of the thesis includes four main chapters and a conclusion section
Chapter 1: Introduction to Bosch Vietnam Co., Ltd
Chapter 3: Analyzes the current situation of Control chart at Bosch Long Thanh factory
Chapter 4: Apply Control chart for thickness requirement in MSE1 department
INTRODUCTION
Overview of Bosch Vietnam Co., Ltd
Founded in 1886 by Robert Bosch in Stuttgart, Germany, the Bosch Group has grown to become a global leader in technology and service solutions With 440 subsidiaries and partners in over 150 countries, Bosch operates in more than 60 regions worldwide As of the end of 2021, the company employed 402,600 people and generated a revenue of 78.7 billion euros.
In 1994, Bosch established its first representative office in Ho Chi Minh City, marking the beginning of its business operations in Vietnam Since 2007, the company has been represented by Robert Bosch (Vietnam) Co., Ltd., engaging in various sectors such as sales, manufacturing, research and development, and international services Bosch Vietnam's corporate office is located in Ho Chi Minh City, with additional offices in Hanoi and Danang, as well as a factory in Dong Nai province that produces power transmission belts for continuously variable transmissions (CVT) The company also operates two research and development facilities focused on automotive technology in Ho Chi Minh City In 2021, Bosch achieved sales exceeding 192 million euros in Vietnam.
Figure 1.1: Factual information and statistics about Bosch in Vietnam in 2021
Figure 1.2: Some of Bosch's brands
Figure 1.3: Some of Bosch’s customers
Bosch Long Thanh factory
Figure 1.4: Bosch Long Thanh factory
Source: Company website Name: Bosch Vietnam Co., Ltd
Figure 1.5: Logo of Bosch Vietnam Co., Ltd
Address: Street No 8, Long Thanh Industrial Park, Long Thanh District, Dong Nai Province
Company website: bosch.com.vn
Market: Global, especially China and Japan.
Formation and Development History
Important turning points in HcP's lengthy growth process include:
October 22, 2007: The factory construction project was started
August 1, 2008: At a rented factory in the high-tech industrial park, HcP installed the first power transmission belt assembly line
December 16, 2010: Began moving operations to the finished factory
January 3, 2011: The start of the first Element production line
July 3, 2012: HcP created its initial Loop set
October 2013: In Vietnam, the TGA technical training program was put into practice
June 2014: 10 million power transmission belts were made by HcP
March 2017: Announced that 20 million goods had been produced at HcP
2018: Marked the achievement of 25 million products manufactured at HcP and celebrated the company's tenth anniversary.
Field of operation and products
The Bosch Long Thanh factory in Dong Nai is the company's inaugural belt manufacturing facility in Vietnam, focusing on continuously variable transmission (CVT) belts Since its establishment in 2008, the plant has produced 1.6 million CVT belts in its initial production cycle and has distributed over 25 million belts by March 2018.
8 each in the Netherlands, Vietnam, and Mexico-produce CVT belts at the moment, with the Vietnam facility being the biggest and bringing in the most money
Figure 1.6: Products of the company
Bosch manufactures the continuously variable transmission (CVT) belt drive, which plays a vital role in enhancing vehicle performance This innovative component is essential for the smooth and fuel-efficient operation of continuously variable transmissions, ensuring a reliable connection between the engine and wheels within the powertrain system.
The CVT belt drive from Bosch is made up of numerous small steel components
To develop a durable and flexible belt, specially designed steel components were meticulously crafted and assembled These precision-engineered steel parts undergo surface treatment, ensuring outstanding durability and stability.
A CVT belt drive consists of two loopsets, each containing numerous longitudinal pieces intricately shaped and connected These components play a crucial role in facilitating vehicle movement by effectively transferring power from the engine to the wheels.
Bosch employs advanced technology and meticulous machining, along with rigorous quality control measures, to ensure the stability and precision of its CVT belts, thereby meeting the highest standards for quality and safety.
Organizational Structure
The Head of Commercial (HcP/PC) and Head of Technical (HcP/PT) at HcP are in charge of making decisions, creating objectives, and giving the company's overall direction
Source: Human resources department There are also divisions that the manufacturing leadership has direct control over:
HcP/CTG (Control Room): oversees the organization's finances and budget
HcP/ICO (Information Coordination and Organization Department): manage information security-related issues and facilitates software installation on the business's system
The HcP/LOG (Logistics Management Department) is responsible for managing inventory levels, monitoring the usage of raw materials for both input and output, and overseeing the quantity of items shipped to consumers with each order.
The Technical Training Center (HcP/TGA) offers technical training courses to develop young, vibrant, and inventive internal resources
HcP/HRL: accountable for hiring, training, compensation, and benefits
The HcP/FCM (Facilities Management Department) oversees the maintenance of the facilities and equipment to meet the needs of the workforce
HcP/HSE (Health, Safety and Environment Department) is in charge of industrial safety and offers safety instruction to workers before they enter the manufacturing lines
HcP/MSE3 (Production Department): in charge of putting together components and loopsets to make finished goods
PS/QMM (Quality and Methodology Department): maintains customer satisfaction through internal assessments and IATF certification and ensures product quality through training on techniques like SPC and FMEA
The PS CT/ETC (Engineering Technology Center) plays a crucial role in supporting Bosch Vietnam's current products and global transmission technology It collaborates in the development of Test Technology, focusing on creating and implementing testing strategies while evaluating their outcomes The center conducts various tests, including elements, product support belts, document control, risk assessment, technical adjustments, and 8D analysis on existing products.
HcP/MSE1 (Production Department), which is in charge of creating elements
HcP/MSE2 (Production Department): responsible for producing loopsets
HcP/TEF (Maintenance Department): oversees the upkeep of document systems and equipment, and handles issues when a breakdown affects the output of the production line
HcP/PRS (Security Department): oversees the factory's general security, offers protocols and registration paperwork when outside partners visit the facility to film or take pictures, and supports pertinent documentation
MSE1 Department
HcP/MSE1: responsible for producing elements
Source: MSE1 department of Bosch Vietnam Co., Ltd Includes:
MSE1.1 (Production Engineering): is responsible for process improvement, quality improvement, troubleshooting, documentation, performing inspections and product releases, and BPS activities in manufacturing Including:
MFE1.12: Hardening, deburring, mixing & end of line washing
MSE1.2 (Line Engineering): is responsible for line performance, production planning, leading quality problem-solving activities, line and product release, and improvement activities (BPS, bottleneck process, etc.) in manufacturing
MFO1 (Operation): is responsible for the daily production of elements, fulfilling element demand, maintaining production standards, directing manpower management, and implementing production improvements
MFL (Manufacturing Engineering) oversees the management of relocation projects, application projects, and support and sustaining (S&S) projects It plays a crucial role in implementing product or process changes via Engineering Change Requests (ECR) and is responsible for launching new model platforms on current series production equipment.
Figure 1.9: Organizational chart in MSE1
Overview of Fine Blanking process
Fine Blanking is a precise manufacturing technique designed to create high-quality parts with stringent tolerances This process involves feeding coil raw material into a Fine Blanking machine, which utilizes a series of tools to punch the material into the specified shape.
Source: European-business.com Fine Blanking machine specifications:
Control stroke for each product type
Steps in the Fine Blanking process
Figure 1.11: Fine blanking production process diagram
The De-coiler system feeds the coil material into the Fine Blanking machine, which utilizes a specialized tool to cut the material into precise shapes The Fine Blanking process is comprised of four distinct phases, ensuring high accuracy and precision in the final product.
The clamming, forming, cutting, and ejecting phases are crucial in the manufacturing process During the clamming phase, coil material is positioned and secured to prevent bending, deformation, and damage prior to forming and cutting The forming phase establishes the pin and hole dimensions of the element, resulting in variations in thickness In the cutting phase, the element's contour is created, and it is cut from the coil material The ejecting phase then pushes the element out of the tool and onto a magnetic arm Finally, the finished element is transported to a conveyor for storage, and sample elements are measured in the quality control room to ensure they meet standards before proceeding to the next steps.
LITERATURE REVIEW
Statistical Process Control
Statistical Process Control (SPC) is a method that monitors manufacturing data through statistical techniques, ensuring processes remain under control (Hoerl et al., 2012) This data can include attribute measurements, such as the dissolution time of tablets in a subgroup SPC is effective in managing and improving process performance by reducing errors from operators, measurement inaccuracies, and variability in raw materials (Rahman et al., 2015) It assesses both the predictability and stability of a manufacturing process in its initial phase and its capability in the subsequent phase, allowing for timely corrective actions when needed.
1) The first step is to determine whether the process is statistically controlled by plotting data in control charts and determining whether the process is statistically controlled using control chart rules The assignable cause must be eliminated as soon as is practically possible if there are any signs that one may exist (for instance, one point may be outside the control boundaries) (Wheeler et al., 1992) This is done to avoid producing unneeded out-of-spec products To this purpose, the root cause of uncontrolled processes should be identified and corrected using a combination of SPC techniques and other quality tools
2) The ability of a process to meet the need is determined by computing the capability indices of the process This is the second phase A strong method for assessing how well the research process can develop products that match design process standards is process capability analysis Using this tool, it is possible to estimate what percentage of the population of manufactured goods will fall outside the customer's agreed-upon process specification limits and result in a defect A comparison of process performance to its process specifications using different capabilities indices is what is referred to as a process capability Process specs are the established requirements for the product, which are typically described by the Lower Specification Limit (LSL) and the Upper Specification Limit (USL) (Figure 2-1)
Seven Quality Control Tools
A check sheet is a straightforward and efficient tool for data collection and analysis, commonly utilized to document information regarding events or processes over a specified timeframe.
Check sheets can be designed in various forms depending on the purpose of use and the type of data collected
Research indicates that check sheets are highly effective for data collection and analysis, particularly in quality and production management According to Montgomery et al (2008), check sheets are a widely used method in quality control, enhancing the efficiency and ease of data collection compared to alternative methods.
Pham et al (2019) demonstrated that check sheets are an effective and user-friendly tool for data collection and analysis in evaluating production processes and suggesting improvements.
In summary, check sheet is a simple and effective tool for data collection and analysis Studies have shown that this tool is very useful in quality control and production management
A histogram is a widely used statistical tool for visualizing the frequency of continuous variables, with stacked bars representing the frequency of values across various ranges This allows users to easily analyze and interpret data effectively.
M Nowakowski et al conducted a study in 2013 that used histograms to present the frequency of genes found in gene data samples By doing so, they were able to better comprehend the mechanisms of gene combinations and correlations with diseases
Histograms serve as a powerful tool for data analysis, offering users significant advantages They enhance the understanding of data frequency and distribution, which can facilitate informed decision-making and effective planning for data-related issues.
A Pareto chart is a visual tool that merges bar and line charts to display data frequency in descending order of significance This chart helps identify and prioritize the most critical factors among numerous issues or causes, making it an essential resource for quality control and problem-solving across various industries.
In a 2019 study by M Bortolotti et al., the use of Pareto charts significantly enhanced the quality of healthcare services The research revealed that these charts helped identify the primary sources of patient complaints, allowing the team to prioritize their efforts effectively Consequently, this approach led to improved patient satisfaction and overall healthcare quality.
This study emphasizes the value of the Pareto chart as a problem-solving tool in the healthcare sector By directing attention to the most critical issues, it helps users tackle root causes and attain measurable enhancements.
In a 2021 study by M Shamsuzzoha et al., the authors utilized a Pareto chart to enhance production quality in a manufacturing company The findings revealed that the Pareto chart effectively identified the primary sources of defects in the production process.
The company implemented a streamlined process that allowed it to focus on enhancing efficiency, resulting in fewer defects and a significant boost in product quality.
In 2019, Raman et al highlighted that Pareto Analysis is a statistical method for identifying the root causes of issues within organizations, grounded in the 80/20 principle, which posits that 80% of results stem from 20% of inputs By employing this analysis, organizations can focus on a select few factors that are most likely to contribute to production challenges, allowing them to prioritize their improvement initiatives effectively.
Pareto charts are powerful tools for problem-solving and quality enhancement across diverse industries They enable organizations to pinpoint the most critical factors and prioritize their efforts, leading to significant improvements in processes and products.
Cause and effect diagrams, also known as fishbone diagrams or Ishikawa diagrams, are a visual tool used to identify and analyze the possible causes of a problem
The diagram features a central spine symbolizing the problem, with branches that illustrate potential causes This tool is commonly utilized across diverse sectors, including manufacturing, healthcare, and service industries.
In a 2017 study by L Dora et al., cause and effect diagrams were utilized to pinpoint the root causes of mining accidents This approach enabled the team to identify critical factors contributing to these incidents and formulate effective prevention strategies Consequently, the study reported a significant reduction in accidents within the mining industry.
ANALYZING THE CURRENT SITUATION OF CONTROL CHART
Introduction
This chapter offers a detailed examination of control chart implementation at Bosch Vietnam, structured into three key sections It begins with an overview of the control chart system utilized by the company, highlighting the historical context, advantages, and fundamental principles of control charts Additionally, the chapter discusses the current status of control chart implementation within the organization, outlining both the benefits and challenges associated with the system.
The author outlines the implementation of control charts in the MSE1 department's manufacturing process, detailing the materials and equipment utilized This section emphasizes the control chart system designed to monitor critical requirements, including hardness, edge straightness, and surface glossiness.
The author highlights the unmet requirements for control chart application and the underlying reasons for this gap This section is crucial as it reveals the areas where the implementation of control charts remains insufficient, despite their established advantages We will investigate these reasons and consider potential solutions to enhance the current situation.
Process flow in MSE1 department
Figure 3.1: Process flow to produce the element
Source: MSE1 department of Bosch Vietnam Co., Ltd
The fabrication of a plant's components involves several key stages Initially, metal coils are decoiled and sliced into individual parts for the Fine Blanking process, followed by inspection and measurement The components then undergo hardening, which includes heating in an oven, quenching in oil, and tempering at a low temperature Next, deburring and body grinding procedures smooth the edges and remove burrs After another round of examination and measurement using a stone, detergent, and water, the components are prepared for the final Mixing and Washing operations This last step combines element lots with similar properties in a machine, followed by thorough cleaning and washing before the components are ready for use.
Overview of Control Chart System in Bosch Vietnam
The Control Chart is a widely used tool in the manufacturing industry for monitoring, controlling, and improving the quality of production processes At Bosch
Vietnam, Control Charts are employed to monitor and control production processes across various departments, including the MSE1 department responsible for producing pushbelt elements
Bosch Vietnam employs a Control Chart system that gathers and analyzes data from sensors, gauges, and measuring devices By applying statistical methods, the system identifies trends and patterns to detect and prevent product defects Real-time data collection and analysis utilize various statistical tools, including Control Charts, frequency histograms, and process capability analysis This system is seamlessly integrated into the company's quality management framework, facilitating continuous improvement in production processes.
The implementation of Control Charts in the MSE1 department for pushbelt element manufacturing involves identifying and monitoring process parameters to detect variations These charts establish upper and lower control limits along with the process mean, allowing for the identification of unacceptable variations When the process is deemed out of control, corrective actions are implemented to restore it within acceptable limits Additionally, the Control Chart system incorporates process capability analysis to assess overall production performance and pinpoint areas for improvement.
The implementation of Control Charts at Bosch Vietnam significantly enhances product quality, boosts productivity, and lowers production costs By enabling early detection of process variations and defects, Control Charts facilitate defect prevention, minimizing the need for rework and scrap, which ultimately leads to higher customer satisfaction Additionally, they help reduce process variability, further increasing productivity and decreasing overall production expenses.
Control Chart Implementation for Element Manufacturing in MSE1
The MSE1 department at Bosch Vietnam plays a vital role in the production of element manufacturing, essential for pushbelt production To ensure quality and efficiency, the department has adopted Control Charts to effectively monitor and manage several critical requirements.
29 the element manufacturing process, including the hardness, edge straightness, surface glossiness, and thickness
In element manufacturing, controlling hardness is crucial as it directly impacts the strength and durability of the elements, which in turn affects the reliability of the pushbelt To maintain hardness within acceptable limits, the MSE1 department has adopted a statistical process control (SPC) system.
During the hardening process, the operator randomly selects five elements from two trays for sampling after each charge Hardness testing is performed using a hardness tester that measures the resistance of the elements to indentation, with results reported in HRC (Hardness Rockwell C).
Source: MTMS Engineers Pvt Ltd
The hardness values are plotted on an X-bar chart, a control chart that monitors the process's central tendency This chart displays the average measured hardness (X-bar) along with the upper and lower specification limits (USL and LSL), which are derived from the specified limits.
Figure 3.3: Control chart of Surface hardness
This control chart is used to monitor the surface hardness requirement variation over 30 days in January 2023 The values are collected daily and presented on the chart
The control chart illustrates that the data values remain within the established upper and lower control limits, signifying a stable production process with minimal variation This stability indicates that the production can be effectively monitored using a control chart.
The MSE1 department performs a capability analysis to verify that the hardness process meets specification limits This analysis includes calculating process capability indices, such as Cpk, which assess the process's ability to produce elements within these limits while accounting for natural variability A higher Cpk value signifies a greater capability of the process to consistently produce compliant elements.
Figure 3.4: Process capability of Surface hardness
The CpK chart assesses the production process's capability to fulfill product quality standards, displaying the daily calculated CpK index over a 30-day period.
The CpK chart illustrates that the production process consistently meets product quality requirements and remains stable without significant variations This indicates that quality standards are being upheld, and the process can be effectively monitored through this chart.
Implementing Control Charts for hardness control in the MSE1 department enables the early identification and correction of process issues, preventing the production of non-conforming elements This proactive approach ensures that the pushbelts manufactured meet high-quality standards and fulfill customer requirements.
The MSE1 department utilizes a precision measuring device to accurately assess the saddle height during the element manufacturing process, ensuring that it meets the required specifications for consistent and reliable results.
In the Deburring process, measurement data is collected and plotted on an X-bar chart to monitor the process over time Control limits are established based on specification limits, and any data point falling outside these limits is deemed out of control Such out-of-control points necessitate an investigation to determine the root cause and implement corrective actions to prevent future occurrences.
Figure 3.5: Control chart of Saddle height
This control chart is used to monitor the saddle height requirement variation over
30 days in January 2023 The values are collected daily and presented on the chart
The chart reveals that the data values remain within the established upper and lower control limits, signifying a stable production process with no significant variation This control chart effectively demonstrates the stability of the production process and its suitability for ongoing monitoring.
The department conducts a capability analysis alongside monitoring with control charts to verify that the process meets the saddle height requirements This analysis includes calculating process capability indices, such as Cpk, which assess the process's ability to produce compliant products.
33 within the specification limits By conducting this analysis, the department can identify areas where the process needs improvement and take actions to improve its capability
Figure 3.6: Process capability of Saddle height
The CpK chart assesses the production process's capability to fulfill product quality standards, displaying the daily calculated CpK index over a 30-day period.
The CpK chart illustrates that the production process consistently meets quality requirements and remains stable without significant variation This indicates that the production process adheres to quality standards and can be effectively monitored through this chart.
Requirements for which Control Chart has not been applied
While the MSE1 department has successfully implemented Control Charts for certain critical requirements, there remain additional requirements where this tool has not been utilized These unaddressed requirements are essential for ensuring product performance and reliability, as their variability could lead to defects or failures.
Bosch prioritizes product quality in its development process to fulfill customer needs and establish trust While Control Charts are essential for monitoring and refining production, their application to certain requirements is not yet adopted at Bosch Vietnam This is primarily due to the minimal errors associated with these requirements and additional challenges faced in implementation.
Investing in equipment and software for Control Charts is essential for quality measurement and data collection in the production process However, these tools can be costly and require regular maintenance to ensure accurate data Consequently, implementing Control Charts can strain the company's budget, necessitating effective resource management and allocation.
Bosch Vietnam encounters challenges not only related to costs but also in the application of Control Charts to meet requirements To successfully implement these charts, the company must develop a sophisticated quality measurement system that guarantees the accuracy of the data collected This design process necessitates both technical and managerial expertise, often requiring collaboration across various departments within the organization.
Implementing Control Charts necessitates modifications in the production process and interdepartmental collaboration The company must establish new regulations to maintain production continuity and ensure synchronization among departments These changes may pose challenges for employees, highlighting the need for additional support and training.
Evaluating the frequency of errors for requirements that have not yet been
In the first quarter of 2023, scrap amounts and associated costs exhibited a slight upward trend while remaining within acceptable limits Consequently, the author opted to gather data on errors linked to requirements not yet monitored by Control Charts to assess their error frequency This analysis focused on identifying error modes and determining which requirements incurred the highest error rates, leading to increased costs The collected data will inform the development of control strategies for these requirements, particularly through the implementation of Control Charts.
This approach would enable the MSE1 department to gain control over the modes of error, reduce the number of errors, and minimize the amount of scrap and related costs
Ultimately, this would improve the efficiency and profitability of the manufacturing process
3.6.1 Determining the primary sources of error
The author analyzed error modes in the MSE1 department over a one-year period from April 2022 to April 2023, utilizing the error checking system of the QMM3 department By employing a Pareto chart, the study identified the most frequently occurring error modes across all processes.
Figure 3.10: Pareto chart for Failure mode at the MSE1 department over a period of 1 year from April 2022 to April 2023
According to the 80-20 principle, four primary types of errors are identified in manufacturing, with thickness inaccuracies being the most common, followed by discoloration, corrosion, and stains, collectively representing 80% of all errors To effectively tackle these issues, I focus on thickness inaccuracies by implementing the Control Chart methodology, a proven process control technique that enhances error management.
39 monitoring and control of manufacturing processes to reduce variations and maintain product quality within specifications
Moreover, Bosch has already successfully implemented Control Charts for some requirements, which further supports the feasibility and effectiveness of this approach
Utilizing Control Charts to tackle thickness inaccuracies can significantly minimize waste, enhance product quality, and decrease costs related to error correction This focused and methodical strategy promises a sustainable, long-term solution to boost the efficiency and profitability of the manufacturing process.
APPLYING CONTROL CHART TO THICKNESS REQUIREMENT IN
Project Initiation and Planning
Project Manager: Nguyen Thi Tu Vy
As the project manager, I will ensure the project remains on track and within budget by overseeing all aspects, including resource management, timelines, and team communication I will serve as the primary contact for the project, providing progress updates and addressing any issues that may arise.
Process Engineer: Thai Moc Nguyen
Nguyen will focus on comprehending the production process and pinpointing critical parameters to enhance efficiency Collaborating with the production supervisor and maintenance technician, he will identify opportunities for improvement Additionally, Nguyen will establish and execute Statistical Process Control (SPC) procedures to monitor and regulate the thickness of mechanical components.
Quality Engineer: Nguyen Hoang Ha Oanh
Oanh will concentrate on statistical data analysis, the selection and implementation of control charts, and the monitoring of process capability (Cpk) Collaborating closely with the process engineer and production supervisor, she will identify potential quality issues and suggest corrective actions Additionally, Oanh will analyze data gathered through Statistical Process Control (SPC) procedures and provide recommendations for process enhancements.
Chinh possesses practical experience in the production process, offering valuable insights into its intricacies He is committed to ensuring consistent and accurate data collection while assisting in the implementation of process changes on the production floor Additionally, Chinh will collaborate with Nguyen to establish and execute Statistical Process Control (SPC) procedures.
Maintenance Technician: Vu Duc Bao
Bao is tasked with maintaining equipment and ensuring it operates within specified parameters, while also helping to identify and resolve equipment-related issues that could affect process control and part thickness He will collaborate closely with Nguyen and Chinh to ensure the production equipment is properly calibrated and maintained in support of SPC procedures.
Management Representative: Nguyen Minh Tri
Tri, as a member of the management team, will provide essential resources, support, and decision-making authority for the project He will ensure that the project goals align with the organization's strategic objectives and will communicate the project's progress to the senior leadership team.
This project aims to enhance process capability and ensure consistent product quality by implementing Control Charts for monitoring and controlling the thickness of mechanical parts The project team will work together to establish baseline process capability, identify areas for improvement, and develop procedures for Control Charts to effectively monitor and manage part thickness.
This project encompasses the complete production process, starting from raw material selection to the final inspection of mechanical parts The project team will collaborate with all stakeholders to effectively integrate Control Charts into the existing process Additionally, a communication plan will be established to keep stakeholders updated on the project's progress and the influence of Control Charts on production efficiency.
The project is scheduled to commence on February 23, 2023, and conclude on April 25, 2023 The team is committed to meeting all project milestones within this timeline to successfully achieve the project's objectives The project manager will oversee the progress and ensure the team remains focused on meeting these goals.
Process Assessment and Data Collection
Thickness requirement is defined as the difference in thickness between the ear and the body of the element
4.2.2 Select critical-to-quality (CTQ) product characteristics
Identifying critical-to-quality (CTQ) characteristics is essential in manufacturing, as these factors significantly influence the overall product quality Monitoring and controlling these CTQs is vital to ensure that the final product adheres to the desired specifications.
The Pareto chart analysis identified that element thickness is the primary factor affecting product quality Deviations from the optimal thickness range can lead to defects in the final product.
To meet this requirement, the Control Chart method will be employed Control charts are essential statistical tools used for monitoring and controlling processes They involve data collection and analysis to assess process stability and ensure it remains within established control limits.
Utilizing Control Charts allows us to set control limits for element thickness and effectively monitor the process to maintain it within the desired range Should the process deviate from control, timely corrective actions can be implemented to realign it.
Overall, the use of Control Charts to monitor and control the thickness of the element will help ensure that the final product meets the customer's requirements and quality expectations
The Fine Blanking process precisely controls the thickness of metal elements by cutting sheets into specific shapes Deviations in this process can lead to thickness variations, potentially causing quality issues.
The Fine Blanking process is essential for maintaining the quality of the element, necessitating careful monitoring and control through Control Charts This approach minimizes variations and ensures the element's thickness remains within the specified range, ultimately meeting the customer's Critical to Quality (CTQ) requirements and ensuring optimal performance.
4.2.4 Select equipment for sample measurement
The sample measurement process utilizes advanced PMA machines, which are capable of accurately measuring various characteristics of metal components with a remarkable resolution of up to 0.1 àm.
24/7 with three shifts per day to ensure that measurements are carried out efficiently and in a timely manner
To create an effective data collection plan for implementing Control Charts, it is essential to monitor and control the thickness of mechanical parts while complying with Bosch's internal requirements and the 5M methodology, which includes Man, Machine, Material, Method, and Measurement.
Operators and quality control personnel undergo systematic training to ensure they can effectively execute data collection procedures and produce accurate measurements These training programs are essential for equipping staff with the knowledge and skills needed to perform their roles, which is vital for maintaining the accuracy and reliability of the data collected.
Proper maintenance and calibration of production equipment are crucial to prevent issues like inaccurate measurements and malfunctions that could compromise data integrity Regular checks and calibrations are necessary to ensure optimal equipment performance.
Ensuring the quality and consistency of raw materials is essential in the production process A rigorous quality control process is implemented to verify that all materials meet the required standards before use, guaranteeing consistent characteristics and overall product quality.
Standardizing production processes and data collection methods reduces variability from differing techniques, ensuring accurate and reliable data This consistency across all batches enhances the overall quality of the production process.
The author employs calibrated and precise measuring devices to gather thickness data, ensuring reliability and accurate detection of any changes To reduce measurement variability, a consistent measuring method and environment are maintained throughout the data collection process.
In the Fine Blanking process, around 120 elements are produced and stored in a box after each machine run To verify that the thickness of these elements meets the required specifications, a random sampling method is utilized An operator randomly selects three elements from the box for thickness measurement, ensuring that the data collected is representative of the entire production process and minimizing bias in the sampling.
To achieve randomness and eliminate bias, a random number generator is employed to select specific measurement areas This technique minimizes variability and enhances the accuracy and reliability of the data collected By implementing a standardized sampling method, the Fine Blanking process is optimized for better results.
46 monitored and controlled effectively to meet the desired specifications and maintain quality standards
Sample Size: Collect a total of 120 samples, with each sample consisting of a single part's thickness measurement
Data Analysis and Monitoring
4.3.1 Calculate the process average (mean) and standard deviation (sigma) using the collected data
Figure 4.3: Probability plot of Thickness
Cpk = min [(USL - mean) / (3 * sigma), (mean - LSL) / (3 * sigma)]
Figure 4.4: Process capability report for Thickness
Comment: The Cpk = 1.43, the process is stable and capable
Apply the SPC
The evaluation results demonstrate that the process is stable and operates effectively, allowing for the application of Control Charts to monitor and control production The subsequent step involves selecting the appropriate chart, which is contingent upon the data type and the process's objectives.
Figure 4.5: Steps when choosing control chart type
Determine the Type of Data
In the project, author examining the thickness measurement of an element in the production process Therefore, the data is continuous data
The author collected thickness values of the element measured in micrometers (µm) and identified that these values exist within a continuous range, confirming that this is continuous data.
Before collecting samples, the author evaluated the necessary sample size to guarantee the accuracy and reliability of the results This required sample size is determined by the desired accuracy level, data variability, and confidence level.
In this project, the author randomly selected three elements from a box to measure thickness, as the focus is on evaluating the homogeneity of the production process rather than measuring production efficiency A sample size of three is deemed adequate to achieve the necessary level of accuracy.
The Xbar-R control chart is ideal for variable data when the sample size is small, typically ranging from 2 to 10 In this project, a sample size of 3 will be utilized, making the Xbar-R control chart an appropriate choice for ensuring reliable results This chart calculates the upper and lower control limits based on the variance of the sample.
The sample will be taken at the beginning and the end of the shift
The first result of collection as below:
Figure 4.6: Xbar-R Chart of Thickness
The control chart shows that product thickness is well managed, with no significant deviations or out-of-control points, indicating effective quality control This success is attributed to the long-term use of Control Charts in the Fine Blanking process for other Critical to Quality (CTQ) parameters Control Charts serve as a proactive quality control tool, enabling the detection and prevention of potential issues before they arise Consequently, applying Control Charts to additional CTQ parameters can further enhance thickness control.
However, it is important to continue closely monitoring the thickness parameter to ensure that it remains within control limits in the long run This is particularly crucial in a
In a high-volume production environment, minor deviations from target values can lead to substantial waste and customer dissatisfaction Utilizing data from control charts allows for the identification of trends and patterns in the process, enabling necessary adjustments to maintain thickness within acceptable limits Furthermore, control charts assist in pinpointing underlying sources of variation that may impact product quality, facilitating timely corrective actions.
In summary, the favorable outcomes observed in the control chart for the thickness parameter can be linked to the effective use of Control Charts for other Critical to Quality (CTQ) parameters in the Fine Blanking process Ongoing monitoring and data collection are crucial to maintain the thickness within control limits and acceptable ranges.
This research investigates the application of Control Charts for thickness requirements at Bosch Long Thanh By utilizing observations, interviews, and statistical analysis, the study highlights challenges in implementing Control Charts and offers targeted solutions to enhance the factory's production process.
The implementation of Control Charts for monitoring the thickness of mechanical parts has proven effective in maintaining process stability, enhancing process capability, and ensuring consistent product quality A structured approach, involving cross-functional team collaboration and adherence to project phases, led to the successful achievement of project objectives while highlighting areas for future improvement.
The author suggests enhancing the production process by utilizing Control Charts to meet thickness requirements Key solutions include training employees, implementing quality control methods, and employing control charts to effectively monitor process variability.
Participation of a cross-functional team: By involving specialists from different fields, the project benefited from diverse perspectives and insights, leading to a more comprehensive and successful implementation of Control Charts
Methodology: The project followed a phased approach, clearly defining objectives, roles, and timelines, ensuring efficient project management and execution
The project implemented a comprehensive data collection strategy that emphasized a robust methodology, incorporating random sampling, appropriate sample size, and sampling frequency By adhering to the 5M approach, the project ensured the acquisition of representative and accurate data, which is essential for the effective adoption of Control Charts.
The project emphasized the importance of education and skill development by prioritizing the training of team members and production staff in the use of Control Chart tools and processes This initiative significantly enhanced their awareness, commitment, and knowledge of the system.
The project focused on fostering a mindset of continuous improvement through process analysis and monitoring, which facilitated the quick identification and resolution of any out-of-control situations or variations This approach significantly contributed to the long-term stability and capabilities of the processes involved.
Potential for resistance to change: Some team members may have been hesitant to adopt Control Charts, requiring additional efforts in communication and training to overcome reluctance
The project encountered challenges stemming from resource limitations, including constraints in time, manpower, and equipment, which may have adversely affected both the efficiency of the project and the effectiveness of the Control Chart system.