Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0

32 Advances in Production Engineering & Management Volume 18 | Number 1 | March 2023 | pp 32–48 https://doi.org/10.14743/apem2023.1.455 ISSN 1854-6250 Journal home: apem-journal.org Research paper Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 Ly Duc, M.a,b,*, Hlavaty, L.b, Bilik, P.b, Martinek, R.b aFaculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam bDepartment of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czech Republic A B S T R A C T A R T I C L E I N F O Improving quality, enhancing productivity, redesigning machining tools, eliminating waste in production, and shortening lead time are all objectives aimed at improving customer satisfaction and increasing profitability for manufacturing companies. This study combines lean manufacturing and six sigma techniques to form a technique called Lean Six Sigma (LSS) by using the DMAIC (Define -Measure-Analysis-Improve-Control) model. This study proposes to use statistical test models to analyze real data collected directly from the operator. The study proposes to use the Taguchi optimization technique to determine the optimal conditions for oil dipping tanks of molybdenum materials. In addition, the study also proposes a computer vision technique to recognize objects using color recognition techniques running on the LABVIEW software platform. This study builds a digital numerical control (DNC) model operating on digital signal processing techniques, linking the data of each process together. The results reduced the rate of defective parts in the whole processing stage from 6.5 % to zero defects, the whole processing line production capacity increased by 7.9 %, and the profit of the whole production line was USD 35762 per year. As a valuable external outcome, the conclusion of the LSS pr oject fostered a spirit of continuous improvement. The utilization of research results from the research environment in the actual production setting is significantly enhanced for the operator. The LSS model is deployed with specific tasks and targets for each member of the LSS project team, and the processing conditions for each specific stage are optimized, such as the oil dipping process and hole grinding process. Industry 4.0 techniques, including computer vision, digital numerical control, and commercial software such as LabVIEW and MINITAB, are optimized for use, simplifying machining operations. Some proposed directions for future research are also presented in detail. For example, studying the improvement of the quality of the 220 V power supply through harmonic mitigation in processing factories is an intriguing area of investigation. Additionally, exploring data security for big data in the context of Industry 4.0 would be a valuable study to enhance customer satisfaction with big data technology in the future. Keywords: Lean Six Sigma; Industry 4.0; Manufacturing; Smart manufacturing; Zero defect manufacturing; DMAIC (Define-Measure-AnalysisImprove -Control); Computer vision * Corresponding author: [email protected] ( Ly Duc, M.) Article history: Received 17 November 2022 Revised 4 April 2023 Accepted 6 April 2023 Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 1. Introduction Industry 4.0 techniques develop strongly in the product factory environment [1]. Specifically, applying computer vision techniques is for object recognition and object classification, artificial intelligence (AI) techniques to create robots that work in place of humans and create high productivity. Using automate manufacturing operations to eliminate waste, shorten lead times, increase productivity, and move towards zero defect production [2-3]. The Six Sigma technique Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 Advances in Production Engineering & Management 18(1) 2023 33 has been implemented and brought to great effect at Motorola since 1980 and is embraced by many other companies as a model of continuous improvement in the manufacturing company. Lean manufacturing tools and models are logically integrated into each phase of Six Sigma implementing a continuous improvement model that delivers efficiency, and that hybrid model is called Lean Six Sigma (LSS) [4-5]. General Electronic Company has implemented the LSS model into its continuous improvement activities since 1998 and has brought high efficiency. Specifically, the company's revenue is more than 1.2 billion USD, which shows to the world the high efficiency in business operations when applying the LSS model to continuous improvement activities. 1.1 Research question The LSS model is implemented by manufacturing companies in continuous improvement activities to improve quality and productivity, enhance customer satisfaction. Holtskog [6] and Sony et al. [7] also highlight the limitations of the LSS model when deployed by manufacturing companies. However, many manufacturing companies implement the LSS model into continuous improvement activities with high efficiency while several companies also implement the LSS model but the results are not as required. The LSS is a project, so it is necessary to plan work specifically, clearly divide work for each member of the project, and describe in detail, specific goals. Completion time for each task is absolutely necessary [8-9]. This study details the process of implementing the DMAIC model of the LSS technique into the product production line at a mechanical company. The specific goals of this research are (1) Optimize production conditions at the oil dipping process, prevent defects and prevent delays for customers. (2) Automatic selection of production tools by Industry 4.0 or computer vision technology at hole grinding process, preventing mistaken selection of production tools caused by careless employees and shortening production time, simply optimize the work performance for operators, improving operator satisfaction on continuous improvement activities. (3) Digital signal technology is deployed to collect data of each individual line by barcode system, call machining programs for automatic machining machines by barcode system and measurement system to collect stored data into the SQL system to form big data in Industry 4.0 [10]. In production activities, there are several questions such as: (1) Determine the specific production conditions for semi-automatic and automatic processing machines such as Oil dipping tanks, and hole grinding machines; (2) Product quality is guaranteed throughout the entire processing stage, which method is appropriate and not too complicated in the operation of employees; (3) The factory is illuminated by LED lights, and the factory is operated by smartphones and computer systems. All of them are sources of harmonics in the power supply to the furnace, the circuitry that controls the semi-automatic and automatic machines, ensuring that harmonic components according to IEEE 519:2022 are essential. The main operating principle of the LSS model is based on manufacturing fundamentals. Lean manufacturing and six sigma techniques are also based on manufacturing fundamentals. The implementation LSS project model is effective in action process planning, operational process analysis, and action process assessment. In addition, the LSS project investigates, analysis, and identifies problems at specific production lines and proposes improvement plans. Werkema [11] detailed evaluation of the results of implementing the LSS model into continuous improvement activities at manufacturing companies and brought high results, enhancing the company's competitiveness. Lean manufacturing technique has not yet clearly formed the detailed structure of each solution corresponding to each specific problem [12]. The Six Sigma technique is highly effective in minimizing process variation but has not really cared about the time and speed completion of production process [13]. These two techniques are deployed in combination to overcome each other's weaknesses and improve efficiency [14]. 1.2 Contribution of research This article details the steps of implementing an LSS model that combines lean manufacturing and six sigma techniques in continuous improvement operations at a manufacturing company [15]. The research results show that the defective rate decreased from 6.5 % to zero defect, the Ly Duc, Hlavaty, Bilik, Martinek 34 Advances in Production Engineering & Management 18(1) 2023 production capacity of the whole production line increased by 7.9 % and the profit increased by 35762 USD per year. This study contributes to improve the working morale of machine operators, the spirit of applying techniques from research to the practical environment, and remove the thinking separating the research environment and the production practices. In this study, it is considered as a model for managers performing each step in the analysis to find out the origin problem. The DMAIC model is used as a guide for members of the continuous improvement project in a variety of companies [16]. The data in this study were collected directly at the production line by the operator at the mechanical product manufacturing company [17-18]. This study proposes the use of statistical tools such as descriptive statistics, statistical hypothesis testing in data analysis, and determination of the current status of the problem on the table of specific analysis results. The Taguchi optimization technique is implemented to specifically determine the optimal condition for the machining condition. Computer vision technology identify and classify objects by color is deployed to redesign production operating conditions at specific production stages. Digital signal processing techniques based on barcodes and QR Codes are developed for real-time data collection at each separate manufacturing process and create a close connection in terms of data between the stages [19]. This study is a big challenge for researchers because its data completely collected from reality at each specific production line. Analytical tools are deployed to analyze data such as PARETO charts, descriptive statistics tools, statistical hypothesis testing tools, fishbone charts, value stream mapping, failure tree analysis, techniques, Taguchi optimization techniques, computer vision techniques, and digital numerical control techniques. This paper details the implementation of the LSS model using DMAIC in continuous improvement and also serves as a reference model for LSS researchers, managers, and project members implemented as a model. The DMAIC model is applicable to many different environments and organizations and serves as a model. The next structure of the research paper is organized as follows: Section 2 details the content of the literature review. Section 3 presents the research method in detail. Section 4 details the results of the experimental study and discusses the results from the experiment, and Section 5 presents the conclusion of this study. 2. Literature review The Lean Six Sigma model combines Lean manufacturing and Six Sigma techniques [20-21]. The literature review for both Lean manufacturing and Six Sigma techniques is performed first. Next is the evaluation of the data for statistical techniques applied to actual data analysis techniques and numerical processing techniques in data collection and analysis. 2.1 Lean manufacturing Key tools used in Lean manufacturing are Value Flow Mapping, Fishbone Diagram, Kanban System, Pareto Diagram, and Failure Tree Analysis [22]. These tools mainly perform analysis to confirm the wastes incurred in production activities and suggest improvement actions to eliminate wastes with the aim of increasing productivity and set goals to improve production methods [23]. Value Stream Mapping (VSM) details valuable and non-valued activities across each activity in the supply chain. The VSM tool identifies valueless activities (waste activities) to help managers easily decide on improvement proposals. The 5 Whys tool and fishbone diagram does the enumeration problem point causes and root cause selection. The system charting tool builds countermeasures based on weighting indicators [24-25]. In addition, Fault Tree Analysis combined with the 5 Whys tool adds extra power to root cause analysis. However, Fault Tree Analysis focuses on detailing the problem points according to the 5 whys method without focusing on finding the root cause and 5 whys analysis only focuses on the starting content of the problem point. A combination of failure tree analysis and 5 whys analysis makes a powerful analyze model and Kanban System is a fundamental tool in Lean manufacturing to control operations. Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 Advances in Production Engineering & Management 18(1) 2023 35 2.2 Six Sigma Fluctuations in the production process stem from machining conditions, human factors, unstable machine accuracy, and power sources that generate many harmonic components that exceed IEEE 519:2022 standards. Six Sigma technique helps to eliminate fluctuations in the production process. Improve product quality, and customer satisfaction. Motorola Company pioneered the implementation of Six Sigma technology in the phones production line and achieved great results in controlling volatility and reducing defect rates below 1 Sigma. Six Sigma techniques use DMAIC (Define-Measure-Analysis-Improve-Control) model implemented as a model for performing problem analysis and measurement [26-27], using the actual database at the machining line. Define phase (D) presents an overview of the environment around the problem to be analyzed and analyzed to determine the content of the problem point using statistical charting tools such as Pareto chart, and bar chart. Measure phase (M) deploys statistical testing tools, and statistical hypothesis testing to measure and analyze real data at the production stage and find the root problem point. Analysis phase (A) deploys the Taguchi optimization technique to determine optimal production conditions for production conditions. Conduct experiments and collect experimental data by operators. Improve phase (I) deploy Industry 4.0 tools such as computer vision to identify and classify objects by color running LabVIEW software and digital numerical control techniques in digital signal processing by barcode or QR Code, data collected into the big data system in Industry 4.0 systems [28-30]. Control phase (C) deploys an online measurement system using digital numerical control (DNC) technology to control the system automatically and completely eliminate workers’ skill. A comprehensive literature review of the Six Sigma technique is needed. 2.3 Lean Six Sigma (LSS) The main goal of the Lean Six Sigma model is to eliminate the fluctuations of the production process and improve product quality by manufacturing according to the zero defects model to improve product quality, process quality, people quality, production capacity, and customer satisfaction [31-32]. Ensuring quality meets customer requirements and delivering products on time is a challenge. Pande et al. [33] pointed out level problem of responsiveness between providing services to customers and meeting customer requirements is a big challenge. Rathi et al. [34] specifically outlined 17 obstacles when implementing LSS projects for enterprises and the main content is data collection. Data is the backbone of the LSS project. The ability monitoring the LSS project to ensure that the projects are completed on schedule and project manager's target met is a challenge. Implementing LSS projects knowledge of team members is also an issue that needs attention. The main tools used in the DMAIC phase such as Process Flowchart, Data Collection Checklist, Fishbone Diagram, Histogram, 5 Whys Analysis, Statistical Hypothesis Test, Experimental Design, Taguchi method, and Statistical analysis are comparing results before and after improvement [35-36]. If the LSS project is not performed well, it can lead to loss of investment, time, and resources, customer trust and the image of the organization implementing the LSS project. Lean Six Sigma technique has been successfully applied in many companies. However, there are still a few companies that still do not give good results when applying Lean Six Sigma. Because of the inconsistent use of statistical techniques, Lean manufacturing techniques. Shokri [37] has implemented LSS technology in a mechanical manufacturing company with the goal of eliminating scrap to zero and achieving a profit of 98 USD per year. Guleria et al. [38] proposed to apply the LSS project to the gear component manufacturing process, the tools applied such as SIPOC (Supplier-Input-Process-Output-Customer) diagram, Pareto chart, Fishbone chart, reject results from 10641 to 3193 (PPM). Minh and Thu [39] proposed to apply the LSS project to the manufacturing process of mechanical components, apply tools such as the Pareto diagram, fishbone diagram, scatter plot, histogram, and lifting work of production capacity from 118 products in 8 hours to 155 products in 8 hours. Ly Duc, Hlavaty, Bilik, Martinek 36 Advances in Production Engineering & Management 18(1) 2023 3. Research methodology 3.1 Research characterization The company produces condensed products using many automatic processing machines and processing conditions, so the variation in the production process is very large. Controlling product quality means controlling fluctuations in production conditions. This study employs both quantitative and qualitative research methods. The quantitative method is to use statistical charts to analyze the data. De Boer [40] highlights the need for a large enough sample size to meet the requirements of statistical models. Qualitative research is seen as a management tool implemented to understand and find solutions for specific problems in the workplace. De Boer [40] demonstrates that quantitative research provides logical information about reasons without using mathematical equations, observable behaviors or problem-solving solutions. This study specializes in a particular mechanical production line, and conducts a process assessment of the status quo that generates problem points for waste and human skill-dependent operations. Hart [41] highlights the need for direct workers at each stage of the LSS project implementation process. Operators at the machining line understand the need for continuous improvement and implementation. The main goal of the project is to implement the research results into practice, the researcher directly participates in the implementation of the project's work together with the operator. Practice research projects, measure the results of practice directly at the production line, and evaluate their effectiveness. The researchers provide instructions on how to collect and analyze samples for LSS project workers and operators who perform direct sampling. The author directly participates in the implementation of the LSS project at a mechanical processing company from April 2021 to March 2022, participates in all project activities as the manager, and performs the works such as defining research, performing data collection, analyzing data to collect results after implementing the LSS project. 3.2 Scope of Lean Six Sigma application The slow implementation of the tasks in the Lean Six Sigma project will waste the company's resources, so the LSS project management team needs to focus on controlling the progress of activities [42]. Changing the production process entails changing a lot of other things such as changing standards and technical conditions, changing operating methods and controls. This study presents a step-by-step implementation of Lean Six Sigma techniques with the DMAIC model as Table 1. Table 1 Activities development in DMAIC phase Activities DMAIC phase Activities 1 Activities 2 Activities 3 Activities 4 Define Present an overview of the environment surrounding the problem point to be analyzed and describe information about the problem point to be studied. Using fishbone diagrams, the 5 whys or failure tree analysis details the causes of the problem score according to 4 aspects: Man-MachineMethod-Material. Gather the opinions of the Lean Six Sigma project team members and identify the root cause of the problem. The system diagram is implemented in the construction of improvement activities according to the rating scale. Measure Make surveys and check sheets to collect data directly from the production line by operators. Planning, methods, and data collection directly on the line [43-44]. Deploy statistical tools, and test statistical hypotheses to analyze data. - Analysis Set up a specific implementation plan for each trial according to the GANTT and WBS diagrams. Analyze the current status of each stage by video recording method and analyze the correlation between man and machine using a Man-Machine diagram [45-46]. Set specific goals for each case according to S.M.A.R.T criteria. - Improve Re-optimize production conditions at each soybean production line by the Taguchi method [47-48]. Rebuild management standards, and work instructions according to ISO9001:2015 and IATF16949:2015. - - Control Build a digital processing system to control the operation of each operation at each corresponding production line, the Industry 4.0 system is built based on the POKA-YOKE theory. Build an online measurement system using digital numerical control techniques [49-50]. - - Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 Advances in Production Engineering & Management 18(1) 2023 37 4. Results and discussion 4.1 Define (D) This phase outlines the Lean Six Sigma (LSS) project scope clearly and specifically. The fish bone diagram combined with the 5 Whys analysis lists individual causes of defects in 4 aspects as Man-Machine-Method-Material (Fig. 1). Failure tree analysis combined with the 5 Whys analysis are also reviewed and verified the cause of defects. A survey questionnaire was established and researchers assessed the accuracy of each question, surveying the opinions of direct operators of the production line such as machine operators, chief and deputy shift staff of production, production line managers, machine maintenance technicians, production line design technicians, and factory managers. All comments show the general content is that there are no specific regulations on machine operating conditions, there is no connection of production data between stages, and employees choose production tools by experience, so they often give rise to defects. Evaluating the current state of the machining process by using the control plan sheet tool is easy to spot gaps in the manufacturing process. Managers who lack understanding of the production process and completely depend on the skills of workers. Interviews with workers of LSS project members contribute to enhancing the culture of sharing in the company. The LSS project needs to clearly show two goals: (1) Clearly define the goals of each specific task and (2) Form a plan table according to Gant and WBS (Works Breakdown Structure) diagrams for each specific task. From Fig. 1. This research paper has 3 main projects: Project A (Make sure to use the right tools to process the hole size of the product at the machining center). Project B (Determine the temperature conditions, time of the oil dipping, and the stabilization time based on the expansion of molybdenum) and Project C (Check the quality of power supply for the oil dipping machine). Fig. 1 Fishbone diagram 4.2 Measure (M) Project A is implemented experimentally at the product hole grinding stage. The product is manufactured with 2 different materials depending on customers’ requirements. (1) Material type FC250 and molybdenum has a hardness of 196 HV, and (2) Material type copper and molybdenum has a hardness of 125 HV. The tool for hole grinding also depends on the respective material. Tools employing FC250 materials used blue-labeled tools, while those using SUJ2 materials used red-labeled tools. However, the shapes of the two types of tools are similar (Fig. 2), make their identification and differentiation is a challenge. Ly Duc, Hlavaty, Bilik, Martinek 38 Advances in Production Engineering & Management 18(1) 2023 Fig. 2 Shape of hole grinding tools Fig. 3 Molybdenum disulfide inside Bush product Fig. 4 Probability plot of Project Project B performs the task of determining the most optimal oil dipping conditions. Bush product line uses molybdenum material inserted into the groove inside the product hole (Fig. 3), the molybdenum material deforms in size according to thermal conditions and heat time. Molybdenum is manufactured by Sumi Steel Co, Ltd. manufacturing, its main ingredient is molybdenum disulfide. It is effective in reducing friction or wear in high-temperature and load-bearing environments, and is effective in grease-restricted environments. Optimizing conditions for oil dipping is a challenge for LSS project team members. Project C performs quality control of the power supply for electronic boards and thermistor devices at the oil dipping stage. The factory uses a lot of LED lighting systems, computer systems, internal and external telephone systems, all the above devices are harmonic sources in the power supply, and harmonics make the image. The sine waveform of the power supply is distorted, and the system of power boards is unstable. Specifically, the extreme resistance of the oil dipping tank does not guarantee thermal stability and is the cause of defects. Designing systems for harmonic mitigation to ensure that total harmonic distortion (THD) falls within the IEEE 519:2022 standard is a major challenge. Shape of tools Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 Advances in Production Engineering & Management 18(1) 2023 39 The statistical testing method is applied to data analysis activities of the LSS project to meet the right objectives. The Anderson-Darling normalization method is performed to check the normality of the hole diameter data with an error of fewer than 3 microns using Minitab 18.0 software. The P-value of Project A), the P-Value of Project B), and the P-Value of Project C) are less than 0.005 (Fig. 4). The hypothesis H0 that data are normally distributed is rejected. A nonparametric method was used to analyze this data, as this data set is not normally distributed. The analyze results will be presented in the analysis phase contributing to the implementation of Project A, Project B, and Project C. 4.3 Analysis (A) Project A (The identification of machine tools by color at the hole grinding process). The product hole grinder has 3 main parts: Mandrel, Nut, and Blade (Fig. 5). The FC250 and the Mandrel Synchronous Material Cutting Kit and the Nut Kit are the same and interchangeable. However, the hardness setting at the blade's cutting surface is different. It should be distinguished when using FC250 and copper materials for machining. Using the chi-square test is for association statistics running on Minitab 18.0 software to evaluate the similarity of the results of correct identification of the cutting tool's blade type H0 or the dissimilarity of the correct identification of the cutting tool blade type H1. Use the difference between the expected number Ei and the actual number of observations Oi to find the chi-square value (called a statistic), according to the Eq. 1. 𝑥𝑥2=�(𝑂𝑂𝑖𝑖−𝐸𝐸𝑖𝑖)2 𝐸𝐸𝑖𝑖 (1) 𝑘𝑘 𝑖𝑖=1 Fig. 5 Structure of cutting tools Fig. 6 Difference between observed and expected counts Ly Duc, Hlavaty, Bilik, Martinek 40 Advances in Production Engineering & Management 18(1) 2023 Randomly select 200 employees of 3 processing lines (65 Bush product line employees, 60 Pin product line employees, and 75 Blank processing line employees) to distinguish between the blade type used for FC250 material and the blade type used for copper material. The P-Value of the Chi-square test is 0.016 less than 0.05, H0 is rejected, and H1 is accepted. Indicating is the results of blade selection of tools cut are not uniform among 3 groups of 200 employees. Line Bush products that the rate of choosing the right type of Blade is 46 % and the rate of choosing the wrong type of Blade is 26 %. In line Pin, the right selection rate reaches 23 % and the wrong selection rate reaches 33 % in Line Blank the right selection rate reaches 31 % and the wrong selection rate reaches 41 %. The percentage difference between Observed and Expected Counts of Line Bush's right choice reaches 42 % and the wrong choice reaches negative 20 %. Line Pin's right choice reached negative 23 % and the wrong choice reaches 11 % and Line Bush correctly pick negative 18 % and the wrong choice reaches 9 %, see Fig. 6. Project B (Re-designed of oil dipping tank processing condition). The oil dipping process of molybdenum-based products is done through 3 steps: Step 1) The product is neatly lined up and immersed in MG815 oil which is heated to 100℃ by the thermistor. Step 2) Store products are in a warehouse environment with a temperature of 70℃. Step 3) Cool the product in a normal temperature environment from 28℃ to 32℃ for about 7 days. To evaluate the size expansion of molybdenum powder after implementing the 3 above conditions, with 5 factors and 2 levels for initial treatment conditions (Table 2). Taguchi method responds well to many factors in the system simultaneously, ensuring independent research and reliable influence of factors. The L32 (2^5) orthogonal matrix table designed for 5 elements is presented in Table 3. In this study, the quality characteristics of the molybdenum set size extension (E6) in the standard region are less than 3 microns. The test prototype is sampled directly from the production process, the results are converted to signal-to-noise ratio (S/N), according to Eq. 2. 𝑆𝑆𝑁𝑁 �=−10 ·log[(𝜇𝜇−𝑚𝑚)2+𝑆𝑆2] (2) The molybdenum expansion dimension of 3 microns was measured with a Koshaka 3D profile tensile tester with a profile measuring distance of 80 mm. The results of the S/N analysis (Fig. 7) show that the product response of cooling time after oil immersion and oil immersion time strongly affects the quality of molybdenum size expansion. Next, the oil immersion temperature has a relative response and the drying time as well as the product drying temperature after oil immersion is responsive but not high. The results of the one-way ANOVA analysis (Table 4) show a 95 % confidence interval for the thermal conditions of the molybdenum product. Project C (Quality control of power supply is for oil dipping tank). A supply voltage of 400V/3 phase/30A provides a thermistor (Fig. 8) to heat the oil bath (Fig. 9). Ensuring a constant and stable power supply to the thermistor is essential. Power meter type PQ3100 manufactured by HIOKI firm is used by the company to measure voltage source, current source for thermistor. 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