Jul 2026· World Journal of Advanced Research and Reviews· Vol 31, pp. 061-075· 0 citations
Abstract
Measuring and controlling dimensional accuracies are very critical in steel manufacturing to meet high specification requirements of customers and to provide competitiveness. MPL Steels, being one of the major steel producers in India, have been experiencing great scrap rates of 9-11% in the context of dimensional inconsistency amidst roller wear, mill instability and cutting errors, a fact that has caused massive loss of finances and Occasional operational difficulties. The purpose of the study was to design and institute an integrated Lean Six Sigma system with the increased predictive maintenance and digital optimization capability to methodically minimize defects and improve process capability in MPL Steels in a sequential manner. Applying the DMAIC Six Sigma approach, the identification of the root causes was fulfilled by means of Pareto, regression, and cause-effect tools applied to a big amount of production data. The important interventions were taken such as monitoring of roller wear using IoTs, auto-adjustment of cutting parameters using an AI program, a prediction-based maintenance schedule, and thorough training of operators, and the pain points were monitored using a statistical process control chart. After its implementation, MPL steels realized 55.4% reduction in its thickness-related scrap, which reduced the scrapping rates to 4.1% downturn 9.2%, whereas process capability (Cpk) also improved up to 1.33 up turned 0.82. Reduction in machine downtimes was 66.7%, the total defects decreased by 61.5%, and the annual saving was much more than half a million dollars. The findings show that a combination of Lean Six Sigma and Industry 4.0 technologies can be used to make significant improvements in quality, operational efficiency, and sustainable competitiveness in a mid-sized steel manufacturing that will serve as a basis of long-term, data-driven continuous improvement.
The metalworking industry plays a fundamental role in Mexico's economic and productive development due to its involvement in the manufacturing, repair, and maintenance of industrial equipment. However, many machining and welding workshops face challenges related to process organization, plant layout, inventory control, standardization of activities, and workplace safety, which limits their operational efficiency and competitiveness. In this context, the objective of this research is to develop an operational optimization proposal for the La Huacana Machining and Welding Workshop, located in Lázaro Cárdenas, Michoacán, through the application of the Lean Six Sigma approach. The research adopts a quantitative approach with a descriptive and propositional scope, supported by the DMAIC methodology (Define, Measure, Analyze, Improve, and Control). Tools such as Failure Mode and Effects Analysis (FMEA), risk analysis, plant layout redesign, the development of standardized operating procedures, and the implementation of an inventory control system and performance indicators are used to diagnose and analyze the current situation. The diagnostic results reveal operational waste, process variability, deficiencies in materials management, and risks associated with occupational health and safety. Furthermore, opportunities for improvement were identified related to workspace organization, optimization of material flow, and strengthening industrial safety conditions. It is concluded that the integration of Lean Six Sigma tools constitutes a viable alternative for increasing productivity, reducing unproductive time, improving decision-making, and strengthening the competitiveness of metalworking shops. Furthermore, the research provides empirical and methodological evidence for the application of continuous improvement strategies in small and medium-sized enterprises in the industrial sector.
Ofelia Barrios Vargas, G. Navarrete, Samuel David Soriano López et al.· Veredas do Direito· 0 citations
This study focuses on analyzing and improving the process capability for waste reduction in an automotive parts manufacturing plant. A cause-and-effects diagram was employed to investigate and identify the root causes of defects. Process capability analysis was then applied to evaluate the effectiveness of improvements of the Work Procedures. The initial defect rate was 2.9 percent, consisting mainly of surface scratches, dents, discontinuous surfaces, and poor surface finishes. The primary causes were inappropriate insert selection and cutting direction, leading to inadequate tool strength and premature wear. Prior to the improvement, the process capability index (Ppk) was 0.67. After implementing the proposed improvements, the three-step modification process includes altering the cutting tool path direction, adjusting the feed rate, and changing the cutting insert. The defects were completely eliminated, tool life was significantly extended, the process capability index increased to 1.39, and production costs were reduced by 5.10 percent
Unknown authors· Suranaree Journal of Science...· 0 citations
Efficiency in pharmaceutical warehousing is critical because accuracy, speed, and regulatory compliance determine overall supply chain reliability. PT X has experienced declining warehouse performance marked by rising defects, long cycle times, and significant non-value-added activities. Purpose: This study aims to analyze the causes of operational inefficiency and formulate structured improvements. Approach: Using the Lean Six Sigma DMAIC framework, the research applies waste identification, Value Stream Mapping, Cost of Poor Quality, DPMO and sigma measurement, Process Activity Mapping, and Failure Mode and Effect Analysis.The Measure stage reveals a total lead time of 4,890 minutes with only 250 minutes of value-added work (Process Cycle Efficiency = 5.11%) and sigma levels between 3.2–3.6 across eight defect types. Analyze findings indicate that waiting, motion, inventory, and defect waste dominate, with the highest risk originating from recording errors, missing items, and FIFO violations. Improvement through WMS digitalization, layout restructuring, SOP standardization, and visual management produces a more efficient future-state VSM. Conclusion: Inefficiency in PT X warehouse is systemic, rooted in human factors, work methods, and information flow. Originality: This study presents an integrated model combining Lean waste analysis and Six Sigma defect reduction as a unified framework for optimizing pharmaceutical warehouse operations.
Abstract
Machine efficiency and productivity are key factors in the manufacturing industry to ensure product quality and smooth production processes. PT XYZ, as a company engaged in manufacturing, faces challenges in optimizing the performance of its Heading machine used during the production process. One of the identified problems is the high downtime, which averages 5,509.7 minutes per month during the research period. This study applies the Overall Equipment Effectiveness (OEE) and Six Big Losses methods to analyze machine effectiveness and designs improvement recommendations through the Total Productive Maintenance (TPM) approach. Data collected over a six-month period, from January to June 2024, shows that the Heading machine has an availability rate of 86.63%, a performance rate of 87.93%, and a quality rate of 99.55%, resulting in a total OEE value of 75.5%. The Six Big Losses analysis indicates that the main factor affecting machine effectiveness is reduced speed losses. Therefore, this study recommends the implementation of TPM to improve the effectiveness of the Heading machine performance at PT XYZ.
Defects in precast concrete spun pile production pose risks to structural reliability, operational efficiency, and project costs. The Company has experienced recurring defects that increase the cost of poor quality and reduce customer satisfaction, highlighting the need for a systematic improvement approach. This study applies the Six Sigma DMAIC methodology to analyze and reduce defects using production data from 25,206 units manufactured between 2020 and 2022. Statistical analysis using P-chart control limits, DPMO calculations, and sigma-level evaluation revealed an overall defect rate of 0.63%, dominated by Broken PC Bars, Cracks in the Stock Area, and Dry Concrete, which together contributed 62.5% of total defects. The initial process performance averaged 3.4 sigma (approximately 2,150 DPMO), which improved to 4.67 sigma following the proposed corrective actions. Root cause analysis identified human factors, unstable machine calibration, procedural inconsistencies, and material variability as the primary contributors. Improvement recommendations include operator training, biweekly machine maintenance, digital standardization of work procedures, and stricter material control. The findings demonstrate the effectiveness of a data-driven DMAIC approach for enhancing product quality in precast manufacturing. Future improvements may incorporate IoT-based monitoring and stronger supplier collaboration to support long-term process stability and operational excellence.
Welly Mahardhika· OPSI· 0 citations
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