Lean Sigma Research Articles

Radial Basis Function Neural Network for the Evaluation of Image Color Quality Shown on Liquid Crystal Displays

The color quality of an image shown on a liquid crystal display (LCD) can be measured with a spectroradiometer; however, this instrument is expensive, work under controlled illumination conditions with an artificial source of light, and measurements take a long time. A spectroradiometer returns measurements of wavelength or CIE color space. A low-cost and fast alternative consists of using a digital camera that outputs RGB measurements. Unfortunately, comparisons between measurements obtained with both instruments cannot be performed; hence, conversion equations must be used. The main problem is that equations do not consider the effects caused by the camera lens, sensor variations, and configurable parameters such as gain and the exposure time. This paper proposes the architecture of a radial basis function neural network (RBFNN) to measure the image color quality displayed by an LCD using a digital camera. The RGB values acquired with a camera are used as inputs to the RBFNN. The output predicted the luminance and chromaticity components in the CIExyY color space and included the corrections to the lens and camera parameters. First, the RBFNN topology is explained, including the calculation of the number of neurons in the hidden layer, and the definition of the dispersion centers and their associated spread. Next, the experiments related to RGB color space reconstruction and conversion from RGB to CIE are presented. The proposed approach was tested on a real automotive scenario. The results obtained were similar to those measured with the spectroradiometer with an accuracy of 93.3%. Moreover, the results remained within limits established by the six-sigma methodology.

Cost of poor quality reduction in auto sector: an exploration with Six-Sigma

The cost of poor quality (COPQ) always helps in analysing the operating costs for effective and profitable business management. COPQ consists of those costs which are generated as a result of producing defective material. The present study deal with implementing the Six-Sigma in the Indian automobile company. This research aims to reduce the problems faced by the company's quality inspection department. The department faces various rejection problems of two vehicles, such as front fork pivot hard movement, grease nipple broke, O-rings struck, edge bends, etc. We used a structural DMAIC methodology to improve the process. This study used multiple Six-Sigma tools such as Pareto chart, process map, fishbone diagram, and process capability chart to solve the rejection problem. Using a Six-Sigma methodology, we improved the sigma level of 1.875 to 4.375, which benefits 199,020 rupees' rework costs annually. This study helps practitioners and academicians to emphasise their effort towards COPQ.

Improved Productivity of Document Verification Process Using the Lean Sigma Method

In the industrial era 4.0 emerged smart companies using the concept of automation and digitization. This has had an impact on printing companies which are experiencing a decline in the global rate of newspaper growth in the next five years. Therefore, many companies conduct Improvement and Innovation activities and eliminate waste in order to continue to survive and be able to compete. This research aims to identify the types of waste that occur in the flow of the process of activities, analyze the factors that cause waste, as well as provide proposals for minimizing waste. The method used in this study is lean sigma which is a combination of lean and six sigma to identify and eliminate waste in an effort to improve the continuous improvement process. In this study conducted with Define, Measure, Analyze, Improve, and Control (DMAIC) stage in accordance with the steps in Six Sigma. This research is focused on improving the productivity of continuous improvement verification process with lean sigma method.

Comparative Analysis Four Different Ways of Calculating Yield Index SSSpk Based on Information of Control Chart, and Six Sigma, to Measuring the Process Performance in Industries: Case Study in Aden’s Oil Refinery, Yemen

Process capability index PCIs have long been used to assess process efficiency in the manufacturing. While PCIs assess the process stability and efficiency, real measurements of product quality depended on estimate the standard deviation for a short-term variance was calculated as the base approach in several previous studies for measuring PCIs. Studies in recent years seek to estimate shift of the process mean from target. In today’s smart manufacturing production climate, manufacturers keep track of production performance. Since the deviance of process mean comparative to the target is different. This allows a more robust norm to be implemented by estimate the relative shift to describe the variance of process in Six Sigma ( SS ). For that purpose, this paper suggests an evaluation method by comparative analysis to four different ways of calculating yield indexes $S_{pk}$ based on six sigma and control information, of $\bar {X}_{S}$ chart. Moreover, this paper extends the univariate process index yield $SSS_{pk}$ to a multi- generalized yield index is called $SSMS_{pk}$ . An established an updated approach to assess univariate and multi-characteristic, based on understanding variation which is critical to quality (CTQ) and focusing on the position and actions of natural process tolerance between defined tolerances limits. To demonstrate the trueness verification proficiency of this approach, this paper includes an industrial case study to assess the process efficiency in Aden’s oil refinery, Yemen. The findings of this study indicated that the indexes which are calculating based on six sigma outperformed existing indices and that this study has important implications for industrial practitioners, researchers and quality control experts interested in the evaluation of process performance.

The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma

Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.

Теоретико-методичні аспекти оцінки управління конкурентоспроможністю страхових компаній

The article is concerned with consideration of the theoretical and methodological aspects of the management of competitiveness of insurance companies. It is specified that insurance activity belongs to the most profitable and risky types of business. Over the past few years, there has been a tendency to reduce the number of insurance companies in the insurance market, which will strengthen the processes of concentration, monopolization and competition between powerful players in the insurance market. Under such conditions, the issue of determining the level of competitiveness management of insurers, as well as their competitive advantages and factors that form a competitive position in the market, acquires special relevance and urgency. The versatility of the concept and the actualization of the study of the competitiveness of insurance companies in the insurance market of Ukraine, along with the significant achievements of Ukrainian scholars in this area, determine the need to apply a comprehensive methodology for assessing these processes. To analyze the state of competition in a certain segment of the market, it is proposed to focus on the concentration coefficients and on the Herfindahl-Hirschman Index. The next stage of the assessment is the study of the factors of influence and their ranking by the weight of the impact on the final result. A characterization of the quantitative, qualitative, matrix and graphic methods that can be used in the course of assessing the competitive position, competitive advantages and competitiveness of the insurance company are carried out. As the final stage, an analysis of the efficiency of the insurer’s competitiveness management process based on the SIX SIGMA concept is proposed. It is concluded that managing the competitiveness of an insurance company is a complex process that is formed under the influence of a number of changing and dynamic factors and components, and also has immense number of manifestation forms. That is why the assessment of the level of competitiveness management of insurance companies should be based both on the use of existing methodologies and on the introduction of innovative approaches to the analysis of the level of competitive advantages of business entities in the insurance market of Ukraine

Process Improvement Using Six-Sigma (DMAIC Process) in Bearing Manufacturing Industry: A Case Study

Propagation, development of technologies and increasing customer demands switch the approach of existing work in the industries. To overcome barricades, the six-sigma DMAIC (define, measure, analyse, improvement, control) approach is trendy along with being advantageous. This approach decreases the variation and set up the way for up gradation in the manufacturing companies. This paper contains the Six-Sigma DMAIC approach that was used to decrease the process variation of inner and outer races of ball bearing for enhancing product quality. Define phase of DMAIC approach begins by difficulty detection through the voice of internal and external customers. The later stage constitute of measuring the data of bearing parts of existing process. This stage followed by the analyze as well as improvement stage, where the Six-Sigma quality improvement tools i.e. statistical process control (SPC), Control charts, MINITAB 18.0 software, fish bone diagram along with significant study of alive process were imposed to identify root causes and minimizing process variability. The improvement stage minimized the assignable causes for variability. The control phase called to maintain the improved process till further improvement.This work expected that the Six-Sigma DMAIC methodology was effective for increasing the sigma level, decreasing the value of standard deviation and also decreasing the expected part per million (PPM) out of specification limits. Values of capability indices i.e. (Cp, Cpk and Cpm) were improved. A Six-Sigma DMAIC methodology is well-known and is capable of playing an efficient role in manufacturing industry by reducing variability in the bearing part process.

İŞLETMELERDE İŞ SAĞLIĞI VE GÜVENLİĞİNİN İYİLEŞTİRİLMESİ AMACIYLA ALTI SİGMA KALİTE YÖNETİM SİSTEMİ İLE MODEL OLUŞTURULMASI

Altı Sigma Kalite Yönetim Sistemi bir ürünün üretim aşamaları esnasında en az hata ve kayıp vererek üretilmesini ve böylelikle ürünün en az üretim maliyetine sahip olmasını hedefleyen kalite kontrol sistemlerinden biridir. İş Sağlığı ve Güvenliği ise kısaca bir işin yürütülmesi esnasında ortaya çıkabilecek risk ve tehlikeleri en aza indirerek çalışan ve toplum sağlığını korumayı kendine amaç edinmektedir. Bu çalışmada iki senelik bir süreç içerisinde bir işletmedeki İş Sağlığı ve Güvenliği sistemi Altı Sigma Kalite Yönetim Sisteminin enstrümanları ile incelenmiş ve bulunan sorunların Altı Sigma Metodolojisi ile çözümleri önerilmiş ve çözüm önerilerinden uygulamaya koyulanların ne ölçüde iyileştirme getirdiği istatistiksel olarak ortaya koyulmuştur. İlgili çalışmanın tam versiyonu Okan Üniversitesinde Haziran 2019 yılında ‘’İŞ SAĞLIĞI ve GÜVENLİĞİNDE ALTI SİGMA YAKLAŞIMI ÜZERİNE BİR MODEL UYGULAMASI’’ adı ile Doktora Tezi olarak kabul görmüş ve onaylanmıştır.

Six-Sigma Quality Management of Additive Manufacturing.

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

Reducing waste to improve product quality in the wooden pallet production processby using lean six sigma approach in PT. XYZ

This company is a company engaged in wood processing. The product produced by this company is wooden pallets. The problems that are found by this company is waste in the production process that are the high defective producs and non value added activities The average disability percentage of wood pallets is 7.52% of the total production per month. The value exceeds the disability tolerance of the company that is 5%. This study proposes to reduce the number of defective products and eliminates non value added activities through design improvement. This problem can be solved by using Lean Six Sigma approach which is a combination of the Lean and the Six Sigma concept with the DMAIC method by providing improvements to increase the number of defective products and eliminate non value added activities. By using the Lean Six Sigma method can provide lean metrics and sigma level calculation values. Based on actual study, the result for the lead time is 212.58 minutes, the process cycle efficiency is 64.40%, the process velocityis 0.0041 processes / hour. Based repair 70.73%, process velocityis 0.0035 processes / hour.

The Fuzzy Process Quality Evaluation Model for the STB Quality Characteristic of Machining

Whether it is important components of a machine tool itself or various important components processed by the machine tool, many vital quality characteristics mostly belong to the smaller-the-better type. When the process quality levels of these quality characteristics do not attain to the criteria, friction loss may increase during the machine operation, affecting not only the process precision and accuracy but also the lifetime of the product. Therefore, this study applied a smaller-the-better six-sigma quality index simultaneously demonstrating process quality level and process yield. Besides, in coping with statistical process control data, a one-tail confidence-interval-based fuzzy testing method was developed to evaluate process quality. Because this approach is built on the basis of confidence intervals, it can reduce the possibility of misjudgment resulting from sampling errors as well as integrate past experience to enhance the accuracy and precision of the assessment, and then it can grasp the timeliness of improvement.

A Proposed Performance Prediction Approach for Manufacturing Process Using Artifical Neural Networks (Dept.M)

This paper aims to provide an approach to predict the performance of parts produced after multi-stages manufacturing processes, as well as assembly. Such approach aims to control and subsequently identify the relationship between the process inputs and outputs so that a process engineer can more accurately predict how the process output will perform based on the system inputs. The work is guided by a six-sigma methodology to obtain improved performance. In this paper a case study of the manufacture of a hermetic reciprocating compressor is presented. Each of manufacturing stages is separate and affects to the functionality of the end product. The application of artificial neural networks (ANNs) technique is introduced to improve performance prediction within this manufacturing environment. The results demonstrate that the approach predicts accurately and effectively.

Barriers and waste in the research grant application process in higher education through a Lean Six Sigma lens

Barriers and waste in the research grant application process in higher education through a Lean Six Sigma lens

The Evolution of NIMHANS Neurotrauma Care

AbstractNeurotrauma services in NIMHANS, Bangalore, have been evolving since its inception in 1958. At present, it is a major referral center for Karnataka and surrounding districts of the southern states. It has many firsts to its credit, from having a separate building to having dedicated trauma teams to manage the service 24/7. It has made rapid strides in healthcare delivery based on data-driven evidence, improvisation with Lean sigma approach, and contribution through research and publications. This center has won best Neurotrauma center award in 2010.

USE OF LEAN SIX SIGMA METHOD TO IMPROVE PROCESS OF ELECTROLYTE REPLACEMENT PROTOCOL IN ICU

TYPE: Abstract Publication TOPIC: Education, Research, and Quality Improvement PURPOSE: Electrolyte imbalance is a common and potentially life threatening phenomenon in critically ill patients. Our data analysis found failure to implement the ICU protocol leads to inadequacy, inconsistency and delay in correcting electrolytes in critically ill patients that can cause life threaterning complications. METHODS: The team utilized a Lean Six Sigma approach to identify the root causes of delays in treatment. This project specifically utilized: affinity diagram, regression analysis and Failure Mode Effects Analysis. Team composition included the Chief Nursing Officer, ICU physicians, nursing director, nurse manager, ICU nurses and a pharmacist. Initial cycle of improvement focused on nurse education and awareness of the impact of a lack of adherence to the protocol. Lab initiated a secondary cycle focused around rapid provision of the lab results to assist in this process. Quality improvement impacts virtually every level of healthcare, from supply chain management to patient safety. RESULTS: Process changes demonstrated a 44% improvement in potassium replacement and 71% improvement in magnesium replacement in the ICU. Significant improvement was demonstrated in the time from lab available to start of electrolyte replacement. CONCLUSIONS: Lean Six Sigma methodology can be used successfully to reduce variation in ICU protocol like electrolytes replacement. CLINICAL IMPLICATIONS: Process variation improvement 71% for Magnesium and 44% for potassium replacement protocol leads to reduction in morbidilty and mortality from arrythmia in ICU. DISCLOSURE: No significant relationships. KEYWORDS: Lean six sigma, process improvement, ICU protocol

Increase Electricity Supply to the Community to Support Sustainable Development by Minimizing House Load in Geothermal Power Plant

Recently, electricity consumption in Indonesia continue to increase rapidly along with the increase in development, economic growth and also the increase in the population. One potential source of electrical energy comes from geothermal energy, where Indonesia has enormous potential. According to data from the Geological Agency, Ministry of Energy and Mineral Resources, until the end of 2016 there were at least 331 geothermal energy source locations throughout Indonesia, where most of the potential was on a volcanic track with a potential of 28,579 MW and this is the largest geothermal energy in the world. In its operation, power plants need energy to supply their own needs (house load) which amounts up to 10 % of the electrical energy produced. Own electricity needs are used to drive brine and condensate pumps, fan drive motors in cooling towers, lamps and electronic appliances in well pad and offices. The use of this energy can be minimized to increase electricity production which is distributed to users. The methodology used in this research is lean sigma process through the Define-Measure-Analyze-Improve (DMAI) steps. The first step is to define the problems regarding opportunity from electricity use for own needs that can be saved and constraints faced, the next step is to measure the electrical usage as baseline data, then analyze to find the causes of inefficiencies in electricity use and the last make improvement programs. From a case study conducted at the Gunung Salak geothermal power plant in West Java, the energy efficiency of house load can be minimized by up to 7.5%-10% in 2018-2019. Greater energy supply to the community will have an impact on improving the economy of the community to support production activities that require electrical energy.

Quality Improvement in the Emergency Department: A Project to Reduce Door-to-Electrocardiography Times for Patients Presenting With Chest Pain

IntroductionThe American Heart Association/American College of Cardiology guidelines recommend obtaining electrocardiography for patients who present to the emergency department with chest pain in less than 10 minutes of arrival. Reducing door-to-electrocardiography time is an important step in adhering to the recommended door-to-balloon times (≤ 90 minutes) for patients who present with ST-segment elevation myocardial infarction. MethodsBased on lean sigma principles, a protocol was implemented in an adult emergency department that included deferring nurse triage for patients with complaints of chest pain, chest tightness, and chest pressure and providing them with a red heart symbol as an indicator for clinical technicians to prioritize their electrocardiography order. Pre- and postintervention data were collected over a 12-month period. ResultsBefore the intervention, the mean door-to-electrocardiography time was 17 minutes for patients with chest pain (n = 893). After the intervention, the mean door-to-electrocardiography time for patients with chest pain significantly decreased to 7 minutes (n = 1,057) (t = 10.47, P ≤ 0.001). Initially, the percentage of compliance with door-to-electrocardiography standard of 10 minutes was 31% and improved to 83% after implementation of the new protocol. DiscussionImplementation of the optimized door-to-electrocardiography protocol decreased the time for obtaining diagnostics and improved compliance with the American Heart Association/American College of Cardiology guidelines, potentially decreasing door-to-balloon times for patients who presented with ST-segment elevation myocardial infarction.

Application of Six Sigma methodology in an Indian chemical company

PurposeThe aim of this paper is to deploy Six Sigma (SS) methodology for addressing the customer complaints pertaining to Chemical-X in an Indian chemical company.Design/methodology/approachThe study followed a structured Define, Measure, Analyze, Improve, Control (DMAIC) approach to address the customer complaints. The complaints have been classified into different categories along with a project charter in the define phase. In the measure phase, measurement system analysis (MSA) and supplier, input, process, output and control (SIPOC) have been applied. In the analyze and improve phase, why–why analysis, process capability study, how–how analysis, Gage repeatability and reproducibility and Taguchi design have been applied to optimize the manufacturing process parameters for Chemical-X. Lastly, in the control phase, validation of 20 batches has been piloted to validate the optimized parameters.FindingsThe findings of this study highlight the optimization and prioritization of the process parameters. It shows that humidity has the least impact on the manufacturing of Chemical-X, whereas shift type has the maximum impact. The experimental output indicates that the 1st Shift, the holding time after grinding should be twenty-four hours, and the temperature after grinding should be 40 °C to reduce the customer complaints concerning lumps formation in Chemical-X.Research limitations/implicationsThe study is performed for a single product (Chemical-X). It has focused only from the manufacturing process view and not from the transportation, suppliers and downstream supply chain view.Originality/valueA systematic and data-driven approach of the SS methodology ensured that the customer complaints due to lumps formation reduced from 5% (approx.) to 1% (approx.) which resulted in the cost saving of INR 4 million (approx.) annually.

Reliability, availability and maintainability analysis of a bag production industry based on the six sigma DMAIC approach

PurposeThe purpose of this paper is to implement the six sigma (SS) strategy in a bag sector under actual operating circumstances based on defining-measure-analyze-improve and control (DMAIC). During the project, several statistical tools and methods have been used efficiently to create inferences. Thus, to measure and enhance system efficiency, the author calculate reliability, availability and maintainability (RAM) indices. Based on this research, the author show how the SS method and RAM analysis are very helpful in determining maintenance intervals, as well as in planning and organizing the appropriate maintenance strategy.Design/methodology/approachThis study introduces the step-by-step application of the DMAIC methodology for the identification and reduction of bag production line downtime and examines the present operations management. Thus, statistical techniques are used to analyze the failure and repair database. Pareto analysis, histograms and descriptive statistics at the machine and line-level of the historical data were conducted. Trend and serial correlation testing validated the hypothesis of independence and identical distribution of database was performed. In addition, with their best fit allocation, the RAM of both the bag production line and its machines was estimated at separate mission times.FindingsThe main goals of the applied method are to understand the nature of the downtime patterns and to accurately and quantitatively estimate the RAM characteristics of the bag production system. The assessment defines the production line's critical points, requiring further enhancement through an efficient maintenance approach. Therefore, by improving plant efficiency and safety, the author can decrease unplanned downtime and equipment failures.Originality/valueThis research is expected to serve as an attempt to conduct SS DMAIC methodology through RAM assessment and its impact on system efficiency under actual circumstances. The benefit of the methodology is that the manufacturing process is continuously monitored by suitable indicators, the use of which leads to a continuous improvement cycle.

Halal six sigma framework for defects reduction

PurposeIn recent years, halal food industries are facing a high level of competition. The growing demand for halal food means firms are working hard to improve quality and reduce halal food defects. The purpose of this study is to propose a halal-based six sigma (SS) framework that could be used to reduce halal food defects and improve compliance.Design/methodology/approachThe proposed halal six sigma (HSS) framework has five phases based on the define-measure-analyse-improve-control (DMAIC) approach. The proposed framework also integrates halal assurance system requirements into HSS to ensure compliance. Tools used in the proposed model include SIPOC tools, Pareto diagram, root cause analysis and the improvement process flows. A case study in a chicken meat company is used to test and validate the proposed framework. In case of study research, brainstorming was used to review an initial proposed framework and focus group discussions were used to determine feasible improvement actions.FindingsThe results showed that the proposed HSS framework could be effective to detect and reduce the halal defects and lower the halal defect costs. By adopting the framework, the case firm was able to lower halal defect costs significantly and increase the SS level.Practical implicationsSS approach can be designed and adapted to specific food industry. HSS framework could provide a systematic approach to reduce halal food defects and sustain the improvement efforts. The proposed HSS framework is also easy to use to understand halal critical points and improve halal compliance.Originality/valueThis study proposed a HSS framework that can be adopted to reduce halal food defects and costs. This proposed framework will benefit the halal food industry intending to realize zero halal food defects and lower production costs. The limited number of publications in the research theme of halal food defects show that there is a significant gap in the existing body of knowledge.