A practical methodology for analysing and improving the measurement system.

Measurement systems are used widely in manufacturing organisations in order to make many important decisions. These decisions range from the acceptability of a given product against tolerance requirements to the level of statistical control of a process or its capability to consistently perform a given task. In most situations, however, little thought is given to the quality of the data generated by such measurement processes. By using potentially flawed data in making fundamental manufacturing decisions, organisations undermine the quality of the decision-making process itself. This paper reviews the approaches presently available from both a technical and a practical point of view, based on the priorities of process improvement practitioners. The conclusion of the paper is that the Evaluation of the Measurement Process proposed by Wheeler and Lyday offers the best balance of accuracy and utility.     

A risk-based approach to manufacturing process control: use in autoclave moulded composite sandwich panels

The quality of a design solution is largely the consequence of making informed decisions based on the quality of available information. This information is not always accurate or reliable and therefore its use is not without risk, forcing designers to be conservative. The design of mechanical components is dependent amongst other things on the materials and manufacturing processes utilised. Composites are unique in that the component configuration, material and manufacturing process can be simultaneously manipulated, allowing highly optimised structures to be designed. Risk analysis is a pragmatic way of managing the variability of such information and therefore the risk associated with design for manufacture decision-making. This paper investigates the use of statistical process control techniques for improving the consistency of autoclave moulded composite components.     

A machine learning approach to yield management in semiconductor manufacturing

Yield improvement is one of the most important topics in semiconductor manufacturing. Traditional statistical methods are no longer feasible nor efficient, if possible, in analysing the vast amounts of data in a modern semiconductor manufacturing process. For instance, a typical wafer fabrication process has more than 1000 process parameters to record on a single wafer and one manufacturing plant may produce tens of thousands wafers a day. Traditional approaches have limits in extracting the full benefits of the data. Therefore, the manufacturing data is poorly exploited even in the most sophisticated processes. Now it is widely accepted that machine learning techniques can provide powerful tools for continuous quality improvement in a large and complex process such as semiconductor manufacturing. In this work, memory based reasoning (MBR) and neural network (NN) learning are combined for yield improvement and an integrated framework is proposed for a yield management system based on hybrid machine learning techniques. In this hybrid system of NN and MBR, the feature weight set which is calculated from the trained neural network plays the core role in connecting both learning strategies and the explanation on prediction can be given by obtaining and presenting the most similar examples from the case base. The proposed system has advantages in typical semiconductor manufacturing problems such as scalability to large datasets, high dimensions and adaptability to dynamic situations.     

A QUALITY CONFIDENCE PROCEDURE FOR GDSS APPLICATION IN MULTICRITERIA DECISION MAKING

This paper introduces a procedure for improving the quality of group decision making. Emphasis is placed on the identification of outliers and on the establishment of confidence limits in group decision making. Participants in group decision making whose opinions fall outside the group's tolerance level are further studied to annex the source of this variation.

The study presents 30 stakeholders with the task of deciding whether or not to adopt a new manufacturing technology (computer integrated manufacturing). The goal in either choice is the improvement of the total productivity of the organization. The decision made by this group is based on a pairwise comparison of seven manufacturing dimensions. Priorities for these criteria are generated using me analytic hierarchy process (AHP). These priorities are analyzed to identify potential outliers. Procedures on how to manage these outliers in order to improve the quality of decisions arrived at by the group are provided.     

面向持续质量改进的过程管理方法研究

提出了过程管理方法在企业实施持续质量改进中的应用模式,解决了过程管理的建模与分析以及制造企业产品实现过程信息层次模型的实现等问题,为制造企业系统地分析和解决过程质量问题提供了一个基本框架。     

A framework for System Excellence assessment of production systems,based on lean thinking,business excellence,and factory physics

This article proposes a production system framework that synthesises lean production, business excellence, and factory physics. The framework, which draws on a deep state-of-the-art understanding, consists of a performance measurement system supporting the achievement of a target condition based on variability and lead time reduction, as well as approaches of continuous improvement. Based on four types of excellence, a System Excellence value is calculated, indicating the distance from a target condition and thus displaying relevant improvement potential. As a key result, the framework proposed provides a contribution to knowledge, as it combines the aforementioned schools of thought, resulting in a holistic framework for action. The measurement system offers a high level of robustness, as it draws on diverse data sources and reflects on the dynamic behaviour over time. It has been successfully implemented in automotive manufacturing plants worldwide, which may suggest considerable practical relevance. Another key result of this research is that through applying the framework, important bottom-line indicators, such as lead time, failure costs, or productivity, could be improved. As the plants are typical automotive industry high-volume plants, it is proposed that the solutions presented offer a suitable standard for this industry and type of plant.     

TOC-based performance measures and five focusing steps in a job-shop manufacturing environment

Theory of constraints (TOC) views a company as a set of interdependent processes working in harmony to achieve the profit goal of the company as a whole, and thus it emphasizes total system performance over localized measures to guide operational decisions. This paper demonstrates the usefulness of employing TOCbased global performance measures to make operational-decisions (e.g. product mix, continuous improvement, inventory management, production planning and scheduling) to strengthen the internal supply chain in a relatively complex manufacturing environment, i.e. a job shop. An ARENA-based simulation model is presented and a number of scenarios are discussed that provide insights regarding the characteristic features of TOC, such as goals and necessary conditions, performance measures, five-focusing steps for continuous improvement, and drumbuffer-rope scheduling. These insights will assist managers in making important decisions regarding approaches to successful TOC implementations, and will provide academics with a broad range of future research opportunities.     

Process Yield Analysis for Nonlinear Profiles in the Presence of Gauge Measurement Errors

Process yield plays an important role in many manufacturing industries for measuring process performance. However, gauge measurement errors have significant effect on process capability analysis. In this study, we present a method based on the yield index to evaluate the process yield of nonlinear profiles in the presence of gauge measurement errors. The results indicate that the presence of gauge measurement errors in the data leads to different behaviors of the yield index estimator according to the existence of the gauge variability. Our proposed test procedure can be easily used to determine whether or not manufacturing processes meet the quality requirements when gauge measurement errors are considered. A real example from a manufacturing process is used to demonstrate the applications of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

A framework for decision makers to design a business analytics platform for distributed organizations

The growth of business analytics applications in decision making is becoming a significant component in today's organizations, and the powerful changes brought by such applications to both centralized and distributed organizations have led decision makers to revise the way they capture, process, and analyze both structured and unstructured data and make decisions. This study discusses how business analytics tools can supply distributed organizations with a new operating model, process, and outlet to disseminate knowledge, and provides a framework for building a business analytics platform that may be employed by decision makers and managers to realize the full potential of a comprehensive decision-making platform in a distributed organizational setting.     

The role of SPC in printed circuit board manufacture

This paper reports on research which examined the use of statistical process control (SPC) in the quality improvement process of a printed circuit board (PCB) manufacturer. The implementation of SPC is discussed along with the difficulties encountered and benefits achieved. The findings indicate that SPC is a tool which can be of considerable assistance in the quality improvement process of PCB manufacture. However, the variety of manufacturing technologies used and the number of interconnecting processes makes the application of SPC more difficult than in other traditional industries. The lessons learned include that the introduction of SPC must not be rushed, that discipline and support from all levels in the organization are crucial to its success, that SPC cannot be used in isolation—it needs the structure of a continuous improvement initiative, and that getting processes in a state of statistical control and capable, and keeping them there, is a difficult task which involves considerable effort and patience.     

DECISION INTEGRATION FUNDAMENTALS IN DISTRIBUTED MANUFACTURING TOPOLOGIES

Advances in control, communications, computer science and engineering have made it possible to design and implement large scale systems, where the decision making, control and information processing are distributed. This research effort attempts to establish the theoretical foundation of operational decision integration for such systems. Decision integration is a method to improve the quality of decision making. The basic elements of the integration process are defined. A simple distributed hypothesis testing model is employed to demonstrate that properly designed integration always improves the quality of the decisions. The problem of organizing decision making agents into architectures of integration (parallel versus hierarchical) is addressed. Several elementary decision architectures for small organizations are analyzed, and their performance is compared. The results are also extended for the case of flexible architectures with adaptive topology. The implications of integration are discussed with respect to the overall performance of an organization.     

Applying quality improvement principles to achieve healthy work organizations

BACKGROUND: Health care has used total quality management (TQM)/quality improvement (QI) methods to improve quality of care and patient safety. Research on healthy work organizations (HWOs) shows that some of the same work organization factors that affect employee outcomes such as quality of life and safety can also affect organizational outcomes such as profits and performance. An HWO is an organization that has both financial success and a healthy workforce. For a health care organization to have financial success it must provide high-quality care with efficient use of scarce resources. To have a healthy workforce, the workplace must be safe, provide good ergonomic design, and provide working conditions that help to mitigate the stress of health care work. INTEGRATING TQM/QI INTO THE HWO PARADIGM: If properly implemented and institutionalized, TQM/QI can serve as the mechanism by which to transform a health care organization into an HWO. To guide future research, a framework is proposed that links research on QI with research on HWOs in the belief that QI methods and interventions might be an effective means by which to create an HWO. Specific areas of research should focus on identifying the work organization, cultural, technological, and environmental factors that affect care processes; affect patient health, safety, and satisfaction; and indirectly affect patient health, safety, and satisfaction through their effects on staff and care process variables. SUMMARY: Integrating QI techniques within the paradigm of the HWO paradigm will make it possible to achieve greater improvements in the health of health care organizations and the populations they serve.     

Comparison of EPA's QMS to SEI's CMMI.

EPA and other government organizations make decisions based on environmental measurements. How good are the data? How well are the data generators performing? What measurements apply to them? How can the data life cycle processes be improved so data generators can continually provide the best data? EPA's Quality Management System requirements go beyond evaluation of environmental data quality itself to examine systems associated with production, collection, processing (validation/verification), transfer, reduction, storage, and retrieval of data throughout a life cycle. This QMS specifies minimum quality requirements for particular environmental programs. But how can you measure and compare programs that go well beyond the minimum, towards optimal quality? This paper compares EPA's requirements for Quality Management Systems (R2) and Project Plans (R5) to the Software Engineering Institute Capability Maturity Model (CMMISM). The CMMISM model provides for growth (staged or continuous) and a comprehensive assessment that is not yet provided in EPA's R2 or R5. Properly implemented, the CMMISM model serves as a quality framework for integrating and aligning organizational processes and implementing a program of continual process improvements. It identifies process areas ("things to do"), and provides measures of performance ("how well things are done") against specific goals and practices. CMMISM uses a Systems Engineering Management approach, built on process models, that helps identify "how good" the system is. Goodness is defined as stages in a complete model for optimal operation. CMMISM provides two methods for evaluating the goodness of the project. The Staged model in CMMISM provides a Maturity Level that is a well-defined evolutionary plateau describing the manner in which a specified set of processes are performed. As the organization advances in maturity, these levels become more defined and processes are tailored for specific project needs. The other method is called the Continuous Model in CMMISM, and it allows you to achieve Capability Levels. These are used to describe how well each project is doing in relationship to the different process areas. There are six Capability Levels from 0-5 that apply to individual process areas. Organizations using the Capability Level approach can select individual process areas that are important to specific projects and work to improve the processes. Improving capability in individual process areas raises the organization's overall quality of products delivered. The Continuous Model, unlike the Staged Model, lets you pick higher maturity level process areas before completing all of the ones below. Environmental measurement programs need to focus on the quality of the systems where data are collected, processed, transferred, and so forth. DynCorp built on the quality foundation from our experience with R2 to successfully implement CMMISM practices in the development of Forms II Lite and other applications. DynCorp is now migrating to the CMMISM model that has evolved from the existing CMM model. The CMMISM model focuses on the full cycle of Requirements Management from identification, development, collection, refinement, analysis, and validation throughout a project life cycle. It also has a more refined focus on the identification, development, collection, analysis, and evaluation of meaningful measurements, so the results can be used to improve a process or product.     

Activity-Based Throughput Management in a manufacturing company

This paper presents an Activity-Based Throughput Management (ABTM) framework that is based on the premise that attaining the goal of making money through activity analysis must be the main focus of manufacturing businesses. The proposed framework integrates the basic concepts of evolving management philosophies of Activity-Based Management and the Theory of Constraints into a cohesive model. The paper explains the concepts and conventions related to building a visual- (i.e. a process map) and spreadsheet-based ABTM model and thereby provides an operational decision-making framework for manufacturing managers in the modern manufacturing environment. The usefulness of the framework and its underlying model are then demonstrated by developing a number of product and process improvement scenarios, e.g. focused quality improvement, cycle time management, technological investment justification, and optimal product mix.     

A quality management framework for implementing additive manufacturing of medical devices

This paper aims to provide a comprehensive review of the implementation of additive manufacturing (AM) for medical devices. A quality management framework is proposed with discussion on topics such as software and data input, product understanding, AM equipment qualification, process understanding and continuous process verification. The interplay between input materials, process controls, and final outcomes of AM were analysed in the framework of quality management. Opportunities and challenges in implementing AM for medical devices are discussed.     

Lean tool selection in a die casting unit: a fuzzy-based decision support heuristic

Lean manufacturing philosophy asks for elimination of wastes hidden in the manufacturing system by focusing on product value stream and eliminating non-value adding activities through continuous improvement efforts. Value stream mapping methodology is subjected to principles of continuous improvement in order to improve the productivity of the process and quality of the product. It provides various tools for data collection and analysis, and identifies the wastes occurring in different stages of manufacturing process. The role of value stream mapping is very important in the identification and subsequently reduction of the wastes. To select the detailed mapping tools for the identification of waste at micro level is a complex decision making problem. In this paper, a case study related to a die casting unit has been taken. A hierarchy related to the decision problem has been developed to select the value stream mapping tools. Here, a fuzzy logic based multi-preference, multi-criteria, and multi-person decision making heuristic has been developed to solve a problem pertaining to above case study. The proposed methodology enjoys logical support from existing decision making tools and pertinently maps the inside details of the underlying problem.     

Multi-sensors data fusion for monitoring of powdered and granule products: Current status and future perspectives

Process Analytical Technology (PAT) is a systematic approach for monitoring of process parameters and product quality attributes and nowadays is considered for continuous processing of many industrial products. It is a mechanism to design, analyse and control manufacturing processes through on-line, in-line, at-line and off-line configurations for monitoring Critical Quality Attributes (CQAs). PAT systems include a combination of reliable in-line sensors, spectroscopic instruments and Multivariate Statistical Methods (MSMs) to provide informative knowledge for quality assessment of powdered and granule products. Nevertheless, monitoring programs of advanced manufacturing processes based on PAT systems typically provide large sets of data which are complex to interpret. The application of appropriate data-driven modelling techniques could assist in the interpretation of complex data matrices to better control of processes. Data fusion is a data-driven approach that could increase performance and robustness of models used for data interpretation to generate more accurate knowledge about process conditions and performance by merging related outputs collected from several instruments and considering synergies from multiple sources. This paper aims at presenting the current state of the art regarding the application of multi-sensors data fusion for powdered and granule manufacturing processes and making a critical review of recent progress and future possible perspectives in this field.     

Robust, Producible Design Process Evolution

Until about 10 years ago, design engineering and manufacturing at our company were separate organizations. Design engineers produced part designs for an integrated engine system and expected manufacturing to make and assemble the parts. Tolerance decisions were influenced by the arguing ability of each discipline along with historic precedent. Most form-fit-function characteristics were about 95% producible or two-sigma designs. Nonconformance control by a Material Review Board (MRB) was used by design engineering to monitor manufacturing quality. Although this process demonstrated the ability to produce excellent engines, it depended on inspecting in quality, multiple rework loops and resulting high cost.

This article will discuss the evolving process used to design engines that are producible and error proofed. Discussion will include the organizational structure supporting the needed culture change, the six-sigma impact of common terminology and data driven decisions, the structured approach using manufacturing process capability data to facilitate producibility, use of assembly defect and customer escape data to drive error-proofing early during the design process, and the focus on standardized notes and automated characteristic accountability for error prevention. Discussion will include examples demonstrating the significant improvements in quality and producibility accomplished by the new process.     

Application of analytical network process for the evaluation of sustainable business practices in an Indian relays manufacturing organization

Sustainable business strategies or technologies determine the market dynamism so as to withstand competitiveness. The evaluation of such strategies is a multi-criteria decision making (MCDM) problem. To deal with such problems, analytical hierarchy process (AHP) and analytical network process (ANP) could be deployed. AHP considers the distribution of goal amongst the elements being compared and judges the element which has the greater influence on the goal. In order to overcome the drawbacks with AHP, ANP could be used which is a holistic approach in which all attributes and alternatives involved are connected in a network system that accepts dependencies. Priorities which are established in ANP are the same way as they are in the AHP using pair wise comparisons and judgments. ANP is the most comprehensive framework for the analysis of societal, governmental and corporate decisions that are available today to the decision-maker. It is a process that allows one to include all the factors and criteria, tangible and intangible that has an effect on making a best decision. The paper reports a research study that is focused on the selection of the best alternative from the perspective of sustainability for an Indian relays manufacturing organization.     

How Can Lean Manufacturing Lead the Manufacturing Sector during Health Pandemics Such as COVID 19: A Multi Response Optimization Framework

Lean manufacturing has been used for the last few decades as a process and performance improvement tool. Initially known as Toyota production system (TPS), lean is now used in almost all service and manufacturing sectors to deliver favorable results such as decreased operational cost, increased customer satisfaction, decreased cycle time, and enhanced profits. During the coronavirus disease (COVID 19) pandemic, the manufacturing sector struggled immensely and could not function well even after lockdown was eased in many countries. Many companies found out there are not ready to conform with new regulations made by authorities in many countries. This paper proposes the use of simulation and multi response optimization in addition to other typical lean tools in order to arrive at optimum performance at the end of each project through an established optimization framework. The framework is used in a real case study performed at an aluminum extrusion factory. Lean manufacturing helps organizations to operate with smaller number of resources. It standardizes all processes so that most of the jobs can be done by most of the workers, but this is not enough to create a healthy, sanitized work place. Our framework utilizes the strengths of lean tools and adds pandemic readiness factor to them to ensure improvement in performance and health pandemic readiness. Implementation of the framework in the case company resulted in 50% reduction in labor, $730000 in expected annual cost savings, reduction in inventory levels, improved employee morale and the achievement of pandemic ready status.