803,736 research outputs found
Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry
Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation
A strategy for achieving manufacturing statistical process control within a highly complex aerospace environment
This paper presents a strategy to achieve process control and overcome the previously mentioned industry constraints by changing the company focus to the process as opposed to the product. The strategy strives to achieve process control by identifying and controlling the process parameters that influence process capability followed by the implementation of a process control framework that marries statistical methods with lean business process and change management principles. The reliability of the proposed strategy is appraised using case study methodology in a state of the art manufacturing facility on Multi-axis CNC machine tools
Statistical process control implementation in the food industry: A systematic review and implications for future research
This study is to illustrate a systematic review application in investigating common issues emerging from Statistical Process Control (SPC) implementation in the food industry. A total of 34 journal articles were rigorously selected from four databases and reviewed. The most common themes emerge in SPC implementation in the food industry is the benefits while the remaining themes are motivation, barriers and critical success factors (CSF). This review found that the evidence of SPC implementation in the food industry is beneficial; however, a lack of both awareness and guidelines relating to SPC implementation in the food industry has resulted in a slow adoption. This systematic review concluded that there is a crucial need for further research into the SPC deployment aspect addressing how to deploy SPC in the food industry in a systematic manner
An exploratory study on statistical process control in the UK food industry
Statistical Process Control (SPC) is an effective technique improving process performance in manufacturing companies; however, the literature shows its implementation in the food industry is still less evident. This research aims to assess the SPC implementation in the UK food industry and subsequently develops an SPC implementation roadmap (SPCIR) and SPC Readiness Self-assessment Tool for food companies to assess their readiness level to adopt SPC. Survey and multiple-case studies were conducted to identify the widespread of SPC, challenges of implementing SPC, Critical Success Factors and the reasons for not implementing SPC in this industry. A five-phase SPCIR was refined through the action research, while five SPC readiness factors were identified through the Delphi study. This study adds value to the current knowledge by extending organisational readiness theories through the identification of SPC readiness factors and expands the organisational learning theory by uncovering type of learning created within SPC implementation. This study is relevant, practical, and useful to both practitioners and academics by providing a holistic implementation roadmap to guide the managers to implement SPC not only at the organisational level but also at the project level. This study offers an itinerary of organisational readiness that enables the managers to confirm the organisational preparedness for the adoption of SPC. The small sample size may limit the generalisability of the findings. But this exploratory study provides critical information to the managers in this sector to develop a strategic plan for a successful SPC implementation
Computing Multivariate Process Capability Indices With Microsoft Excel
In manufacturing industry there is growing interest in measures of process capability under multivariate setting. While there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this paper a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. Our aim is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report formatMultivariate Process capability indices, statistical quality control, Visual Basic, Excel Indici di capacità multivariati, Controllo statistico della qualità
The use of statistical process control in pharmaceuticals industry
The use of statistical process control has gained a major importance in the last years due to very good results that is
provides and due the ease interpretation of the results, even by the people who are not specialists in the field. An
essential quality, that differs the statistical process control to the other quality analysis statistical methods is that it
examines the process in all stages, not only in the final stage. The increase of the competitiveness in all areas of industry
made that the methods used in quality control to be more performant. No organization can maintain a high standard
without a performant quality control. The pharmaceutical industry is one of the most important industries, holding an
essential role in human’s health in particular and in welfare of the whole society in general. This application is meant to
illustrate, by using some of statistical indices, control diagrams and capability process indices, how it is used the
statistical process control in the pharmaceutical industry and highlights both advantages and disadvantages of using it
Statistical process control readiness in the food industry: development of a self-assessment tool
Background: The increasing pressure from the customers, governmental regulations and fierce market competition forced food companies to pursue powerful quality improvement technique. Although Statistical Process Control is widely known for its effectiveness in process control, many food companies faced difficulties to adopt such technique, where being in the state of not ready has always been the reason. There has been a debate about the importance of deciding the state of readiness of a company to initiate their CI techniques such as SPC towards the successful implementation and sustainability of such technique.Scope and approach: This paper emphasises the importance of SPC readiness towards its implementation in the food industry and determines its factors. The SPC readiness factors were identified based on the current literature review and complemented with a three-round Delphi study involving the SPC experts (academics, industry and consultants). Key findings and conclusion: The SPC readiness factors identified are top management support, sense of urgency, measurement system, employees involvement and organisational culture readiness. The developed conceptual self-assessment readiness tool enables food practitioners to identify the current state of organisational readiness and facilitate the companies to plan strategic changes and preparation activities for the adoption of SPC in their businesses
Optimising towards robust metal forming processes
Product improvement and cost saving have always been important goals in the metal forming\ud
industry. Numerical optimisation can help to achieve these goals, but optimisation with a deterministic\ud
approach will often lead to critical process settings, such that the slightest variation in e.g. material behaviour\ud
will result in violation of constraints. To avoid a high scrap ratio, process robustness must be considered in the\ud
optimisation model. Optimising for robustness includes Robust Manufacturing (RM) techniques, Optimisation\ud
Under Uncertainty (OUU) methods and Finite Element (FEM) simulations of the processes. In this paper,\ud
we review RM and OUU. Subsequently, the combination of Statistical Process Control (SPC), robust and\ud
reliability based optimisation methods, and FEM-based process simulation implemented in AutoForm-Sigma\ud
is presented. An automotive deep drawing application demonstrates the potential of strategies that optimise\ud
towards robust metal forming processes
Analisis Pengendalian Kualitas Produk Floordeck dengan Menggunakan Metode Statistical Process Control (SPC) pada PT. Mulcindo Steel Industry
Abstrak
Mulcindo Steel Industry adalah suatu perusahaan di bidang produksi baja, salah satu produk yang dihasilkan pada PT. Mulcindo Steel Industry ini adalah floordeck. Tujuan dari penelitan ini adalah untuk mengetahui permasalahan mutu yang sering muncul pada proses produksi floordeck, mengetahui faktor penyebab permasalahan mutu pada produk floordeck, dan mengetahui pemecahan masalah mutu yang dihadapi oleh PT. Mulcindo Steel Industry untuk memproduksi floordeck. Penerapan metode Statistical Process Control pada pengendalian kualitas floordeck ini dilakukan dengan identifikasi masalah menggunakan Check Sheet, Diagram Pareto, dan Peta Kendali. Proses analisa faktor penyebab permasalahan mutu dengan menggunakan Diagram Sebab Akibat. Langkah terakhir yang dilakukan yaitu melakukan tindakan perbaikan dengan memberikan usulan pemecahan masalah. Hasil penelitian menunjukkan bahwa permasalahan mutu yang sering terjadi adalah kecacatan robek pada produk floordeck dengan nilai presentase sebesar 39%. Faktor yang menyebabkan permasalahan mutu pada produk floordeck adalah faktor metode berupa setting mesin yang tidak standar karena memang belom adanya standar penyettingan pada mesin yang mengakibatkan setting mesin berubah-ubah. Solusi pemecahan masalah mutu yang dapat diberikan agar robek pada produk floordeck dapat berkurang adalah memperbaiki standar waktu setting mesin forming menjadi ± 129 menit.
Kata kunci: Pengendalian Kualitas, Floordeck, Statistical Process Control.
Abstrac
Mulcindo Steel Industry is a company in the field of steel production, one of the products produced at PT. Mulcindo Steel Industry is a floordeck. The purpose of this research is to find out the quality problems that often arise in the floordeck production process, to find out the factors causing quality problems in floordeck products, and to know the solutions to quality problems faced by PT. Mulcindo Steel Industry to produce floordeck. The application of the Statistical Process Control method on the quality control of the floordeck is carried out by identifying problems using Check Sheets, Pareto Diagrams, and Control Chart. The process of analyzing the factors causing quality problems using a Cause and Effect Diagram. The last step is to take corrective action by providing problem solving suggestions. The results showed that quality problems that often occur are tearing defects in floordeck products with a percentage value of 39%. The factors that cause quality problems in floordeck products is the method factor in the form of non standard machine settings because there is no standard setting on the machine which causes the machine settings tochange. The solution to solving quality problems that can be given so that tearing on floordeck products can be reducedis to improvethe standard time of setting the forming machine to ± 129 minutes.
Keywords: Quality Control, Floordeck, Statistical Process Control
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