Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

In the field of industry, especially in the production areas, it is particularly important that the monitoring of assembly efficiency takes place in real-time mode, and that the related data-based estimation also works quickly and reliably. The Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems used by companies provide excellent support in data recording, processes, and storing. For Overall Equipment Effectiveness (OEE) data showing the efficiency of assembly lines, there is a regular need to determine expected values. This paper focuses on OEE values prediction with Multiple Linear Regression (MLR) as supervised machine learning. Many factors affecting OEE (e.g., downtimes, cycle time) are examined and analyzed in order to make a more accurate estimation. Based on real industrial data, we used four different methods to perform prediction with various machine learning algorithms, these were the cumulative, fix rolling horizon, optimal rolling horizon and combined techniques. Each method is evaluated based on similar mathematical formulas

The impact of the convergence of information technology and industrial automation on operational excellence in the manufacturing environment.

Thesis (MBA)-University of KwaZulu-Natal, 2007.The need to increase productivity, improve quality and increase flexibility whist continuously reducing costs is driving manufacturers to search for alternative means of converting the product idea into a manufactured product. Plant automation systems which are the nervous system and increasingly the intelligence of the plant have an integral role to play in this regard. This study investigates the convergence between traditional IT and Industrial Automation with a view to understanding how this phenomenon will affect operational excellence within the manufacturing environment. The study further investigates the key determinants of success for automation systems within the broader business context and how this can lead to an advantage over competitors. The study is limited to manufacturing operations within the greater Durban area. The results revealed that there is a clear relationship between industrial automation and information technology in manufacturing organisations. However, of interest is the fact that in the majority of the organisations surveyed the two functions operate as separate entities within the organisation resulting in overlaps of responsibility and accountability for key equipment and processes. Factory efficiency was found to be the key determinant of success in the majority of the organisations surveyed whilst the provisioning of production data when used strategically was found to have a positive effect in allowing the organisation to gain an advantage over its competitors. Due to the limitation of the short time frame allocated to this research, the study could not go in detail into the drivers of these findings consequently recommendations for further research is made

Design and realisation of an integrated methodology for the analytical design of complex supply chains

Supply chain systems are inherently complex and are dynamically changing webs of relationships. Wider product variety, smaller production lot sizes, more tiers and different actors involved in coordinated supply chains also cause supply chain complexity and presents major challenges to production managers. This context has led modern organizations to implement new supply chain paradigms and adopt new techniques to support rapid design, analysis and implementation of the new paradigms. The present research focuses to develop an integrated methodology which can support the analytical design of complex supply chains. [Continues.

Sistemas de informação na indústria 4.0 : mecanismos de apoio à transferência de dados para conhecimento em ambientes Lean

The paradigm that presently emerges in the organizational context, known as Industry 4.0 (I4.0) or Fourth Industrial Revolution, promises to bring principles of connectivity and flexibility to the companies that embrace it. Industry 4.0 enhances the efficiency in adapting in real time to the customers’ requirements, through the establishment of an intelligent shop floor capable of answering in a flexible and customized way to market changes. However, during the last three decades, it is known that the adoption of the Lean philosophy was absorbed by the industrial environment, with results that proved to be exuberant, considering the simplicity of the tools. In this way, the I4.0 implementation must be prepared to preserve the existing manufacturing systems, proceeding, whenever possible, to upgrade them on a Lean excellence basis. It is said that information systems will be decisive in the foundation of the I4.0 paradigm. Of these, MES systems, with greater connection to the shop floor, will tend to be aligned with existing practices, contributing, through their connectivity, to the introduction of knowledge management practices and data visualization mechanisms. In the specification and architecture phase of these systems, understanding the processes will be crucial. Thus, their documentation is an organizational pillar, with BPMN and UML being able to guide it. However, and in addition to its usefulness in the processes’ mapping, BPMN is also likely to be applied in capturing tacit knowledge, which can be a foundation for the constitution of knowledge repositories, impacting organizational excellence. It is in this context that the present work is implanted, aiming at the creation of guidelines and mechanisms that facilitate the implementation of I4.0 strategies in Lean industrial environments. The adopted methodology first went through an exhaustive literature review, in order to find possible bilateral effects between I4.0 technologies and lean tools. Then, the development of some applications aligned with the I4.0 paradigm, as a technological engine, and the Lean philosophy, as a tool for eliminating waste and / or creating value, was contemplated. From the various development experiences in an industrial context and considering the evidence reported in the literature, this study proposes a Lean 4.0 framework oriented to the shop floor.O paradigma que atualmente emerge no contexto organizacional, conhecido como Indústria 4.0 (I4.0) ou Quarta Revolução Industrial, promete trazer princípios de conectividade e flexibilidade às empresas que a adotam. A Indústria 4.0 potencia a eficácia no ajuste em tempo real aos requisitos dos clientes, através da constituição de um chão de fábrica inteligente e capaz de responder de forma flexível e customizada às mudanças do mercado. Contudo, durante as últimas três décadas, sabe-se que a adoção da filosofia Lean foi absorvida pelo meio industrial, com resultados que se demonstraram exuberantes, tendo em conta a simplicidade das ferramentas. Deste modo, a implementação I4.0 deve ser feita no sentido da preservação dos sistemas de manufatura já existentes, procedendo, desde que possível, ao seu upgrade numa base de excelência Lean. Conta-se que os sistemas de informação serão decisivos na fundação do paradigma I4.0. Destes, os sistemas MES, com maior conexão ao chão de fábrica, tenderão a ser alinhados com as práticas já existentes, contribuindo, através da sua conectividade, para a introdução de práticas de gestão do conhecimento e mecanismos de visualização de dados. Na fase de especificação e arquitetura destes sistemas, o entendimento dos processos será crucial. Assim, a documentação dos mesmos é um pilar organizacional, estando o BPMN e a UML capazes de a orientar. Porém, e a somar à sua utilidade na ilustração de processos, o BPMN está igualmente passível de ser aplicado na captação de conhecimento tácito, o que por si pode ser uma base para a constituição de repositórios de conhecimento, contribuindo para a excelência organizacional. É neste contexto que o presente trabalho se insere, tendo como objetivo a criação de linhas orientadoras e mecanismos que facilitem a implementação de estratégias I4.0 em ambientes industriais Lean. A metodologia adotada passou, primeiramente, por uma exaustiva revisão da literatura, por forma a encontrar possíveis efeitos bilaterais entre tecnologias I4.0 e ferramentas lean. De seguida, contemplou-se o desenvolvimento de alguns aplicativos alinhados ao paradigma I4.0, enquanto motor tecnológico, e à filosofia Lean, enquanto ferramenta de eliminação de desperdícios e/ou criação de valor. Das diversas experiências de desenvolvimento em contexto industrial e considerando as evidências reportadas na literatura o presente estudo propõe uma framework Lean 4.0 orientado ao chão de fábrica.Mestrado em Engenharia e Gestão Industria

Functional specifications of a manufacturing execution system

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.Includes bibliographical references (p. 129-130).by Roland B. Sargeant.S.M.M.B.A

Cyber-physical business systems modelling : advancing Industry 4.0

Abstract: The dynamic digital age drives contemporary multinationals to focus on delivering world-class business solutions with the use of advanced technology. Contemporary multinationals relate to a present-day business primarily engaged to generate profits. These complex multinationals offer value through the manufacture, sale, and management of products and services. Disruptive strategies in operations driven by emerging technological innovations demand continuous business improvements. These insightful opportunities are inclusive of operations, enterprise systems, engineering management, and research. Business sustainability is a strategic priority to deliver exceptional digital solutions. The Fourth Industrial Revolutions (4IR) offer significant technological advancements for total business sustainability. The underlying 4IR technologies include Cyber-Physical Systems (CPS). The collective challenges of a large global business are not easy to predict. CPS protocols deliver sustainable prospects required to integrate and model physical systems in real-time driven by the 4IR implementations. The goal of this thesis is to develop a model (CPS) suitable for self-predicting and to determine ideal operational practice driven by technologies of the 4IR. The model (CPS) seeks a novel tool effective for comprehensive business evaluation and optimisation. The competence of the anticipated tool includes suitability to collaborate current operations and predict the impact of change on a complex business. ..D.Phil. (Engineering Management

Successfully implementing a Manufacturing Execution Systems (MES) solutions.

Thesis (MBA)-University of KwaZulu-Natal, 2007.A Manufacturing Execution System (MES) is a system that companies use to measure and control critical production activities. As the installed base of MES installations grows, claims that MES does not have a positive impact on the day-to-day operations within manufacturing companies are more common. Documented results and anecdotal evidence are also now available. Due to the pace at which this market has grown, more and more vendors and implementation partners are entering the market. Organizations that wish to successfully implement a MES solution need to be well informed and educated about the intricacies of software implementations. Organizations need to ensure that they are in control of the implementation and not at the mercy of the software vendors and implementation partners for success. Organizations need to plan the whole implementation process thoroughly and top level management need to drive the initiatives within the organization to ensure success

A holistic approach for selecting appropriate manufacturing shop floor KPIs

In the era of globalization, manufacturing industries need to monitor their manufacturing operations acutely in order to remain competitive. Manufacturers seek to engineer highly flexible, robust, and efficient manufacturing processes enabling the production of high-quality goods at competitive costs while always addressing and adapting to evolving challenges. As a result, manufacturing industries in the present time have realized the significance of shop floor data analysis. They are implementing performance measurement systems to continually assess and improve the operational state of their manufacturing operations. These systems comprise a set of Key Performance Indicators (KPIs), which can enumerate the effectiveness, competence, efficiency, and proficiency of manufacturing processes. There is a lack of KPI understanding by the manufacturers and no framework or methodology available in the literature to select KPIs systematically, methodically, and/or scientifically for a manufacturing facility. This deficiency typically leads to failures in reporting and monitoring critical performance measures, with resultant losses to achieve key business objectives. Viewing the current industrial needs and limitations highlighted in the literature, this research presents a holistic approach that enables manufacturers to systematically understand, analyze, and select appropriate KPIs for their shop floor operations assessment. The approach is mainly centered on the premise that KPIs can be chosen based on a set of measures that are theoretically grounded. First, a manufacturing shop floor exploration model is developed to 1) recognize the key business objectives, 2) identify the bottlenecks in the manufacturing shop floor facility that negatively impacts the throughput, 3) point out the problems and challenges, and 4) list the KPIs used for monitoring shop floor performance. The model uses a set of questionnaires and structured interviews to collect the required data (i.e., data related to manufacturing shop floor performance) along with the real-time data extracted from the manufacturing shop floor. Second, a novel KPI guideline is developed to systematically guide the manufactures to understand, analyze, and select appropriate KPIs. These guidelines consist of five stages: information stage, discernment stage, scheming stage, the origin of the data stage, and assisting technology to capture the data stage. Every stage consists of a set of measures and their corresponding elements dedicated to providing vital information to help manufacturers better monitor their shop floor operations and improve decision-making capabilities. Last, to streamline the decision-making by prioritizing key business objectives and KPIs, the SMART criteria technique is prudently selected. The practicality of the proposed approach is demonstrated through its application to an automotive seat manufacturing company. It is sensible to indicate that the complete methodology of selecting appropriate KPIs and reviewing the manufacturing shop floor performance is a continuous process. After suggesting and implementing the KPIs, the manufacturers should evaluate the performance regularly since, in the current complex manufacturing environment, both internal and external business factors change over time. Hence it is necessary to incorporate these changes and provide continuous improvement, evaluating the shop floor performance on a regular basis