An Overview of Next-generation Manufacturing Execution Systems:How important is MES for Industry 4.0?

The purpose of this paper is to understand the evolution of manufacturing execution systems (MES) in the digital transformation era. Theoretical propositions made on MES (based on literature survey) were empirically examined using three case studies in Danish companies. Findings gave an overview of Industry 4.0 ready MES and identified its role in factories of the future. It is a first attempt to analyze the concepts behind next-generation MES to give a primer on ‘MES as a digital twin', via first iteration of results from cross-case synthesis of collected data. The paper also maps the current MES research pertaining to Industry 4.0 into key groups to highlight its significance in digital manufacturing

Mallipohjainen järjestelmäintegraatio tuotannonohjausjärjestelmille

Application integration becomes more complex as software becomes more advanced. This thesis investigates the applicability of model-driven application integration methods to the software integration of manufacturing execution systems (MES). The goal was to create a code generator that uses models to generate a working program that transfers data from a MES to another information system. The focus of the implementation was on generality. First, past research of MES was reviewed, the means to integrate it with other information systems were investigated, and the international standard ISA-95 and B2MML as well as model-driven engineering (MDE) were revised. Next, requirements were defined for the system. The requirements were divided into user and developer requirements. A suitable design for a code generator was introduced and, after that, implemented and experimented. The experiment was conducted by reading production data from the database of MES-like Delfoi Planner and then transforming that data to B2MML-styled XML-schema. The experiment verified that the code generator functioned as intended. However, compared to a manually created program, the generated code was longer and less efficient. It should also be considered that adopting MDE methods takes time. Therefore, for MDE to be better than traditional programming, the code generator has to be used multiple times in order to achieve the benefits and the systems cannot be too time-critical either. Based on the findings, it can be said, that model-driven application integration methods can be used to integrate MESs, but there are restrictions.Järjestelmäintegraatio vaikeutuu ohjelmien monimutkaistuessa. Tässä työssä tutkitaan mallipohjaisten järjestelmäintegraatiometodien soveltuvuutta tuotannonohjausjärjestelmille (MES). Tavoitteena oli muodostaa koodigeneraattori, joka käyttää malleja luodakseen toimivan ohjelman, joka siirtää tietoa MES-järjestelmästä johonkin toiseen tietojärjestelmään. Toteutuksessa keskityttiin yleistettävyyteen. Aluksi työssä käytiin läpi aikaisempaa tutkimusta MES-järjestelmistä ja mahdollisuuksista integroida niitä toisiin informaatiojärjestelmiin. Lisäksi otettiiin selvää kansainvälisestä ISA-95 standardista ja B2MML:sta sekä mallipohjaisesta tekniikasta (MDE). Tämän jälkeen järjestelmälle määriteltiin vaatimukset, jotka jaettiin käyttäjän ja kehittäjän vaatimuksiin. Koodigeneraattorista tehtiin ehdot täyttävä suunnitelma, joka toteutettiin ja jolla suoritettiin kokeita. Koe toteutettiin lukemalla tuotantodataa MES:n kaltaisen Delfoi Plannerin tietokannasta, jonka jälkeen data muutettiin B2MML tyyliä noudattavaan XML-schema muotoon. Kokeet osoittivat, että koodigeneraattori toimi kuten toivottiin. Kuitenkin havaittiin, että verrattuna manuaalisesti toteutettuun ohjelmaan, luotu ohjelma ei ollut yhtä tehokas ja lisäksi se oli pidempi. Huomattiin myös, että MDE-metodien käyttöönotto vie paljon aikaa. Jotta MDE olisi perinteistä ohjelmointia parempi vaihtoehto, sitä pitäisi käyttää useita kertoja ja sillä luotu järjestelmä ei saisi olla liian aikariippuvainen. Havaintojen perusteella voidaan sanoa, että mallipohjaisia järjestelmäintegraatiometodeja voidaan käyttää MES-järjestelmien integrointiin, mutta sille on rajoituksia

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario

A Process Mining Based Approach to Complex Manufacturing Process Flow Analysis: A Case Study

Department of Management EngineeringWith recent advances in IT infrastructure in manufacturing environments, a large amount of manufacturing data are collected and stored in a database at various stages of production. These data may include valuable information for manufacturing companies to improve their manufacturing processes. The method of manufacturing data analysis is crucial for understanding the manufacturing data. However, traditional manufacturing data analysis methods such as data mining, simulation, etc. have limitations for this purpose since those are difficult to provide overall process-level information. Therefore, in this thesis, a process mining based approach for analyzing complex manufacturing processes is proposed. Process mining is a useful tool for process-related knowledge acquisition since it enables users to derive not only manufacturing process models, but also several performance measures related to processes, resources, and tasks. This thesis suggests a framework for the manufacturing process analysis. To do this, it applies process mining techniques to perform four types of analysis, which are visualization of production flows, machine-to-machine inter-relationship analysis, machine utilization, and monitoring & diagnosis of task performance regarding yield rate and lead time. Furthermore, a case study is conducted to support the proposed framework with an event log of an electronic components manufacturing process.ope

Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze

Engineering framework for service-oriented automation systems

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

Projeto de adaptação de módulo informático para gestão de auditorias : implementação de normativos no âmbito da qualidade e da segurança em indústrias alimentares

Dissertação de Mestrado Integrado em Medicina VeterináriaA evolução tecnológica das últimas décadas proporcionou à indústria agroalimentar a oportunidade para se munir de ferramentas informáticas ágeis e robustas. Têm surgido sistemas computorizados, particularmente os MES (Manufacture Execution System), que permitem gerir a implementação de planos de gestão da segurança e qualidade na indústria alimentar através da introdução de interfaces tácteis no chão-de-fábrica das indústrias, onde se dá o processamento dos alimentos. Este trabalho teve por objectivo ajudar a implementar, em parceria com uma empresa de “software”, um módulo para gestão de auditorias – MGA – que tenta agilizar, através da integração com um sistema MES, a implementação de normativos internacionais, tais como o ISO 22000, “BRC food” ou o “IFS food”, os quais possibilitam, no caso português, a exportação de produtos alimentares para mercados internacionais. Foi definido um plano para o desenvolvimento do projeto, contribuindo este trabalho com a seleção e compilação de listas de parâmetros de avaliação da segurança e qualidade dos alimentos relativos a pré-requisitos, boas práticas e metodologia HACCP, bem como de parâmetros relativos à “BRC food”, um normativo geralmente requisitado pelo mercado alimentar da Commonwealth. Testes preliminares sugerem que o MGA agiliza o planeamento e a execução de auditorias, tirando vantagem da disponibilização imediata de dados previamente compilados. Outras vantagens podem incluir redução do tempo de produção de documentos de auditoria, e recolha sistematizada de dados relevantes para estudo e antecipação de riscos alimentares. A sua integração com sistemas MES pode também acelerar a implementação de normativos necessários à comercialização internacional de produtos alimentares.ABSTRACT - Project development of a software module for audit management and food safety and quality standards implementation in food industries - Technological development of the last decades provided the opportunity for the Agro- Industries to benefit from agile and robust informatics tools. Computerized systems that are popular nowadays, such as MES’s (Manufacture Execution System), allow to improve the implementation of food safety management plans in food processing facilities by introducing tactile interfaces on the factory floor, where food processing takes place. This work aimed to assist the implementation, in partnership with a software company, of a software module for audit management – MGA – whose goal is also to quicken, through integration with a MES system, the implementation of international norms, such as ISO 22000, BRC food or IFS food, whose certifications may allow the access to most of international food markets. A plan was defined in order to develop the project, to which this work contributed with the selection and compilation of food quality and safety parameters relating to good practice, pre-requisites and HACCP methodology, as well as parameters relating to the BRC food, a norm usually required by the Commonwealth food market. Preliminary tests suggest that the MGA streamlines audit planning and execution, taking advantage of immediate access to previously collected data. Other advantages may be the reduction of time expended on the production of the auditing related documents and systematized input of relevant data to anticipate food related risks. Its integration with a MES system may also accelerate the implementation of international food norms, essential for the international trade of food products