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Data-Driven Failure Management: An Ontology-Based Speech Recognition App for Failure Capturing in Manufacturing Processes
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This paper describes a novel voice interaction mechanism for capturing and managing design knowledge within a collaborative Computer-Aided Design (CAD) environment. We present a software module for speech recognition that integrates with a CAD application to allow the automatic creation of textual annotations in a 3D model directly from voice data. Audio is transcribed automatically, resulting in a textual note that is searchable and available to other users via a Product Data Management (PDM) system, providing an intuitive mechanism to document modeling processes and design knowledge. The system consists of three functional blocks: (1) audio recording, (2) speech recognition, and (3) query management against a cloud-based service. In this paper, we justify the need for our system from a human-computer interaction standpoint and discuss the rationale of its design and implementation in the context of collaborative design communication. Finally, we discuss some application spaces that demonstrate the capability of voice annotations for capturing knowledge.
Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.
In the IS discipline, the formulation of design principles is an important vehicle to convey design knowledge that contributes beyond instantiations applicable in a limited context of use. However, their formulation still varies in terms of orientation, clarity, and precision. In this paper, we focus on the design of artifacts that are oriented towards human use, and we identify and analyze three orientations in the formulation of such design principles in IS journals - action oriented, materiality oriented, and both action and materiality oriented. We propose an effective and actionable formulation of design principles that is both clear and precise.
Recognizing that design is at the core of information systems development, has led to a design-science research paradigm where differing kinds of knowledge goals give form to differing kinds of knowledge processes within a single study. This paper analyzes knowledge production in design-science research to explain how an endogenous form of pluralism characterizes such studies, making it problematic to associate any design-science study with a single view of knowledge production. Instead, a design-science research study exhibits up to four different modes of reasoning, called genres of inquiry. These genres are derived from two dualities that contrast differing knowledge goals and differing knowledge scope in the knowledge production process. The first duality arises from the sometimes seemingly contradictory knowledge goals of science versus design. The second duality reflects the contradiction between the scope of the knowledge produced, which may be idiographic or nomothetic. The evolutionary and iterative nature of a design-science study compels different knowledge goals and scope at different moments throughout a project. Because of this momentary nature, a single design-science study can be associated with multiple genres of inquiry. This understanding of the variety in the genres of inquiry advances the discourse on the nature of design-science research and the justification and evaluation of its outcomes. Consequently, a corresponding set of criteria for knowledge justification and evaluation is provided for each genre of inquiry.
Service-Systems-Engineering (SSE) stellt die systematische Gestaltung und Entwicklung von Servicesystemen in den Mittelpunkt des Forschungsinteresses. Servicesysteme ermöglichen interaktive Wertschöpfung durch eine auf ein Wertversprechen ausgerichtete Konfiguration von Akteuren und Ressourcen. Weil diese Konfiguration unter anderem die Servicearchitektur, die Technologie, die Information und physische Artefakte in der Nutzung einschließt, handelt es sich um soziotechnische Systeme. Die Wirtschaftsinformatik kann mit ihrer interdisziplinären Tradition und Integrationsfähigkeit eine führende Rolle im Verständnis und der Entwicklung dieser Systeme spielen. SSE verdeutlicht das Potenzial, das die Entwicklung anwendbarer Theorien, Methoden und Ansätze der systematischen Gestaltung, Entwicklung und Pilotierung von Servicesystemen besitzt. Dies gilt umso mehr, wenn diese auf einem verbesserten Verständnis der grundlegenden Wirkprinzipien dieser Systeme beruhen. In diesem Zusammenhang stellen sich insbesondere drei zentrale Forschungsherausforderungen: 1.) die Entwicklung von Servicearchitekturen, 2.) die Entwicklung neuer Interaktionsformen mit Servicesystemen sowie 3.) die Mobilisierung von Ressourcen für Servicesysteme. Letzteres bezeichnet eine IT-gestützte Ausweitung des Zugriffs auf und der Nutzung von Ressourcen. Die Forschung in diesem Kontext ist vor allem deshalb herausfordernd, weil die Entwicklung von Modellen, Methoden und Artefakten in Servicesystemen oft die Einbettung in reale oder sogar neuartige Servicesysteme erfordert. Daher erscheinen methodisch vor allem die Pilotierung IT-basierter Innovationen, gestaltungsorientierte Forschung sowie Aktionsforschung sinnvoll. Als eine integrative Disziplin ist die Wirtschaftsinformatik besonders gut positioniert, um die Forschung zur evidenzbasierten Gestaltung der Architekturen, Interaktionen und Ressourcenzugängen von Servicesystemen zu befördern und wichtige Beiträge zu einer Konstruktionslehre für Servicesysteme zu leisten.
Abstract
Service systems engineering (SSE) focuses on the systematic design and development of service systems. Guided by a value proposition, service systems enable value co-creation through a configuration of actors and resources (often including a service architecture, technology, information, and physical artifacts), therefore constituting complex socio-technical systems. IS research can play a leading role in understanding and developing service systems. SSE calls for research leading to actionable design theories, methods and approaches for systematically designing, developing and piloting service systems, based upon understanding the underlying principles of service systems. Three major challenges have been identified: engineering service architectures, engineering service systems interactions, and engineering resource mobilization, i.e. extending the access to and use of resources by means of IT. Researching SSE is challenging. Assessing the models, methods, or artifacts of SSE often requires embedded research within existing or even novel service systems. Consequently, approaches such as piloting IT-based innovations, design research or action research are the most promising for SSE research. As an integrative discipline, IS is in a unique position to spearhead the efforts in advancing the architecture, interaction, and resource base of service systems with evidence-based design.
Service systems engineering (SSE) focuses on the systematic design and development of service systems. Guided by a value proposition, service systems enable value co-creation through a configuration of actors and resources (often including a service architecture, technology, information, and physical artifacts), therefore constituting complex socio-technical systems. IS research can play a leading role in understanding and developing service systems. SSE calls for research leading to actionable design theories, methods and approaches for systematically designing, developing and piloting service systems, based upon understanding the underlying principles of service systems. Three major challenges have been identified: engineering service architectures, engineering service systems interactions, and engineering resource mobilization, i.e. extending the access to and use of resources by means of IT. Researching SSE is challenging. Assessing the models, methods, or artifacts of SSE often requires embedded research within existing or even novel service systems. Consequently, approaches such as piloting IT-based innovations, design research or action research are the most promising for SSE research. As an integrative discipline, IS is in a unique position to spearhead the efforts in advancing the architecture, interaction, and resource base of service systems with evidence-based design.
Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm. We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods. This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base. Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications.
As a commentary to Juhani Iivari's insightful essay, I briefly analyze design science research as an embodiment of three closely related cycles of activities. The Relevance Cycle inputs requirements from the contextual envi- ronment into the research and introduces the research artifacts into environ- mental field testing. The Rigor Cycle provides grounding theories and methods along with domain experience and expertise from the foundations knowledge base into the research and adds the new knowledge generated by the research to the growing knowledge base. The central Design Cycle sup- ports a tighter loop of research activity for the construction and evaluation of design artifacts and processes. The recognition of these three cycles in a research project clearly positions and differentiates design science from other research paradigms. The commentary concludes with a claim to the pragmatic nature of design science.
The central outcome of design science research (DSR) is prescriptive knowledge in the form of IT artifacts and recommendations. However, prescriptive knowledge is considered to have no truth value in itself. Given this assumption, the validity of DSR outcomes can only be assessed by means of descriptive knowledge to be obtained at the conclusion of a DSR process. This is reflected in the build-evaluate pattern of current DSR methodologies. Recognizing the emergent nature of IT artifacts this build-evaluate pattern, however, poses unfavorable implications regarding the achievement of rigor within a DSR project. While it is vital in DSR to prove the usefulness of an artifact a rigorous DSR process also requires justifying and validating the artifact design itself even before it has been put into use. This paper proposes three principles for evaluating DSR artifacts which not only address the evaluation of an artifact’s usefulness but also the evaluation of design decisions made to build an artifact. In particular, it is argued that by following these principles the prescriptive knowledge produced in DSR can be considered to have a truth-like value.
Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both self-reported current usage (r=.63, Study 1) and self-predicted future usage (r =.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r=.45, Study 1) and future usage (r=.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
Manufacturing companies are more frequently confronted with short-term disruptions due to increasing manufacturing complexity. These disruptions lead to additional costs due to e.g. downtimes. Technological reaction potentials resulting from the adaptation of process parameters are currently neglected when reacting to disruptions. Moreover, technological interrelations between the manufacturing processes are not sufficiently examined. Thus, cost saving potentials are not fully utilized. Therefore, an adaptive reaction is necessary to exploit these potentials. In this paper, three challenges in the adaptive design of manufacturing process sequences in case of short-term disruptions are presented. Furthermore, a concept for overcoming these challenges is outlined.
Manufacturing refers to the processes of converting the raw materials into useful products. This is normally accomplished by carrying out a set of activities such as product design, selection of raw material, and materials processing (Kalpakjian and Schmid 2001). There exist a large number of conventional manufacturing processes which are used for the manufacturing of common products. However, the manufacturing engineering is a dynamic field marked with continuous advancement in the traditional approaches and the incorporation of novel approaches for manufacturing advanced products. Not all manufacturing processes can produce a product with equal ease, quality, and economy. Each manufacturing process is generally characterized by some advantages and limitation over the other processes. On the same lines, manufacturing using lasers may offer extraordinary benefits in some cases or may be a total failure in others. In order to keep the manufacturing of materials using lasers in the correct context, this chapter intends to give a brief overview of the various manufacturing processes.
Kurzfassung
Mit zunehmender Komplexität und Vernetzung der Produktionsprozesse steigt das Aufkommen von Störgrößen. Infolgedessen versuchen Unternehmen verstärkt, ein effizientes Störgrößenmanagement zu installieren. Im Rahmen eines Forschungsprojekts wird deshalb eine Systematik entwickelt, mit deren Hilfe KMU befähigt werden, Störgrößen leichter und schneller zu identifizieren sowie Maßnahmen zur Behebung und Prävention abzuleiten. Der Aufbau der Systematik wird in diesem Fachbeitrag beschrieben.
Das Buch beschreibt die strategischen Konzepte für die Fabriken der Zukunft und ordnet diese in ein systematisches Vorgehen ein. Damit verfolgt es eine ganzheitliche Sicht auf das System Produktion. Die Autoren entwickeln einen methodischen Ansatz zur strategischen Planung und Implementierung neuer Technologien und Konzepte. Sie beziehen Zukunftsvorstellungen und Wege zur strukturellen Anpassung der Produktionen von industriellen Unternehmen ein und bieten zahlreiche innovative Lösungen. Auch liefern sie Hinweise zur Implementierung systemverändernder Technologien bis hin zu Industrie 4.0. Der Aufbau des Buches steigert das Verständnis einzelner Strategie- und Technologielinien. Das Buch basiert auf langjährigen Erfahrungen in Industrieunternehmen sowie in der Forschung.
Der Inhalt
Einführung.- Re-Industrialisierung der verarbeitenden Industrie.- Technologiepolitik für das System Produktion.- Das System Produktion unter dem Einfluss globaler Megatrends.- Visionen und strategische Konzepte für das System Produktion.- Implementierung von Strategien in das System Produktion.- Zusammenfassung und Ausblick.
Die Zielgruppen
Das Buch ist besonders für Entscheider in produzierenden Unternehmen, Ingenieure, Informatiker und Betriebswirte interessant, da es praxisnahe Konzepte und Vorgehensweisen aus theoretischen Grundlagen ableitet. Die Studierenden lernen Methoden, Konzepte und Technologien zukünftiger industrieller Produktionen kennen.
Die Autoren
Prof. Dr.-Ing. Engelbert Westkämper war viele Jahre in der Forschung und Wissenschaft auf dem Gebiet der Produktionstechnik und des Managements der Produktion an den Universitäten in Braunschweig und Stuttgart sowie als Leiter des Fraunhofer-Institutes IPA in Stuttgart tätig. Seine Ansätze, basierend auf langjähriger Industrieerfahrung und wissenschaftlichen Arbeiten zu Zukunftskonzepten und Strategien der Produktion, finden sich in diesem Buch.
Dr.-Ing. Carina Löffler arbeitete im Team von Prof. Westkämper und hat an der GSaME (Graduate School of Excellence advanced Manufacturing Engineering) an der Universität Stuttgart mit dem Thema der strategischen Produktions-Strukturplanung im Automobilbau promoviert. Sie ist heute in der Strategieentwicklung Produktion der Dr. Ing. h.c. F. Porsche Aktiengesellschaft in Stuttgart tätig.
Purpose – A common understanding of what events to regard as production disturbances (PD) are essential for effective handling of PDs. Therefore, the purpose of this paper is to answer the two questions: how are individuals with production or maintenance management positions in industry classifying different PD factors? Which factors are being measured and registered as PDs in the companies monitoring systems? Design/methodology/approach – A longitudinal approach using a repeated cross-sectional survey design was adopted. Empirical data were collected from 80 companies in 2001 using a paper-based questionnaire, and from 71 companies in 2014 using a web-based questionnaire.
Findings – A diverging view of 21 proposed PD factors is found between respondents in manufacturing industry, and there is also a lack of correspondence with existing literature. In particular, planned events are not classified and registered to the same extent as downtime losses. Moreover, the respondents are often prone to classify factors as PDs compared to what is actually registered. This diverging view has been consistent for over a decade, and hinders companies to develop systematic and effective strategies for handling of PDs.
Originality/value – There has been no in-depth investigation, especially not from a longitudinal perspective, of the personal interpretation of PDs from people who play a central role in achieving high reliability of production systems.
The combination of high-performance and quality with cost-effective productivity, realizing reconfigurable, adaptive and evolving factories leading to sustainable manufacturing industries is one of the emerging research challenges in the domain of Internet of Things. So, it is important to define strategies and technical solutions to allow manufacturing process to autonomously react to the changing factors. Continuous data collection from heterogeneous sources and infusion of such data into suitable processes to enable almost real-time reactive systems is emerging research domain. This research work provides a technical framework for continuous data collection in support of the supply network and manufacturing assets optimization based on collaborative production planning. This research work explores its connection to legacy systems, software components and hardware devices to provide information to the different data consumers. The most relevant achievement of this work is the framework to bridge differences among computing systems, devices and networks allowing uniformity for data access by application decoupling data consumers from data sources.
Evaluation of design artefacts and design theories is a key activity in Design Science
Research (DSR), as it provides feedback for further development and (if done
correctly) assures the rigour of the research. However, the extant DSR literature
provides insufficient guidance on evaluation to enable Design Science Researchers
to effectively design and incorporate evaluation activities into a DSR project that can
achieve DSR goals and objectives. To address this research gap, this research paper
develops, explicates, and provides evidence for the utility of a Framework for
Evaluation in Design Science (FEDS) together with a process to guide design science
researchers in developing a strategy for evaluating the artefacts they develop within
a DSR project. A FEDS strategy considers why, when, how, and what to evaluate.
FEDS includes a two-dimensional characterisation of DSR evaluation episodes
(particular evaluations), with one dimension being the functional purpose of the
evaluation (formative or summative) and the other dimension being the paradigm
of the evaluation (artificial or naturalistic). The FEDS evaluation design process is
comprised of four steps: (1) explicate the goals of the evaluation, (2) choose the
evaluation strategy or strategies, (3) determine the properties to evaluate, and
(4) design the individual evaluation episode(s). The paper illustrates the framework
with two examples and provides evidence of its utility via a naturalistic, summative
evaluation through its use on an actual DSR project.
European Journal of Information Systems advance online publication,
11 November 2014; doi:10.1057/ejis.2014.36
Open Access
Knowledge about the state of the production system is necessary for proper management of a company. A modern company typically uses an ERP (Enterprise Resources Planning) system for management support, but still there is usually a gap between business and manufacturing layers of a company. There is a need to provide solutions allowing data acquisition directly from the production system, analyse this data and display it in a convenient form. Each type of production systems require a different approach to collect data because of variety of objects and conditions. The ability of production data acquisition mostly depends on the level of automation. This paper presents a comparison of methods of data acquisition from different types of manufacturing systems. Methods of data acquisition, from both automated and non-automated manufacturing systems, are described. Automation equipment resources (sensors, actuators, PLC, DCS, CNC, HMI, SCADA) and automatic identification systems (barcodes, RFID, vision systems etc.), as well as communication solutions (fieldbus, wired and wireless networks) and information exchange standards (OPC, MTConnect) are discussed.
In construction process, defects occur inevitably and repeatedly. It is one of the primary causes of project schedule and cost overruns. Many studies on defect causation analysis and management system have been conducted to facilitate defect measures and rectifications as well as to reduce the reoccurrence of the defect. However, those studies did not sufficiently consider the relationship of defect information flow in the construction process, which resulted in reactive rather than proactive defect management plan. This paper investigates the issues and needs of current defect management practices in the construction industry. It also presents a conceptual system framework for construction defect management that integrates ontology and augmented reality (AR) with building information modeling (BIM). The following three main technical solutions are proposed in the system framework: 1) defect data collection template to assure data quality and accuracy; 2) defect domain ontology to search and retrieve project or work-specific defect information; and 3) AR-based Defect Inspection System to support field defect management. It is envisaged that the system framework and solutions could enable proactive reduction of the defect occurrence during the construction process and that could greatly improve current defect management practices in the construction industry.
Work measurement methods previously proposed require considerable time and effort by time study analysts because they have
to measure the required time through direct observations. In this study, however, we propose a method which efficiently measures
the standard times without involving human analysts by using speech recognition and digital image processing techniques. First,
we implement a prototype system which can acquire the status of manufacturing cells through a speech recognition system. Second,
using image processing, we suggest a method which consists of two main steps: motion representation and cycle segmentation.
In the motion representation step, we first detect the motion of any object distinct from its background by differencing two
consecutive images separated by a constant time interval. The images thus obtained then pass through an edge detector filter.
Finally, the mean values of coordinates of significant pixels of the edge image are obtained. Through these processes, the
motions of the observed worker are represented by two time series of data of worker location in horizontal and vertical axes.
In the cycle segmentation step, we extract the frames which have maximum or minimum coordinates in one cycle, store them in
a stack, and calculate each cycle time using these frames. In this step we also consider methods for detecting work delays
due to unexpected events such as an operator’s movement out of the work area, or interruptions. To conclude, the experimental
results show that the proposed method is very cost-effective and useful for measuring time standards for various work environments.
As business environments become more and more information-intensive, an organization's approach to collecting, processing and disseminating information becomes a significant determinant of its productivity, delivery performance, quality, profitability, and overall competitiveness. Integrated Manufacturing Systems (IMS) of the 1990s and beyond will require a totally integrative management of the manufacturing enterprise. This focus on the integration of the entire enterprise rather than a narrow focus in just the manufacturing function places more stringent demands on data integrity in order to effectively coordinate the activities required to carry out the functions aimed at attaining the organizational mission. An overview is presented on the current body of knowledge on Automated Data Capture (ADC) technologies, including: bar coding, radio frequency identification, radio frequency communication, optical character recognition, voice recognition systems, machine vision, magnetic stripes, Electronic Data Interchange (EDI), and systems design and integration considerations. These technologies, collectively referred to as keyless data entry techniques, automated data capture, techniques, or Automatic Identification (Auto ID) technologies are critical to the success of automated manufacturing systems, distribution, inventory control and other computer facilitated activities related to enterprise integration.
The effect of presentation media on recipient performance and perceived difficulty in text-based information systems was investigated. Based on data collected in a laboratory setting, we examined differences among electronic mail, audio, and audio/video media in terms of message comprehension and perceived difficulty. Twenty-four volunteer business administration faculty and graduate students were subjects for the repeated-measures experiment. The results show that media affects message comprehension, but does not alter the recipient's perception of the difficulty of the message. Media that require reading the message were found to be superior to audio media in terms of comprehension.
A brief survey of text mining: Classification, clustering and extraction techniques
- M Allahyari
- S A Pouriyeh
- M Assefi
- S Safaei
- E D Trippe
- J Guttierez
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K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques.
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Advances in production management systems: Innovative production management towards sustainable growth
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Stich, V., Jordan, F., Birkmeier, M., Oflazgil, K., Reschken, J., & Diews, A. (2015). Big data
technology for resilient failure management in production systems. In S. Umeda, M. Nakano,
H. Mizuyama, N. Hibino, D. Kiritsis, & G. von Cieminski (Eds.), Advances in production
management systems: Innovative production management towards sustainable growth. APMS
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Continuous data collection framework for manufacturing industries
- S Ghmire
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- C Agostinho
- R Goncalves