Intellectualization of the management processes at the enterprise of automotive industry

© 2018 Association for Computing Machinery. Complex production systems, such as the trucks manufacturing, require the intellectualization of processes. Introduction of smart technologies (Industry 4.0) in automotive industry requires new approaches to the managerial decisions making, thus the modelling of a control system becomes the most important part of the manufacturing optimization. In this paper, we present an assembly line simulation model with dynamically changing parameters that allows reacting quickly to the production system’s changes. A software module has been developed to automatically perform such functions, as monitoring the operations (statuses) on positions and developing recommendations to organize logistical flows. The algorithm to determine the state of the assembly line’s position is presented. Introduction of such approach in automotive industry will allow not only optimizing processes and improving product quality, but also establishing favorable conditions for the subsequent intellectualization of the automotive service

Digitalization Roadmap for Turkish Seaports

One of the most effective and necessary application areas of digitalization is seaports. The commercial sophistication of a country is measured by its processing capacity at sea ports, which are the export and import gates. Sea ports are trying to reduce error rates by increasing their operation speeds and capacities through digitalization. Ports like port of Singapore, port of Rotterdam are examples that through digitalization processes the ports become more effective and the handling of loading and unloading is much faster. Turkey has an enormous potential in the area of Seaports but the investment in innovative technologies at Seaports in very low. The importance of digitalization to become and be competitive on the global market will be more and more clear to the port operator. Because of this a guideline is needed. Digital road maps in the manufacturing area of Turkey despite the creation of a road map for the seaport are not formed and given. This study is a roadmap to the digitalization process in Turkey sea port was created. To create and understand this roadmap a general understanding for digitalization and Industry 4.0 will be given. Also the digitalization processes in Seaports of Europe will be analyzed on the examples of Hannover port and port of Rotterdam. Finally the current situation in Turkish Seaports in regarding to digitalization will be investigated and the technologies of the biggest five ports will be shown. After this three points in section 2. Material and Method, in section 3 digitalization steps are presented.Dijitalleşmenin en etkili ve gerekli uygulama alanlarından biri limanlardır. Bir ülkenin ticari karmaşıklığı, ihracat ve ithalat kapıları_x000D_ olan deniz limanlarındaki işleme kapasitesi ile ölçülür. Deniz limanları dijitalleşme yoluyla çalışma hızlarını ve kapasitelerini artırarak_x000D_ hata oranlarını azaltmaya çalışıyor. Singapur limanı, Rotterdam limanı gibi limanlar, dijitalleşme süreçleri ile limanların daha etkili_x000D_ hale geldiği ve yükleme ve boşaltma işlemlerinin çok daha hızlı olduğu örnekleridir. Türkiye, Seaports alanında muazzam bir_x000D_ potansiyele sahiptir, ancak Seaports'ta yenilikçi teknolojilere yapılan yatırım çok düşüktür. Dijitalleşmenin küresel pazarda rekabet_x000D_ edebilmek ve rekabetçi olabilmenin önemi liman işletmecisine daha açık olacaktır. Bu nedenle bir yönerge gereklidir. Liman için bir_x000D_ yol haritası oluşturulmasına rağmen, Türkiye'nin imalat alanında dijital yol haritaları oluşturulmamış ve verilmemiştir. Bu çalışma,_x000D_ Türkiye'nin deniz limanındaki dijitalleşme sürecine bir yol haritası oluşturacaktır. Bu yol haritasını oluşturmak ve anlamak için_x000D_ dijitalleşme ve Endüstri 4.0 hakkında genel bir anlayış verilecektir. Ayrıca Avrupa Limanlarındaki dijitalleşme süreçleri Hannover_x000D_ limanı ve Rotterdam limanı örnekleri üzerinde analiz edilecektir. Son olarak Türk limanlarında dijitalleşme konusundaki mevcut_x000D_ durumu incelenecek ve en büyük beş limanın kullandığı teknolojiler gösterilecektir. Bölüm 2'deki Materyal ve Yöntem de bu üç_x000D_ önemli konuya değinildikten sonra, 3. kısımda dijitalleşme adımları sunulmaktadır.Dijitalleşmenin en etkili ve gerekli uygulama alanlarından biri limanlardır. Bir ülkenin ticari karmaşıklığı, ihracat ve ithalat kapıları olan deniz limanlarındaki işleme kapasitesi ile ölçülür. Deniz limanları dijitalleşme yoluyla çalışma hızlarını ve kapasitelerini artırarak hata oranlarını azaltmaya çalışıyor. Singapur limanı, Rotterdam limanı gibi limanlar, dijitalleşme süreçleri ile limanların daha etkili hale geldiği ve yükleme ve boşaltma işlemlerinin çok daha hızlı olduğu örnekleridir. Türkiye, Seaports alanında muazzam bir potansiyele sahiptir, ancak Seaports'ta yenilikçi teknolojilere yapılan yatırım çok düşüktür. Dijitalleşmenin küresel pazarda rekabet edebilmek ve rekabetçi olabilmenin önemi liman işletmecisine daha açık olacaktır. Bu nedenle bir yönerge gereklidir. Liman için bir yol haritası oluşturulmasına rağmen, Türkiye'nin imalat alanında dijital yol haritaları oluşturulmamış ve verilmemiştir. Bu çalışma, Türkiye'nin deniz limanındaki dijitalleşme sürecine bir yol haritası oluşturacaktır. Bu yol haritasını oluşturmak ve anlamak için dijitalleşme ve Endüstri 4.0 hakkında genel bir anlayış verilecektir. Ayrıca Avrupa Limanlarındaki dijitalleşme süreçleri Hannover limanı ve Rotterdam limanı örnekleri üzerinde analiz edilecektir. Son olarak Türk limanlarında dijitalleşme konusundaki mevcut durumu incelenecek ve en büyük beş limanın kullandığı teknolojiler gösterilecektir. Bölüm 2'deki Materyal ve Yöntem de bu üç önemli konuya değinildikten sonra, 3. kısımda dijitalleşme adımları sunulmaktadır

Simulation of an automotive supply chain in Simio: Data model validation

This paper presents a simulation model of the supply chain of a company of the automotive industry. The purpose of this paper is to use the presented model to validate the considered set of variables that we think are relevant to the problem. This approach was important as it allowed to consider a set of variables that could have been ignored if a different approach had been followed. It should be stressed that, due to privacy concerns, real data was not used, but rather random distributions assigned by the modeler. Notwithstanding, by recognizing that, for the data used, the outputs are in accordance to what happens in the real system, the authors concluded that the set of variables can be considered as validated. Yet, it is still necessary to further complement the model with additional available variables that were not included at this stage, due to its complexity, e.g., customer demand variability, uncertainty associated to suppliers' and impact of external events, such as transportation delays.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, thePortuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operacional Programme for Human Capital (POCH)

An IoT based industry 4.0 architecture for integration of design and manufacturing systems.

This paper proposes an Internet of Things (IoT) based 5-stage Industry 4.0 architecture to integrate the design and manufacturing systems in a Cyber Physical Environment (CPE). It considers the transfer of design and manufacturing systems data through the Cloud/Web-based (CW) services and discusses an effective way to integrate them. In the 1st stage, a Radio-Frequency IDentification (RFID) technology containing Computer Aided Design (CAD) data/models of the product with the ability to design / redesign is scanned and sent to a secure Internet/Cloud Server (CS). Here the CAD models are auto identified and displayed in the Graphical User Interface (GUI) developed for the purpose. From the scanned RFID CAD data/models, the 2nd stage adopts unique machine learning technique(s) and identifies the design & manufacturing features information required for product manufacture. Once identified, the 3rd stage handles the necessary modelling changes as required to manufacture the part by verifying the suitability of process-based product design through user input from the GUI. Then, it performs a Computer Aided Process Planning (CAPP) sequence in a secure design cloud server designed using web-based scripting language. After this, the 4th stage generates Computer Aided Manufacturing (CAM) toolpaths by continuous data retrieval of design and tooling database in the web server by updating the RFID technology with all the information. The various processes involved the 3rd and 4th stages are completed by using ‘Agents’ (a smart program) which uses various search and find algorithms with the ability to handle the changes to the process plan as required. Finally, the 5th stage, approves the product manufacture instructions by completing the production plan with the approved sheets sent to the Computer Numerical Control (CNC) machine. In this article, the proposed architecture is explained through the concept of IoT data transfer to help industries driving towards Industry 4.0 by improving productivity, reducing lead time, protecting security and by maintaining internationals standards / regulations applied in their workplace

Big Data Analytics for vehicle multisensory anomalies detection

Autonomous driving is assisted by different sensors, each providing information about certain parameters. What we are looking for is an integrated perspective of all these parameters to drive us into better decisions. To achieve this goal, a system that can handle these Big Data issues regarding volume, velocity and variety is needed. This paper aims to design and develop a real-time Big Data Warehouse repository, integrating the data generated by the multiple sensors developed in the context of IVS (In-Vehicle Sensing) systems; the data to be stored in this repository should be merged, which will imply its processing, consolidation and preparation for the analytical mechanisms that will be required. This multisensory fusion is important because it allows the integration of different perspectives in terms of sensor data, since they complement each other. Therefore, it can enrich the entire analysis process at the decision-making level, for instance, understanding what is going on inside the cockpit.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039334; Funding Reference: POCI-01-0247-FEDER-039334]

Managing changes initiated by industrial big data technologies : a technochange management model

With the adoption of Internet of Things and advanced data analytical technologies in manufacturing firms, the industrial sector has launched an evolutionary journey toward the 4th industrial revolution, or so called Industry 4.0. Industrial big data is a core component to realize the vision of Industry 4.0. However, the implementation and usage of industrial big data tools in manufacturing firms will not merely be a technical endeavor, but can also lead to a thorough management reform. By means of a comprehensive review of literature related to Industry 4.0, smart manufacturing, industrial big data, information systems (IS) and technochange management, this paper aims to analyze potential changes triggered by the application of industrial big data in manufacturing firms, from technological, individual and organizational perspectives. Furthermore, in order to drive these changes more effectively and eliminate potential resistance, a conceptual technochange management model was developed and proposed. Drawn upon theories reported in literature of IS technochange management, this model proposed four types of interventions that can be used to copy with changes initiated by industrial big data technologies, including human process intervention, techno-structural intervention, human resources management intervention and strategic intervention. This model will be of interests and value to practitioners and researchers concerned with business reforms triggered by Industry 4.0 in general and by industrial big data technologies in particular

Are simulation tools ready for big data? Computational experiments with supply chain models developed in Simio

Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing. The need and potential benefits for the combined use of Simulation and Big Data in Supply Chains (SCs) has been widely recognized. Having worked on such project, some simulation experiments of the modelled SC system were conducted in SIMIO. Different circumstances were tested, including running the model based on the stored data, on statistical distributions and considering risk situations. Thus, this paper aimed to evaluate such experiments, to evaluate the performance of simulations in these contexts. After analyzing the obtained results, it was found that whilst running the model based on the real data required considerable amounts of computer memory, running the model based on statistical distributions reduced such values, albeit required considerable higher time to run a single replication. In all the tested experiments, the simulation took considerable time to run and was not smooth, which can reduce the stakeholders' interest in the developed tool, despite its benefits for the decision-making process. For future researches, it would be beneficial to test other simulation tools and other strategies and compare those results to the ones provided in this paper.This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Data Mesh: concepts and principles of a paradigm shift in data architectures

Inherent to the growing use of the most varied forms of software (e.g., social applications), there is the creation and storage of data that, due to its characteristics (volume, variety, and velocity), make the concept of Big Data emerge. Big Data Warehouses and Data Lakes are concepts already well established and implemented by several organizations, to serve their decision-making needs. After analyzing the various problems demonstrated by those monolithic architectures, it is possible to conclude about the need for a paradigm shift that will make organizations truly data-oriented. In this new paradigm, data is seen as the main concern of the organization, and the pipelining tools and the Data Lake itself are seen as a secondary concern. Thus, the Data Mesh consists in the implementation of an architecture where data is intentionally distributed among several Mesh nodes, in such a way that there is no chaos or data silos, since there are centralized governance strategies and the guarantee that the core principles are shared throughout the Mesh nodes. This paper presents the motivation for the appearance of the Data Mesh paradigm, its features, and approaches for its implementation.- (undefined