A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry

This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion

Towards specification of a software architecture for cross-sectoral big data applications

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.This work is supported by the IBiDaaS project, funded by the European Commission under Grant Agreement No. 780787.Peer ReviewedPostprint (author's final draft

Context-Aware Tuples for the Ambient

In tuple space approaches to context-aware mobile systems, the notion of context is defined by the presence or absence of certain tuples in the tuple space. Existing approaches define such presence either by collocation of devices holding the tuples or by replication of those tuples across all devices. We show that both approaches can lead to an erroneous perception of context. The former ties the perception of context to network connectivity which does not always yield the expected result. The latter causes context to be perceived even if a device has left that context a long time ago. We propose a tuple space approach in which tuples themselves carry a predicate that determines whether they are in the right context or not. We present a practical API for our approach and show its use by means of the implementation of a mobile game

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments

Cybersecurity for industrial Internet of Things: architecture, models and lessons learned

Modern industrial systems now, more than ever, require secure and efficient ways of communication. The trend of making connected, smart architectures is beginning to show in various fields of the industry such as manufacturing and logistics. The number of IoT (Internet of Things) devices used in such systems is naturally increasing and industry leaders want to define business processes which are reliable, reproducible, and can be effortlessly monitored. With the rise in number of connected industrial systems, the number of used IoT devices also grows and with that some challenges arise. Cybersecurity in these types of systems is crucial for their wide adoption. Without safety in communication and threat detection and prevention techniques, it can be very difficult to use smart, connected systems in the industry setting. In this paper we describe two real-world examples of such systems while focusing on our architectural choices and lessons learned. We demonstrate our vision for implementing a connected industrial system with secure data flow and threat detection and mitigation strategies on real-world data and IoT devices. While our system is not an off-the-shelf product, our architecture design and results show advantages of using technologies such as Deep Learning for threat detection and Blockchain enhanced communication in industrial IoT systems and how these technologies can be implemented. We demonstrate empirical results of various components of our system and also the performance of our system as-a-whole

Scenarios for development and demonstration of dynamic maintenance strategies

International audienceThe maintenance of machinery and assets is a huge cost to European industry. Increasing requirements in terms of reliability, availability and quality and safety of production, in addition to the extension of normal operational ranges pose big challenges to machinery maintenance activities. The majority of corrective and preventive schemes adopted by companies are based on local information and in many cases prove to be costly and inadequate. However, today is possible to take advantage of radically new technologies (i.e internet, mobile devices, micro technologies) to re-design maintenance strategies, taking full advantage of the existing information at machinery facilities, and introducing smart and cheap hardware devices (sensors, PDAs) hence making room for cost-effective dynamic e-maintenance systems. This paper gives a short overview of the analysis of existing use cases for development and demonstration of new technologies, that are being developed as part of European Integrated Project DYNAMITE 017498 (Dynamic Decisions in Maintenance) where new technologies will facilitate the implementation of cost-effective maintenance solutions, with a special focus on condition-based strategies

An integrated mathematical model for the optimization of hybrid product-process layouts

The layout of a manufacturing process plays a significant role to maintain a profitable production and make competitive a company. Product-oriented layouts aim to minimize the distance travelled by the manufactured units; the process-oriented approach attempts to maximize the saturation of the facilities. However, in many cases a hybrid approach may be necessary to achieve a compromise between the two objectives. This paper aims to present a mathematical model capable to define a hybrid product-process layout by autonomously: (i) defining the process cells and, for each of them, evaluating the number of machines necessary for stability; (ii) identifying the position of the machines within each cell; (iii) determining the best position for the cells in a given shop-floor area; (iv) evaluating a set of KPIs for the obtained layout proposal. The numerical implementation of the model led to obtain a layout proposal within 10 seconds for a process made of 30 distinct operations. The approach is validated through case-studies taken from the automotive industry; the obtained results show that the model is an effective tool to support the activities of designers of manufacturing processes

Industrial demonstrations of e-maintenance solutions

The aims of some industrial demonstrations were to integrate the technological and information technology strands in a business sense and to verify the effectiveness of the complete Dynaweb Solutions. The feasibility of integrating the DynaWeb components to form e-maintenance architecture has been tested on the TELMA Platform based on a physical process and stands as a test bed relevant to both an automation architecture, as well as to a maintenance architecture

Digital manufacturing applicability of a laser sintered component for automotive industry: A case study

Purpose - Additive manufacturing requires a systemic approach to help industry on technology applicability research. Towards this end, the purpose of this research is to help manufacturing business Ieaders decide whether digitalised manufacturing based on additive manufacturing are suitable for engineering applications and help them plan technology transfer decisions. Design/methodology/approach - The methodology is based on case study research and action research, involving a mix of quantitative and qualitative research methods. The empirical part involved the study of the fatigue life of industrial component manufactured by Iaser sintering as well as a combination of quantitative and qualitative methods to define a strategic decision-making. Findings - Laser-sintered plastic materials are suitable in end use automotive applications, especially when there are multiple product variations. Fatigue life of the tested coupling meets the design requirements. Additionally, production of mechanical parts can be substituted by additive methods while digitalising the manufacturing process to gain productivity, especially when there is a need for mass-customisation. Research limitations/limplications- This research relies on a single case study research. The application used is unique and its technical empirical data cannot be transferred directly to other applications. Practical implications - lndustry practitioners can use this research to shed light on technology transferability challenges considering technical feasibility of additive polymer materials, economic aspects as well as strategic implications for implementing digitalised manufacturing methods based on additive manufacturing. Originality/value- This research presents a combined study of technical and strategic factors for additive manufacturing transferability using an industrial mass-customisation case as an example. ln addition, a new cost comparison model is presented including the impact of geometry variations