Elisabeth Ilie-Zudor

Survey paper: A survey of applications and requirements of unique identification systems and RFID techniques

The paper contains an overview of unique identification issues and of the various radio frequency identification techniques that are available now or will become available in the short term. The paper also compares RFID with traditional ID technologies. It shows application possibilities and gives examples of current implementations. Each application has its own requirements that translate into specific RFID-techniques, -options and -parameters. Techniques include frequency range, tag energy supply and tag writing capabilities. The data to be stored in the tag and transferred to the reader must be selected as one of the options. Parameters influence reliability and confidentiality, among other things. Information interchange issues of identifier-based operations in supply-chains are discussed as well, while the last part of the paper presents a framework for choosing an auto-ID technique in a supply chain.

Towards adaptive and digital manufacturing

The problem each manufacturer repeatedly faces is how to meet demand by making available the required quantities of products in the specified quality and at proper time. From the four main R&D directions, i.e., adaptive manufacturing, digital manufacturing, knowledge-based manufacturing, and networked manufacturing, emphasized by the European initiative Manufuture, mostly the first two are underlined in the paper by illustrating some solution approaches. However, all the related issues, i.e., knowledge-based manufacturing and networked manufacturing, together with the requirements of real-time functioning and cooperativeness are kept in view.

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0

Strongly rooted in the Internet of Things and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms like the Factory of the Future and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart personalized products are created through smart processes and procedures. The 4th industrial revolution will be based on CyberPhysical Systems that will monitor, analyze and automate business processes, transforming production and logistic processes into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used to increase productivity and efficiency. This survey provides insights into the latest developments in these domains, and identifies relevant research challenges and opportunities to shape the future of intelligent manufacturing environments.

Engineering education on supply-chain management for students and for employees in industry

The paper presents a virtual institute, built in the form of an Internet‐based platform, for distance learning in the field of supply‐chain management. The Institute addresses students and teachers on university educational level, as well as employees in industry. Besides learning modules, the platform provides users with means to communicate, respectively to organise and execute project activities from remote locations. Means for on‐line content editing, and course administration are made available as well.

Decision support solutions for factory and network logistics operations

The paper examines the relationship of decision levels, performance measures and modelling and decision support approaches through the example of two implemented decision support systems for manufacturing and logistics application fields. Aside from highlighting the relevance of decision support for making industrial networks fit for emerging challenges, the relevance of the two presented EU FP7 projects VFF and ADVANCE to the Factories of the Future vision is shown. A discussion of the two projects outlines future research, with particular focus on challenges that arise from integration across levels of the decision hierarchy, within an organisationally heterogeneous network.

Data type definition and handling for supporting interoperability across organizational borders

Organisational heterogeneity—especially in networks where new members may join at any time—requires ongoing actions to maintain interoperability. On the level of data interoperability, this highlights the importance of various aspects of data model and dataflow design, as well as handling of data at run-time. The latter is certain to require automated means of data model negotiation, and—while today’s design processes are far from fully automated—such means can leverage productivity and support verification procedures in data modelling and dataflow design as well. The paper presents results in one possible approach to data type definition and manipulation, through the example of the ADVANCE dataflow engine and its type-related features. Aside from an XML-based type system, type inference algorithms are presented which are employed both during design and flow execution.

From tracking operations to IOT-the small business perspective

Mapping individual items onto a virtual representation and keeping track of their properties now finds wide acceptance in larger enterprises and networks in the form of tracking and tracing. However, even if underlying technologies are ripe enough for off-the-shelf frameworks, small enterprises are still largely left unpenetrated due to present-day tracking applications still being optimized for massive use with little variability. Also, higher-level functionalities, such as inter-organizational transparency and integration of different networks - typically attributed to the ¿Internet of Things¿ concept are still awaiting wider implementation. The paper presents a track-and-trace framework along with pilot implementations focusing on the small-business sector and highlighting enhancement possibilities towards an Internet of things.

TraSer: an open-source solution platform for cross-company transparency in tracking and tracing

Abstract Recent trends in industrial production are marked by rapid changes in structures of collaboration or competition, as well as the spreading of customized production and more intricate customer demands regarding quality and visibility of delivery processes. All this calls for efficient means of tracking and tracing beyond company borders—a technological step which is, in principle, available, yet, it is de facto restricted to isolated proprietary solutions excluding countless small and medium-sized enterprises from their application. The EU-funded project TraSer (Identity-Based Tracking and Web-Services for SMEs) was started with the goal of overcoming these obstacles by providing a free, open-source tracking and tracing solution platform which would allow SMEs to set up and maintain tracking and tracing services across company borders requiring low costs of initial investment and operation. The paper presents main goals and envisaged results of the project.

CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications

The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications. In this paper, we present the Complex Event Machine Learning framework which proposes a set of tools for automatic distributed machine learning in (near-) real-time, automatic continuous evaluation tools, and automatic rules management for deployment of rules. These features are implemented for a deployment at the edge of the network instead of the cloud. We evaluate and validate our approach with a well-known classification problem.