IFAC-PapersOnLine 49-12 (2016) 249–254 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2016.07.608 © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Computational methods, Database management systems, Distributed computer control systems, Embedded systems, Industry automation, Intelligent manufacturing systems, Simulators. 1. INTRODUCTION The factory of the future is in the objectives of the Industrie 4.0 strategy. Cyber-physical systems (CPSs) are the new research framework that makes the complexity of the Industrie 4.0 goals treatable and where physical and software components are deeply intertwined to interacting each other in a myriad of ways that change with context (Lee 2015). The efficiency measurements and assessment of industrial production processes in the future scenario have to take into account the increasing role of information flow across processes from the enterprise system level down to the shop- floor. Information is the main vehicle that allows humans to be in control of sustainability and productivity and allows the introduction of artificial intelligence as a decision support tool. The appearance of unforeseen behaviours is a typical phenomenon of complexity: new features and prospects might emerge from the deliberate application of data mining techniques coupled with artificial reasoning and inference on already well known and established data. Indeed new opportunities would originate from the full exploitation of information acquired, stored and communicated in industrial processes. Most of it unfortunately, due to the high volume of data, is usually doomed to be neglected as noise rather than useful added-value information. Though expensive smart metering methods are common, on current manufacturing plants, they should deserve a deeper exploitation. This paper proposes an “information-conservative” approach suggesting a key enabling technology and a methodology for modeling, control, simulation, planning, optimization and scheduling of industrial processes, through dynamical assessment of some common key performance indicators (KPIs). The key approach is the pervasive use of relational database systems that actively support transmission, storage and elaboration of information across the 5 levels defined in the ISA-95 standard – from sensing and actuation to the management of a network of enterprises. The traditional 5 ISA-95 levels seem designated to be blurred and surpassed soon by the new smart factory technologies and concepts. It is expected to move from the existing hierarchical control structures, based on the ISA-95 automation pyramid, towards more decentralized and reconfigurable structures based on the CPS principles (Leitao 2015). Indeed cyber-physical production systems (CPPSs) will show cyber capabilities within every physical component, as distributed computing along with distributed intelligence, and ‘self-*’ methods, namely: self-adaptation and self-configuration, along with self-diagnosis, self-organization and self-reconfiguration dynamics, as required through the Industrie 4.0 strategy. The introduction of an active distributed database mechanism at the shop-floor, through the best embedded database technologies today available, renders the data mining and the optimization of processes viable for the CPS challenge. By the use of the quality of a declarative language, as the database languages in the relational model, most of the techniques for planning and optimization (Jeon 2016) can be enabled dynamically. Decision support systems based on time-aware relational model inference can lead towards results potentially unforeseen at the beginning of the information gathering (Yang 2016, Nickel 2016, Date 2014). The full relational model will require more scientific effort in the future but a restricted database-centric technology based on the established SQL database language standard can create a first technological step towards the challenges made up by the smart manufacturing scenario. We propose guidelines and technology hints that can be effectively used in KPI-based control for the energy efficiency of industrial processes within the sustainable factory of the future research framework (Stiel 2016). In section 2 we introduce related work on the basic ideas of the database-centric technology. In section 3 a problem The path towards Industrie 4.0, requires that factory automation problems cope with the cyber-physical system complexity and its challenges. Some practical experiences and literature in the field testify that the role of the database management systems is becoming central for control and automation technology in the new industrial scenario. This article proposes database-centric technology and architectures that seamlessly integrate networking, artificial intelligence and real-time control issues into a unified model of computing. The proposed methodology is also viable for the development of simulation and rapid prototyping tools for smart and advanced industrial automation. Andrea Bonci*, Massimiliano Pirani*, Sauro Longhi* *Dipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, 60131, Ancona, Italy (e-mail: a.bonci@univpm.it, massimiliano.pirani@gmail.com) A database-centric approach for the modeling, simulation and control of cyber- physical systems in the factory of the future.