Manufacturing enterprise together with its interfaces to the world became nowadays a complex ecosystems.It is composed of various geographically distributed component [production sites, offices, RTD units] connected via logisticsThe connections of the enterprise in the supply chain network have to be dynamically maintained to address changes in the network structure and be able to chose best optionsDifferent layers of the enterprise are supposed to work in synergy, yet objectives and time scale vary from layer to layer.Legacy equipment exists side by side with advanced toolsNumerous interactions occur among factories and devices, which sometimes are part of collaboration towards a common goal and other time is competition in resource constrained environment.
In context of energy performance, it can be seen that overall consumption is influenced by buildings, equipment, logistic operations and personnel.It becomes crucial to obtain holistic view on the enterprise in order to assure energy efficient performance of the enterprise.Without ecosystem oriented view on the problem, some saving opportunities may remain hiddenRerouting the solar energy acquired by the building to the MES system because e.g. Energy prices in a certain time range are high and the energy required to heat/illuminate the building need not be so much because humans do not reside in the area at that time slot of the dayWe propose an integrated energy management architecture to fulfill this taskHaving in mind SOA related developments, researchers at Loughborough  have highlighted two main directions to explore further for a new generation of Digital Business Ecosystems for SMEs: visualization & reconfiguration, and integration of Manufacturing Execution Systems with the higher and lower levels (so that businesses would be able to interrogate on production data and influence production metrics). Aalst and Dustdar  highlight the need to take into consideration the situations of concept drift, i.e. when the process is changing while being analyzed (e.g. changing energy prices in the described scenarios).
Frame 1:Four functionsTheir implementationRE for cross-layer integrationFrame 2:Integrated view: four functions at ERP layerDAE collecting relevant data from BA and FA systems, computation of Key Performance Indicators (KPI) and storing this data in the corresponding (BAS or FAS) database. DSS fwd process chaining evaluates the acquired data and calculated KPI values with respect to the objectives defined in the Knowledge Base, stored in dedicated database, and suggests actions for performance improvement. SYNC check is done whether DSS output may propagate down to BAS and FAS. eEMS Report Engine to build and provide consolidated reports after accessing the database to obtain the required information. The RE functionality can be exposed as web service to the users.Reasoning in eEMS is to be implemented by means of a CEP engine, having the database as input and internal Knowledge Base, stored in dedicated database of CEP engine, for reasoning. The CEP engine analyzes energy consumption patterns, passes the refined information to other ERP systems, and updates the Knowledge Base of mEMS if needed.
Production line located at the premises of the FAST Lab, Tampere University of Technology, FinlandProduction line information (e.g. status of robots and products, quality inspection results, energy consumption of conveyors, robots, and controllers) is exposed as web services. Data concerning the building environment (temperature, relative humidity and ambient light) is captured via wireless sensors and routed to a server through edge routers.
The architecture was implemented as a set of web apps deployed on Apache TomcatTwo WS endpoints are present in the system: one serves as interface for FAS data sources and other provides access to reporting engine for third party apps.Efficiency KPIVisualized in browser
Approaches to modelling and knowledge representation are out of the scope of present paper.
A cross-layer approach to energy management in manufacturing
A cross-layer approach to energymanagement in manufacturing•Date: October, 2012•Linked to: RTD at FASTContact informationTampere University of Technology,FAST Laboratory,P.O. Box 600,FIN-33101 Tampere,FinlandEmail: email@example.com/fastConference:2012 IEEE International Conference onSystems, Man, and CyberneticsTitle of the paper:A cross-layer approach to energymanagement in manufacturingAuthors:Anna FloreaDr. Corina PostelnicuBin ZhangProf. Jose L. Martinez Lastra, Dr.Sc.If you would like to receive a reprint ofthe original paper, please contact us
Ecosystem Oriented EnergyManagement: An ImplementationAnna FloreaDr. Corina PostelnicuBin ZhangProf. Jose L. Martinez Lastra, Dr.Sc.Tampere University of Technology, Finland
Outline Background and motivation. Proposed Architecture Ecosystem Oriented Architecture for EnergyManagement Implementation Summary
6.6.2013 5Background. Implications for energymanagementSaving opportunities: Dynamic Utility contracts Smarter BuildingAutomation Services: Holistic evaluation ofconsumption Status of productionprocess as inputcontrol variable High quality productionEquipment UtilitiesBuildingsPersonnelLogisticsContributors to energy consumption
Architecture proposedFunctions:• Date retrieval and processing• Reasoning• Reporting• VisualizationFunctions:• Date retrieval and processing• Reasoning• Reporting• Visualization
ImplementationIndustrial testbed• 10 manufacturing cells• robot (except the buffer cell) and a• conveyor system• 729 different layouts of mobile phones• Frame• Keyboard• Screen types• Production line information exposed as webservices.• Data concerning the building environment iscaptured via wireless sensors and routed to aserver through edge routers.
Implementation6.6.2013 8• WS• Data acquisition• Reporting• KPI• CEP• Esper Rule Engine• Visualization• web browser• Reporting• Standard web technologies
Summary• Factories and buildings are interdependent and should beapproached as a whole, to efficiently manage associated energyconsumption.• Ecosystem Oriented Energy Management Systems are needed toovercome the fragmentation traditionally residing in factory andbuilding automation.• Future research will focus on optimization methods that take intoconsideration all options for energy saving and transfer.
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