Deliverables

D6.3: STANDARDISATION ACTIVITIES

This document reports achievements on Task 6.3 “Standardisation Activities” by giving a rough overview of technologies and related standards. After all the technical requirements have been realized, it turned out that available standards were strong enough for the implementation and the deployment of the DISRUPT platform.

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D6.1: DISSEMINATION AND COMMUNICATION REPORT

This deliverable oversees activities that took place along project life cycle with particular focus on the activities held in the last year and provides an overview of the set objectives and corresponding results. The current document summarizes all the Dissemination, and Communication results and learnings associated with them.

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D4.4: CPS CONTROL ENGINE

This document is the final version of the deliverable 4.4 of Work Package (WP) 4 on CPS, IoT and analytics and it describes the concept and software solutions underpinning the CPS control engine in the DISRUPT Project.

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D4.3: PREDICTIVE ANALYTICS TOOLKIT – REPORT

The purpose of this deliverable is to provide a Predictive Analytics Toolkit within the DISRUPT platform that allows reasoning on latest data by looking into the past. This is achieved by looking into state-of-the-art technologies and tools on predictive analytics that allow machine learning and data mining techniques to create executable analytics models based on historic data

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D3.3: DECISION SUPPORT TOOLKIT EXPERIMENTATION AND ASSESSMENT

This deliverable is focused on modelling, simulation, and optimization tools as they are seen as the DISRUPT contribution for FoF reference architectures. It reports the results of the experimentations. All the experiments are first implemented on each tool independently and then the collaboration of the data and results among different DISRUPT modules are elaborated

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D5.3: VALIDATION AND IMPACT ASSESSMENT

The Purpose of this deliverable is: (a) to get better understanding of the DISRUPT results based on predefined KPIs in previous deliverables; (b) to clarify the KPIs in detailed and how to calculate the KPIs in DISRUPT system; (c) to get better understanding of the difference with existing status (Reporting System – Data Collection – Existing KPIs results etc.); (d) to define the existing data structure of shop-floor and define the relation with KPIs; (e) to identify the business expectation based on the estimated values.

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D4.2: DATA ANALYTICS TOOLKIT

The purpose of this deliverable is to present the data analytics and knowledge environment and architecture to be used by the use-case organisations involved in the DISRUPT project via the data analytics toolkit. This document outlines the framework for both the lower layers of data collection and subsequent analytics, as well as the upper, knowledge layer via the IBPM approach in order to provide full production transparency.

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D3.1: DESIGN OF DECISION SUPPORT TOOLKIT

The purpose of this deliverable is to a) give an impression of the state of the art of modelling, simulation and optimization, to b) describe how these components need to be advanced within the DISRUPT project to cover the project objectives and building upon the use case requirements and specifications, and c) to define on a methodological (and not on a technical or architectural) level how the components will be deployed to support the use cases

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D2.4: Validation and Improvement of KPIs for the DISRUPT environment

This deliverable reports the identified of KPIs that will play an essential role during the validation and evaluation process. The KPIs are classified into 3 categories: Industry related KPIs that define critical and notable types of performance measurement, Innovation KPIs that will highlight DISRUPT innovation aspects related to ideas, market and output and ICT-related performance KPIs that support the assessment of the DISRUPT platform as such.

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D2.3: The DISRUPT Platform Integration Plan

This deliverable provides the plan for the development activities that the project should follow in order to produce a fully functional and integrated platform for a smart manufacturing ecosystems. The plan for the DISRUPT integration is driven by the data and thus it is specified the data-driven integration plan, in which both data semantics and data synchronization are fundamental in the correct implementation of the analytics and decision-support processes.

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D2.1: Technical specifications and goals

This deliverable provides solid technical specifications for each one of the modules that collectively comprise the DISRUPT system. These functional requirements arise from a set of user scenarios that introduce a functional perspective into user needs and it is discussed how each requirement addresses the identified use cases and business goals.

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D1.4: User requirements, data requirements and management

The purpose of this deliverable is to exhaustively capture, list and justify the user requirements for DISRUPT, upon which the system specifications will be established. This ensures that there is a clear mapping from business to technology, i.e., that the DISRUPT system indeed covers contemporary business needs in manufacturing for both its two industrial partners (and sectors) and for Industry 4.0 transition.

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D1.3: Use-cases and manufacturing goals

The main objectives of D1.3 are: (a) establishing an understanding of the current production settings for the two user manufacturing companies (CRF-FCA and Arcelik), (b) formally describing the current operational environments and the needs for change of the two companies, and (c) situating the requirements and aspirations of the two companies in their respective current settings.

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D1.2: Manufacturing cases elucidation and market analysis

The main objective of D1.2 is to establish the industrial and commercial backdrop against which the DISRUPT technologies will be developed in order to meet business expectations for both end-user industries (i.e., manufacturers) and technology providers (i.e., ICT vendors).

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D1.5: Business Models (interim version M12)

The results presented at this version are the outcome of a broad survey of existing approaches, business models and business cases. This is a interim version for month 12.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723541

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