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Zero Defect Manufacturing (ZDM) is intrinsically a multi-disciplinary approach. It requires parallel progress of different technological pillars, including sensor devices, quality inspection systems, industrial informatics, data analytics and knowledge management. 

Figure 2: RAPpID homepage

A lot of attention is given towards implementing analytics such as Machine Learning (ML), regression analysis etc., upon timeseries data collected in production processes.

Textual data types that capture the knowledge and experience of people working in manufacturing workplaces, addressing, and solving quality issues, can be very useful for ZDM needs. Industrial Social Network (ISN) solutions can be very efficient for serving communication needs in manufacturing environments. They allow informal communication, that is more appealing to the users, regarding internal instances and events occurring in the workplace, as well as the exchange of knowhow and best practices. Such solutions, capturing informal knowledge from employees in the shopfloor, combined with IIoT data, can prove very valuable in servicing ZDM objectives.

In the context of the H2020 MANUWORK project led by LMS, WEP has developed the RAPpID ISN solution (see Figure 1). RAPpID is used by personnel involved in the manufacturing process (engineers, line operating levels, maintenance personnel, etc.), allowing the sharing of knowledge of past and new best practices for line operations, as well as recent lessons learnt. 

Figure 1: RAPpID new DBoard module

It promotes better connectivity and socializing among employees and line operators working in the production area, as well as engineers/other experts working in other parts of the manufacturing plant, or even external suppliers or colleagues from other facilities of the company, allowing to raise immediate problems and seek to solve them. This allows therefore to reduce downtime, improve production quality, and hence reduce losses.

The core activity of the ISN4ZDM project was the integration of two new modules, the DBoard module (see Figure 2), and the KBUI module (see Figure 3), both accessible through RAPpID and integrated with ZDMP environment and components (see Figure 4), to enrich ZDMP with additional “components” that provide new knowledge-based services towards ZDM.

RAPpID and the two new modules DBoard and KBUI, complement existing ZDMP functionalities with new capabilities that are geared towards combining employees’ tacit knowledge with IIoT data (events, incidents, analytics, etc.) that are typically used for ZDM purposes. RAPpID benefits from the integration with ZDMP both from business and from technology aspects. RAPpID, through its integration to ZDMP, can gain access to a large potential customer base of ZDMP. Moreover, by integrating RAPpID to ZDMP, “data in motion” or “data at rest” managed by ZDMP can be accessible to RAPpID users via RAPpID dashboards, allowing employees to use typical social network functions such as comments, discussions, likes, posts, etc., in relation to these IIoT based events. Textual input from users can be combined with IIoT data in a knowledge database implemented by LMS using semantics and ontology technology.


Figure 3: New KBUI module: KB-UI a) SPARQL Query search, b) Advanced Search and c) Graph viewer

ISN4ZDM project has identified two key objectives:

  • To extend the functionality of RAPpID ISN solution with a knowledge base that aims to link IIoT/machine data typically generated in some manufacturing environment with textual information typically available in ISN solutions such as RAPpID. 
Figure 4: ISN4ZDM architecture

This objective fits with the ZDMP ecosystem that provides applications and components to assist manufacturers. In other words, the business objective of ISN4ZDM fits with the business objectives of ZDMP ecosystem.

  • To increase the impact towards the manufacturing community, ISN4ZDM has identified several ZDMP components to integrate (see list below), fostering the extension of the ZDMP ecosystem with additional and unique functionalities offered by ISN4ZDM, and increasing the capability of ISN4ZDM to penetrate the ZDMP market, thus being a win-win case for both ZDMP and ISN4ZDM: Portal – Facilitates access to ZDMP; Secure Authentication and Authorisation – Single Sign-On (SSO) to RAPpID and ZDMP; Data acquisition – Data access, exchange and analytics with ZDMP, for IoT extracted data; Service and Message Bus – ‘Listening’ and communicating critical events to RAPpID, facilitating “data-in-motion” exchange with ZDMP; Application Run-time – Container-based orchestration and hosting of components; Marketplace – Offer and access RAPpID in cloud through the ZDMP ‘Marketplace’.

Figure 4 provides the overall architecture of the ISN4ZDM system (RAPpID tool, with the new ZDM modules) and the integration with several ZDMP components.

Participants’ Details

We Plus S.p.A. (WEP)


Mr Amit Eytan, amit.eytan@we-plus.eu

Laboratory for Manufacturing Systems and Automation (LMS)


Mrs Chrysa Dimitrakopoulou, dimitrakopoulou@lms.mech.upatras.gr

Useful links

Sub project website:


LinkedIn page:


ZDMP Website, Architecture Component(s):

www.zdmp.eu, https://www.zdmp.eu/documentation