A Distributed Registry of Multi-perspective Data Services in Cyber

Physical Production Networks

Ada Bagozi, Devis Bianchini and Anisa Rula

Department of Computer Science, University of Brescia, Via Branze 38, Brescia, Italy

Keywords:

Internet of Services, Service-oriented Architecture, Cyber Physical Production Networks, Industry 4.0.

Abstract:

The advances in smart technologies, such as sensor networks, cloud computing, data management and artiﬁ-

cial intelligence, enable production systems to communicate with each other and rapidly conﬁgure themselves

to meet dynamic production needs. In this context, the adoption of service-oriented computing is aimed at en-

abling modular and standardised software infrastructures, platform-independent interactions between software

components and information hiding for ensuring data sovereignty in a fully distributed environment. However,

for a full-ﬂedged exploitation of service-oriented computing capabilities in the Industry 4.0 production sys-

tems, the existing service design solutions still lack a clear speciﬁcation of what is the data which the service

relies on, what is the business goal of the service and when it is invoked within the information ﬂow through-

out the production network. In this paper, we propose the model of a registry of data-oriented services in an

industrial production chain. The organisation of services in the registry is guided by multiple aspects of the

production network, namely: (i) the business goal of a real production network (ii) the perspective on produc-

tion data that is managed through the service (iii) the high level action performed by the service The modelling

strategy has been conceived to properly guide service design against ad-hoc solutions, thus facilitating future

service selection and composition to meet the business goals of collaborating actors. The resulting portfolio of

services can be declined by each actor of the production network, leading to a distributed registry that allows

each actor to preserve control over the owned data. The application in a case study has been performed to

demonstrate the feasibility of the data-oriented services.

1 INTRODUCTION

The ever-growing application of smart technologies

in modern digital factories, such as sensor net-

works, cloud computing, data management and ar-

tiﬁcial intelligence, is enhancing the integration of

product design, manufacturing processes and gen-

eral collaboration across factories over the supply

chain, where production systems can communicate

with each other and rapidly conﬁgure themselves

to meet dynamic production needs (Mohammed and

Trzcielinski, 2021). Research efforts on Industry 4.0

digital revolution shifted from the design of Digital

Twins (as the digital counterpart of machines or parts

of production plants in isolation at shop ﬂoor level)

to the design of Cyber Physical Production Systems

(CPPS), that is, hybrid networked cyber and engi-

neered physical systems that record data (e.g., us-

ing sensors), analyse it using data-oriented services

(e.g., over cloud computing infrastructures), inﬂu-

ence physical processes and interact with human ac-

tors using multi-channel interfaces (Harrison et al.,

2021). Recently, the Digital Twin and Cyber Phys-

ical Production Systems paradigms is evolving to-

wards a supply chain level, where actors of the pro-

duction environment (e.g., production leaders, sup-

pliers and customers) rely on an integrated vision of

the product, that travels throughout the production

phases, enriching itself step-by-step with data about

the process, the status of industrial assets used in the

production, the outcomes of product quality control.

Such a vision has been envisioned in recent litera-

ture on Digital Threads (Margaria and Schieweck,

2019) and the so-called Cyber Physical Production

Networks (Hawkins, 2021). Nevertheless, it is still

far from being realised in concrete production sup-

ply chains. First, current systems are still isolated

from each other and do not allow data to cross the

borders of actors cooperating in the production en-

vironment. Each actor can access data about his/her

own production phases and industrial assets only, ac-

cording to policies that are necessary to preserve data

174

Bagozi, A., Bianchini, D. and Rula, A.

A Distributed Registry of Multi-perspective Data Services in Cyber Physical Production Networks.

DOI: 10.5220/0011591100003318

In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 174-181

ISBN: 978-989-758-613-2; ISSN: 2184-3252

sovereignty. Second, existing solutions still have a

monolithic organisation of data thus leading to lim-

ited control on the access policies concerning differ-

ent perspectives.

Service-oriented architectures can come to the res-

cue for the implementation of functions spanning

across all the layers of digital factories and all the

phases of the product lifecycle management. The

adoption of service-oriented computing is aimed at

enabling modular and standardised software infras-

tructures, platform-independent interactions between

software components and information hiding for en-

suring data sovereignty in a fully distributed envi-

ronment. However, for a full-ﬂedged exploitation of

service-oriented computing capabilities in the Indus-

try 4.0 production systems, the existing service design

solutions still lack of a clear speciﬁcation of what is

the data which the service relies on, what is the busi-

ness goal of the service and when it is invoked within

the information ﬂow throughout the production net-

work.

To this aim, the contribution of this paper is the model

of a registry of data-oriented services in an indus-

trial production network. The organisation of ser-

vices is guided by multiple aspects to be considered

in a production network: (i) the business goal of a

real production network (e.g., production scheduling,

sustainable energy consumption, process monitoring

and product quality control); (ii) the perspective on

production data that is managed through the service

(e.g., the industrial assets owned by actors in the net-

work, the product over its lifecycle, the production

process); (iii) the high level action performed by the

service (that is, data collection, monitor, dispatch and

display). The registry is distributed in the sense that

the resulting portfolio of services can be declined by

each actor of the production network, thus allowing

each actor to preserve a control over the owned data.

A real case study has been performed. The aim is to

demonstrate how data-oriented services can be fruit-

fully exploited over multiple perspectives.

The paper is organised as follows: Section 2 intro-

duces the case study; cutting edge features of the ap-

proach compared to the state of the art are discussed in

Section 3; Section 4 describes the model adopted for

the organisation of services in the distributed registry;

Section 5 presents the architecture that implements

the service-oriented approach; a use case is presented

in Section 6; ﬁnally, Section 7 closes the paper.

2 MOTIVATING SCENARIO

Figure 1 provides an overview of the considered valve

production network. Valves are placed in prohibitive

environments and, once installed, are difﬁcult to re-

move and maintain over time and require high quality

levels. The production of the valve as the ﬁnal prod-

uct, its installation on-ﬁeld and maintenance are time-

consuming and costly tasks and the product itself is

delivered on-demand in low volumes, very often de-

signed to serve speciﬁc needs of customers. The case

study targets different categories of actors involved in

this kind of production networks in the manufactur-

ing sector: the production leader (e.g., the valves pro-

ducer); the raw materials suppliers (e.g., the forger);

the suppliers of mechanical processing tasks (who is

in charge of machining raw materials provided by

the forger to be assembled in the valves); the sup-

pliers of speciﬁc tools used in the production stages

(e.g., to perform quality tests on the valves). In the

production network, collaboration among actors is a

main aspect in order to deliver on time high qual-

ity products. The production leader and the suppli-

ers may perform different tasks, requiring data owned

by other actors and services delivered across actors’

boundaries. For instance, the supplier of mechani-

cal processing tasks transforms raw materials in valve

sub-parts by using computerised numerical control

machines, that are speciﬁcally conceived for ﬂexible

production. This supplier is interested in monitoring

the performances of the machines by implementing

predictive maintenance and anomaly detection tech-

niques. On the other hand, the same kind of supplier

and the raw materials supplier (i.e., the forger) are

interested in adopting energy efﬁcient strategies on

their assets, given the high cost of energy in this sec-

tor. Furthermore, the production leader is interested

in the optimisation of the production scheduling, that

involves all actors. Therefore, the latter task requires

the leader to (partially) access data about all the pro-

duction phases.

Business Goals. In the real case study considered

above, four important business goals have been iden-

tiﬁed: the production scheduling, the process moni-

toring, the sustainable energy consumption and prod-

uct quality check. Business goals, elicited in the in-

dustrial project through requirements analysis jointly

performed by the consortium of partners, are brieﬂy

sketched in the following. In all goals, some recurring

stages of data management, namely data collection

(Collect), data monitoring (Monitor), data dispatch-

ing towards other actors in the production network

(Dispatch) and data visualisation (Display) have been

highlighted.

A Distributed Registry of Multi-perspective Data Services in Cyber Physical Production Networks

175

Quality Control

Supplier

Production

Leader

Mechanical

Supplier

Forger

Supplier

Raw

Material

Valve

components

Valve

check

Services:

dataAcquisition

productionScheduling

energyEfﬁciency

predictiveMaintenance

dataVisualisation

Services:

dataAcquisition

productionScheduling

processMonitoring

productMonitoring

dataVisualisation

Valves production network

Services:

dataAcquisition

productionScheduling

productMonitoring

dataVisualisation

…

Services:

dataAcquisition

productionScheduling

energyEfﬁciency

dataVisualisation

…

Production Order Quality control order

Other

components

Figure 1: Actors and main tasks within the production network of deep and ultra-deep water valves, considered as real case

study.

Production Scheduling. To implement this business

goal, the production leader gathers from his own

scheduler the data necessary to identify the product,

the resources and the steps necessary for manufac-

turing (Collect stage). Once the planned produc-

tion process generated by the scheduler has been re-

ceived, for each planned phase the actor queries the

supplier’s scheduler, through the connectivity infras-

tructure, to receive the conﬁrmation of the start and

end date of phase execution. Each phase might con-

cern the provisioning of raw material, the production

of semi-worked components or the execution of out-

sourced tasks (e.g., quality controls). During produc-

tion scheduling, suppliers may conﬁrm or propose al-

ternative dates, which are forwarded to the produc-

tion leader, to perform a rescheduling, if necessary

(Dispatch

stage). As the various requests are sent by

the production leader and conﬁrmations are arriving,

the progress of production scheduling is monitored

(Monitor stage).

Sustainable Energy Consumption. In this business

goal, during the Collect stage, each actor of the

production network gathers data necessary to moni-

tor his/her own plants from the energy consumption

viewpoint. This data may be collected as sensor data

directly from monitored work centers/machines (e.g.,

electrical energy consumption, temperature and so

forth). Collected data are processed to identify possi-

ble anomalies in energy consumption (Monitor stage),

in case sending back to monitored machines proper

reconﬁguration commands to reduce or limit energy

consumption (Dispatch stage). Information about the

energy consumption proﬁles and detected anomalies

are reported on the dashboard of the owner of moni-

tored plants/machines (Display stage).

Process Monitoring. In this business goal, during the

Collect stage, each actor gathers data (e.g., times)

concerning the production process phases for which

the actor is responsible. Every delay that might re-

quire a production rescheduling is detected (Monitor

stage), reported to the owner of the process phase with

delays (Display stage) and communicated to the other

actors for whom such a delay might have negative ef-

fects (Dispatch stage).

Product Quality Checking. During the Collect stage

of this business goal, an actor who is interested in

checking the quality of the produced product (or

semi-worked component) gathers the data detected

through product quality check. Collected data is

processed to identify quality issues (Monitor stage),

that are displayed on the actor’s dashboard (Display

stage), and is used to require some rescheduling and

alternative executions of the production process that

might (totally or partially) balance the anomalies that

have been detected on products (Dispatch stage).

3 RELATED WORK

Since the ﬁrst stages of the Industry 4.0 revolution,

service-oriented architectures (SOA) have been

proposed to foster the development of platform-

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

176

independent, interoperable and component-based

integration of Industry 4.0-compliant devices, ma-

chines or parts of production plants (Siqueira and

Davis, 2021). SOA proved to be an efﬁcient approach

to cope with the heterogeneity of the industrial sys-

tems, to assure interoperability, reuse, standardisation

and information hiding (Zhang et al., 2020; Park

et al., 2020). SOA is also the most widely used design

framework in IIoT-based application architectural

designs (Lee et al., 2015). In (Catarci et al., 2019)

recent approaches for handling Digital Twins have

been reviewed, in order to fully understand the

relationship between Digital Twins and Web Ser-

vices. Further research has been performed to study

the application of SOA to the multi-layer structure

of the digital factory, aimed at better representing

the complexity of industrial environment (Park

et al., 2019), also proposing context-aware solu-

tions (Alexopoulos et al., 2018), service-oriented

composition (Derhamy et al., 2019), collaborative

work (Glock et al., 2019). In (Liu et al., 2020)

authors discussed the introduction of model-driven

design of complex Industry 4.0-compliant plants by

starting from modular components exposed as web

services. An alternative proposal based on the design

of production processes as workﬂows is described

in (Kayabay et al., 2018). The adoption of SOA also

enabled to combine this technology with Multi-Agent

Systems (MAS). For instance, in (Cagnin et al.,

2018) an architecture based on MAS, SOA and Se-

mantic Web technologies for the management of IIoT

devices in manufacturing systems has been discussed.

Contributions of This Paper w.r.t the State of the

Art. Design strategies based on SOA are mainly fo-

cused on the integration of devices (or groups of de-

vices) within the infrastructure for the development

of data-driven applications. Moreover, these strate-

gies target speciﬁc needs in the production network,

such as energy efﬁciency (Matsunaga et al., 2022),

anomaly detection (Qi et al., 2021), predictive main-

tenance (Nordal and E-Thalji, 2020) on work centers,

production scheduling (Jiang et al., 2021) and pro-

cess monitoring (Nica and Stehel, 2021). Overall, the

analysis of the literature indicates that a service model

that includes multiple perspectives in a Cyber Physi-

cal Production Network, namely the production pro-

cess and the product lifecycle perspectives, is still an

open issue. Existing approaches already investigated

the relationship between Digital Twins and the Prod-

uct Lifecycle Management (Tao et al., 2018), also

introducing the novel concept of Digital Thread, in-

tended as the cyber side representation of a product, to

enable the holistic view and traceability along its en-

Figure 2: Classiﬁcation of services according to multiple

taxonomies.

tire lifecycle (Margaria and Schieweck, 2019). Never-

theless, the idea of combining data over the three per-

spectives of product, process and industrial assets and

designing services on top of these perspectives, prop-

erly categorised with respect to the data ﬂow within

the Cyber Physical Production Network, is missing in

literature. According to our opinion, the proposal of

some criteria to organise services within a registry is

crucial to avoid the proliferation of ad-hoc service-

oriented solutions. As a ﬁrst attempt in this direction,

we proposed to adopt the business goal, the perspec-

tive on production data and the high level action per-

formed by the service as criteria.

4 A DISTRIBUTED REGISTRY OF

MULTI-PERSPECTIVE

SERVICES

In order to organise the services within the registry,

in this section we introduce the service classiﬁcation

shown in Figure 2. The classiﬁcation is driven by

the three perspectives in the data model (product, pro-

cess and industrial assets) introduced in (Bagozi et al.,

2022), by the business goals identiﬁed during require-

ments analysis in the real case study and by the data

A Distributed Registry of Multi-perspective Data Services in Cyber Physical Production Networks

177

ﬂow stages in the production network, namely data

collection, monitor, dispatch and display.

Implementing business goals corresponds to the de-

sign of several services in order to access, share and

visualise the data between all the actors involved in

the production network. Services are mapped to the

different phases of the information ﬂow according to

the actions they perform, as speciﬁed in the following:

• collect services, used by each actor to acquire

data from the physical side of the production net-

work, such as the scheduled production plan or

sensor data about machine energy consumption;

• monitor services, used to detect anomalies

that may lead to higher consumption or break-

down/damage of the work centers, production

process failures delays or product quality issues;

• dispatch services, used to share data across the

actors of the production network, such as the ser-

vice to collect delivery dates from suppliers in

production scheduling;

• display services, used to visualise data on dash-

boards or ad-hoc GUIs, such as the service to dis-

play the production schedule and the one to dis-

play energy consumption anomalies.

Monitor services can implement either threshold-

based techniques or advanced data analysis solutions.

The approach has been conceived in order to enable

the integration of different data analysis models work-

ing on parameter values. An extensive discussion

about such algorithms is out of the scope of this pa-

per. Their integration within the proposed approach

and their performance validation can be the object for

future experiments.

We formally deﬁne a multi-perspective data-

oriented service S

as a tuple

= hn

, t

, P

, url

, m

, IN

, OUT

where: (i) n

is the name of the service; (ii) t

the service type (collect, monitor, dispatch, display);

(iii) P

is the set of perspectives on which the service

is focused (product, process, industrial assets); (iv)

url

is the endpoint of the service for its invocation;

(v) m

is the HTTP method (e.g., get, post) used to

invoke the service; (vi) IN

is the representation of

the service input; (vii) OUT

is the representation of

the service output. We denote with S the overall set

of data-oriented services.

Services in S are implemented as RESTful ser-

vices; therefore, they are also described through

HTTP methods (e.g., post, get, put and delete)

used in the service, corresponding to the Create-Read-

Update-Delete actions on data. A multi-perspective

Business Goal

Hierarchy

Service Registry

Service 2

Service N

Service 2

Service N

Service 1

Service 2 Service NService 1

…

S11

S12

S13

S21

S22

Figure 3: Service organisation within each node of the dis-

tributed service registry.

service S

deals with different types of data, based on

its input/output parameters according to the process,

product and assets perspectives of the data model.

Figure 3 provides a high level view on how ser-

vices are organised within the service registry. The

registry is distributed in the sense that each actor has

his/her own view on the portfolio of services that

can be used. Business goals in the registry are con-

ceived as a pattern of service stages and perspectives

on which each stage must be focused on. This en-

ables each actor to be guided in the selection of ser-

vices according to the desired perspective and in their

composition to implement a business goal following

the data collection, monitoring, dispatch and display

stages. Moreover, each actor can maintain control

over his/her own data for data sovereignty purposes.

5 APPROACH ARCHITECTURE

Figure 4 presents the architecture that implements

the service-oriented approach described in this pa-

per, including the four business goals introduced in

the previous section. In the architecture each actor is

equipped with: (i) his/her own vision on the data that

the actor can explore (data repository), organised ac-

cording to the multi-perspective data model; (ii) a list

of services the actor has at his/her disposal to inter-

act with internal industrial assets or machines in the

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178

Services repository

Data Repository

Monitor

Services

Monitoring

Data

Collect

Services

Quality Control

Supplier

OEMMechanical

Supplier

Forger

Supplier

Raw

Material

… …

Valves production supply chain

…

Collected

Data

Production

Scheduling

Services

Energy

Eﬃciency

Services

Product

Quality

Services

Display

Services

Multi-perspective dashboards

…

Multi-perspective

Data Model

Dispatch services

Figure 4: Service-oriented architecture for Cyber Physical Production Network in deep and ultra-deep valves production.

form of Cyber Physical Systems and a list of services

exposed by the other actors (service registry); (iii) a

web-based multi-perspective dashboard for data ex-

ploration and visualisation purposes. The data repos-

itory includes both structured data, stored within a re-

lational database according to the multi-perspective

model and semi-structured data, stored within a Mon-

goDB NoSQL installation (Collected Data).

MongoDB database stores ﬁne-grained measures

collected as a continuous ﬂow of data (data streams)

from the production network (e.g., sensors data ac-

quisition). For each measurement, a JSON document

is registered, reporting the value of the measure, the

timestamp, the ID of the target entities. The data

stream collected from a vibration sensor on a speciﬁc

work center or component is an example of this kind

of parameter. JSON documents can be organised in

different collections with respect to the physical pa-

rameter that is being measured (vibration, electrical

current, temperature). Fine-grained processing data

can be collected internally, from resources and indus-

trial assets owned by the actor, or externally, provided

by the other actors.

The service registry contains four kinds of ser-

vices, mirroring the four stages highlighted in the

business goals. Display services populate the web-

based multi-perspective dashboard that each actor

uses for data exploration. From the home page of the

dashboard, it is possible to start the data exploration

by following one of the three perspectives, namely,

product, process and industrial assets. Each perspec-

tive brings to a UI component (tile) implemented us-

ing ReactJS libraries: i) the product synoptic tile al-

lows an exploration from the product perspective of

each single actor; ii) the process phases tile allows

an exploration from the process perspective of each

single actor; iii) the working centers tile allows ex-

ploration from the industrial asset perspective of each

single actor. More details on the web-based multi-

perspective dashboard can be ﬁnd in (Bagozi et al.,

2022).

6 USE CASE

To give a (non exhaustive) example of multi-

perspective service composition to meet business

goals, let us consider the production scheduling goal.

The ﬁrst stage in this goal concerns the registration

of the Engineering Bill of Materials (EBoM) and the

Manufacturing Bill of Materials (MBoM) in the data

model of the production leader. These actions are im-

plemented in the registerEBoM and registerMBoM

Collect services, that are focused on the product per-

spective, as shown in Figure 5.

After the Manufacturing Bill of Materials of the

ﬁnal product has been registered, process phases are

A Distributed Registry of Multi-perspective Data Services in Cyber Physical Production Networks

179

Business goals

Collect DisplayDispatch

Product

Process

Assets

Collect MonitorDispatch

Product

Process

Assets

Data Perspectives

Display

notifyProductionOrder

registerEBoM

registerMBoM

receiveProductionOrder

collectProductionTimes

registerResources

collectMachinesDowntimes

detectProductionDelays

displayMBoM

displayProductionScheduling

displayDelays

displayMachinesDowntimes

Production Scheduling

& Monitoring

Product quality

Monitoring

Energy consumption

optimization

Collect DisplayDispatch

Product

Process

Assets

Collect MonitorDispatch

Product

Process

Assets

Data Perspectives

Display

notifyProductionOrder

registerEBoM

registerMBoM

receiveProductionOrder

collectProductionTimes

registerResources

collectMachinesDowntimes

detectProductionDelays

displayMBoM

displayProductionScheduling

displayDelays

displayMachinesDowntimes

Collect DisplayDispatch

Product

Process

Assets

Collect MonitorDispatch

Product

Process

Assets

Data Perspectives

Service types

Display

notifyProductionOrder

registerEBoM

registerMBoM

receiveProductionOrder

collectProductionTimes

registerResources

collectMachinesDowntimes

detectProductionDelays

displayMBoM

displayProductionScheduling

displayDelays

displayMachinesDowntimes

Figure 5: Multi-perspective service map: services are categorised among collect, dispatch, monitor and display services and

are intersected over the product, process and assets perspectives; furthermore, services are grouped according to the target

business goal.

scheduled and connected to the product to be pro-

duced and to the work centers and resources required

for the production, respectively. These actions are

implemented in the receiveProductionOrder and

in the registerResources Collect services, respec-

tively.

Therefore, the production leader forwards the

Manufacturing BoM, the production order and re-

sources to the scheduler for production schedul-

ing (notifyProductionOrder Dispatch service).

Some missing information in this schedule, con-

cerning the delivery date of some product parts,

must be asked to the parts suppliers by inter-

acting with their schedulers, that will reply with

the delivery date, returned back to the produc-

tion leader (collectProductionTimes Collect ser-

vice). This process is repeated until the ﬁnal

plan is obtained. Finally, display services are ex-

posed to visualise data on the dashboard, such

as the service to display the Manufacturing BoM

(displayMBoM service) and the production schedul-

ing (displayProductionScheduling service).

Monitoring of production advancement

presents a different pattern of stages: possible

downtimes are ﬁrst collected from machines

(collectMachinesDowntimes Collect service)

and checked for the identiﬁcation or prediction

of delays (detectProductionDelays Monitor

service). The machines downtimes and delays are

sent to the production leader, who is interested in

this kind of information, since it has an impact

on the production schedule of the ﬁnal product.

The production leader and his/her suppliers agreed

upon the services that are meant to exchange this

information over the production network. Delays and

machines downtimes are therefore displayed on the

dashboard of the production leader (displayDelays

and displayMachinesDowntimes services). The

ﬁnal service map for the production scheduling

business goal is reported in Figure 5.

7 CONCLUDING REMARKS

In this paper we propose the model of a registry to

organise data-oriented services in a production net-

work, according to different aspects, namely: (i) the

business goal of a real production network (e.g., pro-

duction scheduling, sustainable energy consumption,

process monitoring and product quality control); (ii)

the perspective on production data that is managed

through the service (e.g., the industrial assets owned

by actors in the network, the product over its lifecycle,

the production process); (iii) the high level action per-

formed by the service (that is, data collection, moni-

tor, dispatch and display). The service is distributed,

in the sense that the resulting portfolio of services can

be declined by each actor of the production network,

thus allowing each actor to preserve a control over the

owned data.

The proposed approach will be further developed to

increase the standardisation level through the intro-

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

180

duction of Semantic Web technologies to represent

data on which services are built, as well as standard

service taxonomies. The introduction of an infrastruc-

ture based on micro-services and containers as soft-

ware architectural patterns will also be investigated.

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