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Towards Actionable Cognitive Digital Twins for
Manufacturing
Joˇze M. Roˇzanec1,3,4[0000−0002−3665−639X], Lu Jinzhi2 [0000−0001−5044−2921],
Aljaˇz Koˇsmerlj1, Klemen Kenda3,4[0000−0002−4918−0650] , Kiritsis
Dimitris2[0000−0003−3660−9187] , Viktor Jovanoski3,4, Jan Rupnik1,3, Mario
Karlovˇcec3[0000−0003−4480−082X], and Blaˇz Fortuna1,3[0000−0002−8585−9388]
1Joˇzef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
2EPFL SCI-STI-DK, Station 9, CH-1015 Lausanne, Switzerland
3Qlector d.o.o., Rovˇsnikova 7, 1000 Ljubljana, Slovenia
4Joˇzef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana,
Slovenia
Abstract. Digital Twins (DTs) mirror physical assets and can be en-
riched with software layers that provide different capabilities. In the case
of actionable cognitive twins (CTs), algorithms provide behavior (make
DTs actionable) and a knowledge graph (KG) adds cognitive capabilities.
In this paper we present a new ontology that models a shop-floor DT,
capturing background knowledge regarding shop-floor assets and actors,
data sources, algorithms (with emphasis on artificial intelligence (AI))
and decision-making opportunities as well as their relations. This ontol-
ogy can be used to enhance DTs with cognitive capabilities and instanti-
ated to a KG to provide meaningful context to data and algorithm out-
comes, enhancing decision-making suggestions. We describe this through
two use cases for an automotive parts manufacturing plant in Europe.
Keywords: Actionable Digital Twin ·Knowledge Graph ·Smart Shop
Floor
1 Introduction
Most goods we consume are manufactured in manufacturing plants. These are
organized in buildings with shop-floors - areas devoted to machines and tools
operated by workers to produce goods as established in production plans by
their leaders and managers according to expected demand. The increasing dig-
itization of all aspects of manufacturing allows for greater optimization of the
production process and is becoming a requirement for competitiveness. A part
of this digitization process is the elaboration of digital twins (DT).
A DT can be defined as ”a virtual model of a real product, process or ser-
vice that can monitor, analyze and improve its performance” [19] as well as to
”derive solutions relevant for the real system” [2]. This definition is extended to
consider a systemic perspective, by composing DTs into higher abstraction levels
[20]. With each abstraction level, we gain new context by getting insights into
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2 J. M. Roˇzanec et al.
relationships between elements and other information relevant to that level. Such
a systemic abstraction is the shop-floor. DTs are designed in such a way that
they encapsulate meaningful data (properties) and behavior (operators exposed
through protocols, which make them actionable [11]) and are specific to them.
By doing so, responsibility is delegated to the component with most proximity
and knowledge to a given problem.
Many authors realize the potential of semantics in the domain of DTs since
this approach proved to be effective in many contexts in the past [16, 15]. Boschert
et al. [3] describe how semantic technologies can be leveraged in the NextDT
paradigm to connect multiple DTs into a single value network and make use of
operational data with DTs to offer a wide variety of services. Kharlamov et al.
[12] identify four challenges that, in their opinion, should be solved to take full
advantage of semantic models in the DTs context. These challenges are how to
deal with high-volume streaming and historical data in a semantic context, pro-
vide integration of semantic models with analytical solutions, semantically link
simulations to specific use-cases and how to learn semantic models over time.
Cho et al. [4] understand that one of the main issues of using an ontology in
the context of DTs is that the model should be up-to-date to provide value on
decision making. They propose an approach based on Gaussian Mixture Models
(GMMs) to identify patterns of incoming data and understand if it can be clas-
sified into existing classes or it provides new knowledge that should be included
in the ontology. Banerjee et al. [1] developed a pipeline to extract semantic re-
lations from sensor data, focusing on features that can be built based on the
type of incoming data and information provided by an ontology model to insert
them into a KG where relations can be inferred and knowledge queried using
a semantic querying mechanism. Another approach was developed by Zehnder
et al. [22] who proposed Industrial Data Streams as a novel approach to model
DTs by abstracting data streams into virtual sensors and labeling them with se-
mantic tags that allow to describe data characteristics and provide information
on how can be grouped and used.
This paper focuses on a new proposed concept, actionable Cognitive Twins
(CTs) supporting decision-making in manufacturing systems, particularly on
shop-floor compositions. The main contribution of this paper is the development
of a novel ontology that models shop-floor DTs with entities that describe physi-
cal assets and actors as well as how data is ingested into the digital counterpart,
leveraged by algorithms and AI, and how are their outcomes linked to advice
on potential actions that can be taken to mitigate observed issues to help on
decision making. We describe how it could complement an existing KG by de-
scribing two use cases of actionable shop-floor CTs for decision making in the
context of a manufacturing plant of automotive parts located in Europe.
The rest of the paper is organized as follows. We first describe our approach to
the actionable shop-floor cognitive twin (CT) for decision makings concept and
their composition in Section 2. In Section 3, we describe a use case on how the CT
was developed for an automotive components manufacturing plant in Europe.
Towards Actionable Cognitive Digital Twins for Manufacturing 3
Fig. 1. Cognitive twins supporting decision-making for manufacturing by actions
Finally, we discuss CT concepts in the case study and offer the conclusions with
a summary in Section 4.
2 Cognitive Twin
In this paper, we describe an actionable CT to support decision-making for
manufacturing systems, as shown in Fig. 1. Regular DTs are created as digital
representations of physical entities which the main differences between CTs and
DTs are shown in the previous paper [9]. The CTs can be enhanced with be-
haviors provided by algorithms, AI models and KG models, which make them
actionable. Physical and digital entities, data sources, as well as algorithms and
AI models, can be abstracted into an ontology model. Thus, each CT has their
own ontology for their own environments and the ontology is developed based on
a unified specification with more high level abstractions. Cognition capabilities
are provided by making use of the KG and AI, although their functions differ.
Based on different use cases, the cognitive capabilities can learn the historical
behaviors of physical entities and historical data of digital entities in order to
provide the decision-makings for the operational physical entities.
4 J. M. Roˇzanec et al.
The KG takes advantage of knowledge encoded in the ontology regarding
shop-floor physical entities and their relationships that can be used to contex-
tualize results obtained by algorithms on specific instances. On the other side,
AI algorithms consume available data and provide some response to a proposed
problem (e.g. production forecasting or anomaly detection). A subset of these al-
gorithms (machine learning (ML) algorithms) not only consumes data but learns
from it capturing knowledge regarding dynamics reflected in incoming data. For
the cases we describe in this paper, we make use of the Web Ontology Language
(OWL) to develop the shop-floor CT ontology.
We envision four components that define actionable CTs for shop-floor deci-
sion makings:
1. Ontology and Knowledge Graph: the ontology captures background
knowledge about entities and their relationships in the physical world as
well as their digital counterpart. It is instantiated in a KG, which brings
cognitive capabilities to the DT.
2. Data: recorded information about shop-floor assets, actors and operations.
3. Algorithms: operators that provide specific behavior and capabilities to
DTs. AI models provide cognitive capabilities as well.
4. Actions: decision-making opportunities suggested to users based on insights
obtained through analytics and algorithms ran on the DT.
3 Case Study
3.1 Motivation
Shop-floor is the area of a manufacturing plant where production takes place.
It contains the machines required for production and is the place where work-
ers operate them or manage the production process. Relevant Key Performance
Indicators (KPIs) to the shop-floor are Operational Equipment Effectiveness
(OEE)[14] and Overall Process Efficiency (OPE)[7] among others. OEE (as seen
in equation 5) measures parts produced on a machine versus its theoretical max-
imum capacity; while OPE (as described in equation 6) measures parts pro-
duced versus the theoretical maximum capacity, regardless the cause preventing
to achieve full performance, considering not only machine inefficiencies but the
process as a whole.
Availability =T otal HoursP l anned −LostT ime
T otalH oursP lanned (1)
Speedrate =Actual Machine Speed
Design Machine Speed (2)
Qualityrate =Number of Good Products
Total Products Made (3)
Utilization =Scheduled Time
T otalT ime (4)
Towards Actionable Cognitive Digital Twins for Manufacturing 5
OEE =Availability ×Speed Rate ×Quality Rate (5)
OP E =OEE ×Utilization (6)
Problems we consider in shop-floor are related to the optimization of these
KPIs. The success of the proposed approach can be measured and compared
against other shop-floor control systems regarding improvements over these KPIs.
In this paper we will focus on two problems:
– Problem 1: anomaly detection: what anomalies do occur during produc-
tion? How do they impact the existing production process?
– Problem 2: production planning: how do we re-schedule existing pro-
duction plans based on factors such as early or late terminations, or lack
of skilled workers? How do we mitigate potential issues that could affect
operational up-time such as lack of required materials or skilled workers?
We illustrate our approach with a real scenario of a global enterprise that
has more than 30 manufacturing plants worldwide. In our example, we focus on
a single plant located in Europe and dedicated to the manufacturing of discrete
components for the automotive industry. For our scenario, we consider two lines
(Line 1 and Line 2), each of them has a machine tool (Machine 1 and Machine
2, respectively) to produce the same products (Product 1, Product 2, Product
3). Each of these production lines performs injection molding and takes care of
plastic milling for good product termination. There are many workers (Worker
1, Worker 2, Worker 3, Worker 4, Worker 5) with the right competencies to
operate the line (Competence 1) - for this example, one worker is required per
line per shift (Shift 1, Shift 2, Shift 3). Each worker has certain seniority for a
given competence (e.g.: can be junior, semi-senior or senior), which determines
if certain guidance may be required. Additionally, a worker can choose which
shifts would usually suit them. For the case we present, all workers are willing to
work at Shift 1 and Shift 2 and for demand purposes, there is no need to work
on Shift 3.
Information regarding planned quantities for a given product, workers as-
signed to the line, materials in stock are recorded in the Enterprise Resource
Planning (ERP) system. The amount of produced units and scrap are recorded
by a Manufacturing Execution System (MES) and confirmed by managers at the
end of each shift. This information is later recorded in the ERP software as well.
Information regarding workers, their attendance and competences are registered
in a Human Resources (HR) software. All pieces of software containing relevant
information regarding DTs are connected to them through data sources.
Data is ingested into the DT software though data sources (Datasource 1c,
Datasource 1s, Datasource 1v, Datasource 2c, Datasource 2s, Datasource 2v)
which for this example report production capacity, scrap and velocity for Ma-
chine 1 and Machine 2. Data of listed data sources is consumed by Algorithm
1 and Algorithm 2 which perform some cognitive function, for example, to un-
derstand if some anomaly is detected and propose actions (Action 1, Action 2)
6 J. M. Roˇzanec et al.
which may be taken regarding Machine 1 or Machine 2 to mitigate the issue
detected and reduce impact on Line 1 and Line 2, respectively.
3.2 Use-case overview
In order to solve the problems stated above, we developed a shop-floor CT as
a piece of software that mirrors the physical shop-floor and consists of four
components: ontology and KG, data (historic and current values), algorithms
and actions (suggested decisions that can be made to fix an issue). The current
implementation allows to, at runtime, define data sources for components, specify
relationships as well as decide on which algorithms or analyses are desired in each
case. Action items are suggested based on encoded knowledge and the semantics
and context of a given component.
Ontology and Knowledge Graph
The CT has a KG that stores information regarding workers, lines, and plants
as well as their interactions and decision-making opportunities under different
circumstances. This encoded knowledge provides semantic context to inputs and
outputs (processed data, triggered events and insights) obtained through the
analysis performed by different modules of the shop-floor DT. The KG is built
by custom mapping data from tables and enriched with semantic knowledge
captured in an ontology that describes shop-floor entities. Data is matched to
those entities in order to create specific instances. The ontology may be used, in
a future, to interface with other KGs that share this same convention.
Data
Data allows mapping a physical asset to its digital counterpart. In order to
obtain and feed it to the system, the software provides data source abstractions
to connect to an industrial manufacturing ERP, human resources management
software, Internet of Things (IoT) interfaces and other sources of data. Each
integration may have a different data velocity and the data sources are aware
of that. One such example is data regarding production orders, their execution
through shifts, goods stock, and delivery. Information regarding produced goods
is introduced into the ERP system after each shift: line leaders report on partial
progress and managers confirm information regarding production after each shift.
This data is shortly after ingested into the platform and disseminated to sub-
scribed modules for further processing. In every case, data points are persisted
into a database. This allows accessing current or older states and configurations
so that can be mirrored in the software or used for simulation purposes or train
machine learning models. It also allows us to create and update specific KG
instances, to have an up-to-date shop-floor representation.
Algorithms
Our shop-floor digital twin conceives algorithms as operators that provide
specific behavior and capabilities to the DT representation.
Regarding Problem 1 an anomaly detector [10] runs algorithms for stream
analysis, searches for anomalous behavior and alerts on them as well as on mul-
tiple observed anomalies that are considered to be correlated. Examples of such
Towards Actionable Cognitive Digital Twins for Manufacturing 7
anomalies are high or low levels of produced goods regarding an expected quan-
tity range, higher than expected levels of scrap and operational or technical
downtimes regarding a specific machine or line. High or low levels of produced
goods compared to the expected ones negatively impact the production process
either by increasing stock costs or putting at risk agreed delivery deadlines to
clients. Higher than expected scrap levels impact production costs and even pro-
duction schedules since a greater amount of material is required to produce the
desired goods and thus material stocks need to be reviewed based on this fact.
Finally, technical downtimes affect not only production schedules but also imply
costs of skilled workers paid for dead time.
Events regarding detected anomalies can activate other contextual functions
based on relationships exposed in the KG. Such cases are further analysis and
simulations to understand how downtimes may affect termination dates and
provide expected ones as well as potential deviations.
To solve issues related to Problem 2, a production planning software module
makes use of probabilistic ML and heuristics to assist supervisors when creat-
ing a production schedule in order to ensure workers are assigned to lines of
their own and for which they have the required competences. Heuristics also
validate constraints regarding legislation or shift preferences and ensure they
are respected. The software module regularly reviews existing production plans,
analyzes required materials and skilled workers to handle them and provides
insights about what is needed.
Actions
Insights obtained from the modules described above are put into context
within the knowledge graph, which also provides decision-making opportunities.
This can be considered as advice so that by taking action in the physical world,
detected issues can be mitigated. Such an example is advice provided when
analysis of production plans reveals not enough materials are stocked to meet
production requirements or that workers with required competencies are still to
be assigned or may not be available for planned dates. The worker is advised to
check if stock of material may exist but was not properly recorded in the ERP
software or to issue material orders to get the required stock in time and avoid
operational downtimes. Issues regarding worker assignment to production lines
may be fixed either by making the corresponding assignments or reordering
the production plans. If a shortage of workers with a certain competence is
regularly observed, the issue may be mitigated by hiring people with the required
competencies as well as by training some of the current workers to acquire it.
3.3 Ontology and Knowledge Graph design
In order to develop KG models for the case study, an ontology is defined which
describes shop-floor DT entities. The main focus of the constructed ontology
is to create a unified description for sharing and reusing knowledge about use
cases described considered in this paper. It was constructed following the steps
enumerated below:
8 J. M. Roˇzanec et al.
Fig. 2. Knowledge graph modeling for decision-makings in the manufacturing systems
1. Define the use case.
2. Identify the ontology concepts for class hierarchy development in the use
case.
3. Identify the interrelationships between ontology concepts.
4. Construct the KG models. Tools such as Protege [18] can be used to this
end.
5. Develop Application Processing Interfaces (APIs) for updating KG models
and access encoded knowledge.
6. Extend KG models and APIs to other DT domains.
After analyzing the use case, the ontology is developed based on Basic Formal
Ontology (BFO) [17] and Industrial Ontologies Foundry (IoF) concepts [13]. The
key ontology concepts are introduced in Table 1. Based on the key concepts,
object properties are defined to construct the interrelationships between different
entities. As shown in Figure 2, the ontology is first clarified by two types: 1)
Occurrent, entities that occur or happen; 2) Continuent, entities that continue
or persist through time. The occurrent entities include a process that is specified
by the industrial process (the operational process in the case study). The red
nodes refer to the basic compositions of BFO. The purple nodes refer to the
domain-specific definitions for this case study.
Except for occurrent, the continuent entities include: 1) Generally dependent
continuant, entity specifically dependent on another if another cannot exist, it
does not exist; 2) Independent continuant, referring to the entities existing on
themselves; 3) Specifically dependent continuant, a continuent entity depends
on one or more specific independent continuants for its existence. In summary,
the industrial process and machining tools are implemented by orders with a
Bill of Materials (BoM) during shift schedule. In each industrial process, the
process stock with specific materials (object properties) is processed using one
machine tool operated by a person at the industrial plant site. The person who
Towards Actionable Cognitive Digital Twins for Manufacturing 9
has competency is under an organization. The machine tools construct the pro-
duction lines which are compositions of production plants. In order to support
AI algorithm development, the dataset is defined to represent the data gener-
ated from the machine tool. Such dataset support algorithm development which
is implemented in software. Finally, the software provides actions to be taken in
the physical world (e.g: to control machine tools).
The ontology[8] is modeled in Protege as shown in Fig. 2-B. Concepts are
defined as OWL classes. The interrelationships between classes are defined as ob-
ject properties. Attributes of ontology concepts are defined as data properties.
Based on the case study, individuals are developed to represent the information
of the case study. As described in Section 3.1, two individuals of production lines
are defined as Line1 and Line2 with attribute Competence1. Each of the produc-
tion lines has its own machine: Machine1 and Machine2 defined as individuals
of the machine tool. All their products refer to individuals of Output Processing
Stock as Product1,Product2, and Product3. The five workers are defined as indi-
viduals of person as Worker1-Worker5 with their own competences (individuals
of person competence:Competence1 ) and seniority (person competence senior-
ity: junior,semi-senior and senior). Each worker operates the Line1 and Line2
on individuals of Shift schedule as Shift1-Shift3.
Except for describing the manufacturing systems, the data flow for AI algo-
rithm during the operational process is defined as well. Datasources to ERP for
Machine1 and Machine2 are defined individuals of data source: DS1c,DS1v,
DS1s,DS2c,DS2v, and DS2s. The data sources are used for Algorithm1 and
Algorithm2 (individuals of Algorithm) which is implemented in the software1
(individual of Software) to provide decisions for Machine1 and Machine2 as
individuals of Action:Action1 and Action2.
Table 1. Main Ontology concepts and their attributes
Entity Attribute
Production Plant id, name
Production Line id, name, plant id
Process Stock id, title, materials
Industrial Process id, name, line id, persons per shift, material id, plant id, delay type, delay time
Machining Tool id, name
Production order id, creation date, earliest start date, earliest end date,
person time, machine time, clearing time, BoM version
BoM - Bill of Materials
BoM version, component version, input material id,
input material quantity, plant id, output material id,
output material quantity
Competences id, name, description
Person id, name, active
Shift schedule id, plant id, shift number, week, year, shift start time,
shift end time, shift date
Data source id, title, URL
Algorithm id, title, algorithm type
Software id, title
Action id, title, decision
10 J. M. Roˇzanec et al.
3.4 Use of Artificial Intelligence
Artificial Intelligence can be considered as a subset of algorithmic functions
described above. It involves heuristics and algorithms capable of learning from
data in order to achieve a certain objective.
In order to solve Problem 1, the anomaly detector considers streams of
data and two algorithms to understand if a data point should be considered
an anomaly. The first one is a threshold set in the KG so that any value surpass-
ing it is considered anomalous. The second one uses a t-digest data structure [5]
to efficiently compute quantiles and will consider a new value as an anomaly if
it corresponds to q > 0.99. The KG is leveraged to understand which streams
reflect different aspects of the same reality. Considering that information in ad-
dition to time proximity of detected anomalies, the software can identify which
anomalies may be correlated and provide this insight to the users. Depending
on the context, decision-making opportunities regarded as Action instances can
be retrieved and served to the users as well.
In the case of production planning in Problem 2, we run Monte Carlo simula-
tions [21, 6] based on historic data to perform repeated random sampling based
on existing production information in order to deliver most probable termination
dates as well as expected deviations due to uncertainty. Simulations are run on
a regular basis, taking into account state updates from the physical world in or-
der to provide the most accurate expected status to end-users. Given results are
contrasted with expected termination dates in order to understand if production
will be finished early, on time or late and provide contextual decision making
suggestions based on these insights and leveraging KG encoded knowledge.
4 Conclusion
The increasing digitization of manufacturing processes is leveraged to create ac-
tionable cognitive twins, where not only the state of physical assets is mirrored,
but algorithms are used to provide behavior through heuristic and AI models. In
this paper, we present how actionable shop-floor DTs can be further enhanced
with cognition capabilities. We propose an ontology[8] that encodes background
knowledge regarding shop-floor entities, their relationship to data sources, algo-
rithms and decision-making opportunities based on algorithm outcomes.
For future work, the current KG module can be enhanced with new entities
and relationships required to support new use-cases. In particular, we would
like to address demand forecasting, a relevant problem whose outcomes impact
the whole production process. In order to achieve that, we may need to enrich
the current model with understanding on how particular data should be treated
in order to obtain required features and how this features can be fed to train
complex ML models as well as provide semantics to contextualize forecasted
results within company production lifecycle and global context. We also embrace
the possibility of adding a reasoning module, that would bring new capabilities
regarding how knowledge captured in KG may be used and augmented.
Towards Actionable Cognitive Digital Twins for Manufacturing 11
Acknowledgement
This research was funded by European Union’s Horizon 2020 programme project
FACTLOG (Innovation Action: Energy-aware Factory Analytics for Precess In-
dustries) under grant agreement number 869951.
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