=Paper=
{{Paper
|id=Vol-2518/paper-FOMI5
|storemode=property
|title=Resources in Manufacturing
|pdfUrl=https://ceur-ws.org/Vol-2518/paper-FOMI5.pdf
|volume=Vol-2518
|authors=Emilio M. Sanfilippo,Walter Terkaj,Stefano Borgo
|dblpUrl=https://dblp.org/rec/conf/jowo/SanfilippoTB19
}}
==Resources in Manufacturing==
https://ceur-ws.org/Vol-2518/paper-FOMI5.pdf
Resources in Manufacturing
Emilio M. SANFILIPPO a,b,1 , Walter TERKAJ c and Stefano BORGO d
a Le Studium Loire Valley Institute for Advanced Studies, Orléans & Tours, France
b CESR - University of Tours, France
c Institute of Intelligent Industrial Technologies and Systems for Advanced
Manufacturing, STIIMA-CNR, Milano, Italy
d Laboratory for Applied Ontology, ISTC-CNR, Trento, Italy
Abstract. Standards and ontologies for manufacturing differently understand re-
sources. Because of this heterogeneity, misunderstandings arise concerning the ba-
sic features that characterise them. The purpose of the paper is to shed some light
on the ontology of resources to strive the development of computational models for
manufacturing. In particular, we propose various approaches for the representation
of resources and address their advantages and disadvantages. We also present some
preliminary considerations for the modelling of capabilities and capacities, as well
as information, time, and money.
Keywords. Manufacturing resource, planning, systems design, PSL.
1. Introduction
The concept of resource is fundamental in various domains. Being a general term, it has
been used in a variety of communities without becoming the subject of specific defini-
tions, thus relying on the implicitly shared viewpoint among the community’s members.
This lack of characterisation and the possible mismatches in the understanding of the
term jeopardise the efforts to share data models as well as to integrate information sys-
tems and services. According to the Oxford Dictionary of English (ODE), for instance,
a resource is: “[a] stock or supply of money, materials, staff, and other assets that can be
drawn on by a person or organi[s]ation in order to function effectively [...].”
In this view a resource is like an asset, i.e., an item that has value for and is owned
by an agent. A resource does not need to be related to an action or plan; what is required
is that it has a value for some agent to function as needed. E.g., an adjustable wrench is
of value to a mechanic even though s/he does not foreseen any use for it today.
Once we move from broad dictionary definitions to application contexts such as
manufacturing, the intended semantics of resource-related notions is narrower in such
a way to match engineering views. Despite this, only little agreement is found across
experts and stakeholders in the way in which manufacturing resources are conceived
[22]. Consider, for instance, the following definitions of manufacturing resource: “Any
1 Corresponding Author: Emilio M. Sanfilippo, Université de Tours, 59, rue Néricault-Destouches, 37020
Tours, France; E-mail: emiliosanfilippo@gmail.com (permanent address). Copyright c 2019 for this paper by
its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
device, tool and means, except raw material and final product components, at the disposal
of the enterprise to produce goods and services” (ISO 15531); “A resource is an entity
that provides some or all of the capabilities required by the execution of the enterprise
activities and/or business processes. The types of resources involved in manufacturing
operational management are: personnel, material, equipment (role based and physical
asset) and process segments” (IEC 62264).
First, materials count as resources only in the second case. Second, IEC 62264 states
that resources are needed to execute activities, while ISO 15531 is not explicit about this.
An information system based on ISO 15531 could not therefore easily interoperate with
a system based on IEC 62264 because of the mismatches in the employed models.
The purpose of the paper is to shed some light on the modelling of manufactur-
ing resources to foster the development of domain-specific computational ontologies. In
particular, we present across Section 2 three approaches for resource modelling by ad-
dressing their pros and cons; some general remarks on the approaches are discussed in
Section 3. Section 4 presents an example for resource representation in the context of
manufacturing system design, whereas Section 5 presents some considerations to model
capabilities and capacities, as well as time, information, and money as resources. Finally,
section 6 concludes the paper.
2. Manufacturing Resources
We discuss in this section three main approaches for the conceptualisation and repre-
sentation of manufacturing resources. The purpose is to clearly distinguish and identify
different ways in which resources are understood. Also, we introduce some first-order
formulas to stress the differences between the approaches and to highlight how these
affect the development of computational ontologies. A complete axiomatisation of each
view is beyond the purposes of this work.
As seen in the introduction, the terminology is unfortunate since experts and stake-
holders (as well as standards) rely on different and not well aligned vocabularies. For
the sake of clarity, we will adopt the Process Specification Language (PSL) terminology
[15] (ISO 18629), in particular for the distinction between Activity and Activity Occur-
rence. The latter are processes occurring in time, whereas the former are the conditions
that activity occurrences satisfy. We therefore treat activities as plans, i.e., descriptions
of (possible) activity occurrences. Admittedly, the choice of interpreting PSL activities
as plans is debatable, since they may be also understood as activity types. The distinction
between plans and activity types is however subtle and not relevant for what is presented
in this paper; in addition, the correspondence between activities and plans has already
been documented [15]. We will therefore interchangeably use the terms plan and activity;
process and activity occurrence.
2.1. Manufacturing Resources and Activity Occurrences
Resources are often conceived in manufacturing as entities that participate in manufac-
turing processes [1,6]. This can be formally captured as in (Def1), where PC is the stan-
dard relation of participation holding between endurants and perdurants [7,15], whereas
M f gActOcc stands for manufacturing activity occurrence as defined in (Def2). Also,
PRE(x,t) – ‘x is present at timet t’ – models the time at which an entity exists [7];
and ≺ are the usual order relations on time; outcomeO f is used to model the result of an
activity occurrence, e.g., the product it fabricates.
Def1 M f gResource(r) , ∃ot(M f gActOcc(o) ∧ PC(r, o,t))
(r is a manufacturing resource iff it is the participant of a manufacturing activity
occurrence)
Def2 M f gActOcc(o) , ActOcc(o) ∧ ∃xt(Product(x) ∧ outcomeO f (x, o) ∧
PRE(x,t)∧ (endO f (o),t) ∧ ∀t 0 (≺ (t 0 , endO f (o)) → ¬PRE(x,t 0 )))
(o is a manufacturing activity occurrence iff o is an activity occurrence that ends
with the existence of a new product)
Definition (Def1) is really general. According to it, an entity is a resource even
before the plan requiring its use is provided. Moreover, the product which is the outcome
of a manufacturing activity occurrence might be itself a resource for that very occurrence.
At the same time, this definition violates some basic intuitions. Take, e.g., two entities of
the same type in the same manufacturing site; if one will participate in a manufacturing
activity occurrence tomorrow and the other is never used, the first is a resource and the
other one is not even though we do not know how to distinguish them today.
The generality of this claim is sometimes restricted to capture only certain processes’
participants. For example, manufacturing resources are sometimes understood as persons
or things that add value to a product in its production or delivery (see [21] for a simi-
lar approach). Although it remains unclear what “adding value” means, it suggests that
resources contribute to the completion of the given manufacturing activity occurrence.
A further variation is adopted in works based on PSL [15], according to which man-
ufacturing resources are required for certain activities to occur. The idea is that activities
cannot occur without the specified resources. In the theory PSL resources2 this is cap-
tured by axiom (Ax1) (rewritten in our notation), where occ o f – taken from PSL Core,
see (Ax2) – holds between an activity occurrence and the activity it realises [15].
Ax1 requires(a, r) ∧ occ o f (o, a) → ∃t(PC(r, o,t))
(if activity a requires r, then for all occurrences o of a, r participates in o)
Ax2 occ o f (o, a) → ActOcc(o) ∧ Activity(a)
(occ o f holds between activity occurrences and activities)
According to this approach, the same individual resource is meant to participate
in all occurrences satisfying the same activity. This has undesired consequences. For
instance, the theory cannot deal with cases in which the pre-selected resource is not
available, e.g., because it is under maintenance.
To conclude, this first approach results rather limited. It heavily relies on the relation
between specific resources and activity occurrences. Since in manufacturing contexts
experts often talk of resources in planning or scheduling scenarios where there are no
individual manufacturing activity occurrences to which resources can be bound, it is clear
that we need to extend this view.
2 The reader can refer to the COLORE repository for a complete overview on PSL axioms; https:
//github.com/gruninger/colore/tree/master/ontologies. Last accessed in May 2019.
2.2. Manufacturing Resources and Activities
In this second perspective resources are conceptualised in tight connection to manufac-
turing activities (aka plans), i.e., specifications about the activity occurrences to be re-
alised in manufacturing contexts (see, e.g., [12,22,23]). The rationale for this view is to
be more general with respect to the aforementioned approach; hence, to weaken the di-
rect link between resources and activity occurrences, it allows to refer to resources even
in the absence of individual occurrences where they are meant to participate.
In manufacturing one can distinguish process plans and production plans. A pro-
cess plan defines the sequence of manufacturing activity occurrences to convert a work-
piece from an initial to a final form, where the plan incorporates the description of each
occurrence, its parameters, and possibly even equipment and/or machine tool selection
[19]. On the other hand, based on product demand/orders, a production plan determines
which production resources are to be assigned to each occurrence on each workpiece,
when each occurrence is to take place, while resolving contention for the resources [11].
A production plan incorporates therefore one or more process plans and resolves possi-
ble ambiguities in the process plan. Both process and production planning can be hier-
archically addressed. For the sake of simplicity, the notion of manufacturing activity we
assume includes both process plan and production plan.
The link between manufacturing resources and activities can be captured as in the
following formulas where sat is a primitive relation holding between physical or tempo-
ral entities and the descriptions they satisfy (realise), see (Ax3). The sat relation can be
therefore understood as a generalisation of occ o f in PSL (Ax2) that is useful to bind
objects to the descriptions they satisfy. For the sake of this work, we take Description as
a primitive predicate standing for the repeatable ‘content’ of engineering specifications
[18]. Description is therefore the class of information objects and, as such, it is disjoint
with ActOcc and physical objects. Also, we assume that if descriptions have parts, these
are descriptions, too. Axiom (Ax4) introduces the relation meantToProduce which is
used in (Def3) to define manufacturing activity. Looking at (Ax4), PP stands for proper
parthood in classical extensional mereology [9] (for simplicity, PP is not temporalised),
whereas ProductDescr is the description of the product that occurrences of the activity
at stake are meant to physically realize (cf. relation outcomeO f in Def2).
Ax3 sat(x, y,t) → PRE(x,t) ∧ Description(y)
(if x satisfies y at t then x exists at t and y is a description)
Ax4 meantToProduce(a, x) → Activity(a) ∧ ProductDescr(x) ∧ PP(x, a)
(a is an activity that includes as part the product description x)
Def3 M f gActivity(a) , ∃x(meantToProduce(a, x) ∧ ∀o(occ o f (o, a) →
(∃y(Product(y) ∧ sat(y, x, endO f (o)) ∧ outcomeO f (y, o)))))
(a is a manufacturing activity iff it is an activity such that all its occurrences have
as outcome a product whose description is part of the activity)
With these basic notions, manufacturing resource can be defined as in (Def4)–
(Def5), where resourceFor captures the relational link between resources and activities.
Accordingly, a manufacturing resource is an entity that satisfies the manufacturing re-
source description (primitive predicate) included in the activity at stake.
Def4 resourceFor(r, a,t) , M f gActivity(a) ∧ ∃x(M f gResourceDescr(x) ∧
PP(x, a) ∧ sat(r, x,t))
(r is a resource for the manufacturing activity a iff r satisfies the resource descrip-
tion x included in a)
Def5 M f gResource(r) , ∃at(resourceFor(r, a,t))
(r is a manufacturing resource iff it is a resource for at least one manufacturing
activity)
To conclude, this approach allows to deal with the representation of manufactur-
ing resources without necessarily committing to the manufacturing activity occurrences
where they possibly participate. The approach is therefore more flexible than what dis-
cussed in the previous section, since – as said – one may wish to talk about resources
without binding them directly to occurrences. Note however that the introduced defini-
tions, beside being developed to a limited extent, aims to model only a weak notion of
resource since, for instance, there is no requirement about relevant states of the resource
like availability or ownership. These constraints might be added as additional conditions
depending on the scenario and the specific application one is working with.
2.3. Manufacturing Resources and Goals
We explore in this section a third approach to represent manufacturing resources based
on goal modelling [22]. The idea is that manufacturing resources are entities employed
in manufacturing environments to bring about agents’ goals, e.g., the goal of making a
product with the desired characteristics. The advantage of this view over the previous
ones is the possibility of modelling resources independently from the plans to which they
are possibly related. For instance, one may conceive a driller as a manufacturing resource
only because the driller is functional to achieve certain goals, rather than because there
is a plan for a drilling process covering the description of the driller. From this point
of view, the approach presented in this section weakens what discussed in Section 2.2
in such a way to allow for resources modelling in the absence of manufacturing plans.3
Before presenting some formal aspects, let us look at the notion of goal.
Following the Belief-Desire-Intention approach (BDI) [10], one can understands
goals in terms of agents’ mental qualities referring to desired states of the world. This
view needs however to make sense of goal sharing (common in manufacturing contexts),
e.g., when one wishes to model multiple agents having the same goal towards the pro-
duction of a certain product. An alternative way to represent goals without relying on
individuals’ mental states consists in objectifying and treating them as descriptions of
desired world states. For instance, the goal of agent A (e.g., a manufacturing organisa-
tion) to have a product with characteristics B and C is satisfied by a world state where a
product with characteristics B and C exists.
Similarly to the approach in Section 2.2 to represent descriptions, axiom (Ax5) gives
a minimal constraint on the relation goalForAgent. Looking at the axiom, State is a
primitive predicate standing for a perdurant whose (temporal) parts are all of the same
type [17]. The goalForAgent relation is used in (Def6) to define manufacturing goal.
(Note that, generally speaking, relations goalForAgent and desires should have also a
temporal parameter as an agent may change desires over time.)
3 Reference to goals is often implicitly done in manufacturing through either processes or plans.
Ax5 goalForAgent(x, y) → Description(x) ∧ Agent(y) ∧ ∀st(sat(s, x,t) →
(State(s) ∧ desires(y, s)))
(goalForAgent holds between a description x of a state and an agent y, such that
y desires any state that satisfies x)
Def6 M f gGoal(x) , ∃y(goalForAgent(x, y) ∧ ∀st(sat(s, x,t) →
∃v(Product(v) ∧ PC(v, s,t))))
(x is a manufacturing goal iff there is an agent y such that for each state s
realising x there is a product v which participates in s)
By looking at (Ax5) and (Def6), a manufacturing goal is satisfied by a state of the
world in which there is a product that satisfies the agent’s goal. Also, the formulas bind
goals neither to manufacturing activities nor to their occurrences, i.e., they do not specify
how the state and the product are obtained. As said, this third approach captures the
notion of resource by directly linking it to goals rather than plans or processes.
Manufacturing resource can be now defined by means of a relation resourceFor∗ ,
which binds a resource to an agent’s goal(s) (Def7) independently of the way the goal is
achieved. Both formulas can be strengthen to capture different kinds of resources, e.g.,
mechanisms like machines or tools that ‘actively’ contribute to achieve the goal, or inputs
like amounts of matter that undergo manufacturing activity occurrences [22].
Def7 M f gResource(r) , ∃x(resourceFor∗ (r, x) ∧ M f gGoal(x))
(r is a manufacturing resource iff it is a resource for a manufacturing goal)
In order to model the link between goals and the manufacturing activity occurrences
bringing them about, manufacturing activity can be defined as in (Def8). Accordingly,
a manufacturing activity has a goal so that each occurrence satisfying the activity has
outcome a world state satisfying the goal and therefore the product participating in the
state.4
Def8 M f gActivity(a) , Activity(a) ∧ ∃y(M f gGoal(y) ∧ PP(y, a) ∧ ∀o(occ o f (o, a) →
∃s(sat(s, y, endO f (o)) ∧ outcomeO f (s, o))))
(a manufacturing activity is an activity that includes a manufacturing goal which
is satisfied by the state achieved at the end of any activity occurrence)
3. Remarks
The three approaches presented in the previous section are distinct views on manufac-
turing resources. As seen, the first approach (Section 2.1) is well suited to model re-
sources in tight connection to manufacturing activity occurrences. On the other hand, it is
less exploitable for application cases where resources are to be managed independently
from the manufacturing processes where they only possibly participate. The second view
(Section 2.2) deals with this issue by binding resources to activities (plans) rather than
occurrences. A manufacturing resource is therefore an entity that is functional for a man-
ufacturing activity. Finally, the third approach (Section 2.3) is more general and flexible
4 For simplicity, we use the relation outcomeO f for representing both states and products resulting from
activity occurrences.
than the previous ones, since it models resources independently from both activities and
occurrences but in connection to agents’ goals.
It should be clear that the choice of adopting one approach or the other depends on
experts’ requirements and the way in which they wish to make sense of resources. It can
be argued indeed that each approach fits a subset of factory lifecycle phases: Manufac-
turing execution when the system configuration is given (Sect.2.1); Detailed system de-
sign or production planning when the set of products is known and process planning has
been carried out (Sect.2.2); Early system design when limited details about product and
activities are available (Sect.2.3).
Considering that planning and scheduling tasks play a fundamental role, the second
approach is definitely central to manufacturing modelling. The third approach could be
easily integrated with the second one by representing goals in tandem with plans. In-
evitably, this would increase the (conceptual) expressivity of the ontology, since it would
bring into the overall framework both goal descriptions and plans. From an application
perspective this complexity might be undesired. Also, the approach in Section 2.2 does
refer to the item that an agent wishes to produce even though this is not stated in terms
of goals. In addition, axioms can be added to explicitly model activity occurrences in
tandem with activities and resources.
Finally, it must be noted that – independently from the approach one relies on –
manufacturing resources are conceptualised as roles. Accordingly, an entity is not a man-
ufacturing resource per se but it counts as (has the role of) manufacturing resource only
when certain conditions are met. E.g., when it participates in a manufacturing process or
is bounded to either an activity or goal.
4. Representing Manufacturing Resources: Design of Manufacturing Systems
This section presents an industrial use case where the modelling of manufacturing re-
sources plays a key role. The use case is focused on the design of manufacturing systems
where an ontology-based approach can support the integrated factory design by provid-
ing a shared common model that can consistently evolve during the design phase. In
particular, the use case deals with the design of an assembly line as described in [2,3,5]
(powertrain valve assembly on a cylinder head). The following input data are given in the
current design phase: Process plan of the product to be assembled; set of sequential op-
erations decomposing the process plan; input component or material for each operation;
production resources needed to execute each operation.
Several design decisions need to be taken at this stage, e.g., the assignment of op-
erations to workstations in the line (i.e., line balancing), selection of production re-
sources for each workstation (i.e., line configuration), optimisation of the capacity of
inter-operational buffers (i.e., buffer allocation problem).
Table 1 reports some relevant data defining the use case, namely, the list of opera-
tions (ID and description); component or material inputs for each operation (WIP stands
for ‘work in progress’, i.e., resulting from the previous operation); resources needed to
execute each operation. For the sake of the example, we focus herein only on the re-
sources needed in automatic stations. Each resource can be further characterised in terms
of failure modes, investment and operating cost, size, etc. The processing time may de-
pend on the resources employed to execute the operation. Moreover, if more than one
operation is assigned to the same workstation, the latter must include the production
resources needed for all operations to be performed.
Table 1. Use case data
Operation Operation Description Input component or ma- Resources in Automatic
ID terial Station
op10 Load and identify cylinder cylinder head palletizing robot
head
op20 Apply sealant and lubricant WIP, sealant and lubricant robot, sealant dispensing tool
op30 Install intake and exhaust WIP, intake and exhaust robot, handling gripper
valves valves
op40 Valve blow-by leak test WIP robot, leak test tool
op50 Rollover WIP rollover equipment
op60 Assemble valve stem seal WIP, valve stem seals robot, handling gripper
op70 Press valve stem seals WIP robot, pressing tool
op80 Assemble valve springs WIP, valve springs robot, handling gripper
op90 Assemble valve spring re- WIP, valve spring retainer robot, handling gripper
tainer
op100 In process verification WIP robot, verification tool
op110 Unload cylinder head as- WIP palletizing robot
sembly
Even though the process plan is linear, various feasible system configurations can
be designed based on the requirements and the selectable resources. Line balancing and
line configuration will be carried out by considering objective function and constraints
related to investment cost, operating cost, throughput, etc. [5]
We now apply the approach of Section 2.2 to the data in the table. Input data can be
provided to a methodology supporting the design of the assembly line and its results can
be used to instantiate the ontology specifying the chosen system configuration.
Since the formalizations in Section 2 did not aim at completeness with respect to
manufacturing modelling needs, extensions are necessary. We adopt definition (Def9) for
complex (manufacturing) activities, that is, activities formed by at least two (different)
activities. Conversely, (Def10) introduces manufacturing operations as activities that are
not complex. Admittedly, these two definitions only weakly characterise these notions
but suffice here to present the general view.
Second, by looking at Table 1, each entry in the Operation ID column refers to either
specific operations or to specific complex manufacturing activities. In each case, they are
part of the complex manufacturing activity standing for the entire planned activity; let us
call cxact 1 this latter entity (the activity including all other activities in the table). En-
tries in the Operation column refer to classes of manufacturing activities, e.g., Load and
Identi f yingCylinderHead refer to the activities of loading raw parts and of recognising
the heads of cylinders for assembly purposes (axioms like (Ax6) can be used to exhaus-
tively cover the table). Entries in the final two columns model manufacturing resource
descriptions. In particular, the Input component or material refers to resources that are
inputs for manufacturing processes and are therefore manipulated during the processes,
whereas Resources in automatic station describes resources that either execute a process
(i.e., a robot) or support it (e.g., handling gripper). Domain experts in these cases require
the flexibility of describing either classes of manufacturing resource or individual re-
sources, e.g., descriptions satisfied only by robots owned or sold by a certain enterprise.
For the sake of the example, (Ax7) introduces two (disjoint) resource description classes.
Def9 M f gComplexActivity(x) , M f gActivity(x) ∧ ∃yz(M f gActivity(y) ∧
M f gActivity(z) ∧ PP(y, x) ∧ PP(z, x) ∧ y 6= z)
Def10 M f gOperation(x) , M f gActivity(y) ∧ ¬M f gComplexActivity(x)
Ax6 Load(x) ∨ Identi f yCylinderHead(x) → M f gActivity(x)
Ax7 PalletizingRobotDescr(x) ∨CylinderHeadDescr(x) → M f gResourceDescr(x)
The first row of the table can be represented as follows.5 Formula (f1) intro-
duces the complex activity op10 formed by an instance of Load and an instance of
Identi f yCylinderHead such that whenever op10 occurs, the load occurrence precedes
the identify cylinder head occurrence.6 Formula (f2) introduces the complex activity
cxact 1 comprising op10 and all activities listed in the table above (ideally this formula
should include also their order of execution). Formula (f3) makes explicit the resource
descriptions related to op10. In particular, a specific cylinder head description is in play,
i.e., ds1; this could be further characterised to model, e.g., the dimension, weight, ca-
pability, technology provider, etc. that a physical resource has to satisfy. Differently, the
formula does not cover an individual description for the palletizing robot; it only says
that op10 includes the description of a (generic) palletizing robot. In this way, experts can
avoid to commit to a specific description, which they may introduce along the modelling
process when their requirements are more explicitly defined.
f1 Load(load10) ∧ Identi f yCylinderHead(identi f y10) ∧ PP(load10, op10) ∧
PP(identi f y10, op10) ∧ load10 ≺ identi f y10 ∧ M f gComplexActivity(op10)
f2 M f gComplexActivity(cxact 1) ∧ PP(op10, cxact 1)
f3 CylinderHeadDescr(ds1) ∧ PP(ds1, op10) ∧
∃x(PalletizingRobotDescr(x) ∧ PP(x, op10))
The distinction between input resources and supporting or mechanism resources,
present in Table 1, has not been formalized here. On this topic see, e.g., [16]. In general,
the formal approach presented in Section 2.2 provides only high-level modeling elements
that can be extended and adapted to match specific views in the manufacturing domain.
Also, one can model the activity occurrence(s) satisfying the operations as introduced
above along with the outcome product and the used physical resources. Further axioms
are needed to qualify the participation of resources (e.g., with respect to time constraints),
or to model alternatives in resource selection (e.g., the possibility of using automatic or
manual stations).
5. On Capabilities, Capacities, Information, Time and Money
We discuss in this section preliminary considerations concerning the notions of capability
and capacity (Section 5.1) as well as extensions of the notion of manufacturing resource
to cover information, time, and money (Section 5.2).
5 We label formulas relative to the examples with f .
6 With a slight abuse on notation, we use ≺ to temporally order (the occurrences of) activities.
5.1. Capabilities and Capacities
Resources are commonly characterised in terms of both capabilities and capacities in
manufacturing. According to MANDATE, for example, “a resource capability defines
a group of characteristics specifying manufacturing resources under functional aspects”
(MANDATE-31, p. 12; emphasis is ours). In addition, the standard relies on capabilities
to constrain the participation of resources to manufacturing processes by adding that a
capability is the “quality of being able to perform a given activity.” An example is the
capability of a cutting tool to remove (a certain type of) material within certain dimen-
sional tolerances. Hence, the tool can be used in a cutting activity occurrence aimed at
realising a feature (e.g., hole, slot) only because of its capability. On the other hand, ca-
pacity is a “measure of the quantity of product (or component) a resource can process
per unit of time” (p.24). Although it is hard to find explicit definitions of these notions,
the MANDATE view seems shared by most manufacturing experts and stakeholders.
Focusing on capabilities, from the definition above it is clear that MANDATE un-
derstands them as functionalities. The latter are notoriously differently understood in
engineering [13] and, as a consequence, differently represented in ontologies (see, e.g.,
[14]). A comparative analysis of these approaches with respect to manufacturing, simi-
lar to what done throughout Section 2, is left to future work. As said, we sketch in the
following only some preliminary considerations.
Recent works based on the Basic Formal Ontology (BFO) for manufacturing pro-
pose to conceive and represent capabilities as dispositions [20]. Leaving aside the philo-
sophical debate on these entities, they are commonly understood in applied ontology –
including BFO – as characteristics of objects which are manifested only if specific cir-
cumstances occur (see, e.g., [4]). For instance, the capability of a magnet to attract metal
qualifies the magnet whenever the latter exists. However, this disposition manifests only
when the magnet is close to a piece of metal and, therefore, may never manifest. A sim-
ilar approach is discussed in [8]. Differently from [4,20], the authors do not model the
manifestation of dispositions and treat them as plain (artefacts’) qualities. The two ap-
proaches rely therefore on different ontological views. Approaches like [4,20] inevitably
lead to conceptually and formally complex ontologies, since they must take into account
the conditions triggering the manifestation of dispositions. The approach in [8] avoids
this complexity while being able to represent capabilities. Recalling the way in which
MANDATE binds resources to processes via capabilities, the work in [8] could be used
to specify constraints like: Resource r can participate in activity occurrence o only if it
has capability c.7
Turning now to capacities, note the similarity with capabilities: the latter model what
a resource is able to do (e.g., to cut or add material), the former how many things it
does (e.g., a polished threaded hole each 5 minutes). Capacities could be therefore un-
derstood as (sub-types of) capabilities making explicit the maximum number of products
a resource is able to manipulate for a time unit.
5.2. Information, Money and Time as Resources
The manufacturing literature on resources concentrates on material and agentive re-
sources since these are at the core of manufacturing activities. Section 2 has adopted
7 Considering the differences discussed in Section 2, these constraints need to be specified in relation to the
way in which resources are conceived, e.g., with respect to activities or activity occurrences.
this view. However, material and agentive entities do not exhaust the type of resources
experts talk about in this domain. Even before the Industry 4.0 emphasis on data and
information, it has been known that these have a central role in modern production sys-
tems. Accordingly, the formalisation discussed in the previous sections deals with data
and information along with material and agentive resources, that is, they are not treated
as a separate type of resource that needs a distinct modelling approach.
The situation is different when we move to talk about money and time as resources.
Money is definitely a resource in both colloquial and technical discussions but money is
not discussed in modelling manufacturing scenarios. We believe that the explanation is
quite simple. Money participates in property exchanges (acquiring and selling) and not
in production activities. When one talks of money as a resource, s/he implicitly refers
to a (perhaps just possible) business process where money participates as one of the
exchanged entities. This means that money is not a resource in a mere manufacturing
setting. It is a resource when we augment a manufacturing process with processes about
business transactions, e.g., for acquiring a device or ensuring the availability of an agent
which then participates to the manufacturing activity.
Finally, many discussions about time look at it as a resource: “It takes 48 hrs to
produce this item but we only have 36 available”, “we have enough time to operate that
machine.” We thus posit the question: is time a resource? Since a resource is a role, this
means to answer this other question: what is the role of time in manufacturing processes?
Clearly, time does not participate to a process, nor it acquires a different status when
we look at time from the point of view of an activity. The underlying idea is that when
we talk of time as resource, we actually mean either the availability of the production
resource for a manufacturing process, availability that comes to an end at the delivery
time since this constraint is part of the plan description, or the availability of a device
which is a resource for the plan in the sense discussed in Section 2. In either case, it
turns out that time is not a resource in the manufacturing sense; therefore, talking of time
as a resource should be treated as a way of speaking without any explicit ontological
commitment.
6. Conclusions
The research work presented in this paper strove from the need of making explicit some
of the foundational assumptions behind resource modelling in manufacturing. In particu-
lar, the work presented across the first sections presents existing approaches by address-
ing their pros and cons. As said, the three approaches are well motivated from both an
ontological and manufacturing stance; the choice of relying on one or the other depends
therefore on the modelling requirements that the ontology at stake should satisfy, and
on the level of abstraction one wishes to achieve. The approach discussed in Section 2.2
allows to gain a good compromise between expressivity and modelling flexibility for the
fact that resources are not directly bound to activity occurrences but on their plans.
Future work is required to strengthen both the formal and conceptual treatment of
resources. In particular, Section 5 presented some preliminary ideas which require further
analysis to be modelled in a robust way. A computational formalisation is also needed for
reasoning over manufacturing knowledge and organising data for application purposes.
Acknowledgements: We wish to thank Nicola Guarino, Lorenzo Solano, Pedro Rosado,
Fernando Romero, and Marcello Urgo for previous discussions on the modelling of man-
ufacturing resources that contributed to the materials presented in this paper.
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