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Strategic Action Line LI4: High
Efficiency and Zero Defect
Marcello Urgo, Vittorio Rampa, Renato Cotti Piccinelli, Marco Sortino,
and Pierluigi Petrali
Abstract The objective of this chapter is to describe the strategic action line
related to high efficiency and zero defect production (LI4). In particular, this chapter
proposes research and innovation priorities aimed at studying models for efficiency
in terms of: zero-defect technologies designed to reduce non-conformances, moni-
toring processes during the various phases like quality management, maintenance and
internal logistics of a manufacturing system, upgrading and improving the capacity of
equipment and industrial goods; robustness/flexibility as the capacity to face disrup-
tions, due to the precarious supply of incoming materials and parts, and to the specific
properties of the material (anisotropy, low rigidity, etc.); smart systems for opti-
mized use of available resources (equipment, human operator, knowledge) and for
the control and management of production systems through models (CPS, empirical
models, etc.).
Keywords High efficiency ·Zero defect ·Advanced control ·Maintenance ·
Artificial intelligence
M. Urgo (B
)
Politecnico Di Milano, Milan, Italy
e-mail: marcello.urgo@polimi.it
V. Rampa
CNR-IEIIT, Milan, Italy
R. Cotti Piccinelli
Streparava, Adro, (BS), Italy
M. Sortino
Università Di Udine, Udine, Italy
P. Petrali
DIH-Lombardia, Milan, Italy
© The Author(s) 2024
R.FornasieroandT.A.M.Tolio(eds.),The Future of Manufacturing: The Italian
Roadmap, Springer Tracts in Mechanical Engineering,
https://doi.org/10.1007/978-3-031-60560-4_8
113
114 M. Urgo et al.
1 Introduction
Efficiency is the ability to reduce the effort associated with the achievement of a
goal, optimizing the use of resources, materials and time. Quality plays a significant
role in terms of efficiency in achieving the expected results, as it permits to avoid
rework and waste of products and materials. Efficiency is an enabler of company
competitiveness, since the ability to work efficiently in complex and variable condi-
tions determines the possibility to operate in more demanding and competitive areas,
such as product customization, adoption of new technologies, enabling of remanufac-
turing activities. Finally, the growing complexity of production systems also requires
an efficient use of resources in a more general way. In this perspective, the efficient
use of available machinery and equipment, the ability to exploit available knowledge
and take advantage of advanced digital tools and artificial intelligence towards the
implementation of next generation production systems (Fig. 1).
The goals of this strategic action line fall into three groups:
•Zero defects. Efficiency is considered in terms of reduction of non-conformities
and their impact on the performance of the production system, and involves
monitoring processes in their various phases, quality management, production
systems’ maintenance and internal logistics, updating and improving the capacity
of equipment and industrial assets.
•Robustness/flexibility. Efficiency is considered in terms of ability to carry on
operations during disturbances. In particular, the variability of incoming mate-
rials and pieces, and a material’s specific characteristics (anisotropy, low stiffness,
etc.) are particularly significant for the various types of applications. This is espe-
cially true in a circular economy, which involves rework and/or repair processes.
Fig. 1 Strategic Action Line—High-efficiency and zero defect
Strategic Action Line LI4: High Efficiency and Zero Defect 115
Furthermore, the expansion of the equipment’s scope of application is explored
also in terms of the possibility to operate multiple technologies at the same time
and the use of robots for a wider range of applications.
•Intelligent systems. Efficiency is considered in terms of optimized use of available
resources (equipment, human operator, knowledge). It is necessary to consider
approaches and methodologies for the control and management of production
systems through models (CPS, empirical models, etc.) and approaches that exploit
artificial intelligence, to establish an efficient collaboration between human oper-
ators and automatic tools, as well as approaches and methodologies for the
consolidation of knowledge.
The research and innovation priorities of the strategic action line on High
Efficiency and zero defect are:
PRI4.1: Advanced monitoring and control of production processes (zero defects)
PRI4.2: Approaches for an integrated quality/maintenance/logistics management
(zero defects)
PRI4.3: Updating, retrofitting and valorisation of capital goods (zero defects)
PRI4.4: High efficiency for repair remanufacturing (robustness/flexibility)
PIR4.5: Advanced industrial robot modelling and planning (robustness/flexibility)
PRI4.6: Cyber-physical systems (CPS) for smart factories (intelligent systems)
PRI4.7: Human-artificial intelligence for knowledge consolidation and human-
machine cooperation in high-efficiency production systems (intelligent systems)
PRI4.8: Advanced production, planning and scheduling (intelligent systems)
2 PRI4.1 Monitoring and Advanced Control of Production
Processes (Zero Defect)
Monitoring and control contribute to process efficiency, as all the elements of Overall
Equipment Efficiency (OEE)—i.e. Availability, Performance and Quality—require
accurate and precise measurement in order to:
•Achieve a specific understanding of cause-effect mechanisms and allow the
implementation of advanced closed-loop control techniques;
•Improve the design, execution and maintenance of both physical (machine level)
and virtual (organization level) assets.
Furthermore, the exploitation of sensor systems in combination with stan-
dard methods for collecting, filtering and archiving data will provide an unprece-
dented source of data, useful in understanding complex production phenomena and
scenarios.
The goals of this research and innovation priority are related to:
•Improving data management through the adoption of standard ontologies,
communication protocols and open software that can be easily reused.
116 M. Urgo et al.
•Enhancing HMI (Human Machine Interaction) and UI (User Interface) through
techniques for the visualization of multidimensional data aggregation, to help
users deal with the increased complexity of the reality under control.
•Exploring alternative UI mechanisms based on wearable devices and multisensory
interfaces (visual, audio, tactile and AR/VR techniques)
•Developing a Digital-Twin-based control system, i.e. a control mechanism based
on real time behaviour simulation.
The expected impacts from the introduction of advanced monitoring and control
solutions will allow manufacturing companies to improve process efficiency from
all three perspectives:
(1) More production lines will be available, thanks to an early detection of potential
failures, the introduction of predictive maintenance and a better scheduling of
maintenance operations;
(2) Performance: The process could be contained, or it could self-adapt, in real
time, depending on production needs. Data would help understand the causes
behind speed losses and micro-stops;
(3) Quality: Sensor data and fitting techniques will help reduce process variability
and provide ways for an early detection and correction of deviations.
The integration of process and product parameters will encourage a holistic
approach to the optimization of production processes, and promote waste reduction,
a more energy efficient production and lower CO2 emissions.
Interaction with Other Strategic Action Lines
•LI1—Integration of the study of efficient programming systems to improve the
availability of more intelligent machine tools for product personalization.
•LI2—Integration of advanced machine tool control for a significant increase of
efficiency and reduction of waste and energy consumption.
•LI3—The new advanced functions of machine tools will remodel human–machine
interactions and call for operators with advanced skills and analytical dexterity.
•LI5—The new advanced monitoring and control systems will be particularly bene-
ficial for innovative manufacturing processes such as additive manufacturing and
hybrid manufacturing. More complex machines will have higher costs, and an
effective control system will be needed to avoid problems and increase efficiency.
•LI6—There is a very strong interaction, as more advanced control methodologies
will only be possible by implementing machine learning techniques and smart
components and sensors.
•LI7—The new capabilities of machine tools will certainly have an impact on
management, production, organization and supply chain.
Time Horizon
Short-term goals (2–3 years):
•Development of standard ontologies and communication protocols.
Strategic Action Line LI4: High Efficiency and Zero Defect 117
•Development of basic requirements and regulations to enable the development
of digital twins—3D geometric models of machine components, mathematical
models for handling operations.
•Development of standard adaptive control techniques based on process sensors
(power, temperature, force, vibration).
•HMI innovation through the introduction of virtual reality and basic signal-
analysis functions.
•Development of standards for the creation of digital shadows.
•Off-line monitoring of machine tools with simple process parameter adjustments
(override).
Medium-term goals (4–6 years):
•Introduction of self-programming and more advanced adaptive control techniques
based on the indirect observation of process quantities using mathematical models.
•Innovation of HMI by introducing statistical analysis functions.
•Development of standards for integrating cloud digital twins into the manufac-
turing process.
•Complete off-line control of machine tools, including loading and unloading.
Long-term goals (7–10 years):
•Development of standards for the integration of advanced control logics in
complex systems and production lines.
•Advanced sensor data analysis functions in combination with simulation.
•Development of standards for the integration of digital twins in the critical control
cycle (edge).
3 PRI4.2 Approaches for Integrated Quality/Maintenance/
Logistics Management (Zero Defect)
Digital tools, together with formalized knowledge and data, offer the possibility
to implement complex approaches to quality management, taking into account the
wide range of factors that influence product quality, and improve efficiency of the
production system as a whole.
These factors include: the control of production processes, the management and
supply of materials and components, the maintenance of production assets and their
updated performance, the logistics of the whole system and the interconnection of
the different aspects and actors to determine their performance.
Building on knowledge and data, and exploiting integrated models based on
quality, logistics and maintenance factors, the focus of this research and innova-
tion priority is on methodologies and approaches aimed at improving the overall
efficiency of production systems, in terms of productivity, qualitative characteristics
of the products, use of resources, maintenance policies, etc.
118 M. Urgo et al.
The selected approaches should cover a wide range of products, processes and
resources. For example, large products for which transportation, inspection and
processing are specifically difficult. The considered approaches should be robust,
including in the modelling the intrinsic uncertainty of real production systems, and
any changes in decisions and planning when unexpected events occur, to mitigate
the impact of such events on the systems’ overall performance.
The main goals of this research and innovation priority cover the following areas:
•Methods and tools for quality control in complex products (e.g., product char-
acterized by large dimensions, multiple materials, additive processes, etc.) as well
as in low-volume and/or small-batch production (e.g., personalized products). In
these cases, traditional approaches to quality control and management are inade-
quate, which creates a demand for new advanced approaches. Research will espe-
cially address the use of cyber-physical models for products and processes, adap-
tive approaches, artificial intelligence and machine learning, supervised and unsu-
pervised learning, formalization and structuring of the knowledge of human opera-
tors. The general goal is to improve the ability to predict anomalies and implement
the possibility of feeding and integrating these approaches with knowledge and
data from digital models of production systems (Digital Twins).
•Models and approaches for the integrated management of quality, mainte-
nance and logistics for the entire production system. These approaches are
expected to define and support complex and integrated decisions in relation to
maintenance, quality and logistics. For example, planning the maintenance of
production resources, defining and planning quality controls, activating decisions
for continuous improvement, planning and scheduling production. A possible
class of approaches in this area is opportunistic maintenance, modelled to include
jointly quality (deviations from product/process specifications), maintenance and
system status. The goal is to reduce the impact of maintenance activities on overall
system performance.
Interaction with Other Strategic Action Lines
•LI1—The availability of integrated quality/maintenance/logistics approaches
will improve product customization by reducing the inefficiencies related to
personalized products;
•LI2—Improving efficiency and reducing waste and energy consumption;
•LI7—The development of these approaches requires a strong integration with the
digital platform that operates in the production system.
Time Horizon
Short to medium term goals (2–6 years):
•Models and approaches for an integrated management of decisions related to
quality, maintenance and logistics, considering that a substantial amount of partial
research results and embryonic products are already available.
Strategic Action Line LI4: High Efficiency and Zero Defect 119
Long-term goals (7–10 years):
•Methodologies and tools for identifying quality problems in complex products,
since the main gap with respect to the state of the art is the absence of standard
reference models (ontologies, definition of semantic data), while a secondary gap
is the development of unattended machine learning approaches applied to complex
systems (Cyber-Physical Systems of Systems-CPSoS).
4 PRI4.3 Updating, Retrofitting and Enhancement
of Durable Equipment (Zero Defects)
A substantial portion of the durable equipment of manufacturing companies (for
example, machine tools, assembly systems, etc.) has been designed to have a signif-
icantly long life, in many cases approximately 20–30 years. However, the recent
rapid and radical evolution of production systems has accelerated the obsolescence
of a large part of it, mainly because of the impossibility to integrate it with digital
management and control infrastructures, rather than on its actual process capacity.
Therefore, retrofit and updating geared to the integration of modern digital func-
tionalities into operating but dated durable equipment are extremely significant,
and represent a sustainable, valid and effective approach for the management and
updating of industrial equipment in terms of I4.0 technologies.
The goals of this research and innovation priority cover the following areas:
•Toolboxes for the updating and retrofitting of machine tools and produc-
tion systems. Tools designed to provide operators with the functionalities and
approaches necessary to transform sensors and interconnections of traditional
machine tools. In particular, with regard to sensors, actuators, control devices and
protocols, connection devices and protocols, cyber-physical models of machine
tools’ behaviour and performance.
•Plug-and-play models for the integration of machine tools and production
systems into modern digital control and management platforms. Concep-
tual design of standard connectors to enable a set of standard/optional features.
Standard knowledge-representation models based on semantic technologies will
also be taken into consideration, with a view to enabling and ensuring inter-
operability between different factory objects (machines, conveyors, etc.) by
exploiting existing standards (Supervisory Control and Data Acquisition—
SCADA, Distributed Control System—DCS).
•Assisted methodologies for the characterization of durable equipment for
integration. Durable equipment currently operating in production systems are
based on a wide range of different management and control architectures
which apply different control technologies, communication protocols, control
approaches, electromechanical components. In many cases, they are the result of
multiple upgrade and re-engineering phases. A consistent and secure integration of
these assets into modern IT management and control platforms requires reference
120 M. Urgo et al.
standards to regulate and allow the modelling of the characteristics, functionalities
and capabilities of industrial equipment in a general and unique way. Furthermore,
the assessment of the operational and technological capacity of industrial equip-
ment is influenced by multiple intersecting factors. Therefore, to ensure a coherent
and uniform set of management and control tools, it is important to design and
develop specific approaches as a basis to more general approaches for the eval-
uation and certification of performance in the different situations (architectures,
machine characteristics, control approaches, etc.)
Interaction with Other Strategic Action Lines
•LI2: The possibility to update existing industrial assets has a clear impact on the
sustainability of production activities in general. Furthermore, by its very nature
this research and innovation priority applies a circular economy paradigm.
•LI7: The upgrading and retrofitting of industrial assets is primarily driven by the
need to incorporate them into modern IT platforms.
Time Horizon
Short to medium term goals (2–6 yeas):
•Plug-and-play models for the integration of industrial assets into modern IT
management and control platforms;
•Assisted methodologies for the characterization of durable equipment to support
integration.
Medium term goals (4–6 years):
•Toolbox for updating and retrofitting industrial assets.
5 PRI4.4. High Efficiency for Repair and Remanufacturing
(Robustness/Flexibility)
The transition to circular production models requires, from a technological point of
view, processes, technologies, skills and capital goods for the maintenance, repair,
updating and reworking of products and their components. Therefore, not only the
production, but also the repair and regeneration of products require plants in which
to operate. These plants must be able to work in collaboration with the supply chains
of the original production plants, to manage the entire life cycle of the products.
This research and innovation priority arises from the need for durable equipment
and production facilities to regenerate, repair and recycle products and components.
The focus is on highly efficient technologies and approaches that can partially carry
out processes and/or repeat a limited and/or alternative set of operations to obtain
compliant/degraded and reclassified products. Furthermore, these processes will be
expected to deal effectively and efficiently with the variability of incoming products,
which is typical of reworked products that come from different kinds of uses.
Strategic Action Line LI4: High Efficiency and Zero Defect 121
The goals of this research and innovation priority concern in particular:
•Systems for the automatic characterization of the state of materials and/
or products in support of the recovery/reworking/repair phases. In this context,
DMC/RFID traceability/identification and computerized-vision technologies can
be appropriately developed for characterization of defects/damage/wear.
•Intelligent systems and HMI in support of recovery/rework/repair. The goal
is to provide decision-making and operational support in real time to operators
dealing with recovery/reworking/repair processes in highly variable conditions
and states of the materials to be processed. In this context, decision-support tools
need to be developed, to identify the most suitable strategy for each specific
product, in consideration of its conditions, defining manual/semi-automatic disas-
sembly operations and subsequent restoration and/or completion operations,
compliance of the reprocessed product and its new classification for reintroduc-
tion on the market. AI-based technologies will be investigated, particularly in
support of their interaction with human operators, and for safety and ergonomics
purposes.
•Flexible and efficient production processes and systems to ensure the integra-
tion of repairing and remanufacturing operations in the production flow, assuring
capability of interruption, or partial execution of manufacturing processes, in
support of reworking and repair. This topic includes diversified aspects, such
as the characteristics of equipment and their control systems, the definition of
modular and reconfigurable manufacturing processes, the development of stan-
dards that can explicitly support the functions described. The general objective
is to ensure the possibility of a partial/interrupted execution of processes, while
maintaining the performance levels of the production/reworking systems.
•Repair technologies. Repair operations are geared to restore the products’ charac-
teristics/functionality. For this purpose, it is necessary to develop specific produc-
tion technologies that maximize the recovered value while optimizing repair costs
in terms of energy, material consumption and disposal of non-recoverable items.
In this context, examples are additive manufacturing technologies, which can
play an important role in the formulation/definition of special repair processes,
not only by facilitating the production of spare parts but by introducing specific
methodologies (e.g. return of materials for the subsequent restoration of fastening/
shrinking seats and/or worn profiles) or the reuse of recovered and reconditioned
components to obtain new products.
Interaction with Other Strategic Action Lines
In relation to the strategic action lines of the CFI roadmap:
•LI2: Industrial sustainability.
•LI3: Factories for humans.
•LI6: Evolving and resilient production systems.
•LI7: Strategies and management for next generation production systems.
122 M. Urgo et al.
Time Horizon
Short-term goals (2–3 years):
•Systems for the automatic characterization of the state of materials.
•Intelligent systems and HMI to support recovery/rework/repair.
Medium-term goals (4–6 years):
•Flexible production processes and systems in terms of real/virtual demonstrators.
•Repair technologies in terms of real/virtual demonstrators.
Long-term goals (7–10 years):
•Flexible and efficient production processes and systems up to a Technology
Readiness Level (TRL) suitable for complete industrial implementation and
diffusion.
•Repair technologies up to a Technology Readiness Level (TRL) suitable for full
industrial deployment and diffusion.
6 PRI4.5. Advanced Industrial Robot Modelling
and Planning (Robustness/Flexibility)
The planning and control of industrial robots is essential in ensuring a safe, effective
and reliable use of robots in applications other than those in which they are commonly
used.
The objectives of this research and innovation priority mainly concern:
•Advanced methodologies and sensors to support the safety of industrial
robots, to be used in collaboration with human operators. In this scenario, robots
are meant to perform the heaviest operations or operations that require high accu-
racy or repeatability, and assist humans in their activities. Operators can thus focus
on operations that require greater flexibility (for example those related to product
personalization). As for collaboration between robots and operators, the state of
the art cannot guarantee safety levels in line with regulatory requirements and
the performance standards demanded by the industry. It is necessary to develop
advanced methodologies and sensors capable of predicting human behaviour,
thus avoiding risks and ensuring adequate performance. At the same time, robots
that operate in these conditions have to be built (both in terms of hardware and
control) in such a way as to minimize the impact of possible collisions with
humans, through specific materials, low mechanical resistance, etc.
•Modelling robots to execute technological processes in which the interaction
between tools and parts generates remarkable forces (e.g., milling). Appli-
cations of this type involve a considerable difficulty in terms of controlling the
robot’s movement, as it is impossible to ensure the necessary accuracy in the posi-
tioning of the end-effector when there are significant deformations in the robot’s
Strategic Action Line LI4: High Efficiency and Zero Defect 123
structure. It is necessary to develop modelling techniques that can estimate the
forces generated between the end-effector (tool) and the parts being machined,
implementing appropriate control techniques to compensate actively for devia-
tions from the ideal trajectory, rather than relying on the inherent rigidity that
lightweight structures such as robots cannot provide.
•Specific programming approaches capable of bypassing the need for a defi-
nition of specific trajectories, focusing decisions on the characteristics of the
process to be implemented and avoiding the difficulties associated with the adop-
tion of robots in manufacturing processes. It is necessary to study advanced soft-
ware that can support the programmer in defining the operations to be performed
by the robot. These approaches will be based on features such as learning by
examples, assisted and simplified programming, and the ability to modify and
reconfigure the operations assigned to a robot in a simple and reliable way.
Safe and effective collaboration between robots and operators has strong impacts
in terms of ergonomics and workers’ well-being, as it contributes to relieve people of
heavy and/or tiring tasks. At the same time, it can boost systems’ performance in terms
of flexibility, by calibrating workloads (for both operators and robots) depending on
the volume and characteristics of the products.
Extending the use of robots to the execution of technological operations can
significantly increase the flexibility of production systems. The processes involved
include finishing operations (such as polishing, grinding, deburring, etc.), in addition
to those concerning the removal of materials.
Greater ease in the implementation and reconfiguration of processes assisted or
performed by robots would have a significant impact in terms of the diffusion of
robots in manufacturing industries and their competitiveness in terms of cost, quality
and flexibility.
Interaction with Other Strategic Action Lines
•LI1: The use of industrial robots in a wide range of processes supports product
customization.
•LI3: A widespread use of robots must take into consideration issues related to the
human being.
•LI5: A more reliable positioning of robots’ end effectors can be used to implement
and automate innovative processes.
•LI6: The robots’ high flexibility ensures the adaptability and resilience of
production systems.
•LI7: Digital tools and technologies are needed to support the broad adoption of
industrial robots.
Time Horizon
Short-medium-term goals (3–6 years):
•Human–robot collaboration in production environments.
•Advanced planning approaches for robots.
124 M. Urgo et al.
Medium-long term goals (4–10 years):
•Use of robots instead of machine tools.
7 PRI4.6 Cyber-Physical Systems (CPS) for Smart
Factories (Intelligent Systems)
The flexibility and reconfigurability of production systems require modular and intel-
ligent architectures, as well as monitoring and controlling of logistics and system
quality, compliance with process constraints and safety of man–machine interactions.
Traditional hierarchical control techniques rely on predefined configurations and
statically-designed decision platforms, which do not provide the required degree of
flexibility, adaptability and efficiency.
Furthermore, the behaviour of a production system is usually modelled as a chain
of actions within a purely temporal domain defined in terms of events. The interaction
with the underlying processes requires approaches based on continuous-time control
or, alternatively, discrete-time control but with better temporal resolution. These two
levels are not considered jointly, thus constituting a barrier to the overall optimization
of the system’s behaviour.
It is therefore essential to develop an integrated and distributed platform for moni-
toring, control and supervision. It should consist of intelligent and interacting units,
based on Cyber-Physical Systems (CPS) and a hybrid paradigm, to consider simul-
taneously different temporal domains (discrete and continuous events) related to the
modelling of the behaviour of a production system, at different levels.
This class of approaches is applied for example to modular robotic cells in scalable
production systems, where adaptation to external conditions and to the processes to
be carried out is fundamental. In those cases, coordinating the intelligent compo-
nents that operate in a production system, and integrating them into management
and control platforms are key factors in governing the network of complex inter-
actions between physical, software, robotic and human components, human–robot
interaction and human–robot collaboration.
Using CPS approaches for machines and machine-systems reveals new possibil-
ities that go beyond current control approaches, evolving towards the possibility of
automatic adaptive performance improvement, in terms of efficiency and safety (also
in human–machine interaction) of the whole system at different levels.
The availability of a Digital Twin for a physical system determines the possibility
of developing predictive control algorithms based on the updated status of the plant,
as well as the possibility of using AI methodologies based on the available data.
CPS architectures of individual production units, based on the 5C paradigm
(Connection, Conversion, Cyber, Cognition, Configuration), should therefore evolve
towards a new 6C paradigm, where the additional level would be the Cooperation
between factory objects. It is therefore necessary to develop an integrated level of
Strategic Action Line LI4: High Efficiency and Zero Defect 125
control where intelligent agents can integrate and cooperate towards the collection
of information and data, and the management and optimization of performance.
This level’s key factors are techniques of Massive Data Acquisition, Data
Analytics and Machine Learning, geared to create and integrate the cognitive, self-
configuration and cooperation levels of intelligent CPS units, to drive flexibility, high
performance, and efficiency.
Interaction with Other Strategic Action Lines
•LI2: CPSs offer tools to improve integrated product-process-system modelling.
•LI3: connections with technologies and methods for humans in the factory and
Virtual/Augmented Reality (VR/AR) technologies and applications for product-
process-system management (using a system’s digital twin).
•LI6: highly flexible modular mechatronic systems.
Time Horizon
Short-term goals (2–3 years):
•Development of hybrid simulation/emulation techniques based on digital twins
and predictive control for intelligent CPS units in simplified scenarios.
•Development of efficient technologies for the implementation of CPS
•Development of artificial intelligence systems to be implemented in single
modular CPS units.
Medium term goals (4–6 years):
•Validation and comparison of different predictive and adaptive control systems
for individual CPS units
•Development of artificial intelligence systems capable of coordinating the activity
of single CPS modular units.
•Preliminary development of integrated multilevel platforms for the cooperation
of different CPS and the interaction planning and monitoring activities.
•Validation of artificial intelligence systems to be implemented in single modular
CPS units.
•Validation of simulation/emulation techniques based on digital twins for intelli-
gent CPS units in medium complexity scenarios.
Long-term goals (7–10 years):
•Validation of artificial intelligence systems that can coordinate the activity of
different CPS modular units.
•Validation of simulation/emulation techniques based on digital twins for intelli-
gent CPS units in highly complex scenarios.
126 M. Urgo et al.
8 PRI4.7 Human-Artificial Intelligence for Knowledge
Consolidation and Human–Machine Cooperation
in High-Efficiency Production Systems (Intelligent
Systems)
The challenges faced by production systems and technologies require the ability to
combine the adaptability and flexibility of human intelligence, which can efficiently
handle unexpected and evolving production scenarios, with the capabilities of arti-
ficial intelligence, which can process large amounts of data in real time and manage
complex situations.
In this context, some emerging issues can be identified:
•The experience and knowledge of human operators are a significant part of a
company’s know-how, and represent a strategic asset to be formalized, preserved
and enhanced in order to achieve efficiency. Artificial intelligence technologies
afford the possibility of enhancing this knowledge corpus and the role of man in
production environments. However, the difficulty in retaining and protecting this
knowledge poses a major risk to manufacturing companies.
•Artificial-human knowledge can be structured and consolidated as long as one can
define a sequence of formal steps to interpret the decisions taken both through
automatic approaches (which can be explained through AI-related methods) and
by human operators.
•The gathering, structuring and use of hybrid human-artificial intelligence have to
be based on a network of intelligent systems, for example by applying distributed
federated learning methods or inferring learning models from interconnected
(artificial or human) intelligent nodes. Both flexibility and high efficiency can
be achieved in resilient adaptive production scenarios using distributed learning
methods capable of defining a balance between the analysis of possible optimized
solutions and the use of corporate knowledge.
•Collecting and structuring human-artificial intelligence and knowledge requires
qualified and competent personnel and R&D programs, to exploit modern digital
technologies and integrate them into a manufacturing company’s processes and
know-how.
This research and innovation priority applies to the following areas:
•Structured and formalized approaches (for example, ontologies and semantic-web
technologies) for the representation of knowledge related to production processes
and systems. These approaches must be suitable to support interfaces for access
and use by both human and automatic actors. This will require the ability to operate
on the partial/incremental structuring of knowledge due to missing information
and data, to sequential or incomplete formalization and structuring processes, to
human operator errors.
Strategic Action Line LI4: High Efficiency and Zero Defect 127
•Automatic identification of possible inconsistencies in the knowledge (for
example, Shapes Constraint Language—SHACL). These approaches aim to effi-
ciently integrate human knowledge and artificial intelligence by providing tools
for the validation of knowledge consistency. Based on the specification described
at the previous point, these inference approaches will be expected to predict poten-
tial inconsistencies in the structuring of knowledge and interact with human/
artificial agents to resolve conflicts.
•Human–machine interactions, protocols and interfaces that exploit artificial intel-
ligence with the aim of quickly and efficiently structuring and consolidating
knowledge in relation to production processes and systems. They are based
on human/artificial inference processes, grounded on the methodologies and
approaches described in the previous points.
New developments in this scientific and technological field are important for
the competitiveness and efficiency of manufacturing companies in relation to future
competitive scenarios. The formalization and consolidation of corporate knowledge
bring out and capitalize on the experience and skills of operators. In combination
with the support provided by artificial intelligence approaches, they are important
elements in preserving and profiting from the know-how of companies. These factors
are strategic in future manufacturing scenarios, characterized by the pervasive adop-
tion of digital technologies, the need to interact and collaborate globally while
preserving the intellectual assets of companies, and the drive towards platform-based
collaboration paradigms.
Interaction with Other Strategic Action Lines
•LI3: Involvement of human operators in the process of structuring and consoli-
dating their experience and knowledge.
Time Horizon
•Short-medium term goals (2–6 years)
•Structured and formalized approaches for the representation of knowledge.
•Automatic inference approaches for the formalization of knowledge.
•Human-machine interactions, protocols, and interfaces.
9 PRI4.8 Advanced Production Planning and Scheduling
(Intelligent Systems)
The growing complexity of modern production systems requires advanced planning
approaches to exploit the most of their features and functionality. The objectives of
this research and innovation priority cover the following areas:
•Production planning and scheduling based on artificial intelligence. Approaches
related to the latest developments in artificial intelligence (e.g. deep learning), as
well as more traditional technologies (e.g. expert systems) can play a supporting
128 M. Urgo et al.
role in the planning and programming of production systems. These approaches
can eventually lead to an identification of the system’s current state by expressly
monitoring parts, components, state of production resources, through the analysis
of tracing data and/or images relating to the production system. They can also
help identify the state of the system based on pattern recognition approaches;
select planning rules/policies based on historical data, identify system behaviour
deviations from the plans and determine new planning.
•Robust production planning and scheduling based on risk indicators. In produc-
tion systems, deviations from plans are the rule rather than an exception, due to the
occurrence of unexpected events. Such events can be determined by a number of
possible internal or external causes. Activities might be longer or shorter than orig-
inally planned, resources might not be available, materials might arrive late, supply
arrival times and product delivery dates might change, and new activities such
as reworking may be included in the schedule. Robust approaches are intended
to react to unexpected events (reactive approaches) or mitigate their impact in
advance as much as possible (proactive approaches). Innovative approaches will
be developed based on risk measurements used in the financial sector (e.g., value-
at-risk, conditional-value-at-risk, maximum regret) capable of defining a balance
between performance and mitigation of the impact of uncertainty on the plan’s
performance.
•Model-based approaches for sequencing/control. The request to operate in vari-
able conditions entails the possibility for a production system to work in config-
urations other than those for which they were designed. For example, produc-
tion/assembly lines forced to operate in unbalanced conditions. In these cases,
sequencing and control policies have a significant impact on system performance.
They must therefore be defined and calibrated depending on the changing condi-
tions in which the system operates. Model-based control approaches use a digital
twin for the machine/system, thus providing an effective method for designing
and optimizing sequencing/control policies. Approximate approaches based on
surrogate/low fidelity models can also support a real-time update of the policies
promoting the efficiency of production systems.
•Approaches for the propagation of planning and scheduling constraints. The
complexity of modern production systems requires approximate approaches that
can identify the most important decisions to be made, rather than trying to solve
planning problems altogether. Constraint propagation and automated reasoning
approaches can support the identification and search for appropriate planning and
programming decisions based on complex (nonlinear) models. Furthermore, they
support the identification of the disturbance event that triggers a new planning or
scheduling.
Interaction with Other Strategic Action Lines
•LI1: Planning and scheduling approaches can support the implementation of
personalized production approaches within the limits of the available capacity
of production resources.
Strategic Action Line LI4: High Efficiency and Zero Defect 129
•LI2: Planning and programming can be applied to energy consumption, which
would promote sustainability in operating a production system.
•LI6: Advanced planning and scheduling, especially proactive/reactive approaches
support the adaptability of a production system.
•LI7: Advanced planning and scheduling are relevant approaches for the next
generation of production systems.
Time Horizon
Short-medium term goals (2–6 years).
•Planning and scheduling based on artificial intelligence.
•Robust planning and risk-based production scheduling.
•Model-based control/sequencing approaches.
•Approaches for the propagation of planning and scheduling constraints.
•IT industrial platforms to support the interaction between human and artificial
intelligence.
Acknowledgements The authors would like to thank Gianantonio Susto from the University of
Padova and Roberto Zuffada from Siemens for their support and contribution during the discussion
and collection of materials that have brought to the definition of the chapter.
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