=Paper=
{{Paper
|id=Vol-3762/479
|storemode=property
|title=Design the modern supply chain: The SmarTwin Projec
|pdfUrl=https://ceur-ws.org/Vol-3762/479.pdf
|volume=Vol-3762
|authors=Michele Di Capua,Emanuel Di Nardo,Angelo Ciaramella,Gennaro Iannuzzo,Aniello De Prisco,Daniele Ruggeri Laderchi,Pietro Catalano,Pietro D'Ambrosio,Salvatore Moscariello
|dblpUrl=https://dblp.org/rec/conf/ital-ia/CapuaNCIPLCDM24
}}
==Design the modern supply chain: The SmarTwin Projec==
https://ceur-ws.org/Vol-3762/479.pdf
Design the modern supply chain: The SmarTwin Project
Michele Di Capua1*,†, Emanuel Di Nardo1,†, Angelo Ciaramella2,†, Gennaro
Iannuzzo2,†, Aniello De Prisco3,†, Daniele Ruggeri Laderchi3,†, Pietro Catalano4,† ,
Pietro D’Ambrosio4,†, Salvatore Moscariello4,†
1US srl, via Porzio, Centro Direzionale di Napoli Isola G2, Naples, 80143, Italy.
2DIST – Dipartimento di Scienze e Tecnologie – Università di Napoli “Parthenope”, Naples, 80143, Italy.
3Magsistem spa, Zona Industriale, 81030 Gricignano di Aversa (CE). Italy.
4LinearIT spa, Via Giovanni Severano, 28, 00161 Rome, Italy.
Abstract
The SmarTwin project aims to define and explore an innovative service model with two main
strategic objectives: on the one hand, to anticipate needs arising from both the market (growing
demand for quality and consumer awareness) and the business world (cost reduction,
environmental sustainability, optimization); on the other hand, to identify and begin to occupy a
strategic convergence space related to future and emerging trends in a number of key enabling
technologies such as Artificial Intelligence, IoT, Digital Twin and others. The basic idea is to
enable supply chains (and especially those dealing with perishable goods) to achieve new levels
of efficiency in terms of overall quality and service cost reduction, certification and tracking of
each activity carried out in the production process, minimization of human health risks and
reduction of the ecological footprint of products. The supporting software platform of the project
has been designed on AI (Artificial Intelligence) components and will extend a number of key
enabling technologies such as IoT, Blockchain, AI and Digital Twin to be able to represent and
manage the new levels of complexity expected to be required to effectively address the market
needs identified.
Keywords
Supply Chain, Digital Twin, Machine Learning, Blockchain, Internet of Things.1
1. Introduction and one financial. The methodological aspect is aimed
at providing normative and operational support to
The SmarTwin project aims to investigate an supply chain actors for the implementation of the new
innovative service model for cost optimization, risk hypothesized service model.
reduction, micro-traceability of a product's The technological aspect is aimed at automatically
processing steps, certification of the ecological detecting events, typical activities, quality levels (of
footprint and, finally, financial support for complex products) and operating conditions (of the various
supply chains [2], with a focus on production chains contexts in which activities take place) through the
dealing with perishable products (i.e. vegetables). use of IoT technologies, Artificial Intelligence
The project has several innovative aspects. The (machine learning [3] and computer vision [4]) and
first is represented by the approach to the problem, predictive analytics systems (e.g., with artificial
which is developed on three parallel and integrated neural networks and big data analytics systems). This
logical levels: one methodological, one technological information properly collected and analyzed will
Ital-IA 2024: 4th National Conference on Artificial Intelligence, 0000-0001-8904-180X (M. Di Capua); 0000-0002-6589-9323
organized by CINI, May 29-30, 2024, Naples, Italy (E. Di Nardo); 0000-0001-5592-7995 (A. Ciaramella); 0009-
∗ Corresponding author. 0003-5962-8302 (G. Iannuzzo);
† These authors contributed equally. © 2024 Copyright for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
m.dicapua@usmail.it (M. Di Capua); e.dinardo@untec.it (E. Di
Nardo); angelo.ciaramella@uniparthenope.it (A. Ciaramella);
gennaro.iannuzzo@uniparthenope.it (G. Iannuzzo);
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
specific specification), and reduction of
environmental impact (e.g. minimization of energy
consumption). These objectives are essentially
pursued by realizing a technological platform that
combines the dynamic functionalities required to
manage the configuration and constant evolution of
the proposed service model and those of descriptive
and predictive analysis on the Digital Twin of the
supply chain ecosystem (see figure 1), with a series of
specific technological components, based on AI, for
the recognition of events, activities occurring in the
Figure 1. The Digital Twin schema.
operational context, potential critical scenarios and
for the constant verification of consistency between
enable the feeding of a much more extensive product
what is detected in the real process with what is
certification and tracking system than those currently
provided for at the contractual and regulatory level.
used. The financial aspect, on the other hand,
represents an absolute novelty for this type of
solution and will allow for versatile and secure
3. Data integration
management of economic relations between the As already mentioned, the main idea of the project
various supply chain partners. In particular, within is to approach the problem of managing a complex
the system, in addition to supporting with specific supply chain ecosystem in a holistic way, constantly
functions the process of evaluation and analysis of monitoring every aspect, every phase of work, every
each step of the economic stage, a virtual currency will relationship between partners and every event that
be introduced (to be connected to the Digital Twin of may be useful to ensure the quality specifications
the supply chain [1] model) to manage payments, expected by the customer and to maximize the
anticipate liquidity within the circuit and to securitize benefits for the operators in terms of optimizing costs,
credit. Overall, the presence of the financial time and the objectives set. Obviously, the model must
"dimension" in the governance of supply chain be adaptable to any supply chain context and be able
processes allows us to enhance the new level of "trust" to follow the natural evolutions that occur in these
that we aim to achieve with the application of the contexts to manage seasonality, workload
proposed service model and that not only involves the discontinuities and any contingencies that may arise.
economic operators in the supply chain but also It will therefore be necessary to define all the
extends to end consumers. information relating to the actors, the processes, the
quality objectives to be guaranteed (including
2. The Digital Twin metaphor sustainability objectives, specific production
specifications, etc.) and, of course, the commercial
The proposed service model is characterized by its
relationships established between the various actors.
innovative features and, in particular, by its focus on
Furthermore, for each "measurable" element to be
defining a digital twin of the entire supply chain
detected in the process, it is assumed that an IoT
ecosystem to achieve a holistic view and a new level
sensor network or different types of cameras (e.g.
of detail and pervasiveness of process monitoring,
thermal), whose data streams are constantly analyzed
tracking, quality control and certification activities to
by the AI components of the system, can be plugged in
address and resolve the typical complexity of contexts
and used. All the configuration information required
such as productive supply chains.
to build the model should be defined, captured and
The digital model aims to represent the various
managed by the software platform in such a way that
organizational, economic and regulatory dynamics of
its acquisition is easy and immediate and does not
a modern supply chain, analyzing the huge amount of
create functional access or operational cost barriers to
data collected by the network of integrated sensors
adoption. Therefore, it is necessary to provide
and devices (e.g. cameras), in order to suggest
functions for defining the configuration logic and the
optimization actions aimed at cost reduction (e.g.
rules behavior of the system that are, as far as
avoidance of waste), prevention of risks to people (e.g.
possible, driven by automatic processes and are
timely control of the correct arrangement of goods in
dynamically adaptable and reusable in other phases of
the warehouse), constant monitoring of product
the supply chain. On the other hand, as far as the IoT
quality (e.g. verification of consistency of storage and
sensor network and image stream acquisition sources
transport conditions with what is required by the
is used to detect the status of activities and
operational contexts both referring to working
environments (e.g., warehouses for the storage of
goods) and to the mobile network of multimodal
transportation (e.g., sensors on board refrigerated
trucks for the transport of perishable goods).
These data, appropriately normalized and
aggregated, can already represent an element of
interest for the purposes of tracking and monitoring
Figure 2: The general architecture of the designed the operating conditions of the supply chain
software platform. ecosystem, but at the same time they also constitute a
knowledge base that can be used for the creation of
are concerned [6], the system must be able to census predictive models suitable for recognizing
the devices, identify their location and usefulness for characteristic behaviors or events, that can be
the purpose of assessing the operating conditions and associated with the statements of supply chain
the associated rules for normalizing, aggregating and operators or for evaluating elements of optimization
analyzing the collected data. and risk prevention. From this point of view the IoT
The goal of the system in this case is to be able to platform, integrated into the SmarTwin system,
verify continuous compliance with the optimal provides for collecting data, aggregating them on a
operating conditions and, at the same time, to certify time basis, doing systematic checking of predefined
the consistency of what is detected in the real rules and triggering any alerts, making them
environment with what is stipulated in the searchable by the application functions that
agreements and work processes defined in the service implement the supply chain Digital Twin and making
model. them available to the various AI-based analytics tools
Finally, the monitoring of all events in the supply and optimization components working in the
chain (micro-traceability) and the certification of background.
processes and activities carried out, together with the
digitalization of contractual agreements through 4.2. Video analysis and machine learning
smart contracts, will also enable the designed
platform to automatically settle economic aspects This subsystem is in charge of the camera network
between parties, both by generating payment flows census activities and the management of video
and by offering operators the possibility of streams, from the various devices installed in the
securitizing their acquired (and certified) receivables supply chain environment (in places where typical
for use within the system as an alternative form of activities take place such as warehouses, loading and
liquidity. unloading pallets, etc.), and for each of them it is
concerned with submitting the streams to the analysis
and image processing components to identify events,
4. The software platform
typical activities or situations of potential interest.
The software platform, that implements the The final components (see figure 3) that will be in
service model, represents the basic technological charge of image stream analysis (thus based on
infrastructure into which are integrated both the machine learning and computer vision), will be
dynamic system configuration functionalities designed as Artificial Neural Networks (ANN) with
(necessary to digitize the model), the services offered different tasks, each of which is concerned with
by the specific components (such as the IoT platform, detecting particular types of events (e.g. the presence
Blockchain and AI components), and the operational of people in the area) or capturing particular
functionalities and those that make the Digital Twin of information (e.g. the license plate of a vehicle that is in
the overall supply chain ecosystem usable to various a loading/unloading area). Obviously due to the
types of users. The different components of the specificity of the technology used, the context and the
platform will be detailed below and are shown in purpose of the analysis, it is not always possible to
figure 2. successfully use pre-trained or fully reusable
components in different similar contexts. We will
4.1. The IoT layer therefore proceed on the one hand with the
identification of a set of typical events or actions that
It presides over all activities of continuous
can be generalized and, on the other hand, with the
acquisition of data from the IoT sensor network that
artificial neural networks. The main objective here is
to predict potential problems and recurring data
patterns, to improve supply chain performance, from
the data collected by the IoT platform envisaged by
the project. From a design perspective, it's necessary
to develop versatile and reusable machine learning
modules that can be "plugged" into the platform to
provide information about potential problems that
may arise along the supply chain being analyzed. The
models will learn the normal dynamics of information
flows that characterize one or more processes and
will be able to recognize "anomalous" patterns of data
to prevent risks and consequent process-related
damage or waste, with a view to making the supply
chain more effective and sustainable. The theoretical
approach adopted for this purpose is the time-series
analysis, which focuses on understanding data that
vary over time. It is particularly relevant in IoT, as
much data produced by connected devices is
Figure 3: Component Diagram (UML) of the software organized into time series, such as sensor data and
platform of the project. measurements at regular intervals.
In the more recent landscape, transformer
identification of technological solutions that can allow models, which were initially introduced for natural
the integration into the system of specific components language processing problems, are also showing good
trained ad hoc for a particular task. For example, it results in this area. Transformers can handle data
may be necessary to monitor the state of preservation sequences of varying lengths and can be used for time
of a particular type of product in a warehouse, and to series prediction and analysis tasks. Finally, it is useful
achieve this type of monitoring it may be necessary to to mention also autoencoders, which are deep
employ two cameras of different technology (e.g. a learning models used for dimensionality reduction
thermal camera and an RGB camera) and to train a and feature representation of data, and which can be
specific neural network to process these video used in time series analysis to extract relevant
streams and detect the state of preservation at a features and reduce dimensionality of data, which is a
specific frequency. Obviously, this component will potential issue to be addressed in the application
have to be purpose-built and can only be used in the scenarios of the project. Our analysis is confirming
same environments, conditions and for the same type that these deep learning techniques can be used for
of product for which it was originally designed. In the various time series analysis tasks related to a supply
context of the final prototype, we will try to imagine chain, such as short or long-term forecasting, anomaly
some case scenarios where such conditions might detection, trend and pattern analysis. Relative to the
occur, to try to integrate purpose-built AI components transformer models identified in the first part of the
into the system for a specific function. state-of-the-art analysis of the project, some
As a whole, the subsystem that deals with the preliminary tests were conducted on literature
acquisition of image streams collects images at a set datasets to verify the effective ability of these models
frequency from the various devices and submits them to also be used in the context of time series
to the network of specialized AI components for forecasting. Preliminary results confirmed that
recognition of typical events and activities, it collects transformer models can be used with good
all outputs towards the Digital Twin, in order to make effectiveness also with these types of data.
data available (API) to the monitoring and alerting In the specific area of IoT sensor data analysis, an
component of the service platform. autoencoder-based neural network was tested to
identify vibration anomalies from sensor readings
4.2.1. Predictive tasks based on ML installed on a series of bearings. The aim of the
preliminary test was to be able to predict future
In order to integrate predictive task capabilities bearing failures before they occur.
into the project, recent machine learning techniques
have been explored, with a focus on the use of deep
4.2.2. Quality inspection and computer vision
In the next stages of the project, possible
automated approaches will be analyzed, to support
quality control processes within warehouses and the
quality verification of perishable products, through
the adoption of computer vision techniques. The
analysis referred to should be non-invasive, i.e. based
on the placement of fixed cameras in places where
operators normally carry out visual inspections. Figure 4: Pre/Post explainability modelling approach
Digital images from cameras and videos can be used schema [10].
to train computer vision models dedicated to the
inspection and analysis of products/goods. The help better understand why an AI model generates
computational vision tasks that will be implemented certain decisions by describing how it works.
next will cover both general and specific tasks in the According to recent research made by Forrester,
area of supply chain process control [5]. For example, XAI represents a phenomenon capable of generating
machine learning models will be trained to detect numerous tangible benefits for those who regularly
specific machinery (e.g. forklifts) moving in logistics adopt it within their process management:
aisles, rather than the presence of people in storage
areas. It will also be possible to define and integrate • a reduction in model monitoring efforts
more specific and advanced neural network models ranging from 35 percent to 50 percent;
that instead perform Human Activity Recognition • an increase of up to eight times the number
(HAR) tasks, i.e. the recognition of specific human of models in production;
activities (e.g. picking) according to specific logistics • an overall accuracy of the model itself that
use cases. can be estimated at 15 percent to 30 percent;
• an increase in the profit range that can even
4.3. Explainable AI (XAI) triple.
Within the designed platform of the project there Multimodal data, which characterize control
are several components that adopt Artificial functions, will be treated with innovative computer
Intelligence (AI) or Machine Learning (ML) vision techniques, like image classification, visual
techniques to perform tasks traditionally reserved for object tracking, and reliable and interpretable AI
the human operator and in particular: for the techniques (ante-hoc and post-hoc methods). In
automatic classification and analysis of information addition, methodologies to improve resilience will be
acquired from the supply chain activities, the used (see figure 4). For the design of decision support
performance of automatic checks, to make predictions models adopted for risk assessment [8] from
about operating conditions and finally to suggest information flows that characterize one or more
possible optimizations. In this case, the technological supply chain processes, XAI methodologies will be
issues to be addressed are essentially: the recognition used to improve multimodal data aggregation and
of typical events and activities (HAR - Human Activity decision logic (e.g., Fuzzy Logic). Data mining
Recognition) using image processing techniques and methodologies will be explored for preprocessing
methodologies based on deep architectures, the multimodal data (e.g., intrinsic dimension) along with
creation of predictive models from the large amount signal processing and computer vision techniques.
of data collected from the IoT sensor network (using Data for learning and testing the designed models will
machine learning techniques), and also the be acquired from information flows, images and
implementation of a decision support system (DSS), videos obtained during the operational phases of the
which is necessary to be able to apply the expected supply chain. In the early design stages of the
automatic control rules. SmarTwin system, synthetically created realistic data
In this regard, to mitigate the distrust that often will be used and data augmentation, concept drift and
arises in adopting AI components as mere functional Active Learning methodologies will be adopted to
black boxes, whose actual behavior in terms of make the models more robust. Among the possible
decisions made is not always clear and justifiable in methodologies adopted for explainability purposes,
detail [7], development approaches, based on the Neuro-Symbolic (NeSy) AI has been explored in this
emerging branch of eXplainable, AI will be adopted. early design stage of the project.
The set of tools and techniques used in XAI aims to
4.3.1 Neuro-Symbolic Artificial Intelligence 5. Conclusions
The goal is to create a system that can learn from This paper presents the SmarTwin project, which
large amounts of data, much like neural networks, but aims to integrate the latest machine learning and
also has the high explainability and provable blockchain technologies into production supply
correctness of symbolic systems. One of the key chains in order to improve the quality of their
differences between symbolic and neural systems is component processes, as well as their associated
how they represent knowledge. Symbolic systems use economic and environmental impacts. The project is
explicit representations that humans can understand. still at an early stage and will be completed in 2026,
They structure knowledge in a logical way, often using but the preliminary results of the research activities
logic-based languages. Neural systems, on the other outlined here confirm the validity of the initial idea.
hand, use distributed representations that are hard to
interpret. They learn representations from data in an
Acknowledgements
implicit way, which makes them highly adaptable but
also opaque. It’s seen as a promising approach to The project was funded by the Italian Ministry of
overcoming the limitations of purely symbolic or Economic Development (MISE): it started in 2023 and
purely neural methods and to advance towards more it will end in 2026. Project reference no.
intelligent and human-like AI systems. However, NeSy F/310218/01-02-03-04-05/X56.
AI also faces several challenges. These include the
difficulty of integrating symbolic and neural methods,
the need for large amounts of data to train neural
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