=Paper=
{{Paper
|id=Vol-3779/paper5
|storemode=property
|title=An End-To-End Execution of a Logistic Process in an AI-Augmented Business Process Management System
|pdfUrl=https://ceur-ws.org/Vol-3779/short1.pdf
|volume=Vol-3779
|authors=Giacomo Acitelli,Simone Agostinelli,Angelo Casciani,Andrea Marrella
|dblpUrl=https://dblp.org/rec/conf/pmai/AcitelliACM24
}}
==An End-To-End Execution of a Logistic Process in an AI-Augmented Business Process Management System==
https://ceur-ws.org/Vol-3779/short1.pdf
An End-To-End Execution of a Logistic Process in an
AI-Augmented Business Process Management System
Giacomo Acitelli1,∗ , Simone Agostinelli1 , Angelo Casciani1 and Andrea Marrella1
1
Sapienza Universitá di Roma, Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti
Abstract
This paper presents an end-to-end execution of a real-world business process (BP) in the logistics domain
to illustrate how an AI-Augmented Business Process Management System (ABPMS) can increase BP
automation compared to a traditional BPMS. In addition, we discuss concrete AI-based solutions for the
implementation of the ABPMS lifecycle stages.
Keywords
AI-augmented Business Process Management System (ABPMS), Process Framing, Sense-Think-Act Cycle,
AI Planning, Knowledge Representation and Reasoning, Robotic Process Automation, Conversational AI
1. Introduction
AI-augmented Business Process Management Systems (ABPMSs) [1] are an emerging class of
process-aware information systems infused with AI that autonomously unfold and adapt the
execution flow of business processes (BPs) through continuous conversation with their human
principals. The lifecycle of an ABPMS extends that of a conventional BPMS in two directions:
• The traditional lifecycle stages of a BPMS (i.e., frame, perceive, reason, enact) are aug-
mented with AI. Process framing entails establishing multiple constraints encompassing
procedural rules, best practices, and norms that must be considered during the execution
of a BP [2]. Once an initial framing is completed, the lifecycle enters its central stage,
namely process-aware execution. This consists of the rotation between perceiving, reason-
ing, and enactment. First, the ABPMS perceives data concerning the BP execution and
produced by the working environment (e.g., collected through IoT sensors). Following
data perception, the system engages in reasoning activities, converting collected data
into relevant events, pondering about their uncertainty, and combining them with the
BP towards deciding which actions to take next. This pertains to a wide repertoire of
AI techniques, such as data integration, knowledge graph construction, AI planning, etc.
Finally, the ABPMS leverages its actuators to interact with the environment and enact
the BP as long as the boundaries imposed by the frame are respected.
PMAI@ECAI24: International ECAI Workshop on Process Management in the AI era, October 19, 2024, Santiago De
Compostela, Spain
∗
Corresponding author.
Envelope-Open acitelli@diag.uniroma1.it (G. Acitelli); agostinelli@diag.uniroma1.it (S. Agostinelli); casciani@diag.uniroma1.it
(A. Casciani); marrella@diag.uniroma1.it (A. Marrella)
Orcid 0000-0002-8194-3611 (G. Acitelli); 0000-0002-6500-9802 (S. Agostinelli); 0009-0003-7843-8045 (A. Casciani);
0000-0002-1031-0374 (A. Marrella)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
• At any stage throughout the lifecycle, the ABPMS may perform one of the advanced
stages (i.e., explain, adapt, improve) that are specific to ABPMSs and only feasible when AI
is an integral part of the system. The ABPMS may decide to: provide explanations about
the past, current, and anticipated states of the system; adapt itself to new circumstances
and drifts; and optimize its execution against its goals, the available resources, and the
framing constraints. The implementation of these advanced stages could lead the ABPMS
to update its internal knowledge, consequently reframing itself in an autonomous manner.
Note that the ABPMS can proactively communicate with its human principals (who may
oversee the system decision) about BP-related actions, goals, and intentions.
The BPM literature is plenty of AI-based techniques that cover the life-cycle stages of an
ABPMS in isolation. For example, in [2] the author positions in favor of constraint-based
declarative BP specifications for process framing without delving into the execution stages.
Other studies focus on conversational systems as tools to enhance the execution stages of
ABPMSs [3, 4], neglecting the flexible nature of process framing. In [5, 6], AI planning and
reasoning-based approaches are employed to the automated adaptation of BPs in response to
unanticipated exceptions. However, these adaptations are not explained nor used to reframe the
original BP specification. The need for explainable solutions to increase trust in the interaction
with the ABPMS is investigated in [7]. In [8], the authors present a methodology for BP
optimization based on conformance checking and performance analysis via statistical inference.
However, the literature lacks a holistic description of how an ABPMS should execute a
real-world BP encompassing all its life-cycle stages, thus concealing the advanced automated
features of ABPMSs over traditional BPMSs. In this paper, we mitigate this gap by presenting
the details of an end-to-end execution of a BP from the logistics domain performed through an
ABPMS, showing how introducing AI technology into conventional BPM creates a range of
opportunities to boost BP automation in all the ABPMS life-cycle stages.
2. Executing a Logistic Process with an ABPMS
In this section we illustrate with a real-world example drawn from the logistics domain and
presented in [9], how an ABPMS can enhance the execution of a BP compared to a traditional
BPMS not empowered by AI technology. Specifically, we use a running example of a BP
for transporting perishable products whose safety and quality highly depend on controlling
temperature and humidity from origin (harvest fields) to consumption, and we show how the
stages provided by an ABPMS can enhance the execution of the BP. Fig. 1 presents the BP model
in BPMN.1 In our example, this constitutes the instantiation of the frame stage, which relies
on the aforementioned BPMN model to guide and constrain the execution of the BP. While
the frame for this specific BP is rigid, as we are adopting a prescriptive model, in an ABPMS
the frame specification can be enriched through declarative constraints (e.g., based on Linear
Temporal Logic over finite traces (LTLf) [10]) to be monitored at run-time. For example, the
frame can include constraints to capture and react to events that break the BP boundaries (e.g.,
if a pallet lacks a label to identify it), deviating from what is expected.
1
We refer here to the last release of BPMN, namely BPMN v2.0 – http://www.omg.org/spec/BPMN/2.0/
Figure 1: BPMN representation of a real-world BP from the logistics domain.
The process-aware execution (i.e., perceive, reason, and enact) of the ABPMS is triggered
when the BP starts, that is, when a container with a pallet arrives at the smart distribution
center. Here, a human operator verifies the quality of the products of the pallet. Subsequently,
information about the received product (such as origin, harvest date, etc.) is automatically
detected by reading the pallet labels (barcodes or QR codes). The temperature and humidity
of the container are also automatically detected through dedicated sensors. Based on all this
collected information, a decision is made regarding whether the products are of good quality or
not. Moving forward from the initial phase, if the products are considered not for distribution,
the rejection of the pallet is registered and it is discarded by moving it to a garbage. Conversely,
if the products meet quality standards, the pallet is logged and sent to a climate-controlled
chamber in the distribution center. This ensures proper storage conditions (e.g., oranges must
be kept between 2 and 12 Celsius degrees and at 90% relative humidity). Continuing with the
execution of the BP, besides this first product control, a second one is performed over a sample
in the laboratory. This analysis will determine whether moulds, yeast, and certain bacteria have
grown in the received products. When any of these contaminants is detected, an alarm triggers
and the pallet is discarded by transporting it to the garbage. Conversely, the shipment task of
the received products can start. At the end, if products quality is not excellent, such as being
suitable for distribution but lacking optimal firmness or color, their prices are reduced and the
pallet is prioritized to avoid their spoilage. Finally, once a truck is ready for transportation, all
shipped pallets are registered in the system. This is essentially the sense-think-act cycle [11]
that is implemented by the ABPMS within this scenario.
Indeed, guided by the view exposed in [1], an ABPMS could enhance its process-aware
execution by augmenting the perceive, reasoning and enact stages with AI technologies. For
example, AI techniques for data filtering [12] can indeed be utilized to augment the perceive
stage, e.g., to process the sensed data of a pallet (origin, harvest date, temperature, humidity,
etc.) and automatically rejecting those ones that do not meet quality standards. Meanwhile, AI
techniques such as Situation Calculus [13] can bolster the reasoning stage, thereby assisting
human operators in the decision-making phases of the BP, potentially even automating them
[14]. For instance, when the pallet is sent to a climate-controlled chamber in the distribution
center a Situation Calculus reasoner could be employed to reason on the actions to be taken to
guarantee its optimal conservation by automatically regulating the temperature of the fridge.
When considering the enact stage, for instance, when all shipped pallets must be recorded in
the system, Robotic Process Automation (RPA) [15, 16] techniques could be leveraged. The
registration procedure could be automated using traditional vendors of RPA tools (e.g., UiPath2 ).
The previous stages can be extended also with the advanced stages unique to ABPMSs, as
explained as follows. The explain stage can be enacted whenever explanations regarding the
system’s states are needed within the BP execution. For instance, it can provide a detailed
explanation of quality controls, why a product was evaluated as being in good condition or not,
as well as why the product was rejected and the actions taken in response to the rejection. Lastly,
through explanation, the ABPMS could provide transparent insights into the reasoning behind
pricing decisions and shipment prioritization. To this end, eXplainable Artificial Intelligence
(XAI), is a field of study focused on developing techniques to make decisions more transparent
and understandable to humans. In the context of explaining decision logic to users, XAI
methods could provide insights into why an ABPMS made a particular decision or prediction.
Two popular techniques for this purpose are LIME [17] (Local Interpretable Model-agnostic
Explanations) and SHAP (SHapley Additive exPlanations) [18] which can be customized ad-hoc.
The other stage implemented by the ABPMS is adapt, wherein the system modifies its
operations in response to any unexpected change. This advanced stage could yield numerous
benefits for the BP. For example, the ABPMS could dynamically adjust pricing strategies and
shipment prioritization based on real-time factors such as demand fluctuations or perishability
levels to optimize revenue and minimize waste. Also, if there is a sudden spike in laboratory
contamination levels, the ABPMS could automatically trigger enhanced sanitation protocols or
halt shipments until the issue is resolved. In this direction, Automated Planning techniques in
AI [19] could be employed for the automated synthesis of these strategies, thus adapting the
execution of the BP according to the many factors of interest.
Finally, the BP execution of the presented scenario can be further enhanced in the improve
stage. By analyzing historical data, the system can identify recurring patterns of rejected
products using patterns recognition techniques [12]. For example, if certain types of product
damage have not been previously detected, the system can recommend the introduction of
new evaluation criteria and updates to control procedures. Additionally, it may detect patterns
indicating a supplier’s tendency to deliver defective products or a specific product’s susceptibility
to damage, thus enabling self-improvement by suggesting additional checks for specific suppliers
and/or products. Improvement efforts could also involve utilizing performance metrics to
refine storage and preservation processes over time. Through the analysis of patterns in
product spoilage or storage inefficiencies, the ABPMS could propose optimizations such as
revised temperature control algorithms or enhanced packaging protocols to minimize waste
and maximize product lifespan.
2
https://www.uipath.com/
3. Concluding Remarks
In this paper we examined an end-to-end instantiation of a real-world logistic BP within the
stages of an ABPMS. In particular, we emphasized the role of AI to boost BP automation in all
the ABPMS life-cycle stages. Through this analysis, we not only offered a tangible end-to-end
perspective on how ABPMSs execute and optimize BPs, but we also highlighted their potential
to support the human workforce in performing related tasks.
Acknowledgments. This work is supported by the H2020 project DataCloud (Grant 101016835),
the Sapienza project FOND-AIBPM, the PRIN 2022 project MOTOWN and the PNRR MUR
project PE0000013-FAIR. The work of Angelo Casciani is in the range of the Italian National
Doctorate on AI run by Sapienza.
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