Elsevier

Information Systems

Volume 95, January 2021, 101639
Information Systems

A knowledge-intensive adaptive business process management framework

https://doi.org/10.1016/j.is.2020.101639Get rights and content

Highlights

  • Agent and process models has many concepts in common and can be modeled together.

  • QoS perspectives enable BPM system’s case based automatic configuration.

  • Intent based process abstractions enable runtime agility.

  • Decoupling of constraints from process models increases adaptability.

  • Agents can handle task failure ratios up to 10% on behalf of business experts.

Abstract

Business process management has been the driving force of optimization and operational efficiency for companies until now, but the digitalization era we have been experiencing requires businesses to be agile and responsive as well. In order to be a part of this digital transformation, delivering new levels of automation-fueled agility through digitalization of BPM itself is required. However, the automation of BPM cannot be achieved by solely focusing on process space and classical planning techniques. It requires a holistic approach that also captures the social aspects of the business environment, such as corporate strategies, organization policies, negotiations, and cooperation. For this purpose, we combine BPM, knowledge-intensive systems and intelligent agent technologies, and yield one consolidated intelligent business process management framework, namely agileBPM, that governs the entire BPM life-cycle. Accordingly, agileBPM proposes a modeling methodology to semantically capture the business interests, enterprise environment and process space in accordance with the agent-oriented software engineering paradigm. The proposed agent-based process execution environment provides cognitive capabilities (such as goal-driven planning, norm compliance, knowledge-driven actions, and dynamic cooperation) on top of the developed business models to support knowledge workers’ multi-criteria decision making tasks. The context awareness and exception handling capabilities of the proposed approach have been presented with experimental studies. Through comparative evaluations, it is shown that agileBPM is the most comprehensive knowledge-intensive process management solution.

Introduction

The three industrial revolutions of the past were all triggered by technical innovations: The introduction of steam-powered mechanical production at the end of the 18th century, electrification at the beginning of the 20th century and digitalization through the introduction of programmable logic controllers for the automation of production processes in the 1970s. According to experts, the fourth industrial revolution—triggered by the internet—is no longer a “future trend”, it is now at the center of many companies’ strategic and research agendas [1]. The wave of Industry 4.0 has already started disrupting markets, spawning new business models, blurring industry boundaries and enabling multi-company virtual business networks and ecosystems. Business complexity is growing exponentially and current enterprise systems are becoming insufficient to meet the agility and dynamism that are required by this new business environment [1]. Companies need to combine advanced automation, cloud computing, computer-powered processes and intelligent algorithms to transform their businesses and stay competitive [2].

While computer-powered processes constitute an important part of the new enterprise transformation, currently businesses still have very incomplete and deferred control over their process spaces. It is recognized that while classical business process management (BPM) systems are suitable for predefined and standardized flows, they are less convenient when it comes to capturing business processes whose conduct and execution are heavily dependent on an expert’s knowledge-intensive decision-making tasks [3]. In addition to the traditional needs of efficiency and optimization, BPM needs to deliver automation-fueled agility in order to capture knowledge-intensive processes and be a key driver of digital transformation [4]. New approaches and methods that extend BPM are needed to enable building complex adaptive systems that react dynamically to changes and bring order out of the chaos.

In literature, various researches have been conducted for decades to ensure the agility of business process management systems (BPMS). Some studies have investigated how techniques from the planning community could be used to synthesize new processes and repair previously defined ones that are no longer suitable for a given context [5], [6]. Another set of approaches bringing together ideas from both the Semantic Web and BPM communities to automatically mediate between business experts’ requirements and the semantic web services [7], [8], [9]. In addition, a number of studies have investigated goal or precondition and effect based process selection approaches to build adaptive process management systems [10], [11], [12].

While task/service orchestration and planning driven approaches are considered mature enough for supporting business processes, these methodologies show limitations and pitfalls while dealing with the collaborative, non-deterministic and evolving nature of the enterprise knowledge-intensive processes [1]. Besides, these approaches lack to capture the alignment of planning decisions with corporate strategies and to recognize organization values, beliefs, and policies. As Di Ciccio et al. [13] stated, knowledge-intensive business process management should not focus only on a single dimension (such as tasks or flows), it should capture and manage a series of interrelated elements (data, actions, rules, goals, processes, knowledge workers and the environment) along all the phases of the process life-cycle.

The solution introduced in this work for the management of knowledge-intensive business processes with high variability relies on three hypotheses. The first hypothesis is, process design is not limited to the modeling of task and control-flows, but rather data, rule, goal, environment and process perspectives should be captured in a holistic way. Second, to maintain encapsulation and componentization, process executions should not be managed in terms of task-flows altering the enterprise information space, but rather in terms of interacting entities, each with its own goals, knowledge, decisions and life-cycle [3]. The ultimate goal of BPM systems shifts from maintaining process automation to assisting business expert’s decision making [13]. Accordingly, as the third hypothesis, at least some fragments of the knowledge worker’s expertise can be formalized and supported by autonomous proxies.

In this sense, the concept of knowledge-based multi-agent systems, composed of multiple interacting actors is a promising modeling approach that can simulate the way complex, dynamic processes emerge from the goal-driven collaboration of individual actors at the bottom level [14]. Also, belief–desire–intention (BDI) agents provide a natural encapsulation for knowledge, goal and rule driven decision making that knowledge-intensive process management requires [11]. The agileBPM utilizes BDI agents as autonomous entities that pursue business goals, sense physical and virtual stimuli in the environment, recognize the business context, reason about how they should and should not behave, and assist the business expert accordingly.

In modern BPM systems, discrete process design and execution phases are gradually disappearing and become continuous and integrated activities of design, execution and adaptation [13]. The agileBPM proposes a consolidated modeling methodology that seamlessly integrates business processes, knowledge- intensive and multi-agent systems modeling paradigms and design components. This integration enables seamless utilization and transformation of design time building blocks (such as knowledge, rules, goals, processes, events, etc.) through the whole process execution phases (planning, execution, decision making and adaptation) driven by the intelligent agents.

The objective of this research is to augment, enrich and support business activities to make it easier for business experts to make more informed and accurate decisions. Knowledge workers are still the main drivers of the business, and next-generation BPM systems need to capture and digitalize the behavior of experts in decision-making, cooperation, and negotiation. For this purpose, the agileBPM aims to formalize goal-directed action selection, process quality assessment, rule compliance control, exception management, and dynamic cooperation capabilities of business experts and serves as agent capabilities.

The agileBPM provides a consolidated methodology that captures both cognitive (such as knowledge, rules, goals) and control flow dimensions of the enterprise process space in an incremental manner. The developed multi-agent based process execution environment utilizes and infers the captured business process knowledge fabric through the whole BPM life-cycle and transforms it as the enterprise itself evolves. While most of the existing approaches [5], [6], [12] solely focus on the adaptiveness of business processes through unanticipated exception handling, the proposed agileBPM architecture also enables addressing the problem from collaboration (enterprise goals, dynamic collaborations, quality of service) and context awareness (rule adoption and compliance, data-driven actions, reasoning) dimensions. Accordingly, the agileBPM has been the most comprehensive solution, providing the majority of the key requirements of knowledge-intensive process management systems identified by Di Ciccio et al. [13].

The remainder of this paper is structured as follows: After this introduction, Section 2 presents a consolidated methodology for knowledge-intensive business process modeling and introduces a set of models that conceptualize various aspects of the business space and enable autonomous runtime agility. Section 3 describes the planning module that drives the decision support through utilizing constructed business models and Section 4 presents four use cases that demonstrate the business expert assistance capabilities of the proposed approach. Then in Section 5, the adaptiveness and exception management capabilities of the agileBPM system are evaluated through simulation. Section 6 presents the most prominent approaches to business processes automation and accordingly positions the agileBPM solution. The paper concludes with a summary and an outlook on further research directions.

Section snippets

Knowledge-intensive process modeling methodology

An agent-based business process management system is a complex adaptive system that is able to autonomously pursue organizational goals, perceive the business environment, recognize the application context, and respond accordingly [15]. However, deciding how a given enterprise system should respond to changes through adopting an agent, knowledge, and business process-centered view is a challenging task since there are many factors that need to be considered, such as managerial tendencies,

Business models in decision support

The goal of BPM systems is shifting from maintaining process automation to assisting business experts’ decision making [13]. To this end, next-generation BPM suites should not only focus on the execution of control flows but also capture the cognitive aspects of actors and support decision making. The ultimate decision support that a BPMS can provide is to recommend actions to achieve strategic goals. For this purpose, the questions to be answered are: which business processes can be utilized

Application scenarios

Recommending goal-achieving processes for effectively guiding the business expert during the knowledge-intensive process (KIP) enactment is very important. However, as Di Ciccio et al. [13] identify, a business process management system should also provide other capabilities (such as data-driven actions, rule management, and flexible process execution) to support and enable the knowledge worker adequately. In this section, four potential business scenarios displaying further knowledge worker

Evaluation

To evaluate the adaptiveness and exception management capabilities of the proposed agileBPM system, a business process simulator module, which automatically builds business processes and sets up various challenging execution environments, is developed.

To our knowledge, there are no publicly available real-world datasets for evaluating semantic business processes in any standard notation [27], [28], [54]. In order to evaluate the agileBPM system on a standard set of business processes, a

Related work

For many organizations, process modeling has become an essential part of documenting, managing and analyzing their business operations. Despite its success, there are still some issues that BPM systems have to cope with, such as terminological ambiguities and the low degree of automation of the BPM life-cycle [68], [69], [70]. In order to overcome these problems, researchers are tackling the construction of process management systems that can manage complex processes, while remaining robust,

Conclusions

BPM has successfully delivered benefits over a long period of time and continues to do so, but new realities of business have created new imperatives for business information systems. Today’s BPM systems need new methods for thinking about intelligent processes in ways that build on the concepts of collaboration, adaptiveness, and context awareness. In the literature, there already exists a set of research that aim adaptive processes through implementing business logic with specialized agent

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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