Jul 2026· Journal of Industrial Integration and Management· pp. 1-26· 0 citations· 28 references
TL;DR
This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations.
Abstract
Digitalization and regulatory compliance pose substantial challenges to companies, requiring adjustments to operations and business processes. Smooth transitions can be facilitated by analyzing discrepancies between current and target processes, enabling the identification of necessary organizational changes. Based on these insights, change managers can develop action plans to support effective implementation and ensure return on investment. Although scholars emphasize the importance of data-driven evaluation in change management (CM) and recognize the value of information embedded in business process models, the literature lacks systematic methods for extracting and integrating such information, particularly from text-based sources. In collaboration with industrial partners, we developed a method to address this gap. Our approach integrates semantic business process management, text analytics, and CM to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations. The method also resolves terminological inconsistencies across heterogeneous sources. This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers. To demonstrate applicability, we implemented a proof of concept in an industrial environment to validate process adherence against natural language documents such as industry standards
Managing legal change is a growing challenge for compliance departments due to the increasing complexity and volume of regulations. Failure to adapt business processes to legal updates can result in severe consequences such as fines or reputational damage. Despite the importance of early legal change analysis, research on legal knowledge change management and business process compliance remains fragmented. This work addresses this gap through a systematic literature review covering outlets in Artificial Intelligence, Law, Business Process Management, Natural Language Processing, and Requirements Engineering. The authors identify four research streams and four key activities from change representation to impact analysis, and highlight a lack of integration between legal changes and process compliance. As a first step towards this integration, LegalChanges4BPC is proposed, an automated approach that detects legal changes and analyzes their relevance for business process compliance. To evaluate its feasibility, the authors apply prompt-based techniques with Large Language Models (LLMs) across two regulatory datasets. GPT-5 and Mistral-3.1 show the best balance of completeness and correctness, while Phi-4 and LLaMA-4 excel in efficiency, revealing trade-offs between models across the evaluated aspects. The automated approach employs prompting strategies for legal change analysis and contributes toward automated compliance, legal traceability, and contextual reasoning with LLMs.
Marisol Barrientos, Johannes Loebbecke, Karolin Winter et al.· Business & Information Syste...· 0 citations
Large language models (LLMs) are increasingly relevant to Business Process Management (BPM), particularly when process knowledge is dispersed across documents, conversations, and other unstructured sources. Their probabilistic outputs, however, raise questions about validation, traceability, and accountability. This paper develops a lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle. The framework separates generative interpretation from formal, empirical, and expert validation. It comprises five interdependent layers and six operational principles, implemented through a stage-risk-validation matrix, a principle-intensity map, and four evaluation dimensions. Governance requirements increase as outputs approach live execution or decisions that are difficult to reverse, with controls aligned with the NIST AI Risk Management Framework, the EU AI Act, and the GDPR. A customer complaint-handling scenario demonstrates how the framework can be applied. An illustrative stress test using the BPI Challenge 2017 event log and ten independent LLM generations instantiates the validation layer under information-asymmetric conditions. Although all generated models were structurally valid, the event log revealed incomplete activity coverage and control-flow mismatch. This illustrates the value of an external referent but does not establish comparative performance or a general difference in error detectability between LLM-generated and process-mining artefacts. The framework therefore positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential outputs.
Today, many businesses suffer from fragmented workflows, isolated data, and uncoordinated process execution. Together these problems not only restrict operation efficiency but also decision making capacity. When organizations grow, the existence of non-standardized workflows results in work duplication, transparency loss and difficulty in quality maintenance among different departments. So, in this situation, standardization is not only a good practice but is also a means to guarantee agility, responsibility, and growth. At the beginning, Customer Relationship Management (CRM) systems were mainly considered as a means to handle customer interactions but nowadays they are also used for process modeling and workflow orchestration. Centralization of data and inclusion of processes into CRM environments allow companies to coordinate cross-functional activities, automate mundane jobs, and have a single source of truth. This paper presents a CRM-based method for standardizing enterprise workflows and includes the following steps: process mapping, stakeholder agreement, and system configuration on an ongoing basis. A case study is used to illustrate how a company in the phase of expansion managed to convert its scattered operations into a single streamlined, CRM-based workflow environment. The results show that the company has achieved major benefits such as making their processes more transparent, reducing the cycle time, increasing user understandability, improving data quality, and providing better decision support on top of that. This research above all offers implementation tips to create scalable workflows in CRM environments and it also highlights the fact that one cannot get rid of the trade-off between flexibility and structure. As a whole, the paper reveals that process modeling powered by CRM may be seen as a major instrument that companies can rely on when transforming their operations to standard, modern, and resilient systems.
Satyendra Kumar Vanapalli· American International Journ...· 0 citations
The study examines informatisation and digitalisation of business processes as integrated tools of strategic logistics management in trade enterprises operating under conditions of military risk, supply chain instability, and accelerating European integration. The research combines a systematic synthesis of scholarly literature on digital platforms, regulatory governance, and sustainable supply chains with conceptual modelling used to formalise the relationships identified. Unlike approaches that treat digitalisation mainly as a means of automating operations and cutting costs, the analysis demonstrates its system-forming role in reshaping the architecture of logistics management, integrating business processes, and building digital trade ecosystems; digital platforms are shown to simultaneously lower traditional market-entry barriers and generate new strategic dependencies for enterprises joining established ecosystems, while technologies such as the Internet of Things, Big Data Analytics, and cloud solutions create competitive advantage only when embedded within a coherent management architecture. The regulatory dimension, anchored in European Union digital-services legislation, is found to transform compliance from a technical obligation into a source of strategic advantage for enterprises that adapt proactively. On this basis, the study proposes an original Digital-Sustainable Logistics Strategy that integrates three equally weighted dimensions - efficiency, resilience, and environmental, social, and governance–oriented responsibility - into a unified strategic management architecture, with digitalisation positioned as a system-forming factor of strategic sustainability rather than an auxiliary operational tool, linking digital transformation, regulatory compliance, and sustainability outcomes within a single decision-making framework. The results offer a methodological basis for designing corporate digital transformation programmes and sustainability-oriented logistics strategies, with further research needed on the environmental and social effects of logistics digitalisation and on the post-war restructuring of digital logistics ecosystems in Ukraine.
Valentyn Dranus, L. Dranus, O. Prokopyshyn· Intellectualization of logis...· 0 citations
Supply chain performance assessment requires models that are capable of linking strategic, tactical, and operational data in a consistent and traceable manner. SCOR provides a standardized process and performance framework, while Value Stream Mapping (VSM) captures operational flows, waste, and machine-level data. However, SCOR remains weakly connected to shop-floor observations, and VSM often supports local improvement without aggregating data into global supply chain indicators. To address this gap, this paper proposes a conceptual architecture integrating SCOR, VSM, and ontology through a semantic, data-driven pivot layer. Rather than extending SCOR to a universal Level 4, which would be subject to industry limitations, our approach normalizes VSM data at the machine level and semantically maps it to SCOR Level 3 processes. The ontology formalizes relationships among operators, activities, resources, flows, contexts, constraints, and metrics. The proposed prototype supports traceability from shop-floor data to global performance evaluation and prepares future implementation in digital supply chain environments, including process mining, industrial IoT, and digital twin-based decision support.
In the context of rapid digitalisation and a high level of heterogeneity of contemporary corporate ecosystems, the problem of fragmentation of the information space, and the emergence of isolated data warehouses becomes a critical barrier to business efficiency. The purpose of the study was to develop and substantiate a comprehensive methodology for automating business processes of an enterprise based on the application programming interface – integration of the application programming interface, combining the choice of optimal architectural patterns with mechanisms for secure data exchange. To achieve this goal, the researchers applied system analysis of architectural styles, prototyping of software gateways in Python, and computer modelling of load scenarios in an isolated virtual environment using Docker containers and the Locust tool. The study involved a comparative analysis of the Representational State Transfer, Simple Object Access Protocol, and Graph Query Language protocols in terms of performance and network load, which revealed significant traffic overhead and delays of up to 185 ms when using outdated standards. A hybrid integration methodology was proposed that combines the Representation State Transfer architecture for mobile clients and Graph Query Language for web interfaces, which reduced the amount of transmitted data by 73% and reduced the system response time to 65 ms. A secure data exchange algorithm has been developed based on the use of an intermediate layer (middleware) and a security gateway (API Gateway), which implement two-level request validation and centralised access control to neutralise the risks of unauthorised interference. The expediency of using the "Strangler Fig" migration pattern was substantiated, which allowed for a gradual transition from monolithic enterprise resource planning systems to microservice architecture through a specialised Python gateway without stopping operational activities. It has been experimentally proven that the introduction of asynchronous message queues allows the system to maintain stable operation within 320 ms even at peak loads of up to 5,000 requests per second, in contrast to monolithic solutions that demonstrate exponential performance degradation
Dmytro Zahorulko· Information Technology and C...· 0 citations
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