Robotic Process Automation (RPA) is an emerging software technology for automating business processes. RPA uses software robots to perform repetitive and error‐prone tasks previously done by human actors quickly and accurately. These robots mimic humans by interacting with existing software applications through user interfaces (UI). The goal of RPA is to relieve employees from repetitive and tedious tasks to increase productivity and to provide better service quality. Yet, despite all the RPA benefits, most organizations fail to adopt RPA. One of the main reasons for the lack of adoption is that organizations are unable to effectively identify the processes that are suitable for RPA. This paper proposes a new method, called Rule‐based robotic process analysis (RRPA), that assists process automation practitioners to classify business processes according to their suitability for RPA. The RRPA method computes a suitability score for RPA using a combination of two RPA goals: (i) the RPA feasibility, which assesses the extent to which the process or the activity lends itself to automation with RPA and (ii) the RPA relevance, which assesses whether the RPA automation is worthwhile. We tested the RRPA method on a set of 13 processes. The results showed that the method is effective at 82.05% and efficient at 76.19%.

Operational resilience in modern global supply chains depends on timely and accurate identification of emerging risks. While daily news has become a primary source for such insights, the sheer volume and unstructured nature of these data pose significant analytical challenges, requiring advanced tools to extract relevant and actionable information. This paper introduces an extended evaluation of the LARD-SC framework, a service-oriented architecture for supply chain risk management, by benchmarking five diverse variants of the large language model (LLM) in their capacity to detect, classify, and interpret risks. Drawing on a curated set of 120 real-world news articles on Apple’s Tier 1 suppliers, we adopt a standardized, prompt-based assessment to compare GPT-3.5 turbo, GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, and Claude 3.5 Haiku. Using expert-reviewed metrics, namely the Risk Validation Rate (RVR), Potential Risk Rate (PRR), and False Identification Rate (FIR), we derive a comprehensive Relative Performance Index (RPI) for comparison. Our analysis confirms that advanced GPT-4o variants produce the most consistent accurate risk identifications, achieving higher proportions of validated outcomes while minimizing false positives. Through these results, we highlight the significant promise of LLM-driven analytics for early risk detection in complex supply chains, along with practical considerations such as the influence of prompt engineering, interpretability demands, and the impact of data availability. The findings offer a blueprint for organizations seeking to improve resilience by systematically harnessing the capabilities of LLM within service-oriented risk management ecosystems.

Business process integration (BPI) allows organizations to connect and automate their business processes in order to deliver the right economic resources at the right time, place, and price. BPI requires the integration of business processes and their supporting systems across multiple autonomous organizations. However, such integration is complex and can face coordination complexities that occur during the resource exchanges between the partners’ processes. This article proposes a new method called Process Mining for Business Process Integration (PM4BPI) that helps process designers to perform BPI by creating new process models that cross the boundaries of multiple organizations from a collection of process event logs. PM4BPI uses federated process mining techniques to detect incompatibilities before the integration of the partners’ processes. Then, it applies process adaptation patterns to solve detected incompatibilities. Finally, organizations’ processes are merged to build a collaborative process model that crosses the organizations’ boundaries. Adapt _{WF_Net} , an extension of a Petri net, is used to design inter-organizational business processes and adaptation patterns. An integrated care pathway is used as a case study to assess the applicability and effectiveness of the proposed method.