Search Results (9)

This study presents a practical implementation of Business Process Reengineering (BPR) to streamline teaching assignment workflows at the University of Basilicata, a higher education institution (HEI) facing administrative inefficiencies exacerbated by rigid regulatory frameworks. By integrating process modeling, simulation, and digital tools, the research addresses systemic bottlenecks in resource allocation, transparency, and procedural delays inherent in traditional academic workflows. The methodology employs a dual-phase approach: (1) a detailed “AS-IS” analysis using BPMN 2.0 to map existing processes and (2) a data-driven “TO-BE” redesign validated through discrete event simulation (Simul8^®, Version 31). Key innovations include the automation of approval workflows, dynamic resource prioritization, and stakeholder communication protocols. Simulation results demonstrate a 35% reduction in end-to-end processing time and a 22% improvement in administrative staff utilization while maintaining compliance with national accreditation standards (the AVA framework) and legislative mandates (Law 240/2010). The case study underscores BPR’s role in balancing bureaucratic constraints with operational agility, offering actionable insights for HEIs navigating digital transformation. By prioritizing transparency and stakeholder alignment, the redesigned process not only enhances efficiency but also strengthens accountability in resource management—a critical factor for public institutions under increasing scrutiny for fiscal and educational quality outcomes. This work contributes to the growing discourse on BPR in academia, advocating for simulation-driven methodologies as catalysts for sustainable, stakeholder-centric process innovation in bureaucratic environments. Full article

In an era where sustainability is paramount, understanding the environmental impact of digitalizing business processes is critical. Despite the growing emphasis on sustainable practices, there is a lack of comprehensive methodologies to evaluate how digitalization impacts environmental sustainability compared to traditional processes. This paper introduces a carbon-aware methodological framework specifically designed to assess the sustainability of business process reengineering through digitalization. The Digital Green framework quantitatively analyzes the environmental costs associated with digital transformation, ensuring that truly sustainable digitalization results in lower resource consumption relative to the complexity of the process being digitalized. To demonstrate its effectiveness, the framework was applied to a case study involving the reengineering of an administrative process at a small university in southern Italy. The case study highlighted the framework’s ability to quantify the environmental benefits or detriments of digital transformation, thus guiding organizations toward more sustainable digital practices. This research contributes to the field by offering a concrete tool for aligning digitalization efforts with ecological sustainability, and by paving the way for integration with initiatives such as the Green Software Foundation’s Software Carbon Intensity (SCI) specifications. Full article

Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry. Full article

The ideas of the sharing economy have facilitated innovative business applications, such as Uber and Airbnb. As an example of a sharing economy application, ridesharing services take advantage of underutilized resources to create economic value. However, the unruly design of ridesharing systems may make urban traffic more congested and cause other technology-organization-environment issues. This study explores the application of blockchain and smart contract technologies to enhance ridesharing services by harvesting the blockchain benefits of transaction traceability, process transparency, system automation and disintermediation. After presenting system design and implementation details for building and deploying a blockchain-based system to support the reengineered ridesharing service with required business functions, we conduct functionality/performance tests and theory-based comparative analysis to confirm its feasibility and applicability. The results reveal that our system with blockchain-enabled benefits is superior to incumbent ridesharing systems. Moreover, while prior research rarely reports the design and implementation details of blockchain-based systems to support sharing economy services, this paper primarily contributes to extant literature by not only proposing a layered system architecture adapting blockchain and smart contracts into the desired ridesharing service but also demonstrating the design and implementation details, covering the development tools, the deployment environment and the deployed smart contracts. Full article

Business process re-engineering (BPR) is an approach to improving organizational performance. It evolved mostly within the private sector to maintain a successful business model despite increasing global competition. BPR presents a fundamental improvement in the essential organizational design. This paper investigates recent studies of BPR and identifies the success factors of BPR projects and their connection to the human–technology–organization (HTO) framework. By examining the relevant literature, we study various factors and their effects on the implementation of BPR and how these factors can affect process performance, successfully or otherwise. The aim is to study the literature to determine the success factors and challenges for BPR in the HTO framework. The article concludes by emphasizing the factors that will help to allow BPR to be implemented with a wider use in different sectors. Full article

Quality control is an essential element of manufacturing operations that reduces product defects and provides excellent products of the right specifications to the end consumer. Industry 4.0 solutions, such as digitalisation, along with lean manufacturing tools, may support quality control operations. This paper presents a case study of a food company wherein quality control checks were optimised using business process re-engineering to reduce physical waste and resource usage. Following close analysis of the company’s pack-house operations, it was proposed to adopt elements of Industry 4.0 by digitalising the quality control process. Implementing such a solution led to a reduction in the time needed to complete recorded checks, an increase in the time the pack-house quality control team spends with packers on the production lines, and the facilitation of defects identification. It also ensured that the product met the customers’ specifications and reduced the likelihood of rejection at the customers’ depot. The new system also enabled monitoring of each line in real-time and gathering of additional information faster and more accurately. This article proves how employing lean principles in combination with Industry 4.0 technologies can lead to savings in resources and a reduction in waste, which leads to improvements in operational efficiency. Full article

A business process re-engineering value in improving the business process is undoubted. Nevertheless, it is incredibly complex, time-consuming and costly. This study aims to review available literature in the use of machine learning for business process re-engineering. The review investigates available literature in business process re-engineering frameworks, methodologies, tools, techniques, and machine-learning applications in automating business process re-engineering. The study covers 200+ research papers published between 2015 and 2020 in reputable scientific publication platforms: Scopus, Emerald, Science Direct, IEEE, and British Library. The results indicate that business process re-engineering is a well-established field with scientifically solid frameworks, methodologies, tools, and techniques, which support decision making by generating and analysing relevant data. The study indicates a wealth of data generated, analysed and utilised throughout business process re-engineering projects, thus making it a potential greenfield for innovative machine-learning applications aiming to reduce implementation costs and manage complexity by exploiting the data’s hiding patterns. This suggests that there were attempts towards applying machine learning in business process management and improvement in general. They address process discovery, process behaviour prediction, process improvement, and process optimisation. The review suggests that expanding the applications to business process re-engineering is promising. The study proposed a machine-learning model for automating business process re-engineering, inspired by the Lean Six Sigma principles of eliminating waste and variance in the business process. Full article

Current industrial production is driven by increasing globalization, which has led to a steady increase in production volumes and complexity of products aimed at the pursuit of meeting the needs of customers. In this context, one of the main tools in the management of customer value is Lean Manufacturing or Production, though it is considered primarily as a set of tools to reduce the total cost of the resources needed to achieve such needs. This philosophy has recently been enriched in the literature with case studies that link Lean Management (LM) with the improvement of environmental sustainability. The consequence is an expansion of the Computer Integrated Manufacturing (CIM); indeed, CIM, currently, combining and integrating the key business functions (e.g., business, engineering, manufacturing, and information management) with a view of the life cycle, does not highlight the strategic role of the environmental aspects. In order to deal with the increasingly rapid environmental degradation that is reflected in society, in terms of both economy and quality of life, Industrial Ecology (IE) introduced a new paradigm of principles and instruments of analysis and decision support (e.g., Life Cycle Assessment—LCA, Social Life Cycle Assessment -SLCA, Material Flow Account—MFA, etc.) that can be considered as the main basis for integrating the environmental aspects in each strategy, design, production, final product, and end of life management, through the re-engineering of processes and activities towards the development of an eco-industrial system. This paper presents the preliminary observations based on a analysis of both theories (LM-IE) and provides a possible assessment of the key factors relevant to their integration in a “lean environmental management”, highlighting both positives (lights) and possible barriers (shadows). Full article

We present a discrete event simulation model reproducing the adoption of Radio Frequency Identification (RFID) technology for the optimal management of common logistics processes of a Fast Moving Consumer Goods (FMCG) warehouse. In this study, simulation is exploited as a powerful tool to replicate both the reengineered RFID logistics processes and the flows of Electronic Product Code (EPC) data generated by such processes. Moreover, a complex tool has been developed to analyze data resulting from the simulation runs, thus addressing the issue of how the flows of EPC data generated by RFID technology can be exploited to provide value-added information for optimally managing the logistics processes. Specifically, an EPCIS compliant Data Warehouse has been designed to act as EPCIS Repository and store EPC data resulting from simulation. Starting from EPC data, properly designed tools, referred to as Business Intelligence Modules, provide value-added information for processes optimization. Due to the newness of RFID adoption in the logistics context and to the lack of real case examples that can be examined, we believe that both the model and the data management system developed can be very useful to understand the practical implications of the technology and related information flow, as well as to show how to leverage EPC data for process management. Results of the study can provide a proof-of-concept to substantiate the adoption of RFID technology in the FMCG industry. Full article