Purpose The purpose of this paper is to demonstrate how small manufacturing firms can leverage their Information Technology (IT) resources to develop the lean-digitized manufacturing system that offers sustained competitiveness in the Industry 4.0 era. Design/methodology/approach The study performs an in-depth five years case study of a manufacturing firm, and reports its journey from failure in the implementation of enterprise resource planning to its success in integrating IT-based technology trends of Industry 4.0 with the firm’s core capabilities and competencies while pursuing manufacturing digitization. Findings Industry 4.0 transition requires the organizational integration of many IT-based modern technologies and the digitization of entire value chains. However, Industry 4.0 transition for smaller manufacturers can begin with digitization of certain areas of operations in support of organizational core strategies. The development of lean-digitized manufacturing system is a viable business strategy for corporate survivability in the Industry 4.0 setting. Research limitations/implications Although the implementation of lean-digitized manufacturing system is costly and challenging, this manufacturing strategy offers superior corporate competitiveness in the long run. Since this finding is rather limited to the present case study, assessing the business value of lean-digitized manufacturing system in a larger scale research context would be an interesting avenue for future research. Practical implications Industry 4.0 transition for typical manufacturers should commensurate with their organizational, operational and technical particularities. Digitization of certain operations and processes, when aligned with the firm’s core strategies, capabilities and procedures, can offer superior competitiveness even in Industry 4.0 era, meaning that the strategic plan for successful Industry 4.0 transition is idiosyncratic to each particular manufacturer. Social implications Manufacturing digitization can have deep social implications as it alters inter- and intra-organizational relationships, causes unemployment among low-skilled workforce, and raises data security and privacy concerns. Manufacturers should take responsibility for their digitization process and steer it in a direction that simultaneously safeguards economic, social and environmental sustainability. Originality/value The strategic roadmap devised and employed by the case company for managing its digitization process can better reveal what manufacturing digitization, mandated by Industry 4.0, might require of typical manufacturers, and further enable them to better facilitate their digital transformation process.
Abstract Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.
Research background: Digital transformation has become a strategic imperative for companies worldwide, yet the heterogeneity in adoption patterns remains poorly understood, particularly in transitional economies. Existing research predominantly focuses on large economies, whilst small European countries face distinct institutional environments. The interplay between digital maturity, strategic orientation, and economic performance represents a critical knowledge gap. Purpose of the article: The study aims to identify distinct clusters of Slovak companies based on their attitudes towards artificial intelligence, sustainability orientation, and strategic resilience. The research determines how these clusters differ in technology adoption, perceived barriers, and economic performance outcomes. Methods: The study employs quantitative research based on a structured survey of 402 Slovak companies conducted between February and July 2025. The methodological framework integrates k-means cluster analysis using twelve indicators. The optimal two-cluster solution was validated through the elbow method and silhouette analysis, with principal component analysis providing visualization. Chi-square tests examined differences between clusters across technology adoption, barriers, and performance indicators. Findings & Value added: The analysis reveals a fundamental structural divide within Slovak companies, identifying digitally advanced and traditional companies with significantly different profiles. Advanced companies demonstrate substantially higher adoption of Big Data analytics, ERP systems, and e-commerce platforms. Paradoxically, these companies perceive stronger barriers to transformation, including legal and regulatory uncertainty, implementation costs, system integration issues, and financial and network constraints, revealing a paradox of digital advancement. Most significantly, advanced companies report superior economic performance in turnover and EBIT changes. The research contributes novel evidence from a transitional economy, addressing gaps in comparative digital transformation research. Findings provide actionable insights for policymakers and managers navigating transformation complexities.
This study explores the relationship between E-commerce business operations, Technology-Based Accounting Information Systems (AIS) adoption, and corporate audit needs through a quantitative analysis approach. Drawing on data collected from 234 participants representing E-commerce firms and audit professionals, the study employs Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to analyze the relationships among key variables. The findings reveal significant positive associations between E-commerce characteristics and Technology-Based AIS adoption, as well as between Technology-Based AIS adoption and corporate audit needs. Furthermore, mediation analysis demonstrates that Technology-Based AIS adoption partially mediates the relationship between E-commerce characteristics and corporate audit needs. Additionally, moderation analysis indicates that firm size and industry type moderate these relationships, with larger firms and those in technology-intensive industries exhibiting stronger associations. These findings underscore the importance of technological innovation and organizational context in shaping audit readiness and effectiveness within E-commerce environments.
Driven by the concept of mass customization, manufacturing enterprises are now required to transform their traditional workshops into smart workshops. The integration of cyber-physical systems (CPS), which serves as the core of Industry 4.0, is crucial in achieving smart manufacturing. By implementing CPS, the conventional workshop can transition into a new paradigm characterized by intelligence and flexibility. However, the implementation of CPS in workshops is a complex undertaking that is still in its early stages. Therefore, this research paper aims to provide a comprehensive perspective on CPS-based workshops, with the intention of facilitating their implementation in the industry. Initially, the paper identifies seven key features of CPS-based workshops, namely self-sensing, self-awareness, self-assessment, self-optimization, self-adjustment, self-configuration, and self-control. Subsequently, the paper proposes the architectural framework of CPS-based workshops from a technical standpoint. Furthermore, a conceptual model of CPS-based workshops is developed, which highlights the three fundamental elements and closed-loop mechanism of such workshops. Finally, a case study of a Rolling stock manufacturing company in Indonesia is presented to demonstrate the feasibility of implementing CPS-based workshops in the industry.
The current way of consumption and production is unsustainable in that society is exceeding the earth’s planetary boundaries and creating long-term and irreversible effects on an environmental, social, and economic level. There is a recognized need on all system levels to change the way that we operate. Industry is a key area that requires this attention, as it is largely responsible for the impacts from the production of various societal needs and functions. In particular, the building and construction industry accounts for about 37% of global CO2 emissions. Rapid decarbonization is needed, and technological innovation for electrification, as well as business model innovation towards circularity and sustainability is crucial. With the influence of business on production and the natural environment, it is important to understand how business models can contribute to sustainability transitions. Incumbents have often been perceived as creating barriers towards sustainable transitions, but they also have a large potential to transform industries due to their market power. Previous research has shown that innovative firms are essential for transitions, however, there are not enough insights on how significant business models are for transitions. This paper explores the connections of sustainable business model innovation research with sustainability transitions research and reflects on the ability of incumbents to innovate and contribute to sustainability transitions through insights from a construction equipment manufacturer.
The sector of healthcare is one of the most growing and developing sector of the current economy. The leaders of healthcare system need keys that would help them to advance business processes, decision-making, communication between physicians, administration andpatients, as-well-as effective data access. In this case, Business Intelligence (BI) systems may be useful.BI is a new multidisciplinary research field that is being used in a variety of industries. It entails extracting information from large amounts of data and delivering it to stakeholders in a decision-making context that is correct. Many BI applications in the healthcare industryattempt to analysing data, predictions, supporting decision-making, and attaining total sector improvements. In today’s rapidly evolving health-care industry, decision-makers must cope with increasing demands for administrative and clinical data in order to meet regulatory and public-specific standards. The application of BI is realized as a viable resolution to this problem.As the current data on BI is mainly focusing on the area of industry, So the aim of the current input is to adapt and translate the present research findings for the health-care industry.For this reason, various BI definitions are explored and consolidated into a framework. The objective of this review is to give an overview of how to use BI to aid decision-making in healthcare companies. Along these the sector specific requisites for effective BI-application and role in future are discussed.
Location data rapidly grow with fast-changing logistics and business rules. Due to fast-growing business ventures and their diverse operations locally and globally, location-based information systems are in demand in resource industries. Data sources in these industries are spatial-temporal, with petabytes in size. Managing volumes and various data in periodic and geographic dimensions using the existing modelling methods is challenging. The current relational database models have implementation challenges, including the interpretation of data views. Multidimensional models are articulated to integrate resource databases with spatial-temporal attribute dimensions. Location and periodic attribute dimensions are incorporated into various schemas to minimise ambiguity during database operations, ensuring resource data's uniqueness and monotonic characteristics. We develop an integrated framework compatible with the multidimensional repository and implement its metadata in resource industries. The resources’ metadata with spatial-temporal attributes enables business research analysts a scope for data views’ interpretation in new geospatial knowledge domains for financial decision support.
Abstract Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7%–99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress.
Research institutions and companies capture quintillions of data about their users’ interactions, business, and social media and also from devices such as sensors mobile phones and automobiles. The data are generated at high speed need to be processed and analyzed quickly to identify useful insights and patterns. Nowadays most of the industries are utilizing the big data analytics in various applications. These days’ businesses are broadly utilizing big data tools to analyze huge volumes of datasets. These tools are utilized for speeding up in figuring enormous complex datasets. This paper focuses on how large information is created and the need of examining such information. This paper likewise gives a brief idea about Big Data analytics suggestions in reality and its application in each field alongside difficulties and benefits. This paper also examines various tools for analysis of huge volume of data in different areas of real world.
Elias Olivares Benitez, María Beatríz Bernábe-Loranca, Santiago-Omar Caballero-Morales
et al.
Sales territory design is an important research field because salesforce allocation within territories impacts sales organization effectiveness and customer service. This work presents a novel multi-objective model for re-designing sales territories with three main objectives: sales balancing, workload balancing, and geographic balancing. To measure sales and workload balancing, the variance among territories was calculated. The metric considered for geographic balancing was the sum of the distances from every salesperson to their assigned customers. A metaheuristic algorithm based on Tabu search was developed to solve a weighted aggregate function that integrates the three objectives. The algorithm is embedded in a procedure to systematically change the weights in the aggregate objective function to produce an approximate Pareto front of solutions. The algorithm was tested with instances based on data from a company in Mexico, providing salesperson-customer assignments that can be projected in territories in geographic information systems. The algorithm converges very fast for the instances studied and produces a Pareto front efficiently. Comparing the current situation of the company to a dominating solution obtained with the algorithm in the Pareto front, a significant improvement in the balance is achieved, in the order of 42.0 - 47.1% on average in the three objective functions. Another managerial benefit achieved by the company was a better understanding for the top managers of the salesforce, the customer preferences, and the challenge of serving a large and dispersed market.
Management. Industrial management, Large industry. Factory system. Big business
The number and complexity of embedded system platforms used in mixed-criticality applications are rapidly growing. They run large and evolving applications on heterogeneous multi- or manycore processing platforms requiring dependable operation and long lifetime. Examples include automated and autonomous driving, smart buildings, industry 4.0, and personal medical devices. The Information Processing Factory (IPF) applies principles inspired by factory management to master the complexity of future, highly- integrated embedded systems and to provide continuous operation and optimization at runtime. A general objective is to identify a sweet spot between a maximum of autonomy among IPF constituent components and a minimum of centralized control in order to ensure guaranteed service even under strict safety and availability requirements. This paper addresses the challenges of IPF and how to tackle them with a set of techniques: self-diagnosis for early detection of degradation and imminent failures combined with unsupervised platform self-adaptation to meet performance and safety targets.
Sarah El Hamdi, Mustapha Oudani, Abdellah Abouabdellah
The industrial sector has historically been linked to the prosperity, development and evolution of nations, hence to the genesis of the philosophy of competitiveness, mainly at the manufacturing level. An environment is in perpetual adaptation to trends, and the best example is the change of industrial ideology, as was the case in the past. The global society is facing the same challenges. Customer demands are becoming more and more specific and personalized, which means that factories have to innovate in terms of production and management of mudas in an eco-environmental context closely followed by several global organizations. Information and communication technologies have become the joker to master in order to stay in a market that is global and very competitive. At the heart of this evolution is the emergence of the Industry 4.0 concept, which includes a key element that is intelligent production in factories of the future that can meet different obstacles. The purpose of the current research on one hand is to highlight the relation between I4.0, smart manufacturing and smart factory, and on the other hand to present a synthesis of Industry 4.0 national initiatives linked to the fourth industrial revolution worldwide.
Management. Industrial management, Large industry. Factory system. Big business
Insecurity undermines the economic and business prowess of a nation. Tallying to the human cost, it sabotaged the right to life, liberty, and freedom. This study tests the long run effect and cause of insecurity from 2007-2017 in Nigeria. Using a framework of the Auto-Regressive Distributed Lag Model (ARDL) and Error Correction Model (ECM). Findings revealed that insecurity in Nigeria is majorly internal factors and government expenditure on security in Nigeria is far below the United Nations Standard. The ECM report that disequilibrium caused by internal factors instigating insecurity can be revised back to equilibrium at 25% annually. To improve the economic and business climate competitive advantage.
Management. Industrial management, Large industry. Factory system. Big business
North-South Preferential Trade Agreements (PTAs) are an intensified version of the Uruguay round’s bargain, in which developing countries gain access to developed countries’ markets, expecting increase in inflows of foreign direct investment, but see their ‘policy space’ reduced (Shadlen, 2005). Focusing on United States’ PTAs in the Latin American region, this article seeks to answer why some Latin American countries found this bargain attractive while others did not. I argue that modern PTAs generate uncertainty over their costs and benefits, because there are not standardized tools to estimate the impact of the ‘trade-related’ provisions they include. As a result, policymakers turn to their general ideas about economic development, which assign different meanings to them, producing differing decisions. Empirically, it is shown that the argument complements previous explanations based on structural and societal variables.
Regulation of industry, trade, and commerce. Occupational law, International relations
Cybersecurity ventures expect that cyber-attack damage costs will rise to $11.5 billion in 2019 and that a business will fall victim to a cyber-attack every 14 seconds. Notice here that the time frame for such an event is seconds. With petabytes of data generated each day, this is a challenging task for traditional intrusion detection systems (IDSs). Protecting sensitive information is a major concern for both businesses and governments. Therefore, the need for a real-time, large-scale and effective IDS is a must. In this work, we present a cloud-based, fault tolerant, scalable and distributed IDS that uses Apache Spark Structured Streaming and its Machine Learning library (MLlib) to detect intrusions in real-time. To demonstrate the efficacy and effectivity of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities. A decision tree algorithm is used to predict the nature of incoming data. For this task, the use of the MAWILab dataset as a data source will give better insights about the system capabilities against cyber-attacks. The experimental results showed a 99.95% accuracy and more than 55,175 events per second were processed by the proposed system on a small cluster.