Academic literature on the topic 'Digital Transformation. Technological Innovation. Logistics Optimization'

The world we live in today is pervaded by digital, the net is increasingly present and mixes the dimensions of the physical and the virtual, changing the way we understand, decide and evaluate things and also the way we do business. Artificial intelligence (AI) and related technologies are transforming the way we think and do marketing and the way companies relate to consumers and society.Internet has assumed a key role in nurturing innovation within business ecosystems. AI, big data and Internet of things (IoT) are key drivers of the current revolution in the way of communicating and relating among both individuals and products. This change is mainly due to the impact of algorithms’ mediations on the creation of value and customer engagement.Recent years, growing attention has been devoted to consumer brand engagement through emerging technological platforms (e.g., social media/artificial intelligence-based). However, despite important knowledge advancement, much remains unknown regarding the effect of Consumers’ Technology-Facilitated Brand Engagement (CTFBE) on individuals’ wellbeing, thus determining an important research gap (Hollebeek and Belk, 2021). CTFBE comprises a vital social facet. Hollebeek and Belk (2021) define CTFBE as a consumer’s bloodedly volitional resource investment in technology-mediated brand interactions (Kumar et al., 2019; Hollebeek et al, 2020). Online behavioral customer engagement occurs because of the rise of the new media and the advancement of technology, which have changed the way customers connect and interact with firms (Jahn and Kunz, 2012). One of the most active channels for such an aim are social media (Gummerus et al, 2012) where customers share their own experiences, information, review brands and manifest enthusiasm, delight, or disgust about a brand with others (Hollebeek and Chen, 2014).Digital transformation has totally transformed the value creation process (Reinartz et al., 2019) revolutionizing the way of doing business using the large mass of available data and information, through sophisticated service platforms that increase both effectiveness and efficiency in the value creation processes. AI has been a key component of digital transformation, substantially affecting consumer decision-making (Duan et al., 2021).AI, big data and the IoT are supporting and / or automating many decision-making processes: product, price, channel, supply chain, communication, etc. The customer experience is also redesigned starting from new value creation objectives and can become a stimulus for the creation of new business models. This, in turn, can provide a customized experience that is highly valued by consumers (Lemon and Verhoef, 2016). While new technologies have brought more ways for customers to interact with brands and companies, digital technologies have similarly enabled the automation of company’s interactions with customers (Kunz et al., 2017).According to Kumar et al (2010), AI represents the enabling technology for the transformation of marketing theory and practices: the enormous availability of data, the explosion of the possibilities to reach and interact on the markets and an increased speed of transactions. AI-enabled digital platform helps organizations to attract their customers (Bag et al, 2021; Chawla and Goyal, 2021).An increasing number of marketing decisions already use artificial intelligence in some way, and with the rise of big data is becoming easier to incorporate AI into business practices. Marketers may develop a more effective and personalized communication approach (Mogaji et al., 2020). For this reason, today AI is adopted in all activities where classification, forecasts and clustering are useful or necessary to solve problems and support decisions (management of anomalies in processes, logistics and optimization planning, customer service and customization).In the contemporary world the ubiquity of digital has made fluid the distinctions between channels and has integrated two dimensions of reality (physical and virtual one in phygital), the management of complex processes has become agile and adaptive, the advantages of integration and dynamic use of resources condition the operation of entire businesses. Well, what influence all this changes, new technologies and brand algorithms will have on social engagement?Prior studies on artificial intelligence in service and marketing research have not addressed customer engagement (Kaartemo & Helkkula, 2018). Perhaps, even Kaartemo & Helkkula (2018) specifically called for more research to answer the question: “How can we improve customer engagement through AI?”The article proposal is theoretical/conceptual in nature and starts from an updated review of academic literature on the aforementioned topics, mainly within marketing and business management disciplines, to achieve an interpretative attempt of Brand algorithm and social engagement (role) in digital era. References on request.

Digital transformation plays a vital role in enhancing competitiveness within the logistics sector, yet many organizations struggle to align technological innovation with strategic goals. To address this gap, the study examines how decision making enhances the adoption of digital technologies to strengthen competitive advantage. A five-stage methodology, including case study analysis and expert evaluation, ensures analytical depth and practical relevance. Using a mixed-methods approach, the research integrates qualitative insights from logistics professionals with ordinal logistic regression analysis to evaluate their impact on competitiveness. Findings confirm a positive and statistically significant relationship between digital implementation and performance outcomes, emphasizing the importance of strategic foresight and implementation planning. The study underscores the need to assess technological investments through a long-term strategic lens. These findings offer both academic and managerial value, providing a roadmap for logistics firms seeking to enhance resilience, efficiency, competitive advantage, and strategic alignment in a rapidly evolving digital environment.

Abstract: Spain is the EU country with the largest area of cultivated vineyards. However, it experiences a phenomenon of disaggregation and smallholdings, making it complicated for winegrowers to invest in technification. This paper describes an experiment that will be conducted in the Span- ish wine company Pago de Carraovejas. The experiment aims at optimising storage and decision-making when packaging for shipment by developing an Artificial Intelligence system. A module will be created for the improvement of the arrangement of products in the warehouse. This way, the efficiency of the warehous0e and productivity of the winery will increase.

This paper examines the critical role of Lean optimization in successful digital transformation for project-based organizations. Adopting digital technologies often brings challenges like inefficiencies, waste, and misaligned goals. Lean principles – reducing waste, continuous improvement, and value creation – offer an effective framework to address these issues. Integrating lean methods with automation, AI, and data analytics enhances efficiency, decision-making, and sustainable growth. The paper presents an overview of such integrations, supported by case studies and industry examples. In doing so, it highlights the importance of aligning technological adoption with business objectives and process optimization. By applying Lean thinking to digital transformation efforts, organizations can not only streamline operations but also foster innovation and adaptability in increasingly dynamic market environments. This synthesis of Lean and digital strategies provides a roadmap for navigating the complexities of modern project management while maximizing long-term value and performance. The conclusion emphasizes that this synergy also mitigates common digital transformation challenges, ensuring better alignment between technology investments and strategic objectives.

The digital transformation of entire economic sectors and occupational profiles as well as the introduction of new forms of human-machine collaboration through the increased use of cognitive systems require completely new approaches. The key to success in coping with this change, which can be seen in all industries, is to break up old structures and venture something new. The ability to adapt and innovate is becoming a central success-critical factor in entrepreneurial activity. In order to continue to achieve market success and ensure sustainable growth in an extremely dynamic and disruptive environment, companies and organizations are called upon to proactively shape change. In addition to the establishment of flexible working models and agile processes, the increased generation and integration of knowledge into and around technical systems in the course of targeted competence development of employees is indispensable. The introduction and use of technical systems thus must go hand in hand with the flexibilization of innovation and collaboration processes as well as the development of employee skills in order to generate the currently missing socio-technological link – for companies´ added value and for the benefit of people. In this paper, the authors present an overview of currently used creativity techniques and innovation methods and work out the strengths and weaknesses of the respective tools. Furthermore, the resulting need for action for the optimization of innovation processes in the interaction of established techniques and possibilities of cognitive systems is presented.

There has been a paradigm shift in the functioning of global supply chains, particularly in thepost-pandemic era. This shift is characterized by the convergence of technology. deployment, geopolitical dynamics, and an increased awareness of environmental and social responsibilities. These transformative changes compel businesses to navigate a complex. web of challenges while simultaneously creating many opportunities for global supply chains. This includes delegating responsibilities to the supplier side, empowering suppliers, and promoting digital supply chain networks. Ongoing trade tensions between major economies, such as the United States and China, have resulted in fluctuations in global logistics patterns. Alterations in tariffs and trade agreements impact shipping routes, transportation costs, and the overall flow of goods. Consequently, supply chains must focus on end-to-end supply chains originating from low-cost sources to withstand unforeseen events like black swan occurrences and geopolitical and trade wars. The pandemic has underscored the critical importance of building resilient supply chains. Many firms reevaluate their supply chain strategies, emphasizing diversification to mitigate risk. This involves reassessing suppliers, exploring alternative sourcing options, and increasing safety stock levels. Geopolitical uncertainties and disruptions have prompted reconsidering global sourcing strategies, with reshoring and nearshoring gaining prominence. Companies seek to mitigate risks associated with distant and complex supply chains by bringing production closer to target markets, enhancing responsiveness, reducing lead times, and fortifying supply chain resilience amid uncertain business conditions. From the traditional supply chain model, there is a continuous effort to integrate information technology to transform value streams and increase processes' transparency. Blockchain is playing a significant role, from tracking the provenance of raw materials to reducing transaction fraud. This technology fosters a new level of trust among supply chain partners, safeguarding against potential disruptions and reinforcing the integrity of the entire supply chain ecosystem. These rapid transformations amid uncertain conditions have prompted a reevaluation of supply chain resiliency with a greater focus on risk mitigation strategies. In global logistics, owning ships and manufacturing/leasing containers for extended periods have become the guiding principles. The recent Brexit has also impacted global transport's European and UK legs. The European industry is grappling with significant shortages of resources, including drivers and trained supply chain manpower, affecting last-mile delivery and land transportation in numerous geographies. The growth of e-commerce has significantly influenced supply chains, leading to innovations in last-mile delivery solutions. Companies are investing in warehouse automation, drone deliveries, and other technologies to meet the demands of the rapidly expanding e-commerce market. x Furthermore, a seismic shift toward sustainability reshapes the global supply chain landscape. Businesses recognize the imperative to address environmental, social, and governance (ESG) concerns. Initiatives to reduce carbon footprints, optimize transportation routes, and embrace ethical sourcing practices are becoming integral components of supply chain strategies. Sustainability is no longer merely a corporate buzzword but a critical factor in ensuring long-term viability and meeting the expectations of socially conscious consumers. Overall, global supply chains are undergoing significant transformation driven by technological innovation, environmental consciousness, and a commitment to building resilient networks. Businesses that embrace these trends are not only adapting to the challenges of today but are also positioning themselves to thrive in the dynamic landscape of tomorrow. The key to success is navigating change, leveraging technology, and building sustainable businesses.

Abstract The titanium alloy Ti-6Al-4V is widely used across various applications, such as aircraft structural components, machinery parts, and particularly in parts for medical devices. Understanding the deformation behavior and microstructural changes of this material during thermal processing is essential for achieving the required product dimensions and ultimate mechanical properties. The transformation of Ti-6Al-4V’s crystal structure from the beta phase (body-centered cubic structure) to the alpha phase (hexagonal close-packed structure) occurs over a temperature range of 980°C to 650°C. Therefore, investigating the mechanical behavior of Ti-6Al-4V within this temperature spectrum is crucial for optimizing the heat treatment process and improving the alloy’s mechanical properties. Existing studies have revealed that in this temperature range, the increase in flow stress of Ti-6Al-4V as the temperature decreases exhibits a unique non-linear behavior. Utilizing a Finite Element Analysis (FEA)-Based Digital Twin enables precise simulations of complex behaviors, offering deep insights into stress distribution and deformation patterns under various thermal conditions. This predictive model plays a crucial role in enhancing heat treatment methodologies, crafting components with improved mechanical properties, and reducing both the cost and time associated with experimental procedures. The utilization of the Gleeble 3500 physical simulator emerges as a cornerstone in this study for the systematic collection of experimental data, specifically targeting a range of predefined temperatures and strain rates. This investigation notably pioneers the use of a variable temperature tensile test, a methodological innovation aimed at an in-depth exploration of the phase transformation dynamics inherent to the Ti-6Al-4V alloy. This technique is distinguished by its gradual temperature reduction strategy, enabling a nuanced analysis of thermal effects on material properties. In the realm of high-temperature alloy research, empirical constitutive models, particularly the Johnson-Cook constitutive relationship, have traditionally served to model the mechanical behaviors of metals subjected to high-temperature environments and varying strain rates. However, a critical analysis reveals that the Johnson-Cook model simplistically predicts temperature-induced flow stress variations in a linear manner, a representation that falls short of capturing the complex behaviors observed in real-world applications. To address this discrepancy, the current research advances a “Modified Johnson-Cook” model, an adaptation specifically formulated to capture the intricate non-linear flow stress variations observed in the Ti-6Al-4V alloy. This refinement is pivotal in accurately characterizing the alloy’s response to thermal processing. To operationalize this enhanced model, a Fortran code was meticulously developed, enabling its integration into finite element analysis workflows. This technological integration marks a significant stride towards achieving a comprehensive and precise representation of the Ti-6Al-4V alloy’s mechanical behavior under a spectrum of thermal conditions, spanning from moderate to elevated temperatures. This advancement underscores the study’s contribution to the field, potentially guiding the optimization of heat treatment processes and material design strategies for high-temperature applications.

Abstract In recent years, artificial intelligence (AI) has been changing the way the industry operates, especially the oil industry. In the oil exploration and production activity, specifically in the Reservoirs and Wells areas, the use of AI has been growing exponentially, with applications ranging from reservoir drainage plan evaluation (CARDOSO et al., 2017) to predicting well instability issues during drilling (LENWOUE et al., 2023). The Selection of Alternatives via Artificial Intelligence – SAVIA, arises from the Internal Startups Program, an initiative of Petrobras' Digital Transformation that aims to enable innovations conceived by the employees themselves for value generation, maximizing results and reducing costs for the Company. Centro de Estudos Avançados do Recife (CESAR), as a digital innovation center, partners with Petrobras in the development of these projects to make the ideas feasible. SAVIA emerges from the need to streamline and innovate the well conception process, known as SELEPOÇO, by improving the technological incorporation stage aligned with cost optimization expectations. AI proposes to well Designers alternatives of well configuration for a given investment project. The Minimum Viable Product (MVP) of SAVIA was developed through the construction of a decision tree algorithm based on the accumulated knowledge of Petrobras' technicians and engineers, in the form of business rules implemented in Python, and the construction of a machine learning model based on historical data from built wells. Data from lithology and other information from about 1600 offshore wells built by Petrobras in the last two decades were used to train and assess SAVIA's performance.

Abstract This paper details the transformative journey of the Oman Shell Well Reservoir and Facility Management (WRFM) team in designing and deploying an advanced, automated, and engineering-based monitoring system. The initiative was driven by the need to enhance operational efficiency, ensure safety, and optimize resource utilization across oil and gas facilities. The team achieved this by developing an integrated digital solution that seamlessly connects real-time field data with an Integrated Visual Management (IVM) platform. By embedding engineering logic and physics-based models into the IVM system, the solution enables Exception-Based Surveillance (EBS) alarms to be triggered automatically. This innovation has revolutionized the monitoring process, reducing reliance on manual interventions and enabling faster, more accurate decision-making. The primary objective of the project was to streamline the tracking of safety and business-critical performance indicators by automating the surveillance of wells and surface facilities. This automation not only minimized the need for manual data monitoring but also established clear roles and responsibilities for various teams, including WRFM and Operations. By leveraging engineering calculations rooted in physics models and data-driven logic, the team developed a robust system capable of detecting critical issues such as well exceeding erosional velocity, tubing leaks, and other operational anomalies. To enhance usability, the system incorporates diagnostic plots for intuitive visualization and a consolidated event view that includes features such as user acknowledgment and automated email notifications. These functionalities ensure that issues are promptly identified, communicated, and addressed. The EBS engine, a core component of the system, delivers direct value by generating live, accurate, and instantaneous data-driven alarms. These alarms, when acted upon in a timely manner by the field and subsurface teams, have yielded significant operational and financial benefits. Key outcomes include: 100% Well Integrity Compliance: The system ensures that all wells adhere to stringent integrity standards, mitigating potential hazards and safeguarding operational safety. This proactive approach has significantly reduced the risk of well failures and environmental incidents.Reduction in Deferment reaction time: Since its implementation in May 2024, the deferment reaction time have successfully reduced 15%. This reduction has contributed to uninterrupted production, maximizing revenue generation and minimizing operational disruptions.Reduction in Daily Well Review Process time: The automation of the daily well review process has led to a 20% improvement in Full-Time Equivalent (FTE) efficiency. This optimization has allowed for better resource allocation, reduced operational costs, and enhanced productivity. The integration of the IVM platform as a physical modeling tool for system performance, combined with real-time data from surface facilities and wells, represents a groundbreaking achievement in the oil and gas industry. This first-of-its-kind implementation involved hardwiring an operating envelope database with the IVM prediction engine, creating a robust, real-time, and fully automated data surveillance system that is linked to a physical gas field model. The system's ability to predict and monitor operational performance in real-time has set a new benchmark for efficiency and reliability in the sector. Beyond the immediate operational benefits, this innovation has broader implications for the industry. It demonstrates the potential of digital transformation to enhance safety, reduce costs, and improve decision-making processes. The success of this project underscores the importance of collaboration between engineering, data science, and operations teams in driving technological advancements. Furthermore, it highlights the value of leveraging real-time data and predictive analytics to address complex challenges in oil and gas operations. Through development of this automated monitoring system, a significant milestone is achieved in the evolution of well and facility management. By combining advanced engineering models, real-time data integration, and user-friendly visualization tools, the team has created a solution that not only addresses current operational challenges but also paves the way for future innovations in the industry. This project serves as a testament to the power of digital transformation in achieving operational excellence and driving sustainable growth.