Articles

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Pharmaceutical Product serialization aims to assign distinct serial numbers to items within a pharmaceutical supply chain. However, this process faces several security challenges like Theft of valid serial numbers may occur, enabling the labelling of counterfeit products. Therefore, it's essential to ensure the uniqueness of serial numbers can be verified at any point in the product's lifecycle within the supply chain. Intimidatory nodes along the distribution network could corrupt planned changes of custody for products. Ensuring verifiability of compliance with these changes is crucial. Manufacturers and consumers need assurance that perishable goods with expired shelf lives are appropriately discarded. In this paper, we review a product serialization method leveraging blockchain technology to address these security concerns within a multi-party perishable goods supply chain. Blockchains offer potential solutions by providing a secure platform for data sharing in multi-party environments, enhancing security and transparency. Within Blockchain technology, each distribution partner is registered to uphold transparency regarding drug information. The system facilitates real-time transfer of ownership changes, recording them as blocks with date and time stamps. This ensures visibility to all partners in real time, maintaining the authenticity of drugs. This article aims to outline how Blockchain technology benefits the pharmaceutical industry by enhancing traceability and trackability of drugs throughout the entire pharmaceutical supply chain.

The SAP HANA Application to handle operational workloads that are consistent with transactions while also supporting intricate business analytics operations. Technically speaking, the SAP HANA database is made up of several data processing engines that work together with a distributed query processing environment to provide the entire range of data processing capabilities. This includes graph and text processing for managing semi-structured and unstructured data within the same system, as well as classical relational data that supports both row- and column-oriented physical representations in a hybrid engine. The next-generation SAP Business Suite program designed specifically for the SAP HANA Platform is called SAP S/4HANA. The key features of SAP S/4HANA are an intuitive, contemporary user interface (SAP Fiori); planning and simulation options in many conventional transactions; simplification of business processes; significantly improved transaction efficiency; faster analytics.

Among the most significant systems that organizations of all stripes, whether public or private, use is the Enterprise Resource Planning (ERP) system. Due in large part to the rapid growth of Internet services and the growing reliance on the infrastructure of Cloud service providers, ERP design has advanced, and numerous types of Internet-service-dependent ERP systems have emerged. In addition to the traditional ERP system, the most significant ERP types are Web-based ERP and Cloud ERP. As a result, ERP system vendors and designers, including Oracle and SAP, are relying on cloud-based ERP system design, and offering the ERP system as a service for monthly and annual subscription, where the system is external to the organization and does not need to exist within the organization.

Our lives are becoming more and more digital, and this has an impact on how we work, study, communicate, and interact. Businesses are currently digitally altering their information systems, procedures, culture, and strategy. Existing businesses and economies are severely disrupted by the digital revolution. The Internet of Things, microservices, and mobile services are examples of IT systems with numerous, dispersed, and very small structures that are made possible by digitization. Utilizing the possibilities of cloud computing, mobile systems, big data and analytics, services computing, Internet of Things, collaborative networks, and decision support, numerous new business prospects have emerged throughout the years. The logical basis for robust and self-optimizing run-time environments for intelligent business services and adaptable distributed information systems with service-oriented enterprise architectures comes from biological metaphors of living, dynamic ecosystems. This has a significant effect on how digital services and products are designed from a value- and service-oriented perspective. The evolution of enterprise architectures and the shift from a closed-world modeling environment to a more flexible open-world composition establish the dynamic framework for highly distributed and adaptive systems, which are crucial for enabling the digital transformation. This study examines how enterprise architecture has changed over time, taking into account newly established, value-based relationships between digital business models, digital strategies, and enhanced enterprise architecture.

Regardless of their traditional ERP Systems, it is essential for every business to acquire a universal advantage in the contemporary international market. When everything is considered, end users in these kinds of businesses have to deal with poorly designed interfaces and unusable technologies. Despite the claims of significant benefits from using S4 Hana cloud ERP software, the possibility of achieving maximum productivity is not fully utilized. One of the causes of this reality is the underfunding of ergonomic measures and the newest technologies. Through the design of S4 Hana cloud ERP software applications, we will demonstrate how important and highly recommended ergonomic research is in order to minimize the financial and human costs that enterprises are currently facing.

Antenatal and postpartum major depressive episode (MDE) according to Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM-V) is defined as either daily sustained sad mood or lack of enjoyment or desire for a minimum two weeks plus four associated manifestations (only three if the two major symptoms are present) that start throughout pregnancy or during the first 4 weeks postpartum respectively: 1) Unintentional notable slimming up or down; 2) Sleepiness or sleeplessness; 3) Tiredness sensation; 4) Guilty or futility sensation; 5) Declined concentration capacity; 6) Frequent suicidal thoughts; 7) Psychomotor excitation or delay. Perinatal depression carries vital and adverse consequences on mother’s psychosocial aspects of life, pregnancy and delivery outcomes, her interrelations specifically with the new born with poorer overall health and influences negatively on offspring from the intrauterine life passing by complicated delivery experiencing hard unstable childhood reaching unhealthy adolescence and adulthood. These negative consequences necessitate a great attention for prevention, screening and prompt treatment for antenatal and postnatal depression to prevent such disastrous effects.

Both crucial success and critical failure factors are included in the current review work. The method relies on creating surveys to collect optional data. It describes the terms that are used to obtain research papers on the ERP deployment in Enterprise Business from databases and scholarly research. In order to enhance the quality of papers, it also includes the consideration and restriction criteria. At that time, a thorough audit of the available papers is conducted to determine the impact of ERP use in Enterprise Business. Important elements are found that determine whether ERP deployments are successful or unsuccessful, as well as how they actually affect Enterprise Business (insert actual success and failure variables here aside from impact). The time span during which research publications have been evaluated limits the scope of the study presented in this paper. One implicit drawback is that it only considers the state of the art in the field of study, without taking into account an empirical investigation. Nevertheless, its findings may prove advantageous, and the directions for future research aid in expanding the field of study. This work advances the body of knowledge regarding the potential benefits and drawbacks of ERP adoption for small and medium-sized enterprises. It uses a secondary data collection strategy to identify important success factors, important failure factors, and their impact. The insights will assist Enterprise Business, Enterprise Business' stakeholders, and ERP service providers in understanding the causes of success or failure and in taking the appropriate action.

The review article presents how emerging technologies improves the business enterprise applications for process management. The paper considers certain technologies of enterprise applications and justifies the updated methodological and analytical tools for assessing, selecting, and regulating business processes in a single enterprise resource planning (ERP) system. Information technology must be used to identify products, track their movement into and out of the warehouse using code scanning technology, and streamline the product management procedure. To increase the dependability of management techniques, guarantee that the business operates flawlessly, and maintain a regular management mode, the process management form should be implemented in the enterprise management process. The implementation of digital information technology is essential for achieving effective corporate management. In addition to providing ideal operational circumstances for businesses, it is essential to analyse information technology and manage businesses economically. The foundation for implementing the enterprise applications method strategy is the creation of a process management system and an in-depth, methodical review of the enterprise as a collection of processes. Process-oriented enterprise applications should be the foundation of contemporary novel technologies for modelling business processes. It shares a tight relationship with workflow management systems (WFM), enterprise resource planning (ERP), and total quality management (TQM).

The technologies for cloud ERP (Enterprise Resource Planning) have revolutionized the field of information technologies. Any kind of business can benefit from their flexibility, affordability, scalability, adaptation, availability, and customizable data. An advancement of classic ERP, cloud enterprise resource planning (C-ERP) provides the benefits of cloud computing (CC), including resource elasticity and ease of use. The rise of cloud computing affects on-premise ERP systems in terms of architecture and cost. Cloud-based ERP systems make the claim to be appropriate for digital corporate settings. System quality, security, vendor lock-in, and data accessibility are recognized as the technological issues. Industry 4.0 refers to the re-engineering and revitalization of modern factories through the integration of cloud-based operations, industrial internet connectivity, additive manufacturing, and cybersecurity platforms. One of the four main pillars of Industry 4.0, cloud-based Enterprise Resource Planning (Cloud ERP), is a component of cloud operations that aids in achieving greater standards of sustainable performance.

The extensive globalization of services and rapid technological advancements driven by IT have heightened the competitiveness of organizations in introducing innovative products and services. Among the noteworthy innovations is enterprise resource planning (ERP). An integral field in computer science, known as artificial intelligence (AI), is undergoing a transformative integration into various industries. Grasping the concept of artificial intelligence and its application in diverse business applications is crucial, given its broad and intricate nature. The primary focus of this paper is to delve into the realm of artificial intelligence and its utilization within enterprise resource planning. The study not only explores artificial intelligence but also delves into related concepts such as machine learning, deep learning, and neural networks in greater detail. Drawing upon existing literature, this research examines various books and online resources discussing the intersection of artificial intelligence and ERP. The findings reveal that the impact of AI is evident as businesses attain heightened levels of analytical efficiency across different ERP domains, thanks to remarkable advancements in AI, machine learning, and deep learning. Artificial intelligence is extensively employed in numerous ERP areas, with a particular emphasis on customer support, predictive analysis, operational planning, and sales projections.

For conducting advanced analytics initiatives to acquire in-depth data into usage habits, regional access, sales, and promotional success, etc., unique identification of packaged pharmaceuticals will be a fantastic enabler. The main objective of this study is to prevent and reduce the production of erroneous and counterfeit drugs using the enterprise system, which has become a serious threat because it damages the reputation of legitimate drug manufacturers by trying to produce and market placebo medications that are identical to the real thing. Due to federal government procedures and priorities that frequently change over time, the majority of implementation takes time. To achieve compliance with numerous federal regulatory authorities, including drug traceability for patient safety, the pharmaceutical industry must implement a systematic procedure in an ERP environment. The goals would be to guarantee medical drug traceability and provide real-time warnings to supply chain stakeholders and regulatory bodies to maximize the benefit of integrating a drug traceability system into an ERP environment. Additionally, manufacturers are compelled to maintain product costs on the higher side due to a heavy burden of unchecked manufacturing cost spikes. As a result, innovative marketing schemes must be introduced in order to increase the reach to consumers by putting into practice successful strategies.

Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.