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This study is aimed at examining the function of EAM in medical device production and concentrates on the integration of the architecture of GenAI with SAP EAM capable of effective analytics. Using secondary qualitative research, the study shows significant development in AI applied to asset management, and its improvement on operations. It discusses how GenAI can help minimize loss of time in equipment, identify the right approach to maintenance, and comply with legal requirements. The research also assesses other key parameters that are vital in an organiZation such as decrease in down time, costs of maintenance, reliability, user satisfaction, and compliance. Research supports a significant market increase in health care asset management and is estimated to rise from 42.95 billion to 410.29 billion. The study also reveals several threats like cybersecurity threats and regulatory issues when predicting further development and possibilities of digital twins and IoT-based real-time assets’ management. The study provides specific implementation tactics to optimally utilize GenAI for SAP EAM to reliably and efficiently manufacture medical devices in the future.
The industry of medical device manufacturing needs more reliable tools for their specific operations in regulating better compliance and safety of the patients. Systems like EAM “Enterprise Asset Management” like the tools of “SAP EAM” help in analysing the tool for optimising the life cycle [1]. Integration of GenAI “Generative AI” along with predictive analytics ensures a better transformational approach to decrease downtime and uplifting the maintenance strategies in a specific way. The potential of “GenAI driven SAP EAM” could typically increase the efficiency in operation and maintain regulatory compliance for manufacturing medical grade devices [2].
There is a critical role in “(EAM) Enterprise Asset Management” in managing assets and decreasing disruptions in the operation of manufacturing medical devices. The incorporation of “SAP EAM” helps in providing an advanced tool for asset performance monitoring, allocating time for scheduled maintenance, and ensuring better compliance [3]. This study will explore the incorporation of SAP EAM and incorporation of GenAI in medical device manufacturing for the health industries.
As per the traditional system of asset management, a lack of predictive analytics resulted in a downturn in manufacturing due to unplanned scenarios. These inefficiencies caused an increase in risk in the operations of manufacturing for medical devices, causing a barrier in both regulatory compliance and productivity.
This study aims to analyze the impact of integrating GenAI along with “SAP EAM” for the manufacturing of medical devices, putting a focus on modern predictive analytics to get effective asset management and better efficiency in operations. The objectives of the study are as follows: 1. To explore the significance and role of SAP EAM in asset lifecycle management, and operational efficiency in the manufacturing of medical devices. 2. To analyze the incorporation of GenAI in predictive analytics for improving maintenance strategy in the healthcare industry. 3. To analyze the impact of GenAI for enhancing SAP EAM as per the regulatory need in the manufacturing of medical devices. 4. To explore the challenges of manufacturing medical devices by analysing asset management. 5. To recommend best strategies for incorporating GenAI into SAP EAM for transforming solutions for asset management.
The study holds specific significance in addressing the critical aspect of limitations for an unplanned downtime scenario in asset management for manufacturing medical devices. By incorporating “GenAI”, the study would focus on the innovative process of predictive maintenance, mitigation of risk factors in operations, and options for cost-effective management [2]. This scope would provide a better analysis of the role of SAP EAM along with GenAI aligning them to the needs of the industry, giving focus on the manufacturing practice of sustainability as per the industrial framework.
The manufacturing industry has evolved in lifecycle management through different generations. The primary generation allows giving a manual process and measure of prevention towards managing the asset and effective operation management [4]. The second generation allows for the introduction of certain computerized methods which would allow for advanced tracking systems and give proper scheduling for maintenance strategies. For example in the electrical industry, asset management facilitates controlling the risk factors of certain reliable tasks, by increasing the performance of assets and optimization of a certain frequency of intervention [4]. This ultimately allows for reducing the resources and making the operation cost-effective for strategic decision-making.
Similarly, in case of asset lifecycle management for the sustainable mining industry, highlights the need for asset management for designing, and developing products with better modelling, prototyping, simulating, maintenance, and trouble-shooting, targeting the dynamic market, usage, and end-of-life for the product [5]. This proves that the current generation incorporates advanced tools like IoT, AI tools, and predictive analytics for transforming the lifecycle asset by properly monitoring and optimising the performance of assets through different forms of lifecycle.
Figure 1: Advancement in GenAI in Oncology
[7]
GenAI helps in incorporating and revolutionising predictive analytics through models of Machine learning for interpreting wide sets of data and identification of particular patterns [6]. This tool helps in proper predictions with real-time value and data sets, increasing the strategies for maintenance and decreasing the risk of operations. The robust security of Google Cloud enhances the scalability, and regulatory standards for incorporating Gen-AI in predictive analytics across various industries like finance, retail, health care, and others [6]. Similarly, clear evidence shows that GenAI enhances the improved analysis of imagery, integration for generic datasets, and predictive modelling analysis towards increasing the diagnosis and its efficiency in the oncology healthcare system [7]. As shown above in figure 1, in the 2023 year GenAI ranked between 80% to 90% in maintaining accuracy in diagnosis, personalized treatment and monitoring of patient condition [7].
Figure 2: 3D printing medical grade devices
[8]
While manufacturing medical devices, various industries face different challenges in the regulatory compliance sector like “The FDA”, and” ISO 13485”, which ensure the high quality of the product and the safety of a patient respectively [10]. However, incorporating 3D printing tools and techniques as per the modern scenario shows low viscosity bearing it as a challenge to the medical industry [8]. In some other aspects, research shows that incorporating IoT as a part of advanced tools and techniques shows security concerns in the medical manufacturing of devices [9]. Thus, incorporating high-cost and advanced systems of innovation in the healthcare system needs both upgrades of supply chain and employee skills. The manufacturer of the medical device must ensure the threats of cybersecurity risk and other concerns related to sustainability.
Both predictive analytics and AI-driven “EAM” allow for optimization of the performance of ASSET by allowing an organization to leverage real-time data analytics, as per the advancement in algorithm towards forecasting certain failures and maintenance of schedule. The analysis of AI models allows for getting data for IoT sensor management, allowing for predictive data analysis, by reducing downtime and making it cost-efficient. Implementation of data mining allows the incorporation of AI in various fields of business showing accuracy in flexibility and cost-effectiveness based on certain challenging scenarios [11]. In the oil and gas industry, for instance, traditional asset maintenance caused critical and complex situations. However, with the application of both predictive analysis and AI-driven EAM, companies have achieved up to 20-30 % of the decrease in the expense of their maintenance cost, by improving the reliability of assets and more efficient operations [12].
Case study 1: “Medical Device Customer Enhances Asset Visibility, Risk Management and M&A Integration with Armis Centrix”
Figure 3: A case study for asset visibility of medical devices
[13]
As per the case study analysis, A manufacturing company that serves to be the leading producer of electric wheelchairs and “assistive devices”, in partnership with “Armis Centrix” helps in bringing improvement in their asset management and visibility, by managing risk factors after incorporating GenAI [13]. Different challenges for asset management and visibility across different regions were faced by the company. The firm also lacked enough visibility for devices that are either managed or unmanaged like assets for “IT, IoT, and OT” respectively [13]. Implementation of Armis Centrix allowed for providing real-time tracking visibility and allowed in controlling their assets with enhanced management of risk factors and improved level of efficiency in operations.
Case study 2: “Integrating Multiple Systems for medical equipment asset management.”
Figure 4: Medical equipment asset management
[14]
The case study highlights a leading US-based supplier of medical equipment, who has collaborated with “Savinat Consulting”, towards integrating better solutions of medical equipment by incorporating cloud technology [14]. The case study shows that the company has incorporated various wide and distributed assets of the network in their services through various forms of operations and applications of ERP systems. All these systems allowed for real-time tracking of assets and efficient operations towards increasing the delivery of services [14].
The study has adopted an explanatory structure of research design for making a better investigation of the impact of Gen-AI as per the predictive analysis through “SAP-EAM” in their process of manufacturing. This design helped in exploring different causes and impacts of AI integration in the management of assets [17]. Also, this strategy allows for optimal utilization of assets towards maintenance, an efficient matter of improvement in operations.
This study has adopted both qualitative and quantitative data collection methods. The quantitative data has been collected from different graphs, metrics, and statistical data which allowed for analysis of the records in the medical sector [18]. However qualitative data involves the collection of secondary sets of data through authentic journals, and other sources which allows for the analysis of the study with the authentic ones.
|
Metrics |
Description |
Evaluation Method |
|
Downtime Reduction |
Percentage decrease in equipment downtime [19] |
Operational records analysis |
|
Maintenance Cost Savings |
Cost reduction achieved through predictive analytics |
Financial reports |
|
Asset Reliability |
Improved asset uptime and performance [20] |
IoT/sensor data review |
|
User Satisfaction |
Perceptions of ease and effectiveness |
Employee feedback analysis |
|
Compliance Rate |
Adherence to regulatory standards |
Audit reports analysis |
Table 1: Metrics of evaluation
(Source: Self-Developed)
The above table of evaluation shows different forms of key metrics for evaluation of the impact of AI-driven “predictive analytics” in “SAP-EAM”. This involves the decrease of downtime, savings cost of maintenance, and other reliability of asset management, as per the satisfaction rate of customers and regulatory measures [20]. All these metrics help in making an assessment and effective measures through AI role in the operational analysis of data through various forms of feedback analysis.
Figure 5: Enterprise Asset Management Market Size
[15]
The graph represents the estimated growth of the global healthcare asset management market from 2023 to 2032. With a market value of USD 42.95 billion in 2023, the market is estimated to grow at a compound annual growth rate of 28.50% and reach USD 410.29 billion by 2032 [15]. The steady year-to-year growth demonstrates the rapid growth of the market, fueled by the advancement of asset tracking technologies and the increased adoption of healthcare management solutions globally. The sharp upward trajectory indicates growing healthcare facility significance in efficient resource utilization, cost reduction, and better patient outcomes. Some of the key contributors to this trend are innovations such as RFID tags, IoT integration, and real-time location systems [15]. The chart above shows that significant investment and technological development opportunities in healthcare asset management will be realized over the next ten years.
Figure 6: Generative AI in Asset Management Market
[16]
The graph below highlights the generative AI in Asset Management Market from 2022 to 2032 by application. This has been marked with a start in 2022 at USD 312 million. In 2023, it will increase to USD 371.3 million and will progress at a CAGR of 19.0% till the end. There is USD 2,024.3 million by the end of 2033 [16]. This includes applications of portfolio optimization, risk analysis, asset valuation, allocation, performance prediction, and market analysis. The rapid growth can be attributed to increasing adoption of AI technologies by the asset management industry in enhancing decision-making, forecasting trends, and portfolio optimization. Portfolio optimization and risk analysis were still the highest contributors, though market analysis and forecasting also were steady [16]. It represents the necessity for innovative solutions in managing complex data as well as improvement in operational efficiency within the sector.
The findings suggest strong growth in the use of generative AI and healthcare asset management. This reflects an increased trend of reliance on advanced technologies in the pursuit of enhanced operational efficiency and optimized resource utilization. Health care innovations such as IoT, RFID tags, and real-time location systems are revolutionising health care as it is associated with better cost management and patient outcomes [15]. Generative AI is revolutionising decision-making and risk analysis while optimising the portfolios in asset management, giving them more precise forecast and trend analysis. This trend in both markets indicates an emphasis on data-driven strategies driven by the desire to find novel ways of tackling sophisticated problems [16]. Trends in such aspects portray the wide adaptation of technology into industries focused on increased productivity and minimal inefficiencies through automation and intelligent practices. This change is expected to revolutionize the management of assets in most sectors.
|
Case Study |
Key Outcomes |
|
Case study 1: Medical Device Customer Enhances Asset Visibility, Risk management and M&A Integration with Armis Centrix. |
● Armis Centrix™ provided real-time visibility into IT, IoT, and OT assets, with security enhanced through automated risk detection and monthly KPIs [13]. ● This simplified M&A integration, cutting down on the complexity and the operational efficiency improvement between global production sites. |
|
Case study 2: Integrating Multiple systems for medical equipment asset management. |
● The client improved operation efficiency by integrating siloed applications and using real-time asset tracking to reduce delays, costs, and overheads. ● Using mobile apps and Azure PaaS, customer satisfaction increased and the business could expand into international markets like Canada and the UK [14]. |
Table 2: Key Outcomes of Case Studies
(Source: Self-developed)
The case studies are used to highlight how advanced asset management systems like Armis Centrix™ and real-time tracking are used to optimize medical device operations in line with GenAI-driven predictive maintenance strategies.
|
Author |
Focus |
Key Findings |
Literature Gap |
|
[4] |
Industry 4.0 in asset management |
Improved asset performance and sustainability using Industry 4.0. [4] |
Limited application to medical devices. |
|
[5] |
Digital twin in mining asset management |
Proposed multi-layered framework for lifecycle efficiency. |
Lack of focus on GenAI integration beyond mining. |
|
[6] |
GenAI in real-time data analytics |
Enhanced decision-making with real-time GenAI analytics [6] |
No exploration in enterprise asset management. |
|
[7] |
GenAI in oncology data analytics |
Improved cancer screening with predictive analytics. |
No focus beyond healthcare diagnostics. |
|
[8] |
3D printing in medical devices |
Reviewed biodegradable medical device techniques. |
No link to asset management strategies. |
|
[9] |
IoT in biomedical instruments |
Addressed challenges in biomedical monitoring with IoT [9] |
Limited IoT and predictive analytics integration for EAM. |
|
[10] |
Patient-centered medical device regulation |
Framework for improving patient-focused regulations. |
Excludes asset lifecycle management. |
|
[11] |
AI-driven data mining for predictions |
Developed AI frameworks for decision-making in dynamic environments [11] |
No industry-specific EAM applications. |
|
[12] |
AI in predictive maintenance (oil and gas) |
Reduced downtime with AI in equipment maintenance. |
Does not cover healthcare or medical device sectors. |
Table 3: Comparative Analysis
(Source: Self-developed)
The table summarizes the focus, findings, and gaps of each journal of literature and underscores areas like Industry 4.0, GenAI, digital twins, and IoT while still identifying the little exploration done to their applications for enterprise asset management in medical devices.
The results show various quantitative data sets that show the trends in the healthcare industry and the application of GenAI in asset management in a dynamic market. The statistics show that EAM in the healthcare industry has shown growth from “USD 42.95 billion” from 2023 to “USD 410.29 billion” in the upcoming 2032 year, after incorporating advanced technologies like “RFID”, “IoT” and another form of system for real-time tracking [15]. All these advancements and increases in the utilization of resources show efficient cost management and solutions for healthcare innovation.
Similarly, the application of Gen-AI in the assessment of the market has been found to expand from “USD 371.3 million” in the year 2023 to nearly “USD 2,024.3 million” by the upcoming years 2033 by growing at a CAGR rate of 19% respectively [16]. The key findings show that the application of Gen-AI can help in the optimization of portfolios, analysis of risk factors, and forecasting the current trends in strategic decision-making and efficient forms of operations. Further, the case study outcome has also shown the key outcomes from the analysis done with the future scope. The comparative analysis of the literature review compactly highlighted the major focus of their research, key findings, and gaps found in their literature.
As per the overall analysis in the study, the incorporation of Gen-AI aligns with “SAP EAM” for manufacturing medical devices allows for challenges for high maintenance of cost, mitigating risk for cybersecurity factors, and certain complex regulatory measures as per “FDA, ISO 13485” [10]. Besides, another limitation includes the necessity for a skilful workforce, constraints of the supply chain, and breaches of potential data security factors. Towards mitigating the risk factor as per the alignment of meeting industrial standards.
Incorporating Gen-AI aligning to the SAP EAM helps in increasing the efficient operation management and predictive analytics in manufacturing medical devices. Real-time data analytics allow for the incorporation of IoT and help in reducing downtime, and optimising sets of asset performance metrics, which leads to decreased cost and improved compliance with regulations [21]. The organization would certainly achieve success and long-term sustainability after making strategic decision-making and mitigating risk factors for the safety of the patient.
The research work focuses on transforming the field of medical device manufacturing by merging SAP Enterprise Asset Management and Generative AI. Organizations use GenAI with its deep-level predictive analytics strength to make them shift from reactionary to predictive kinds of maintenance thus reducing down-time, maximize asset performance and cutting down operation and maintenance cost. GenAI is applicable in real-time analysis of vast, complex data sets, which reveals trends and insights unknown to traditional methods. These technological advancements are also very critical within the highly regulated medical device sector, characterized by efficiency, compliance, and reliability.
Future research may be conducted on the scalability and customization of GenAI-enhanced EAM solutions across various manufacturing industries. Industry-specific models would be developed to address unique asset management needs, thus improving adoption and effectiveness [22]. Further, integration with emerging technologies such as digital twins and IoT may open greater potential for real-time monitoring and predictive maintenance. It is going to be an important avenue for further work, incorporating data security and compliance challenges as it incorporates sensitive information while working through AI-driven tools. There could be comparative studies conducted on the economic and environmental impacts of incorporating GenAI into EAM versus more traditional systems, providing depth into its long-term value.
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