DB-FPX9801

DB FPX 9801 Assessment 1 Student Name Capella University DB FPX 9801 Professor Name Submission Date   Access the complete assessment details in the PDF below: DB FPX 9801 Assessment 1

DB FPX 9801 Assessment 2 Student Name Capella University DB-FPX9801 Proposal Writing Professor Name Submission Date   Access the complete assessment details in the PDF below: DB FPX 9801 Assessment 2

DB FPX 9801 Assessment 3 Student Name Capella University DB-FPX9801 Proposal Writing Professor Name Submission Date   Access the complete assessment details in the PDF below: DB FPX 9801 Assessment 3

DB FPX 9801 Assessment 4 Pre-Implementation Conference Call Presentation Student Name Capella University DB-FPX9801 Proposal Writing Professor Name Submission Date [blur_section id=”postDownload” button=”Free Download” form_id=”e9df100″] Slide 01 Pre-Implementation Conference Call Presentation Hello, my name is _________, and I came to see you today to deliver a pre-implementation conference call presentation on the idea of integrating a transformational leadership approach and using digital technologies to reduce supply chain disruptions. Slide 02 Introduction The current competition and high rate of globalization in the global markets has made the global markets extremely competitive. These dynamics have resulted in lean supply chain operations such as just-in-time and proper scheduling (Katsaliaki et al., 2021). Nevertheless, these plans make it harder and more disruptive (Deep et al., 2025). The U.S. supply chains are challenged by such problems as pandemics, shortage of labor, and geopolitical tension. A large number of managers do not have transformational leadership and risk management tools digital (Dura et al., 2025). The out-of-date systems lead to delays, inventory shortages, and demoralization (Alicke and Foster, 2024). The leaders are required to be resilient by adopting transformational leadership and using digital technologies. Slide 03 Statement of the Problem Problem Statement The overall business issue is that supply chain industry businesses have become more susceptible to supply chain disruptions, therefore amplifying the operational and financial cost of supply chain disruptions (Katsaliaki et al., 2021). The issue under concern is that not all supply chain managers in the U.S. have sound supply chain risk management principles and information technologies to create the supply chain resiliency, which leads to the decline of operational efficiency and employee motivation during disruptions (Deep et al., 2025). Purpose Statement The qualitative inquiry project aims to examine the views of supply chain managers in the U.S. regarding the application of transformational leadership and digital technologies as a means of creating organizational and workforce resilience in case of a supply chain disruption. Gap in Practice The practice gap is that U.S. supply chain managers do not have evidence-based approaches to integrating transformational leadership and utilizing digital technologies to achieve resilience of operations and workforce in case of disruptions in the supply chain (Yeboah et al., 2025). Slide 04 Theory(s) Supporting Research Figure 1 Visual Representation of Project Framework (Munir et al. 2020). The project theoretical framework is founded on Munir et al. (2020) and incorporates the supplier, consumer, and internal coordination. The internal integration is considered as the most successful risk management approach when the supply chain has been disrupted. The framework is focused on information processing power, which allows firms to collect and process information of suppliers and consumers. Nevertheless, internal integration is not enough in the current unstable world. The framework also includes transformational leadership that puts emphasis on visioning, empowering, and innovating (Taseer & Ahmed, 2022). Is also compatible with the dynamic capability theory, which emphasizes the capacity of an organization to change (Ayaz, 2022). Better control, planning, and predictive decision-making are supported with the help of technological factors, in particular, AI tools (Dura et al., 2025). The tools increase efficiency and velocity in supply chains. Workforce training and collaboration are essential to human resource development, which is critical in creating resilience (Saeed et al., 2022). The model links leadership, technology, and integration to develop a strong and flexible supply chain system. Slide 05 Methodology The generic qualitative inquiry technique is one of the most appropriate techniques of research that uncovers detailed information about the lived experiences of the participants in terms of their behaviors and choices made when facing a disruptive occurrence in the supply chain. The paper is grounded on the necessity of studying how U.S. supply chain managers can combine the idea of transformational leadership with digital technologies to create resiliency. The generic qualitative inquiry is aimed at knowing the how and why of the deployment of particular strategies in the complicated high-risk setting, particularly where numeric data cannot reproduce the entire picture (Creswell and Poth, 2018). The method is a flexible yet strict framework of studying rich, contextualized human experiences affirming the problem and purpose of the study Yeboah et al., 2025(Yeboah et al., 2025). The consistency of the research objectives with the research methodology also denotes that the study will capture finer viewpoints and adaptive behaviors that are important in supply chain resilience. Slide 06 Sample The study will target supply chain managers of the middle and senior level, who will be located in the United States, and work in various industries including manufacturing, logistics, retail, and healthcare. The sample consists of those who have been in charge of supply chain management and experienced disrupted supply chain management through the application of leadership or digital technology (Palinkas et al., 2015). The participants will enhance validity of the results since the individuals will present expert, experience-based views pertaining to the research questions. The purposive sampling will be applied to select intentionally the ones with the direct experience in disruption management to ensure that they are relevant and in-depth (Palinkas et al., 2015). The sample size will consist of 12 to 15 participants so that it becomes manageable yet the data saturation can be attained as a minimum of 10 finished interviews. Slide 07 Data Collection The proposed data collection tool includes semi-structured interviews to understand the application of transformational leadership and digital technologies by supply chain managers in the United States in case of disruption. The interviews will be carried out online through Zoom to provide the concept of flexibility and availability anywhere throughout the U.S. The participants will be those with 10+ years of experience and will be a part of the study. The interview guides will consist of 10 open-ended questions that are consistent with the digital resilience frameworks and transformational leadership theory (DeJonckheere and Vaughn, 2019). The audio-recorded data will be transcribed literally by means of the Otter.ai, and the finished version will be checked manually. Furthermore, thematic analysis will be utilized as the main means of determining the patterns of responses. The interviews will last approximately 45 60 minutes. Member-checking and peer debriefing will be used to assure reliability as accuracy and interpretation will […]

DB FPX 9801 Assessment 5 Student Name Capella University DB FPX 9801 Professor Name Submission Date   Access the complete assessment resource in PDF format for study and reference: DB FPX 9801 Assessment 5

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DB FPX 9801 Assessment 8 Student Name Capella University DB-FPX9801 Proposal Writing Professor Name Submission Date [blur_section id=”postDownload” button=”Free Download” form_id=”e9df100″] Access the complete assessment document in PDF format. DB FPX 9801 Assessment 8 [/blur_section]

DB FPX 9801 Assessment 9 LEVERAGING ARTIFICIAL INTELLIGENCE FOR STRATEGIC DECISION-MAKING IN HEALTH CARE by Student Name Professor Name Maja Zelihic, PhD, Dean Academics and Doctoral Studies School of Business, Technology and Health Care Administration A Capstone Work Presented in Partial Fulfillment Of the Requirements for the Degree Doctor of Business Administration Strategy and Innovation Capella University Month & year of dean’s approval   Professional Development Plan I could be a doctoral researcher working in the field of AI implementation in healthcare, which is why my opportunities to develop according to Applied Research Competencies are high and multidimensional. The multifaceted nature of the healthcare AI adoption, in light of the fact that only 25% of executives admit to realising potential of AI, despite a 75 percent recognition (Bain & Company, 2024) states the necessity to establish sophisticated research capabilities. Of course, these three sub domains are qualitative research method, analysis of the results and the distribution of the research findings. As to particular research methodologies, I am looking forward to acquiring high-level skills in semi-structured interviews and thematic research, as they will play a critical role in the scenario in which the proposed topic is applied due to the knowledge of the technology and the nature of the organization (AlKuwaiti et al., 2023). As far as data analysis is concerned, I will build my competence in terms of the organizational data about AI deployment since most healthcare organizations report problems related to data integration; on average, 40 percent of these institutions list data standardization among the systems among their issues (Hummelsberger et al., 2023). The research dissemination subdomain aims to provide adequate dissemination and presentation of the findings to the audience of various other stakeholders as the authorities of healthcare need this information to design the appropriate strategies and implementation procedures (Steerling et al., 2023). Figure 1 Professional Development Matrix The strategies that I intend to implement involve creating a classical structure of my work that will encompass the academic part and the practical part that is focused on achieving the goals, mechanisms that will assist me to be motivated and conquer difficulties. To enhance the quality of this practice, I will engage in communication with more advanced researchers and enroll in special methodology classes, such as interview strategy and thematic analysis of the work as it is conducted in the latest researches in the sphere of healthcare technology (Rahman et al., 2024). Regarding data analysis competencies, I will seek job placements in healthcare organisations that are also implementing AI solutions to analyse data in order to improve the analytical skills of the person undertaking the process as demonstrated by Krishingan et al., 2023. To a certain degree this model of development has already impacted my professional practice, since it encouraged me to become more involved in various types of activities, e.g. working on AI-implementation projects, collaborating with healthcare-technology teams. These exercises assisted me in the process of getting acquainted with utilizing various data sets to examine and synthesize the relevant to the fact that the healthcare organizations implementing AI solutions decrease the costs of operations by 25-30 percent (Rojek et al., 2023). The framework has also improved my skill to develop viable solutions to significant challenges in healthcare AI deployment, in particular, the problems related to data integration and change management, which are some of the critical impediments to successful implementation as Dwivedi et al. (2021) recognize. Conclusion According to this professional development model, I have outlined the plan in terms of developing the applied research competence to deal with the challenge of AI application in healthcare. Such competencies of merging qualitative research techniques, data analysis, and research communication capabilities equip me with the task when applied in the area of healthcare technology adoption. These research competencies enhancements will prove very helpful when healthcare organizations move to the direction of AI development. These skills will be applicable in making sure that the knowledge acquired through the organizational practice is reflected back in the healthcare academia to be analyzed further. The accumulation of these skills as thoroughly organized as well as the practical training opportunities in practice are more than good in terms of enriching my academic along with professional training as a healthcare AI practitioner. References AlKuwaiti, A., Nazer, K., AlReedy, A., AlShehri, S., AlMuhanna, A., Subbarayalu, A. V., Al Muhanna, D., & AlMuhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951. https://doi.org/10.3390/jpm13060951 Bain & Company. (2024). Majority of health system executives believe generative AI will reshape the industry, yet only 6% have a strategy in place. Bain. https://www.bain.com/about/media-center/press-releases/2023/majority-of-health-system-executives-believe-generative-ai-will-reshape-the-industry-yet-only-6-have-a-strategy-in-place/#:~:text=Press%20release- Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., & Medaglia, R. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Hummelsberger, P., Koch, T., Rauh, S., Dorn, J., Lermer, E., Raue, M., Matthias, F., Schicho, A., Colak, E., Ghassemi, M., & Gaube, S. (2023). Insights on the current state and future outlook of artificial intelligence in healthcare from expert interviews. Journal of Medical Internet Research  Artificial Intelligence. https://doi.org/10.2196/47353 Krishnan, G., Singh, S., Pathania, M., & Dhar, M. (2023). Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm. Frontiers in Artificial Intelligence, 6(6), 8–12. https://doi.org/10.3389/frai.2023.1227091 Rahman, Md. A., Victoros, E., Ernest, J. T., Davis, R., Shanjana, Y., & Islam, Md. R. (2024). Impact of artificial intelligence (AI) technology in healthcare sector: A critical evaluation of both sides of the coin. Clinical Pathology, 17. https://doi.org/10.1177/2632010×241226887 Rojek, I., Piechowski, M., & Mikołajewski, D. (2023). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Applied Sciences, 13(8), 4–11. https://doi.org/10.3390/app13084971 Steerling, E., Siira, E., Nilsen, P., Svedberg, P., & Nygren, J. (2023). Implementing AI in healthcare-the relevance of trust: A scoping review. Frontiers in Health Services, 3(3), 12–50. https://doi.org/10.3389/frhs.2023.1211150