NURS FPX 9901 Assessment 2 Quality/Performance Improvement Framework
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In today’s competitive business landscape, efficient supply chain management is essential for organizational success. Many organizations still need help with product delivery delays and ineffective inventory management. This Quality/Performance Improvement Framework outlines a systematic approach to addressing these issues through targeted interventions and continuous improvement methodologies.
Current Practice Needing Improvement
The dysfunction to be corrected runs through the ineffectiveness of our organization’s supply chain management system. Although these endeavors have led to some efficiency in business operations, time lags in product delivery and inappropriate stock management still exist, and the company spends more and gets clients’ feedback (Dwivedi et al., 2023). The problems inherited from this incident are poor integration between the respective systems, outdated IT structures, and lack of homogeneity in the communication channels across the departments. The performance data for the last few quarters also authenticate this by displaying a considerable increase in the delivery lead times and an improvement in the inventory holding cost due to overstocking selected products and running out of stock for other items (Tadayonrad & Ndiaye, 2023). Besides that, employee feedback surveys reveal dissatisfaction with old methods and apprenticeship inadequacies for new technologies, intensifying the operational challenges even further.
Gap performance is evident through the office system of standards and the indicators that enable us to measure the benchmarks for other companies that compare less favorably in crucial metrics like order processing cycle length and inventory retention ratio. These discrepancies impair our capability to meet customer’s requirements within time and jeopardize our competitive strength (Dwivedi et al., 2021). The present condition of a supply chain management system suddenly becomes the one that calls for improvement to raise performance levels, cut down expenses, and gain customer satisfaction. Identifying the keys to the issues through interventions and lean practices seems the right way to fill the performance gap and maintain profitability.
QI/QP Framework
One of the issues identified in the supply chain operation is Quality/Performance Improvement (QI/QP), so we are proposing a framework based on the Plan-Do-Study-Act (PDSA) model to address the problem. This nonended approach ensures that quality is embedded in the process through deliberate planning, execution, testing, and then making changes based on the evaluation phase. By practicing the PDSA cycle methodically, we can spot the issues and analyze better implementation solutions while continuously monitoring the changes in real time. There’s literature that substantiates the PDSA approach for driving progress improvement among organizations, including in the healthcare and manufacturing sectors (Coury et al., 2022). Thus, a study states the significance of the same in improving safety and quality of care. The redesigned model PDSA, which was applied in research, proved that this model can decrease defects and improve process efficiency in manufacturing areas.
The PDSA framework will guide us in ineffective intervention targeting and eradicating the root causes identified in the current supply chain management system. The Plan will be implemented by identifying specific goals, setting an action plan, and getting resources. In the phase, assisted by the set ILTT criteria, efforts including system upgrades, process automation, and employee training will be undertaken. The Review phase will examine the operation performance to detect which indicators positively influence lead time, inventory turnover, and customer satisfaction (Faveto et al., 2023). In essence, this data-based approach helps us assess the options implemented and the areas where there is still a scope for improvement. The Heads of the organization’s different departments will be asked for their insights on the effectiveness of the changes made. The most effective interventions will be scaled up across the organization’s departments, and best practices will be disseminated to ensure continuous improvement (von Thiele Schwarz et al., 2020). This can be implemented through the PDSA model and subsequent literature ANALYSIS. We may thus gradually resolve the identified and related distribution management challenges and achieve performance improvements.
QI/PI Data Collected and Analyzed
Qualitative and quantitative QI/PI data will be obtained using different techniques. The data will be integrated with quality assurance aims to make improvement initiatives effective. The quantitative data collected will be based on the performance indicators, including lead time, inventory turnover, order accuracy scores, and timely delivery rate. This data will be recorded systematically using the transaction systems in an ERP system, and manual data collection will be the source where there is a need (Karamatova, 2023). On top of that, the qualitative data will be gathered through employee feedback surveys, focus groups, and stakeholder talks that will lead to indirect observations and subjective experiences, experts’ perceptions, and their evaluations of the process improvements.
We will use sophisticated statistical techniques involving trend, root cause, and process capability analysis to ascertain trends, correlations, and problem areas. SPC charts will be used to check the stability of the process and respond to any rules of the operation that do not agree with the established standards (Omar, 2020). The floor qualitative data will also be examined using thematic and content analysis techniques to identify meaningful insights. At the same time, relevant themes will be taken. By mixing qualitative and quantitative data analysis techniques, we can get a more comprehensive opinion on the influence of quality improvement and performance improvement efforts on the organization’s performance and the stakeholders’ satisfaction. Through regular monitoring and evaluation meetings and reporting, we will be getting a clear picture of progress toward the QI/PI objectives, and it will be possible to make any necessary adjustments and corrections at any time.
Proposed QI/PI Changes and Expected Outcomes
The purpose of the above QI/PI is to overcome the issues of ineffective business systems, especially the logistics department, by making business entities attain higher performance levels through proper process optimization, utilization of technology, and training of employees. For example, the adoption of an integrated ERP system for handling purchase order processing and inventory management, automation of routine work to eliminate the appearance of inaccuracies and improve efficiency and up-skilling, as well as training programs for the extension staff to be competent, are among the main changes to be done (AB, 2022).
The expected outcomes of these changes are threefold. First, there will be a time-to-lead for product delivery, allowing the organization to meet the rising customer demand by increasing responsiveness. Furthermore, consumption of fewer raw materials due to improved demand and inventory forecasting reduces production costs per unit and leads to a higher profit margin.
Changes in Quality or Performance
For feedback, stakeholders such as staff, customers, and suppliers shall be consulted directly through face-to-face interviews, surveys, and focus groups to obtain their impressions of this implementation. Personal opinions will also be presented, and the patient’s level of satisfaction may not be traced by observation alone. References to benchmarking with standard and best practices in the industry can provide a scale for performance improvement, thus indicating performance areas that require more attention or those in which the comparative advantage is gained. The evidence-based approach is adopted when we differentiate quantitative and qualitative data sources and thus come to reliable conclusions regarding the progress of the performance quality (Noyes et al., 2022). As a result, we can make reasonable decisions about iterative improvements of QI/PI processes. Regular review meetings and performance assessments will be introduced to keep monitoring steady and make adjustments when needed. Through the performance assessments, the project shall maintain the improvements over a long period, which is crucial for sustainable development.
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Conclusion
By implementing the proposed QI/PI framework, organizations can enhance their supply chain operations, reduce costs, and improve customer satisfaction. Through a combination of data-driven analysis, strategic changes, and ongoing evaluation, sustainable improvements can be achieved, ensuring long-term success in today’s dynamic marketplace.
References
AB, E. (2022). Automation of supply processes A study at Ericsson AB MARIANA PIMENTA DE ALMEIDA KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT.
https://www.diva-portal.org/smash/get/diva2:1373399/FULLTEXT01.pdf
Coury, J., Schneider, J. L., Rivelli, J. S., Petrik, A. F., Seibel, E., D’Agostini, B., Taplin, S. H., Green, B. B., & Coronado, G. D. (2022). Applying the plan-do-study-act (PDSA) approach to a large pragmatic study involving safety net clinics. BMC Health Services Research, 17(1).
https://doi.org/10.1186/s12913-017-2364-3
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., & Carlson, J. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59(1), 1–37. ScienceDirect.
https://doi.org/10.1016/j.ijinfomgt.2020.102168
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., & Carter, L. (2023). “So what if chatbot wrote it?” multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71(0268-4012), 102–642.
https://doi.org/10.1016/j.ijinfomgt.2023.102642
Faveto, A., Traini, E., Bruno, G., & Paolo Chiabert. (2023). Review-based method for evaluating key performance indicators: An application on warehouse system. The International Journal of Advanced Manufacturing Technology, 3(5).
https://doi.org/10.1007/s00170-023-12684-4
Karamatova, L. (2023). Management accounting and ERP systems are factors behind choosing information systems when exercising management accounting.
https://www.diva-portal.org/smash/get/diva2:1107616/FULLTEXT01.pdf
Noyes, J., Booth, A., Moore, G., Flemming, K., Tunçalp, Ö., & Shakibazadeh, E. (2022). Synthesizing quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes designs and outlining some methods. BMJ Global Health, 4(1), e000893. ncbi.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350750/
Omar, M. H. (2020). Statistical process control charts are used to measure and monitor the temporal consistency of ratings. Journal of Educational Measurement, 47(1), 18–35.
https://www.jstor.org/stable/25651534
Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3(9), 10–26. ScienceDirect.
https://doi.org/10.1016/j.sca.2023.100026
van, Lara, P., Athanasopoulou, A., Aysolmaz, B., & Türetken, O. (2023). Key performance indicators for business models: A systematic review and catalog. Information Systems and E-Business Management, 21(2).
https://doi.org/10.1007/s10257-023-00650-2
von Thiele Schwarz, U., Nielsen, K., Edwards, K., Hasson, H., Ipsen, C., Savage, C., Simonsen Abildgaard, J., Richter, A., Lornudd, C., Mazzocato, P., & Reed, J. E. (2020). How to design, implement and evaluate organizational interventions for maximum impact: The Sigtuna principles. European Journal of Work and Organizational Psychology, 30(3), 1–13.
https://doi.org/10.1080/1359432x.2020.1803960
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