Introduction to MBA FPX 5014 Assessment 3
MBA FPX 5014 Assessment 3 is all about using data analytics and decision models to drive organizational performance. Assessment 3 is all about using managerial decision models to solve a dummy business case with both qualitative and quantitative data. This paper will examine a business issue, look at the options, use models, and achieve an optimum solution.
Assessment Objectives
- Utilize the leadership decision model to solve some levels of complexity.
- Utilize data analysis to substantiate recommendations.
- Assess multiple possibilities and substantiate a final conclusion on justification.
1. Business Problem Identification
Case: A retail company is experiencing a decline in customers’ satisfaction and rising goods costs because of inadequate demand forecasts.
Problem Statement:
How can the company’s control system for storage be expanded to enhance low costs and customer satisfaction?
2. Data Collection and Analysis
The following information articles were analyzed:
- Historical Sales Data (previous 12 months)
- Inventory turnover rate
- Customer satisfaction survey
- Supplier’s management time
MBA FPX 5014 Assessment 3 A trend analysis showed ups and downs in seasonal demand, which had extreme overkings in the 3rd quarter and Q4.
Data Insights:
Inventory costs rose by 15% annually.
Customer satisfaction evaluations dropped by 20% throughout the share period.
To learn more about data-driven decision making, go to Data.gov.
3. Application of Decision-Making Models
Model 1: Cost-Benefit Analysis
Used to contrast investment in a demand forecasting system with the expense of lost sales and inventory holding.
Model 2: Decision Tree Analysis
Couched consequences of continuing current practices against the implementation of an AI-powered forecasting system.
Model 3: SWOT Analysis
- Strengths: Strong vendor relationships
- Weaknesses: Inefficient inventory systems
- Opportunities: Technology-enabled forecasting tools
- Threats: Advanced logistics competitors
Learn more about decision trees at Harvard Business School Online.
4. Evaluation of Alternatives
Option 1: Upgrade existing ERP with AI forecasting module.
- Pros: Greater accuracy, no integration cost
- Cons: astronomical initial investment
Option 2: Outsource inventory planning to a third-party logistics firm.
- Pros: Specialist service, zero initial cost
- Cons: Loss of control, risk of dependency
Option 3: Maintain current system
- Pros: No extra outlay
- Cons: Continuing inefficiency and dissatisfaction
5. Recommendation and Justification
MBA FPX 5014 Assessment 3 Recommended Option: Install AI forecasting in the existing ERP system.
Justification:
- ROI analysis gave 18-months cost recovery.
- Decision tree indicated a high likelihood of lower inventory costs and higher customer satisfaction.
- Aligns with organizational strategy for embracing digital transformation.
How To Complete MBA FPX 5014 Assessment 3
- Clearly articulate a business issue.
- Collect appropriate qualitative and quantitative information.
- Use at least two decision-making models.
- Assess several solutions.
- Make and support a recommendation on adequate grounds.
FAQs
1. What information should I collect?
Collect qualitative (customer survey) and quantitative (sales and costs) information.
2. How many decision models do I have to apply?
Two at least, but three will make your analysis more improved.
3. Am I allowed to apply to real companies for analysis?
You can apply to real companies to make your analysis even better.
Conclusion
MBA FPX 5014 Assessment 3 is the platform of data-based leadership. With the application of decision models and analysis of actual business data, leaders are able to provide strategic recommendations that contribute to performance and sustainability.
References
- Harvard Business School Online. (n.d.). Decision Tree Analysis. Retrieved from https://online.hbs.edu
- Data.gov. (n.d.). U.S. Government’s Open Data. Retrieved from https://www.data.gov/
- Drucker, P. (2007). The Effective Executive. Harper Business.
- Robbins, S. P., & Coulter, M. (2022). Management (15th ed.). Pearson
- McKinsey & Company. (2023). AI in Retail Forecasting. Retrieved from https://www.mckinsey.com/
