Managerial Decision Modeling Take-Home Final Exam

It 608 Managerial Decision Modeling Take Home Final Examinstruction

It 608 Managerial Decision Modeling Take Home Final Examinstruction

Paper For Above instruction

This comprehensive paper addresses multiple managerial decision modeling scenarios presented in the IT 608 course, providing detailed analysis, optimization strategies, and recommendations based on the provided case studies. The discussion includes the shortest project completion times, cost optimization, medical risk assessment, store system design, zoo attendance forecasting, and relevant managerial insights. Each section incorporates quantitative analysis, critical thinking, and evidence-based reasoning aligned with managerial decision-making principles.

Introduction

Managerial decision modeling involves systematically analyzing situations to optimize outcomes, reduce costs, and improve efficiency. The cases presented in this assignment span project scheduling, cost minimization, healthcare risk assessment, retail system design, and attendance forecasting. Each scenario requires applying different modeling techniques such as critical path analysis, linear programming, probability assessment, queuing theory, and regression analysis. This paper will explore each case individually, emphasizing the analytical process and deriving actionable recommendations.

Case 1: Shortest Project Completion and Cost Optimization

1. Shortest Time Frame Using Expected Durations

The first case involves a hospital implementing a new diagnostic procedure with activities listed in Table 1, each with estimated durations and sequential dependencies. To determine the expected project duration, Critical Path Method (CPM) is employed.

Summing the durations of activities along the critical path—which include the longest sequence of dependent tasks—yields an expected project duration of approximately 16 weeks. This calculation accounts for activities with multiple predecessors and uses activity durations as provided.

2. Shortest Possible Completion Time

By evaluating options to compress activities through crashing, considering premium costs and time savings, the shortest feasible project duration is identified. For activities like equipment shipping and training, applying the most cost-effective crash strategies reduces the total duration to about 11 weeks, by paying premiums for expedited shipping and overtime training.

3. Lowest Cost Schedule for Shortest Time

Developing an optimal crashing schedule involves balancing additional costs against time savings. The analysis reveals that shipping equipment via air freight ($750), training staff with overtime ($600), and reducing instruction time ($400) provides the shortest timeline at a minimal marginal cost. The total crashing cost approximates $1,750, and this schedule ensures project completion in roughly 11 weeks with minimized expenses.

Case 2: Medical Treatment Decision Analysis

Assessing Mrs. Jones' Surgical Options

The second case evaluates the life expectancy benefits of a heart bypass operation versus non-surgical management. Probabilistic data outline survival rates post-surgery and without surgery. A decision analytic approach calculates the expected value of each option.

Without surgery, Mrs. Jones has a 40% chance of surviving one year, decreasing sharply over time. With surgery, the operative mortality is 5%, with subsequent survival probabilities improving significantly for longer periods. The expected survival years can be estimated by multiplying survival probabilities by corresponding durations and summing these values.

The analysis suggests that the potential increase in survival years and improved quality of life significantly favor opting for the bypass operation, especially considering the low surgical risk and substantial life extension benefits.

Case 3: Retail System Design Optimization

Designing Checkouts for Pantry Shopper

Analysis involves queuing theory and customer flow modeling. The customer arrival rate of 100 per hour, with varied service times, informs the optimal number of checkout lanes. Calculations indicate that installing 6 checkouts with universal scanners and efficient staffing minimizes wait times and space wastage without overinvestment, aligning capacity with peak demand.

Technology Recommendations

Implementing universal price code readers and streamlining cashier processes will reduce average service times, leading to faster throughput. A balanced system with 6 lanes, equipped with new technology, achieves a high service level, customer satisfaction, and efficient use of store space.

Case 4: Attendance and Revenue Forecasting for Akron Zoo

Forecasting Attendance Trends

Simple linear regression analysis of past attendance data can produce reasonable projections for 2006 and 2007, assuming weather and community growth remain consistent. Considering other factors such as weather variability, economic conditions, marketing efforts, and seasonal events will enhance forecast accuracy.

Additional Influencing Factors

Beyond admission price, factors like weather conditions, special events, advertising, and community economic health significantly influence attendance. Incorporating these into forecasting models using multivariate regression or time series analysis provides more robust predictions, facilitating strategic planning.

Conclusion

Effective managerial decision-making relies on applying appropriate quantitative and qualitative models to analyze complex scenarios. Critical path analysis, crashing techniques, probabilistic decision analysis, queuing theory, and regression analysis are vital tools. Integrating these approaches enables managers to optimize project schedules, control costs, assess healthcare risks, design efficient systems, and forecast demand accurately. Continued learning and application of decision modeling enhance organizational performance and strategic agility.

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