Optimization Rubric Dashboard Unacceptable 10 Points 786005
Optimization Rubricdashboardunacceptable 10 Pointsacceptable 20 P
Compare the criteria for the dashboard, Part 1, Part 2, and Part 3, focusing on the requirements for accuracy, completeness, organization, and clarity in an optimization project, particularly in the context of the perfume production problem for Overlooked Oils. The assignment involves formulating and running linear programming models, analyzing production constraints, re-running models with modifications, and creating an Excel dashboard with instructions for a manager unfamiliar with optimization concepts.
Paper For Above instruction
Optimization plays a crucial role in various manufacturing and business decision-making processes, especially when resources are limited and demands are high. The project revolves around Overlooked Oils, a perfume producer using natural essential oils, which seeks to maximize profits and optimize production strategies under specific constraints. The task involves constructing linear programming (LP) models, interpreting the results, and creating a user-friendly dashboard for managerial decision-making.
Initially, the core objective is to develop an LP model that maximizes profit by determining the optimal number of bottles for each perfume. The model must incorporate the costs of raw ingredients and the constraints imposed by inventory levels, production minimums, and shelf-life limitations. For example, each perfume requires specific quantities of essential oils, with the constraint that Bergamot must be fully utilized due to its limited shelf life, costing $0.06 per gram if unused. The LP model should include decision variables representing the number of each perfume to produce, the total constraints for ingredient usage, and the production minimums of 100 bottles per perfume to ensure production viability.
The formulation of the objective function will be to maximize total profit, calculated as the revenue from selling each bottle ($10.00) minus the cost of the ingredients used, based on their per-mL costs and quantities per perfume. Constraints will include ingredient availability, minimum production quantities, and the Bergamot shelf life constraint. Solving this LP will determine the efficient production mix, resource utilization, and leftover inventory.
Next, the model is to be run and analyzed. The results must specify precisely how many bottles of each perfume are produced, the leftover quantities of each essential oil, and the limiting factors that restrict further production or profit optimization. Limiting factors could be specific ingredient shortages, shelf life limits, or capacity constraints. These insights are critical for managers to understand bottlenecks in production.
The project advances with a re-run of the model to accommodate additional constraints—specifically, production must be exactly 1500 bottles, aligning with operational limitations. The goal here shifts to minimizing total costs under this fixed total production constraint, prompting adjustments in production quantities while maintaining ingredient constraints and shelf-life considerations. The results should reveal the new production distribution, leftover inventory, and any changes in limiting factors caused by the fixed production level.
Further, a cost increase scenario is simulated by doubling the cost of sandalwood. This amendment necessitates re-running the cost minimization model to observe how a significant change in one ingredient’s cost impacts the optimal product mix, ingredient utilization, and profit margins. It underscores how sensitive production decisions are to fluctuating raw material costs and how these influence overall profitability.
The deliverable is an Excel spreadsheet containing multiple tabs: one for each part of the project, presenting the LP models, solution reports, and summaries. Crucially, a dedicated dashboard tab must include detailed instructions tailored for a manager unfamiliar with optimization. It should guide them on how to interpret the models, what variables they can adjust—such as recipe formulations, ingredient costs, inventory levels—and what strategic decisions can enhance production efficiency and profitability. Clear labeling, organization, and a walkthrough of each tab are essential to ensure usability and comprehension, enabling informed decision-making based on the analysis.
This comprehensive approach integrates mathematical modeling, strategic analysis, and user-centered presentation, emphasizing the importance of clear communication in technical decision-support tools and the application of optimization methodologies to real-world problems like perfume manufacturing at Overlooked Oils.