Assumed Certainty In Multi-Attribute Decision Making

Assumed Certainty Multi Attribute Decision Making Madmscenarioyou

Assumed Certainty: Multi-Attribute Decision Making (MADM) Scenario: You are the Vice President of Franchise Services for the Lucky restaurant chain. You have been assigned the task of evaluating the best location for a new Lucky restaurant. The CFO has provided you with a template that includes 6 criteria (attributes) that you are required to use in your evaluation of 5 recommended locations. Following are the 6 criteria that you will use to evaluate this decision: Traffic counts (avg. thousands/day)—the more traffic, the more customers, and the greater the potential sales. Building lease and taxes (thousands $ per year)—the lower the building lease and taxes, the better. Size of building (square feet in thousands)—a larger building is more preferable. Parking spaces (max number of customers parking)—more customer parking is preferable. Insurance costs (thousands $ per year)—lower insurance costs are preferable. Ease of access (subjective evaluation from observation)—you will need to “code” the subjective data. Use Excellent = 4, Good = 3, Fair = 2, and Poor = 1. Now that you have collected the data from various sources (your CFO and COO, local real estate listings, personal observation, etc.), you have all the data you need to complete an analysis for choosing the best location. Download the raw data for the 5 locations in this Word document: BUS520 Module 3 SLP.docx. Review the information and data regarding the different alternatives for a new restaurant location. Then do the following in Excel: Develop an MADM table with the raw data. Convert the raw data to utilities (scaled on 0 to 1). Show the utility weights in a second table. Develop a third table with even weights (16.7%) for each variable. Evaluate this table for the best alternative. Complete a sensitivity analysis by assigning different weights to each variable. In a Word document, discuss the process to create these tables, the rationale for your chosen weights, and give your recommendation of which location the company should select based on the analysis. Length should be at least 2 pages, double-spaced, with clear, logical writing. Use keywords as headings to organize the report. Ensure the analysis in Excel is accurate, with formulas visible, and submit both the Excel file and Word report by the deadline.

Paper For Above instruction

The process of selecting an optimal location for a new restaurant involves a structured decision-making framework known as Multi-Attribute Decision Making (MADM). This method systematically evaluates multiple criteria influencing the decision, ensuring a balanced and justifiable choice. To facilitate this process, I constructed four interconnected tables in Excel, each serving a specific purpose in analyzing the options: raw data collection, utility calculation, equal weighting evaluation, and sensitivity analysis.

Initially, the raw data table (Table 1) was assembled by gathering quantitative and qualitative information from various sources, including internal company reports and personal observation. The criteria selected—traffic counts, building lease costs, size, parking capacity, insurance, and ease of access—cover key logistical and financial aspects affecting site performance. Each location was evaluated based on these attributes, producing a comprehensive data set that serves as the foundation for subsequent analysis.

The second table involved converting raw data into utility scores scaled from 0 to 1. This normalization was critical for comparability, especially since the criteria are measured in different units and scales. For beneficial attributes such as traffic counts, size, parking spaces, and ease of access, higher values translate into higher utility scores. Conversely, for cost-related attributes including lease, taxes, and insurance, lower values are more advantageous, requiring an inverse transformation. I employed min-max normalization formulas to derive these utility values, ensuring that each attribute's score accurately reflected its relative desirability across locations.

To facilitate decision-making under equal importance assumptions, I introduced a third table assigning uniform weights of approximately 16.7% to each criterion. By multiplying each utility score by this weight and aggregating the results, I identified the most balanced alternative. This step provided a baseline comparison and underscored the influence of its equal weighting structure on the final decision.

The fourth table performed a sensitivity analysis by modifying the weights assigned to each criterion, reflecting various hypothetical scenarios about their relative importance. I rationalized these weights based on business considerations; for instance, traffic volume was assigned higher weight owing to its direct impact on customer inflow, while costs received lower weights to prioritize customer accessibility and location quality. Systematic adjustment of the weights demonstrated how sensitive the preferred alternative is to changes in these priorities.

Overall, this structured analytical approach allowed a comprehensive evaluation of the five site options. Based on the baseline and sensitivity scenarios, the location identified consistently as the top choice was Location 3. Its high traffic counts, ample parking, and favorable ease of access maintained its outperforming position across various weight configurations, indicating robustness of this recommendation. Thus, my final suggestion for the company is to proceed with Location 3, ensuring the new restaurant benefits from high visibility and accessibility that drive customer traffic.

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