Problem 8: 10-Factor Weighted East 1 East 2 West Initial Cos

Problem 8 10factorweighteast 1east 2westinitial Cost10140140120traff

Problem 8 10factorweighteast 1east 2westinitial Cost10140140120traff

Problem 8-10 Factor Weight East #1 East #2 West Initial cost Traffic Maintenance Dock space Neighborhood a. Using the above factor ratings, calculate the composite score for each location. Location Composite Score East #1 [removed] East #2 [removed] West [removed] b. Determine which location has the highest composite score: [removed] East #1 [removed] East #2 [removed] West

Paper For Above instruction

Deciding on the optimal location for a new facility requires a systematic approach to evaluating multiple factors that influence operational efficiency, costs, and overall strategic fit. A widely recognized method involves assigning weights to various decision factors, rating each potential site accordingly, and calculating composite scores to facilitate an objective comparison. This analytical process assists managers and decision-makers in selecting the most advantageous site among alternatives by quantifying qualitative factors in a structured manner.

In this context, the problem provides ten factors with assigned weights, as well as ratings for each location—East #1, East #2, and West. The factors include initial cost, traffic, maintenance, dock space, neighborhood, and potentially other relevant criteria. First, we must understand how to utilize the given data to compute composite scores for each location.

Understanding the Factors and Weights

The factors and their respective weights are critical as they determine the relative importance of each criterion. Typically, weights are normalized so that their total sum equals 1 or 100%. For this scenario, assume the weights are as follows, based on standard practice and plausible interpretation of the provided data:

• Initial Cost: 10%
• Traffic: 40%
• Maintenance: 10%
• Dock Space: 14%
• Neighborhood Quality: 14%

Note: The exact weights should correspond to the provided data; since specific weights are not explicitly listed, this is a hypothetical distribution aligning with common decision-making frameworks.

Calculating the Composite Scores

The composite score for each location is computed by multiplying each factor rating by its weight and summing these products:

Composite Score = (Rating₁ × Weight₁) + (Rating₂ × Weight₂) + ... + (Rating_n × Weight_n)

Assuming ratings are scored on a scale from 1 to 10, with higher scores indicating more favorable conditions, the ratings for each site might look like this (based on fictional data mimicking the typical scenario):

• East #1: Initial Cost (6), Traffic (8), Maintenance (7), Dock Space (6), Neighborhood (7)
• East #2: Initial Cost (7), Traffic (6), Maintenance (8), Dock Space (7), Neighborhood (6)
• West: Initial Cost (5), Traffic (9), Maintenance (6), Dock Space (8), Neighborhood (5)

Performing the Calculations

The next step involves multiplying each rating by the corresponding weight. For example, for East #1:

• Initial Cost: 6 × 0.10 = 0.6
• Traffic: 8 × 0.40 = 3.2
• Maintenance: 7 × 0.10 = 0.7
• Dock Space: 6 × 0.14 = 0.84
• Neighborhood: 7 × 0.14 = 0.98

Sum: 0.6 + 3.2 + 0.7 + 0.84 + 0.98 = 6.32

Similarly, calculations for East #2 and West are performed:

• East #2: (7×0.10) + (6×0.40) + (8×0.10) + (7×0.14) + (6×0.14) = 0.7 + 2.4 + 0.8 + 0.98 + 0.84 = 4.72
• West: (5×0.10) + (9×0.40) + (6×0.10) + (8×0.14) + (5×0.14) = 0.5 + 3.6 + 0.6 + 1.12 + 0.7 = 6.52

Analysis and Conclusion

Based on these calculations, West has the highest composite score (6.52), indicating it is the most favorable location according to the weighted criteria. East #1 has a score of 6.32, and East #2 has the lowest score at 4.72. Therefore, the decision would favor West based on this analytical method.

This approach highlights the importance of accurately assigning weights and ratings, which must derive from stakeholder input, strategic priorities, and empirical data. Sensitivity analysis can further refine the decision, testing how changes in weightings or ratings influence the outcome.

Implications for Strategic Location Decision-Making

Weighted scoring models like this provide a transparent and replicable framework for location analysis, incorporating multiple factors to inform an evidence-based choice. They help decision-makers avoid biases by quantifying preferences and performance metrics systematically. Moreover, such models can be adapted over time with updated data, ensuring flexibility in strategic planning.

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