A Manager Has Received An Analysis Of Several Cities Being C
A Manager Has Received An Analysis Of Several Cities Being Considered
A manager has received an analysis of several cities being considered for a new office complex. The data are as follows: Location, and the factors for each city (A, B, C), including Business services, Community services, Real estate cost, Construction costs, Cost of living, Taxes, and Transportation. The respective scores for each factor are provided for each city.
a. If the manager weights the factors equally, how would the locations rank based on their composite factor rating scores?
b. If business services and construction costs are given weights that are double the weights of the other factors, how would the locations rank?
Paper For Above instruction
The decision-making process involved in selecting a new city for an office complex requires a comprehensive analysis of various relevant factors, each bearing different degrees of importance depending on strategic priorities. The case at hand provides data for three potential locations—A, B, and C—assessed across seven factors, with particular emphasis positioned on different weighting schemes for their evaluation. This paper explores the methodologies for rating these locations under equal and weighted importance schemes, and examines the implications of these evaluations on managerial decision-making.
Introduction
Choosing an optimal city for establishing an office entails balancing multiple criteria such as business environment, community amenities, infrastructure costs, and taxation regimes. Quantitative analysis through composite scoring enables managers to objectively compare options based on key performance indicators. Yet, the weighting of these indicators significantly influences the final rankings—they may be equally weighted or differentially valued depending on strategic priorities.
Data Summary
The provided data indicates scores for each city across the following factors:
| Factor | A | B | C |
|------------------------|---|---|---|
| Business services | 9 | 6 | 6 |
| Community services | 7 | 6 | 7 |
| Real estate cost | 3 | 8 | 7 |
| Construction costs | 5 | 8 | 7 |
| Cost of living | 4 | 7 | 8 |
| Taxes | 5 | 5 | 4 |
| Transportation | 6 | 7 | 8 |
Analysis with Equal Weights
When assigning equal importance to all factors, each factor is assigned a weight of 1/7 (approximately 0.143). Calculating the composite score involves multiplying each factor's score by this weight and summing across factors for each city.
- City A:
= (9 + 7 + 3 + 5 + 4 + 5 + 6) × 0.143 ≈ 39 × 0.143 ≈ 5.57
- City B:
= (6 + 6 + 8 + 8 + 7 + 5 + 7) × 0.143 ≈ 47 × 0.143 ≈ 6.72
- City C:
= (6 + 7 + 7 + 7 + 8 + 4 + 8) × 0.143 ≈ 47 × 0.143 ≈ 6.72
Based on this calculation, both Cities B and C tie for the top position, with City A trailing.
Analysis with Differential Weights
In the alternative scenario, business services and construction costs are given double the weight of other factors, emphasizing their importance.
Total weights are allocated as follows:
- Business services: 2 parts
- Construction costs: 2 parts
- Other factors (Community services, Real estate cost, Cost of living, Taxes, Transportation): 1 part each
Total parts = 2 + 2 + 6 × 1 = 10 parts
Corresponding weight assignments:
- Business services: 2/10 = 0.2
- Construction costs: 0.2
- Each of the other factors: 0.1
Calculating weighted scores:
- City A:
= (9×0.2) + (7×0.1) + (3×0.1) + (5×0.2) + (4×0.1) + (5×0.1) + (6×0.1)
= 1.8 + 0.7 + 0.3 + 1.0 + 0.4 + 0.5 + 0.6 = 5.3
- City B:
= (6×0.2) + (6×0.1) + (8×0.1) + (8×0.2) + (7×0.1) + (5×0.1) + (7×0.1)
= 1.2 + 0.6 + 0.8 + 1.6 + 0.7 + 0.5 + 0.7 = 6.4
- City C:
= (6×0.2) + (7×0.1) + (7×0.1) + (7×0.2) + (8×0.1) + (4×0.1) + (8×0.1)
= 1.2 + 0.7 + 0.7 + 1.4 + 0.8 + 0.4 + 0.8 = 5.8
The reweighted scores suggest that City B remains the most favorable, followed by C and then A. The increased importance of business services and construction costs, areas where City B scored highly, influences this final ranking.
Implications
These different weighting schemes illustrate how strategic priorities influence decision outcomes. An equal-weight approach provides an unbiased assessment, while emphasizing certain factors aligns the decision with specific organizational needs, such as cost efficiency or service quality.
Conclusion
The comparative evaluation underscores the importance of weight selection in multi-criteria decision-making. For the city selection, the analysis indicates that City B scores most favorably under both weighting schemes, reinforcing its status as the most suitable candidate based on the provided data. Managers should tailor weighting schemes to reflect organizational priorities for optimal decision-making in real-world scenarios.
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