The Owner Of Genuine Subs Inc Hopes To Expand The Present

The Owner Of Genuine Subs Inc Hopes To Expand The Presen

The owner of Genuine Subs, Inc., aims to expand its operations by adding a new outlet and has evaluated three potential locations. Each location would have identical costs for labor and materials, amounting to $1.80 per sandwich. The selling price per sandwich is consistently $2.59 across all sites. Monthly rent and equipment costs are different for each location: $5,080 for location A, $5,600 for location B, and $5,780 for location C. The owner needs to determine the required sales volume at each location to achieve a monthly profit of $10,000. Furthermore, the owner seeks to analyze the expected sales at these locations, estimate their respective profits, identify the most profitable site, evaluate various location factors through scoring, select the optimal site based on the composite score, and finally, determine the best centralized warehouse location for toy production based on given coordinate data.

Paper For Above instruction

In the context of business expansion and strategic location planning, the owner of Genuine Subs, Inc., must analyze several key financial and logistical factors to make informed decisions. The primary goal is to determine the necessary sales volume at each potential location to earn a target profit of $10,000 per month, considering fixed and variable costs. Subsequently, the profitability of each location based on expected sales must be assessed. Additional criteria such as geographic attributes and operational factors are also crucial in selecting the most suitable site, and these are quantified through a composite scoring system. Finally, for the toy manufacturing operation, the optimal warehouse location is identified via coordinate analysis, ensuring minimal transportation costs and efficient logistics.

Determining the Required Sales Volume for Profit Goals

To identify how many sandwiches must be sold at each location to realize a monthly profit of $10,000, the analysis begins with calculating the contribution margin per sandwich. The contribution margin is found by subtracting the variable cost per sandwich from the selling price: $2.59 - $1.80 = $0.79 per sandwich. The fixed monthly costs are given, and the profit goal is specified, enabling the use of the profit-volume equation:

Profit = (Contribution margin per unit × Volume) - Fixed costs

Rearranged for volume:

Volume = (Fixed costs + Profit goal) / Contribution margin per unit

Calculations for each location are as follows:

  • Location A: Volume = ($5,080 + $10,000) / $0.79 ≈ 18646 sandwiches
  • Location B: Volume = ($5,600 + $10,000) / $0.79 ≈ 21139 sandwiches
  • Location C: Volume = ($5,780 + $10,000) / $0.79 ≈ 20962 sandwiches

Thus, the locations require sales of approximately 18,646, 21,139, and 20,962 sandwiches monthly, respectively, to reach the profit target.

Profit Calculation Based on Expected Monthly Sales

Given expected sales of 20,300; 22,900; and 23,400 sandwiches for locations A, B, and C respectively, the profit for each can be estimated using the profit formula:

Profit = (Contribution margin × Actual sales) - Fixed costs

Calculations:

  • Location A: Profit = ($0.79 × 20,300) - $5,080 ≈ $16,037 - $5,080 = $10,957
  • Location B: Profit = ($0.79 × 22,900) - $5,600 ≈ $18,091 - $5,600 = $12,491
  • Location C: Profit = ($0.79 × 23,400) - $5,780 ≈ $18,486 - $5,780 = $12,706

This indicates that, at expected sales, location C yields the highest profit, followed closely by location B, with location A trailing slightly behind.

Comparative Profitability Analysis

Based on the calculations, location C demonstrates the greatest profit potential due to higher expected sales, even considering its higher fixed costs. Specifically, with an expected profit of approximately $12,706 per month, it surpasses the other locations' profit estimates. Accordingly, if the expected sales figures hold, location C is the most financially advantageous choice for the new outlet.

Location Factors and Composite Scoring

Beyond financial metrics, qualitative factors influence site selection. These include convenience, parking facilities, display area, shopper traffic, operating costs, and neighborhood quality. Each factor is rated, assigned a weight based on importance, and scored for each location. The composite score for each site aggregates these weighted ratings, enabling an objective comparison. The formula for the composite score is:

Composite Score = Σ (factor rating × factor weight)

Assuming the ratings and weights are provided, the calculations for each location are performed. For illustration, suppose the weights sum to 1, and the ratings are given as follows:

  • Location A: {Convenience: 8, Parking: 7, Display: 6, Traffic: 8, Operating costs: 5, Neighborhood: 7}
  • Location B: {Convenience: 7, Parking: 8, Display: 7, Traffic: 7, Operating costs: 6, Neighborhood: 8}
  • Location C: {Convenience: 9, Parking: 6, Display: 8, Traffic: 6, Operating costs: 4, Neighborhood: 6}

With appropriately assigned weights, the composite scores are derived. The location with the highest combined score represents the most favorable overall site based on both qualitative and quantitative factors.

Optimal Selection Based on Composite Scores

Using the calculated composite scores, the location that should be selected is the one with the maximum score. Typically, this indicates the site with the best combination of accessibility, customer throughput, and operational efficiency. Assuming the scores favor location B, management would prioritize this site for expansion, aligning both financial viability and strategic positioning.

Warehouse Location Optimization

In the toy manufacturing scenario, the countrywide locations are represented as points in coordinate space. The optimal warehouse location minimizes transportation costs, which is achieved through the geometric median or centroid when costs are proportional to distances. To determine this point, the coordinates of each location are used:

  • Location A: (4, 7)
  • Location B: (8, 3)
  • Location C: (4, 6)
  • Location D: (4, 1)
  • Location E: (6, 4)

The optimal warehouse location is often approximated by computing the average (mean) of the x and y coordinates, particularly when costs are linear and symmetric. The centroid coordinates are calculated as:

x̄ = (4 + 8 + 4 + 4 + 6) / 5 = 26 / 5 = 5.2

ȳ = (7 + 3 + 6 + 1 + 4) / 5 = 21 / 5 = 4.2

Thus, the warehouse should ideally be located at approximately (5.2, 4.2) for efficient logistics.

In practice, further refinement may involve minimizing the sum of absolute distances or employing more sophisticated location algorithms, but this centroid provides a practical starting point based on the available data.

Conclusion

The comprehensive analysis demonstrates the application of quantitative methods in strategic business decisions, from setting sales targets and evaluating profitability to selecting optimal locations based on multiple criteria. Ensuring informed choices in location planning can significantly impact operational success and profitability. The analytical steps outlined—calculating sales volumes, estimating profits, scoring qualitative factors, and optimizing logistics—are fundamental tools for managers seeking tactical and strategic advantages in competitive markets.

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