Case Study Background: American International Automotive

Case Study Background: American International Automotive Industries

American International Automotive Industries (AIAI) manufactures auto and truck engine, transmission, and chassis parts for manufacturers and repair companies across multiple continents, including the United States, South America, Canada, Mexico, Asia, and Europe. The company ships its products to foreign markets primarily via container ships, establishing warehouses and distribution centers to facilitate logistics. Recently, AIAI has sought to expand its European operations by selecting an optimal site for a new warehouse and distribution center. This site must be strategically located to minimize transportation distances to key customer locations, thereby optimizing logistics costs and delivery efficiency.

The main goal is to identify a site among several potential locations—Dresden, Lodz, Hamburg, Gdansk, and Frankfurt—that best serves seven major customers situated in Vienna, Leipzig, Budapest, Prague, Krakow, Munich, and Frankfurt. Quantitative methods, particularly load-distance analysis, have been employed to evaluate the feasibility and optimality of each candidate site based on geographic distances, shipping costs, and container flow requirements. The analysis involves calculating distances, shipping times, and associated costs, considering the number of containers shipped to each customer and the cumulative transportation expense.

Initial data collection involved mapping distances and travel times between each candidate site and the customer locations using geographic information and map tools. The data matrices include the number of containers routed from each potential site to each customer, along with the corresponding distances and travel durations. This enables the calculation of load-distance metrics, which quantify the overall transportation effort and costs resulting from establishing a distribution center at each candidate site. In addition, accounting for shipping costs proportional to distance ensures a comprehensive evaluation of economic feasibility.

Analysis results suggest that Dresden emerges as the most advantageous location based on load-distance metrics, although other considerations such as proximity to port facilities or regional infrastructure quality could influence final decision-making. In particular, Hamburg's proximity to the port might offset some logistical disadvantages despite its lower load-distance score. While the load-distance and center of gravity analyses provide robust quantitative insights, qualitative factors—such as regional infrastructure, political stability, and future expansion potential—should also inform the final site selection process.

Paper For Above instruction

This paper presents a comprehensive quantitative and qualitative analysis to assist American International Automotive Industries (AIAI) in selecting the optimal European warehouse/distribution site. Given AIAI’s strategic goal to minimize transportation costs and delivery times to major European customers, this report utilizes load-distance techniques and center of gravity models to evaluate potential sites, considering geographic, economic, and infrastructural factors. The findings highlight Dresden as the most suitable location for establishing a new distribution hub, supporting cost-effective logistics operations in Europe.

Introduction

AIAI’s recent expansion in the European market necessitates the establishment of a centralized distribution facility that enhances supply chain efficiency. The company's critical challenge is selecting a site that minimizes total transportation costs while maintaining reliable delivery schedules for key customer regions. Efficient facility location decisions are crucial in supply chain management, affecting both operational costs and service quality. In response, this report employs quantitative models—load-distance analysis and the center of gravity method—to identify the most advantageous site among Dresden, Lodz, Hamburg, Gdansk, and Frankfurt, considering the geographic distribution of major customers.

Background and Context

AIAI’s logistics network in Europe has historically relied on ports of Hamburg and Gdansk, with goods distributed via contracted third-party providers. The company’s increased demand, driven by falling trade barriers and growing customer expectations, underscores the necessity of optimizing distribution activities. The selection process involves analyzing distance, shipping time, and cost data to determine the best location that can serve key customers efficiently. These strategic decisions are aligned with operations management principles, emphasizing cost minimization and service level improvement (Russell, 2019).

Methodology and Analysis

The primary analytical tools employed include load-distance analysis, which evaluates the total weighted transportation effort relative to candidate sites, and the center of gravity approach, which calculates an optimal geometric middle point based on demand and distance. Data collection involved mapping distances and travel times from each proposed site to the seven major customer locations, along with the volume of containers to be shipped (Russell, 2019). The load-distance calculations integrated these data points to produce a quantitative score indicative of each site’s logistical efficiency.

Results of Load-Distance and Center of Gravity Analyses

The load-distance analysis revealed Dresden as the site with the lowest total load-distance value, indicating minimal weighted transportation effort across the major customer base. The coordinate-based center of gravity calculation closely aligned with Dresden’s geographic position, further validating its suitability. These models carefully incorporated the demand volume and distance metrics, offering a robust quantitative foundation for decision-making.

Additional Considerations

While the quantitative analyses favor Dresden, other qualitative factors must be considered. Hamburg, with its proximity to the port, potentially reduces initial inbound transportation costs, especially for goods arriving via sea routes (Furlan et al., 2019). Infrastructure quality in Dresden, particularly in the older eastern regions, warrants consideration, as logistical robustness impacts long-term operations. Regional political stability, infrastructure development, and potential future expansion opportunities are also critical factors.

Recommendations

Based on the quantitative load-distance and center of gravity analyses, Dresden is recommended as the optimal site for the new European distribution center. This location offers minimal logistical effort to serve the core customer regions, particularly Vienna, Budapest, Prague, and Krakow. Nonetheless, integrating qualitative factors—such as port proximity, infrastructure quality, and regional economic stability—into the final decision is essential to ensure sustainable and cost-efficient operations. Strategic contingency planning, including considering alternative sites like Hamburg, could mitigate potential supply chain disruptions.

Conclusion

The site selection process for AIAI’s European distribution center demonstrates the effective application of quantitative models in logistics decision-making. Through load-distance and center of gravity analyses, Dresden emerged as the most advantageous location, supporting the company's supply chain objectives. Future decisions should incorporate qualitative assessments of regional infrastructure and port logistics to refine site selection further, fostering resilient and efficient European operations.

References

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