Corsicana Mattress: Optimizing Shipping Routes for Efficiency and Sustainability
Technology Category
- Sensors - Autonomous Driving Sensors
- Sensors - GPS
Applicable Industries
- Buildings
- Transportation
Applicable Functions
- Facility Management
- Logistics & Transportation
Use Cases
- Last Mile Delivery
- Transportation Simulation
About The Customer
Corsicana Mattress Company is a leading mattress manufacturer in the United States. The company, which is celebrating its 50th anniversary, has grown from a family-owned business to one of the largest in the industry. Despite its rapid growth, the company faced challenges in maintaining efficiency, particularly in its logistics operations. The nature of its product made freight costly, and the company was exploring every possible option to reduce fixed and variable freight costs. However, it was limited by its analytical capabilities. The company was also grappling with the challenge of fulfilling orders from non-default facilities, which led to increased costs and inefficiencies.
The Challenge
Corsicana Mattress Company, one of the largest mattress manufacturers in the United States, was facing challenges in optimizing its shipping routes. The company, which had grown from a family-owned business, was grappling with inefficiencies in its logistics operations. The freight costs were high due to the nature of their product, and the logistics team was limited by their analytical capabilities. The company had instances of orders being fulfilled from non-default facilities, leading to increased costs and inefficiencies. In some cases, shipments were routed through facilities hundreds of miles away, leading to unnecessary costs and increased carbon emissions. The company lacked a system to audit default facility assignments, which was a manual and time-consuming process.
The Solution
The solution to Corsicana Mattress Company's challenge was developed in collaboration with the Logistics Director. The company leveraged Alteryx, a data analytics platform, to automate the process of auditing facility assignments. The first step involved geocoding the company's facilities and shipping locations and using the Find Nearest macro to generate a list of the closest facility for every shipping location. The company then compared this list with their existing default facility assignments, revealing thousands of incorrect assignments. The second phase involved narrowing the scope and defining the impact on capacity utilization at each facility. The company filtered out prospective default facility reassignments where the distance savings were relatively small. The analysis resulted in a list of potential default facility reassignments ranked by their savings potential. The company was also able to simulate the closure of a facility to identify the optimal way to reassign the locations serviced by the defunct plant.
Operational Impact
Quantitative Benefit
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