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closer look at a Tunnell Consulting effort
Evaluate and Reduce Distribution Costs with Network Optimization Modeling
One of our major chemical company clients, based in Texas, produces and distributes a base product to customers in the eastern half of the country. When they came to us questioning high distribution costs and quality control issues, they had five distribution centers in operation, but realized that two or perhaps three of the centers could probably manage the full load. Considering that high costs were largely the result of handling and fixed costs at each center - the total distribution costs were some $20 million - it made sense to explore if the network could be improved by closing one or more of the DCs.
For the purpose of protecting the sensitive information our client shared with us, we have changed the exact locations of the chemical plant and its distribution centers. For the sake of easy comprehension, we have fictitiously located the plant in Houston, and have located the DCs in five cities that correspond to the spread of the original distribution network.
To start our study, we needed to build a baseline scenario on which other distribution alternatives could be compared.
We did this through network optimization modeling, using state-of-the-art CAPS logistics software(see map 1). Our client provided the necessary historical shipping data and all sites were located on the CAPS map: customers (CU), plant (PL), and distribution centers (DC). Costs for each mode of transportation, capacity constraints, and handling costs were incorporated into the model. The Houston DC was located directly at the plant, the Chicago DC was served by rail, the Cincinnati DC by a combination of rail and barge, the Atlanta DC by rail and the Philadelphia DC by vessel.
Distribution Scenarios Evaluated
After entering all relevant information, we reviewed multiple distribution scenarios, evaluating the pros and cons of each in light of both monetary and nonmonetary factors(see map 2). In addition to costs, this particular client had also been concerned about quality control at certain DCs -- partly the result of product volume imbalances -- and about jeopardizing critical relationships by changing distribution channels. After reviewing all possible scenarios, the client decided the best solution for the company and its customers was to close two centers -- Chicago and Atlanta.
According to the optimized scenario, the closing of the Chicago and Atlanta DCs would result not only in a reduction in fixed and handling costs, but in a drop in overall transportation costs, since these centers had used rail service only. The only rail service still to be used would flow from the Houston DC to its direct customers. Meantime, the barge rate from the plant to Cincinnati was lower than the rail rate, and thus savings could be realized by shipping totally by barge. In addition to its lower-cost barge connections, Cincinnati's central location better serves many of the company's higher volume customers, thus increasing the flow to Cincinnati could also cut transportation costs from the DC to customers. Traffic to Cincinnati thus tripled, while flow to Philadelphia decreased substantially. Because Cincinnati's location and barge rate gave it an advantage, capacity at this site was significantly boosted to accommodate the increased traffic.
Distribution Costs Reduced
By closing the Chicago and Atlanta centers, and switching to lower cost transportation when possible, the company was able to save 16% or $3.2 million off baseline costs. Modeling also revealed that closing yet a third center could cut another 14% of baseline costs, although nonmonetary concerns currently make that an unwise choice. For today's realistic market requirements, the scenario chosen saves an impressive amount of money, improves quality control issues, and keeps customers satisfied with quick, reliable deliveries.
For this client, network optimization modeling netted substantial results, supplying a wealth of information in a quick timeframe - and leading the firm to both cut costs and boost customer satisfaction. Other clients choose to model their production network as well as their distribution network - learning how to thoroughly evaluate sourcing and distribution options, leading to solutions that reduce costs and improve customer service.
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