Team
The costs associated with product logistics represent one of the main drivers of a product’s final cost, where warehousing, inventory and transportation average approximately 8% of the final price (Davis and Drumm 1996). It is important to note that this cost is in many instances similar to or higher than the manufacturing cost of the final product. Furthermore, due to outsourcing, off shoring and other trends related to globalization, logistics costs are likely to rise in the future. This will make logistics play an expanding role in the overall performance of all companies, particularly manufacturing companies. It is thus imperative to carefully assess and monitor the performance of individual components of the logistics system to identify opportunities for improvement and correction of these systems.
Paradoxically, although logistics costs already represent a very important component of the product’s final cost, techniques for the continuous monitoring and control of these costs are not very common in industry (Keebler and Manrodt, 1999). This is even more surprising given the large amount of data currently available from enterprise execution and other real-time tracking systems.
Observing the lack of monitoring tools, this project focuses on developing a framework for tracking and improving the efficiency of logistics and transportation resources at an aggregate as well as individual scale. The developed framework yields two results: a logistics cost index to monitor system trends and overall efficiency and logistics performance metrics to benchmark the efficiency of specific freight movement rates.
The project focuses on the analysis of traffic lanes, a common unit of measure for all logistics companies; specifically, the project focuses on the behavior of logistics costs and freight rates throughout these traffic lanes. Data was collected for all major U.S. lanes, which were segmented and analyzed to determine which factors directly affect the costs of transporting goods. Subsequently, the relevant factors identified were used to form a small set of clusters with the purpose of grouping lanes with similar behavior together. Ultimately, this small set of five clusters can be used to summarize the behavior of the overall system and provide insights on the structure of the logistics network.
The first result of the project is a global logistics cost index, developed as an aggregation of the rates on the five clusters. This index was validated through comparison with other well known indicators of economic activity including the PMI, USIIP and CASS freight index. The analysis showed that the developed logistics cost index had desirable properties when compared to these indicators.
Although the developed index is a valuable tool for reflecting the behavior of the logistics system, using it for benchmarking specific lanes is difficult. Therefore, the alternative of using data envelopment analysis (DEA) to rate a lane’s efficiency was considered. The second result of this study is the analysis of a DEA based techniques to achieve this goal.
The resulting index and corresponding efficiency rating metrics open a great amount of future applications and opportunities for research. Some of these include a sharing platform for companies to submit their information to allow for aggregation of information, publishing of a complete logistics index and obtaining full visibility into the status of the logistics network.
All of these expansions have great potential to improve logistic performance; nonetheless, we place specific focus on the publishing of a single index which can be used for predicting future rates. In particular, the ability to make projections will allow companies to effectively manage their resources and can open the possibility of creating a futures market for the trading of logistics related contracts.
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