This research combines the analytical tools required for data-driven management and applies them to the freight and logistics field. Specifically, this research focuses on non asset-based third-party logistics providers (3PLs) offering truckload services in the transportation spot market. Non asset-based 3PLs are companies that provide logistics services for shippers, but own no assets used in the physical distribution of goods. The transportation spot market consists of shipments handled on a one-time load-by-load basis and is characterized by price volatility and uncertainty in the availability of capacity. It exists to serve urgent or unfulfilled demand and is an integral part of the U.S. freight system. Non asset-based 3PLs act as brokers in the spot market matching supply (carriers' capacity) with demand (shippers' shipments).;Because their systems, along with their expertise and knowledge of the market, are the primary assets by which non-asset-based 3PLs survive, methodologies and tools that help leverage this data and knowledge into more effective operational decisions will contribute significantly to their financial success. This research develops such methodologies. Specifically, it creates tools to help 3PLs better conduct their two fundamental tasks: pricing (the price at which 3PLs offer their services to shippers) and sourcing (securing capacity from carriers). This research employs discrete choice and optimization models to develop strategies that support the pricing and sourcing tasks.;Throughout the dissertation a series of conceptual frameworks intended to improve non asset-based 3PL operations in the transportation spot market are presented. As opposed to past works, this research focuses on 3PLs that must actively rather than passively seek capacity in the spot market. These frameworks are then demonstrated and supported using empirical analyses. The empirical analyses rely on two primary data sets: a 3PL provider transaction-level database of shipments and a stated preference experiment administered via an online survey to truckload motor carriers.;The first data set was obtained from a U.S.-based 3PL provider operating in North America. The database consists of transaction-level information on shipments, attributes of carriers and shippers, the location of warehouses, and the amount of available capacity in the market among other information. The second data set consists of the results of a stated preference experiment that assessed the responsiveness of carriers to hypothetical spot market pricing scenarios. The scenarios were presented in the form of discrete responses to offers of compensation for capacity from a shipper; the carrier chose the best available option which included declining all offers.;A major component of the presented frameworks is developing strategies by which 3PLs can leverage their own transactional data within the spot market. Despite its importance, there is a lack of theoretically sound data-driven strategies meant to address the needs of 3PLs. Thus, there is a critical need for this type of research. Furthermore, this research yields insights into the considerable behavioral dynamics of the spot market resulting from its speculative nature. This marks a significant contribution of the research.
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