Comparing FSO, DIY TMS, and Traditional Outsourcing

Executive Summary

Small and mid-sized trucking fleets (1–250 trucks) face a critical strategic choice in how to manage their operations and technology. This white paper evaluates three models: Fleet Super Operator (FSO), DIY TMS (do-it-yourself Transportation Management System), and Traditional Outsourcing, analyzing their impact on key performance indicators (KPIs), costs, staffing, and overall business outcomes. An FSO is an emerging hybrid model where a third-party partner provides an integrated "managed operations + technology" solution, allowing fleets to retain ownership of assets and authority while outsourcing day-to-day logistics and leveraging advanced systems. In contrast, a DIY TMS approach means the fleet implements its own TMS software and manages all operations in-house. Traditional outsourcing refers to fully contracting operations out – for example, leasing onto a larger carrier or hiring a 3PL/managed transportation provider – thereby offloading operational tasks at the expense of direct control. Each model entails trade-offs in ownership burden (the workload and responsibility the fleet must carry) versus operational visibility (the level of transparency and control over freight, drivers, and service performance).

In this paper, we compare these models across financial and operational metrics, using industry data and case examples. We present a 2×2 framework plotting ownership burden against operational visibility (Figure 1) to illustrate where each model lies. Detailed KPI comparisons (service levels, cost efficiency, safety, etc.) are tabulated with documented benchmarks. We develop a total cost of ownership (TCO) model to show how costs and required staff scale as a fleet grows under each approach. A decision-making framework is provided: a scored rubric (with a worked example) and a self-assessment questionnaire (Appendix E) to help fleet owners determine "Which model fits me?" given their specific situation. We also include a definitions and measurement reference box for clarity on terms and KPIs, and a due diligence checklist highlighting what to consider when evaluating vendors or solutions.

Overall, our research finds that Fleet Super Operator (FSO) models can offer small fleets a balance of lower operational burden with high visibility, essentially providing enterprise-grade capabilities "as-a-service." DIY TMS in-house management gives maximum control and visibility but at the cost of significant management effort and overhead – which only larger fleets can efficiently absorb. Traditional outsourcing minimizes internal workload but often sacrifices transparency and agility. The best choice depends on each fleet's size, growth ambitions, operational complexity, and priorities in cost vs. control. Table 1 provides a snapshot of how the models compare on key metrics. In general, very small fleets (under ~10 trucks) often struggle with the fixed overhead of DIY and may benefit from either leasing on with a carrier or using an FSO to achieve scale advantages. Mid-sized fleets (20–100 trucks) can justify some in-house infrastructure but still may find value in FSOs to boost performance. Larger fleets (100+ trucks) tend to have the volume to efficiently insource with a TMS, though even they may outsource select functions to optimize costs. Each model has inherent pros and cons – this paper delineates those in detail and provides tools to guide an objective evaluation.

Methods at a Glance: This white paper's findings are based on a combination of industry research and modeling. We analyzed data from authoritative sources including the American Transportation Research Institute (ATRI) for cost and performance benchmarks, Federal Motor Carrier Safety Administration (FMCSA) and U.S. Department of Transportation reports for safety and demographic statistics, and industry analyses from the American Trucking Associations (ATA) and others. We also reviewed emerging fleet technology case studies (e.g. SmartHop, CloudTrucks) to gauge the impact of tech-enabled services. Where applicable, we cite recent data (2019–2024) to ensure relevance. We constructed a comparative cost model using 2021–2023 average figures (e.g. cost per mile, percent empty miles) to illustrate TCO implications by fleet size. The decision rubric and self-assessment tool were developed based on common criteria identified in fleet management literature and refined with input from fleet owners. All data sources and assumptions are documented in the Methods section and references provided so that analysis can be reproduced or adjusted to a particular fleet's profile. (examples for illustration, not endorsements).