Our proprietary techniques, use of commercially available platforms, and curated datasets are amplified by machine learning models and purpose-built AI agents to help clients make confident, data-driven decisions. Regardless of what is being analyzed– from benchmarking, cost modeling, or price analysis to freight or route optimization during acquisition due diligence, we take a thorough approach to ensure our outputs are accurate and actionable.
Pre-bid or post-LOI diligence requires on-demand availability, business model expertise, and concise communications on findings. If the focus is operational differentiation, IT/systems landscape competitiveness, or strategic integration, expertise in these areas (alongside other diligence partners) paired with AI-driven insights on performance, EBITA, and risk improves decisions making ability, increases confidence during close, and gives perspective on organizational needs. In addition to traditional deliverables and deciphering target-specific nuances, a strategic near-term roadmap helps the investor(s) understand immediate opex or capex investment requirements for what a near, mid and long term post-acquisition integration would look like.
While this should be a strategic decision made across supply chain organizational functions, freight terms (domestic) and incoterms (international) are usually determined without transportation, trade, and logistics’ perspective. Therefore, analyzing prepaid vs. collect or EXW vs. FOB vs. CIF terms with customers and suppliers is a high–value opportunity area regardless of data quality or availability. Both master data (e.g., items and supplier locations) and transactional data (e.g., ASNs, shipments, and warehouse appointments) can be built, re-built, or simulated quickly with AI agents to model landed cost alternatives, simulate risk transfer points against working capital, and evaluate compliance. Term conversion does not stop at the analysis – it is a program which requires investment in people and systems, justifying multi-year maintenance and continuous ROI.
Pricing & Cost modeling requires operational understanding, unique skills, and analysis techniques to tie together the core variable cost components of any operation to its related revenue model. When building these types of models, it is important to stress test the outputs to ensure that the model is reasonable both at scale and at go-live. AI-enabled capabilities are a key differentiator in this type of analysis. Generate synthetic peer baselines using anonymized/modeling datasets, simulate ramp-up curves and volume volatility, and detect unrealistic margin assumptions quickly with the support of AI. Cost models can also be leveraged to compare operating efficiency to that of your peers when benchmarking data is insufficient or unavailable.
Evaluation of software or managed services is both analytical and strategic. It is analytical in that evaluation criteria are best summarized using quantifiable metrics and indicators based on the potential partners’ ability to meet stated requirements. However, it is also strategic in that there is no mathematical formula to deploy to ensure a perfect partner selection. The approach merges the two disciplines to provide detailed, AI-driven insights in support of higher-level decision making around TCO and technical viability, while aligning the various internal stakeholder orgs. and documenting new and existing processes. The result for organizations is a disciplined evaluation process that supports confident decision-making while recognizing that partner selection is both and strategic.
Street-level route modeling identifies optimal solutions against constraints and maximization/minimization variables. While this can be analyzed as part of a broader strategic project, discrete project types are scoped to help determine fleet sizing, static vs. dynamic routing changes, static routing refresh events, private to dedicated conversions, AI integration to simulate demand variability and detect service degradation, dedicated contract carriage sourcing support, customer DC re-assignment impacts, and many more. But routing exercises are not just about moving orders from one route to the next or creating less or more routes, they have implications for order cut-off times, delivery windows, distribution center waving or terminal loading operations, and software/system utilization.
Purchased Transportation Modeling leverages commercially available optimization software using historical order or shipment data with market-available data to create potential transportation plans which consider opportunities for shifts in modal decisions, service-levels, order consolidation, shipment consolidation, and more. Historical transportation decisions are static – inbound paper-based route guides with weight-breaks by region or outbound orders following ERP-determined ship methods, however modern software and simple integrations exist to support best-practice processes to meet dynamic (and evolving) demands. Enhancements in AI capabilities around scenario simulations, predictive rate forecasting and service sensitivity modeling can enrich the off-the-shelf software offerings further when integrated. When approaching modeling it is important to consider your business’ goals, whether they be to drive down cost or focus on service, as well as constraints to help you create actionable, data-backed plans.