Every successful initiative begins with a strategic plan grounded in hard-dollar returns and measurable service improvements. Our approach challenges conventional thinking by combining our operational expertise with advanced analytics, machine learning, and AI-driven insights to uncover untapped opportunities. Rather than relying solely on traditional analysis, we leverage predictive modeling, pattern recognition, and scenario simulation to quantify impact and de-risk decisions before implementation roadmap creation. We are passionate about getting to the best answers, whether that is a re–designed network, reworked software application, a new service line, an updated data strategy, or transformation.
Taking an internal center of excellence to market? Driving into a completely new space? Leveraging existing assets and systems in a new way? New Service lines launch – for Logistics Service Providers and Shippers – is a daunting task. The most successful launches align process, enabling systems/integration, and a go-to-market strategy with ideal customer profiles, voice of the customer analysis, and market growth AI agents to generate the specific business case for the offering. That focus drives revenue opportunities and profitability goals that meet the expectations of shareholders, stakeholders, and service capabilities that meet new requirements for customers, vendors, and future operators.
Supply chain organizations and logistics service providers typically think of change management as a project-based activity but it’s so much more. Implementing change is a broader initiative to drive improvements across processes, technology, structure and strategy. While change impacts are typically discussed related to IT transformations, they should be considered a strategic capability and a competency of front-line hourly employees up to senior level executives. Change capabilities are built and enabled by internal dedicated resources, technologies and processes, both within human capital departments and throughout other enterprise divisions and geographies. To be clear, change capabilities are not just for the Fortune 500. Many small to medium-sized companies can build this capability, as well. Creating this capability efficiently starts with the right approach and partner to take you from current to future state, while supporting individuals, teams and your organization all along the way.
Determining where facilities should be located is considered one of the most strategic decisions due to the impact on cost, service, and risk in a supply chain. Optimizing product flow, through existing and/or greenfield nodes, considers the broadest set of variables such as local labor, real estate, inventory, service levels, risk tolerance, and transportation costs. To enhance this analysis, we leverage inventory placement and product flow AI agents that model demand variability, simulate pooling effects, and evaluate alternative routing structures at scale. Our people and agents identify non-obvious tradeoffs, surface sensitivity drivers, and stress-test configurations against shifting demand and disruption scenarios. With the variable set this broad, the analysis is performed at a higher level than the more targeted strategy project approaches described below. The goal is to align the business strategy with the modeled outcomes, but not try to be too precise.
Sourcing can be approached as a standalone initiative or as a core capability, depending on an organization’s strategy and market position. Whether a company is resource-constrained, time-limited, data-poor, or lacking the tools to execute a formal sourcing event, delaying action can mean missing meaningful cost opportunities. Our first step is to validate that a true opportunity exists — then quantify savings, risk exposure, and performance gaps with AI enabled risk assessment and performance pattern recognition—before issuing notifications to potential suppliers. This helps maintain credibility in the market, not just for this event, but for the long-term as well. If the goal is to develop a center of excellence around sourcing and offer it to yourself or your customers, the methodology accounts for the organizational implications, AI-driven monitoring and adoption when building out the internal capability.
Document what you do today, determining how you want to do it, and then calculate the payback. That sounds simple. Unfortunately, transportation is one of the most managed aspects of a supply chain, even when there is a strategy in place. Our approach considers properly defining current state, designing best-practice future state process and information flow with AI agent support, and evaluating total-cost-of-ownership and time-to-value. Allocating focus to these areas will allow for an achievable roll-out plan with a clear understanding of organizational impact that is backed by AI-driven insights.
Operational pain is often visible in processes, but the root cause typically lies in how data flows across systems and trading partners. Are we fully leveraging the tools we already have? Are system gaps creating unnecessary manual work? Is our integration architecture scalable enough to support growth? Are we leveraging automation or have we attempted implementing agentic AI appropriately? A modern systems strategy goes beyond execution to enable intelligent orchestration. By aligning core platforms with integrated data models and AI enabled insights, organizations move from reactive operations to predictive, data-informed decision-making. The goal is not just automation, but smarter automation. Additionally, system landscape design supports pre/post-acquisition/divestiture activity as new entities are considered for their ability to plug in to a modern technology ecosystem and to extract itself from the legacy environments.
When companies struggle to access timely, reliable business metrics, their ability to make informed decisions suffers. A modern data strategy begins with a clear understanding of the business strategy, decision-making frameworks, and priority use cases defined by operational leaders. From there, we classify, normalize, and map master, reference, and transactional data, which establishes clear systems of record and identifies all downstream consumers. But in today’s environment, data architecture must do more than support reporting; it must enable advanced analytics, machine learning, and AI-driven decision support. That means structuring data so it can power predictive models, automate pattern recognition, and support emerging use cases such as large language model (LLM)-based insights and natural language querying. A critical design component is the integration strategy, driving key functionality into governed master data objects, enriching and transforming data as it moves across systems, and modernizing legacy environments to create an analytics-ready ecosystem. When done effectively, this foundation not only improves visibility but unlocks the ability to move from reactive reporting to predictive and prescriptive intelligence.
A Facility Design project defines the product flow requirements and model’s various layout options to maximize/minimize key variables like storage locations, inventory holding capacity, picks per hour, etc. We utilize AI enable capabilities such as discrete event simulation (digital twinning), labor and productivity models, and automation/robotics ROI evaluations to suggest the optimal time-to-value layout. Facilities can be modeled even without a specific site identified, which can drive the real estate search based on specific design requirements such as dock/rolled doors, racking, automation, etc.