Unit economics (how-to)
How to analyze the unit economics consequences of shifting fulfillment closer to customers geographically.
A practical guide to quantifying the financial impact of moving fulfillment nearer end customers, examining cost structures, margins, and risk shifts across inventory, transportation, and service levels.
July 21, 2025 - 3 min Read
When a business contemplates relocating fulfillment closer to customers, the first question is often about unit cost changes. Shifting geographies typically affects variable costs such as shipping, handling, and last-mile fees, while also impacting fixed costs tied to warehouse footprint, staff, and technology platforms. A clear model starts by isolating the unit economics: revenue per unit, plus or minus all direct costs, and then the contribution margin after variable costs. The analysis should also account for seasonality and demand dispersion, which influence space utilization and capacity planning. By building a scenario set that compares current operations with several proximity options, executives can observe how marginal costs evolve as fulfillment moves, and where scale benefits begin to appear.
A rigorous assessment requires translating strategic choices into measurable numbers. Begin with a baseline: current average order value, shipping cost per unit, handling time, and labor costs per fulfillment line. Then estimate the new costs when, for example, a regional fulfillment center handles a subset of orders. Accounting for inventory carrying costs, service level penalties, and potential safety stock adjustments is essential. Sensitivity analyses help reveal which factors drive profitability under proximity scenarios. The model should also tie to capital expenditure: real estate, automation, IT integrations, and whether savings on per-unit shipping offset new fixed costs. The goal is to understand net impact rather than isolated line items.
Map out the financial implications of proximity with disciplined, scenario-based framing.
Beyond pure cost accounting, the geographic shift changes cash flow dynamics. Reducing the distance to customers cuts lead times, which can decrease returns and improve customer satisfaction, potentially lifting repeat purchases. Shorter cycles may also lower the risk of stockouts in high-demand zones, because replenishment cycles align more closely with actual demand patterns. However, shorter cycles demand tighter inventory governance and faster information flows. The company must decide whether to push more inventory into regional centers or rely on a lighter, demand-driven replenishment. In either case, forecasting accuracy becomes more important, as misalignment between supply and demand erodes the anticipated margins of proximity.
A well-structured financial model should separate timing effects from permanent cost changes. In practice, this means distinguishing transient savings from durable reductions in unit cost structures. Timing matters: upfront investments in regional facilities may be amortized over years, but the benefits could emerge rapidly in quarterly results if shipping savings kick in quickly. Another critical aspect is transportation mode mix; shifting from consolidated, long-haul shipments to frequent, smaller local deliveries can alter carrier terms and service levels. The analysis should quantify these dynamics under different demand scenarios, including peak seasons and promotional periods, to capture resilience or vulnerability in margins.
Link capacity, demand, and economics to drive strategic decisions.
One essential metric is the landed cost per unit, which aggregates material, production, and logistics expenses to a single figure. Proximity often compresses this metric by reducing freight, expedited fees, and last-mile surcharges, while potentially adding handling and cross-docking costs. The trade-off arises when local fulfillment incurs higher fixed costs per square foot, employee benefits, and energy consumption. A robust approach is to model landed costs under multiple geographic configurations and then compare these with the baseline. The exercise should also include the effect of inventory carrying costs and obsolescence risk, especially for seasonal products with narrow windows of demand. The resulting insight highlights where proximity delivers the strongest margin improvements and where it merely shifts costs.
Complement the cost view with operating leverage considerations. As fulfillment becomes more localized, unit economics can improve from higher velocity and better fill rates, but this depends on demand concentration and the ability to utilize capacity efficiently. If a regional network operates near full utilization, small changes in demand can disproportionately affect margins. Conversely, underutilized facilities may erode savings. Consequently, firms should stress-test capacity scenarios, evaluating the impact of gradually increasing volumes in target regions. This exercise reveals the breakeven point for new facilities and helps decide whether a phased rollout or a single, scalable hub best preserves profitability.
Build a data-driven framework that isolates real profit impacts.
Another key angle is customer experience and lifetime value. Shorter delivery times can lift conversion rates and repeat purchase probability, indirectly boosting revenue. However, these gains must be weighed against the risk of service level failures if near-term demand surges outstrip localized capacity. Incorporating service level metrics into the economic model ensures that improved speed translates into financial returns rather than just satisfaction scores. The analysis should quantify how fast shipping correlates with customer retention across segments, and whether proximity strategies enable profitable upsell opportunities during the post-purchase window.
Technology enablers are critical when shifting fulfillment geographically. A unified order management system, real-time inventory visibility, and integrated transportation management are essential for accurate costing and timely decision-making. The model should allocate costs to specific channels and regions, so decision-makers can see which routes deliver the best margins. Digital dashboards that track live metrics—ship times, stockouts, and order accuracy—allow leadership to adjust capacity, pricing, and service levels swiftly. Without strong data governance, proximity yields questionable economics because inputs become noisy and decisions lag behind reality.
Practical steps to implement proximity-informed unit economics.
Supplier and carrier negotiations are also affected by geography. Localized fulfillment can enable closer collaboration with regional suppliers, possibly unlocking tiered pricing or favorable terms for smaller, faster replenishment. Conversely, maintaining multiple regional contracts introduces complexity and administrative costs. The economic model should capture these contractual nuances, including tier-based discounts, minimums, and service-level penalties. By translating supplier terms into per-unit cost adjustments within the proximity framework, the company gains a clearer view of where value is created and where friction erodes margins.
Finally, perform an investment-light test before committing to major expansion. A pilot in one or two markets can reveal real-world frictions—labor productivity gaps, IT integration challenges, and cross-border compliance if applicable. The pilot should measure not only cost savings but also changes in lead time, stock availability, and customer satisfaction. The insights from pilots help refine forecast assumptions, validate the economics, and shape the full-scale rollout plan. Treat the pilot as a learning loop that steadily improves the model’s accuracy and confidence in the decision.
To operationalize the insights, start by aligning executive incentives with the key economics of proximity. Ensure that the incentive structure rewards improvements in margin per unit, not just top-line growth or throughput. Then standardize a shared costing framework across regions so comparisons are apples-to-apples. This requires consistent definitions of variable costs, fixed costs, and allocated overhead. Also invest in scenario planning capabilities that dynamically adjust to demand changes, pricing shifts, and capacity constraints. The goal is to maintain a living model that evolves with geography, customer behavior, and supplier dynamics, enabling faster, more confident decision-making.
As a closing note, the decision to shift fulfillment geographically should rest on a clear link between proximity-driven changes in cost, lead time, and customer value. The unit economics approach must capture how marginal improvements compound into stronger profitability over multiple periods. The strength of the analysis lies in its transparency: it shows which cost levers move in which direction, under which conditions, and why. When executed with rigor, proximity becomes a strategic differentiator that aligns operations, economics, and customer satisfaction into a cohesive growth engine.
