The new logistics playbook for consumer and retail growth

From the January-February 2026 edition of Supply Chain Management Review.

January-February 2026

The January 2026 issue of Supply Chain Management Review explores how rapid advances in autonomous trucking, AI-driven optimization, and workforce development are redefining what it means to lead a modern supply…

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Logistics has evolved from a back-office function to a strategic lever for growth, profitability, and customer loyalty. The companies winning today treat supply chain performance as inseparable from commercial success, placing it on par with marketing or product innovation.

This shift coincides with a new wave of technology. Agentic AI has matured beyond experimental pilots and is now being embedded into everyday workflows. The following dynamics show where execution challenges are emerging most quickly and how operators are adapting.

1. Last-mile complexity is the new normal

Customer expectations are diverging into two extremes, with some shoppers prioritizing ultra-fast delivery and others seeking the lowest possible cost. This shift has accelerated the rise of “buy online, pick up in store” (BOPIS) and “buy online, return in store” (BORIS), as retailers increasingly rely on store networks as fulfillment nodes. As stores take on this role, networks designed for bulk shipments must now handle more frequent, smaller, and faster runs. These tighter delivery windows change how inventory moves and create a daily rhythm that is very different from traditional operations.

Store-based fulfillment also moved inventory closer to customers, which increases the demand for domestic transportation, staging space, and labor. The model tends to work well in dense urban markets where stores are near large customer bases. However, it becomes significantly more costly and complex in suburban and rural areas where distances are longer and fulfillment density is lower.

Relying on a single carrier is no longer viable. Leading retailers are now managing a mix of national, regional, and gig partners, each with different service level agreements (SLAs) and tracking capabilities, making visibility and performance management harder. Add tariffs into the mix, and logistics organizations are dealing with declining small-parcel imports while trying to scale bulk and store fulfillment simultaneously.

The new playbook

• Delivery and fulfillment strategy. Make reliability your edge by prioritizing consistency, reserving ultrafast delivery for high-conversion SKUs, and using a service-tier P&L to flag unprofitable options and enforce pricing.
• Store network operations. Define a clear role for each store location: fulfillment-first, experience-first, or build-to-order; then staff and equip each location according to its primary function, not a one-size-fits-all model.
• Technology and optimization. Deploy AI to optimize routing and batching in real-time, merging orders and re-sequencing deliveries as demand shifts. Use dynamic algorithms to adjust delivery windows and carrier selection based on live signals.
• Supply chain agility. Treat import lanes and node networks as flexible, not fixed, and adjust them as trade policies and tariffs evolve. Build the capability to rebalance between small-parcel, bulk, and store fulfillment channels quickly.

2. Working capital pressure is reshaping daily planning

The operational intensity reshaping last-mile logistics is also transforming working capital management. Higher interest rates have elevated carrying costs for every unit of inventory, making excess stock a direct hit to budgets and forcing capital velocity to drive daily decisions. Legacy planning systems struggle with large SKU counts, tariff volatility, and inconsistent lead times, resulting in inflated stock levels, poor visibility, and persistent margin pressure at distribution centers. To address this, operators are embedding intelligent agents into warehouse and transport management systems to accelerate product movement, reduce dwell time, and release cash tied up across facilities.

Traditional network designs optimized for lowest unit cost rather than inventory velocity, but each additional day of inventory now carries measurable cost. Tariff fluctuations trigger overbuying that locks up capital, while fragmented inventories across channels obscure waste and prevent efficient allocation.

Flow-based models offer a solution. By leveraging cross-docks, rapid-transit points, and synchronized transportation, these approaches minimize idle inventory and keep goods moving. Retailers using AI-supported flow planning have reduced safety stock by double-digit percentages and freed substantial working capital, demonstrating that velocity—not just volume—now defines competitive advantage in logistics.

The new playbook

• End-to-end visibility. Connect forecasting, transport, and warehouse data to spot rising inventory and shift it early, supported by simple dashboards showing turns, dwell time, and tied-up capital.
• Intelligent planning agents. Use AI to read demand and automate transfers while agents help supervisors right-size safety stock and reduce manual replanning.
• Dynamic policy design. Replace fixed rules with flexible guardrails and set clear thresholds for tightening or loosening safety stock to manage liquidity actively.
• Scenario-based decisioning. Bring finance and operations together to model shifts and use weekly scenarios to speed alignment so managers adjust plans quickly.

3. Personalization is pushing secondary manufacturing into logistics operations

Consumer expectations for personalized and seasonal variations—from Snapple variety packs to TikTok-inspired limited editions—are accelerating innovation cycles across beauty, beverage, snack, and household categories. Teams now support frequent launches and line extensions throughout the year, which has shifted secondary manufacturing from production lines into logistics sites. Displays, multipacks, and promotional packs require extra touches inside warehouses and cross-docks, while planogram resets have moved from annual to quarterly or even monthly schedules, increasing facility workload.

This shift creates steady pressure on logistics capacity by adding labor, interrupting flow, and consuming space not designed for light assembly. Centralized sites deliver consistency but extend lead times, while regional sites accelerate demand response at the cost of higher labor and overhead. The operational complexity compounds quickly: automation struggles to adapt to rapid SKU changes, quality checks create additional touchpoints, and faster SKU turnover expands working capital requirements as inventory moves through more steps. Together, these factors drive up the cost-to-serve across the network.

The new playbook

• Workload placement. Build a framework to identify products suited for regional secondary work, balance speed with site costs, and shift low-variability items upstream to reduce strain.
• Automation and AI support. Apply automation where SKU churn is manageable, use AI to sense demand and guide customization, and use vision and generative tools to accelerate pack and planogram design.
• Labor, flow, and inventory coordination. Use agent tools to manage spikes and labor, as better forecasting and coordination reduce idle stock and keep personalization efficient.

4. Retailer and supplier collaboration is becoming a daily operational requirement

Consumers expect constant product availability, requiring retailers and suppliers to operate from synchronized demand signals. Retailers monitor real-time point of sale (POS) trends, online traffic, and inventory levels, yet suppliers often receive this information with significant delays that result in missed sales and margin pressure. The consequences compound quickly, in the form of redundant storage, duplicated inventory, and misaligned transport plans, which in turn raise cost-to-serve and slow recovery when demand shifts. Data silos and inconsistent formats delay integration, forcing teams to maintain higher safety stock simply to protect service levels.

Misaligned incentives widen these operational gaps, making coordination difficult even when both parties recognize the problem. Emerging solutions are helping to bridge this divide. Data clean rooms enable secure information sharing without compromising competitive data, while digital twins create shared visibility across partners. Advanced modeling links inventory and transport planning, allowing suppliers and retailers to respond to demand changes in concert and systematically reduce buffer stock requirements.

The new playbook

• Joint scenario planning. Run planning sessions to test demand, supply, and transport shifts, then use insights to cut redundancy, refine safety stock, and align service.
• AI-supported reconciliation. Use agent tools to spot mismatches, propose reallocations, and shrink reconciliation cycles, enabling faster team adjustments.
• Simulation and testing. Use digital twins to test inventory pooling, consolidation, and routing so teams improve availability while reducing cost.

5. AI agents are becoming the operators behind daily retail execution

AI is moving from limited pilots to tools managing operational workflows at scale. Consumer shopping agents now influence demand patterns, while retailers deploy agents for customers, suppliers, employees, and developers, including platforms such as Walmart’s Sparky and Marty.

Agentic AI supports critical logistics tasks, including lane forecasting, carrier discovery, contract and rate management, bid evaluation, and freight audit. When these agents integrate ERP, TMS, procurement platforms, and market data, they close information gaps and accelerate decision cycles. Forecasting and inventory models must adapt to algorithm-driven demand signals, requiring tighter ERP-level connectivity with suppliers to maintain replenishment stability as automation scales. However, productivity gains depend on clean data and consistent change management across teams.

Early adopters demonstrate measurable impact. One global shipper automated $400 million in freight audits across 350,000 annual invoices, achieving a 93% first-time match rate. Another company managing $6 billion in freight spend improved charter air visibility by 35%, raised first-time match rates by 53%, and reduced leakage by more than 30%.

The new playbook

• System integration. Link suppliers at the ERP level to boost reliability and free capital, starting with high-volume or long-lead partners for fast ROI.
• Modular agent deployment. Deploy agents through knowledge graphs for sourcing and rate improvements, beginning with simple high-volume tasks and scaling as teams gain confidence.
• Governance and oversight. Let teams prioritize partner strategy and risk as agents manage execution within strict approvals, exception rules, and data-quality guardrails.
• Process and workflow design. Redesign roles and workflows so AI fits naturally into sourcing and planning, map manual touchpoints to guide adoption, and align performance metrics to reward effective collaboration.

Preparing for what comes next

Logistics teams are navigating converging pressures that require faster pivots, tighter coordination, and greater precision across daily operations. As trade policies shift, demand patterns evolve, and automation advances, competitive advantage will increasingly favor networks that can adapt with speed and consistency. This adaptability grows when logistics teams integrate earlier with commercial and product planning, since early alignment reduces reactive firefighting and creates more room for proactive decision-making.

At the same time, Agentic AI is beginning to shorten the path from issue identification to resolution, which gives operators clearer signals and a more stable foundation for daily execution. Organizations that view logistics as an evolving system, rather than a fixed set of assets, position themselves to respond more effectively to these pressures and to capture the benefits that come with a more flexible and resilient network.

About the authors

Korhan Acar is a Kearney partner who specializes in end-to-end logistics strategy and execution and can be reached at [email protected].

Steven Cunix is a Kearney partner who focuses on large-scale consumer and retail transformations and can be reached at [email protected].

Roy Kamar is a Kearney principal driving data and AI initiatives for consumer and retail clients and can be reached at [email protected].

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