Supply Chain and Logistics Forecasting with TSFMs

Supply chain forecasting sits at the intersection of some of the hardest problems in time series prediction. A global manufacturer might need to forecast demand for 100,000 SKUs flowing through dozens of warehouses, ports, and distribution centers, each with different lead times, capacity constraints, and demand patterns. Traditional statistical methods struggle with this complexity. Time series foundation models bring pretrained temporal reasoning that generalizes across the heterogeneous signals found in modern supply chains.

Why Supply Chain Forecasting Is So Challenging

Several structural characteristics distinguish supply chain forecasting from simpler univariate prediction tasks.

The bullwhip effect amplifies demand variability as signals propagate upstream through the supply chain. A 5% increase in consumer purchases can translate into a 40% spike in orders at the component supplier level. Each echelon adds its own safety stock buffers and reorder logic, distorting the true demand signal. Forecasting models must account for this amplification or risk perpetuating it.

Intermittent and lumpy demand dominates industrial supply chains. Spare parts, specialized components, and B2B orders often arrive in irregular bursts separated by long stretches of zero demand. A warehouse might see zero orders for a hydraulic valve assembly for three weeks, then receive a bulk order for 200 units. Standard smoothing models fail on these patterns, a challenge retailers face as well with long-tail SKUs.

Long and variable lead times compound forecast uncertainty. When ocean freight from Asia to North America takes 25 to 45 days depending on port congestion and routing, the effective forecast horizon stretches accordingly. Planners need accurate predictions not for next week, but for six to eight weeks out, where traditional model accuracy degrades sharply.

Multi-echelon inventory means that forecasts at one level of the network must be consistent with forecasts at every other level. Store-level demand must reconcile with regional distribution center throughput, which must reconcile with national import volumes. Hierarchical reconciliation, rolling up from store to region to national level, is essential for coherent planning.

Key Signals Across the Logistics Network

Effective supply chain forecasting draws on a richer set of time series than point-of-sale data alone. Order volumes and shipment quantities provide the primary demand signal. Container throughput at ports indicates upstream capacity and congestion. Warehouse fill rates and dock-to-stock cycle times reflect operational efficiency. Port dwell times, the duration containers sit before being picked up, serve as leading indicators of downstream delays. Supplier lead times measured from purchase order to goods receipt capture procurement variability.

These signals benefit from covariate-aware models. Promotional calendars, holiday schedules, weather disruptions, and port delay reports all influence demand timing and magnitude. TSFMs that accept exogenous covariates can incorporate these factors without requiring hand-engineered feature pipelines for each signal type.

How TSFMs Address These Challenges

Zero-shot generalization is the single most impactful capability for supply chain applications. A logistics operator managing 50,000 SKU-location combinations cannot afford to train and maintain individual models for each. A pretrained TSFM generates reasonable forecasts from raw historical context alone, with no per-series training. Models like Moirai, trained on the LOTSA dataset that includes logistics and transportation series, bring particularly relevant pretrained knowledge to this domain.

Prediction intervals are arguably more valuable than point forecasts in supply chain planning. Safety stock calculations depend on understanding the upper tail of the demand distribution. If the 95th percentile of forecast demand over lead time is 1,200 units, then the reorder point is set to cover that scenario. Point forecasts alone force planners to apply crude multipliers to derive safety stock, often resulting in either excess inventory or stockouts. Probabilistic TSFMs output the full forecast distribution, enabling mathematically grounded service level targeting.

Multivariate forecasting captures cross-product dynamics that univariate models miss entirely. When a manufacturer promotes Product A, demand for substitute Product B may drop, while complementary Product C sees a lift. Cannibalization and substitution effects are pervasive in supply chains with overlapping product portfolios. Multivariate TSFMs that jointly forecast related series can capture these dependencies without explicit causal modeling.

The Cold-Start Problem in Logistics

New product launches, new trade routes, and new warehouse locations all present the cold-start problem. A freight forwarder opening a new lane between Rotterdam and Houston has zero shipment history for that corridor. Traditional models cannot produce a forecast. Zero-shot TSFMs can ingest even a few weeks of early data and generate actionable volume projections by transferring learned patterns from similar routes in their pretraining corpus. This mirrors the cold-start advantages seen in retail, but applied to logistics network expansion rather than product assortment.

Real-World Impact

The practical benefits compound across the supply chain. More accurate demand forecasts reduce safety stock requirements, directly lowering carrying costs that typically run 20-30% of inventory value annually. Better lead time predictions enable tighter replenishment cycles, reducing both stockouts and overstock. Probabilistic forecasts allow differentiated service level policies: 99% fill rate for critical A-items, 90% for slow-moving C-items, optimizing the trade-off between service and cost.

Benchmarks from supply chain datasets, including the M5 competition on Walmart's retail supply chain data, demonstrate that foundation model approaches achieve accuracy competitive with heavily engineered competition solutions while requiring a fraction of the development and maintenance effort.

Getting Started

Supply chain teams can begin by targeting a single forecasting use case, such as weekly SKU-level demand at a distribution center, and comparing TSFM zero-shot accuracy against their existing baseline. TSFM.ai's forecast API accepts historical context and returns probabilistic forecasts in a single call, making integration straightforward. For teams ready to incorporate promotional calendars, weather data, or port delay signals, the API supports covariate inputs natively. Explore available models on the model catalog or read more about building production forecast pipelines to plan your deployment.