Transforming Supply Chain Forecasting with TimeGPT

Demand Forecasting in Supply Chain Management

Demand forecasting is a critical function in supply chain management, informing production planning, inventory positioning, workforce scheduling, and capital allocation. Accurate and timely forecasts align procurement, manufacturing, logistics, merchandising, and sales; they reduce stockouts, raise fill rates, curb expedites, and lower waste in perishables and slow movers, improving margins and sustainability.

Financially, better forecast quality enables lower safety stocks for a given service target, freeing working capital and strengthening overall performance.

Supply chains of global enterprises span multiple levels with thousands of SKUs, channels, and locations. Multiple forecasts are required across horizons and granularities: long-horizon, coarse-grained projections guide budgets, capacity, and manufacturing strategy, while short-horizon, fine-grained forecasts by SKU and location drive distribution, replenishment, and labor scheduling. In this context, demand forecasting is not a one-time pipeline but a system that supports decisions at every level of the organization.

Current Practice and Common Pain Points

Through our open source and enterprise products we have supported hundreds of organizations across industries, sizes, and regions in building demand forecasting pipelines. Most maintain dedicated teams of engineers and data scientists who design, implement, and operate these systems.

In most organizations, each use case is addressed with a separate pipeline that follows a similar lifecycle: data collection from ERP, POS, and planning systems; cleaning, alignment, and calendarization; pre-processing; model selection; training and hyperparameter tuning; backtesting and benchmarking; deployment to batch or streaming inference; and ongoing monitoring with periodic retraining. For demand forecasting, practitioners utilize general-purpose models that span a range from classical statistics to machine learning and deep learning.

We have identified the following common pain points:

1) Model obsolescence and talent constraints

Many organizations, particularly non-technical enterprises and public agencies, rely on outdated models. Developing and maintaining state-of-the-art models requires scarce expertise, sustained experimentation, and careful MLOps practices, which are often unavailable or underfunded.

2) Diminishing accuracy returns

Initial accuracy gains are attainable with traditional methods. Subsequent improvement typically demands domain-specific feature engineering, extensive hyperparameter search, and data-specific tuning. The marginal benefit declines as effort and complexity rise.

3) High maintenance and compute costs

Traditional pipelines depend on frequent expensive retraining, handcrafted features, and model ensembles to address heterogeneous demand patterns. These practices increase infrastructure spend, prolong release cycles, and produce large codebases that are costly to maintain.

4) Siloed and ad hoc solutions

Enterprises face multiple use cases across levels, regions, and horizons. Problems are often addressed one at a time, or not at all, yielding inconsistent methodologies, duplicated work, and technical debt. Important applications can remain on simple baselines or are left unmodeled.

5) Inherent data and structure challenges

Intermittent sales, holiday spikes, promotional events, hierarchical coherence, all complicate learning. Traditional models often require manual interventions to handle missing data, calendar effects, and new product introductions. Achieving coherent and accurate forecasts across hierarchies remains a persistent challenge.

Our Product: TimeGPT

At the core of our enterprise solution is TimeGPT, the first published pretrained foundation model for time series. TimeGPT relies on a proprietary transformer-based architecture designed to model time series, trained on a massive and diverse corpus of temporal data. TimeGPT produces point forecasts and calibrated prediction intervals, incorporates exogenous variables, and can be finetuned at different layers.

TimeGPT “bypasses” the traditional cost versus accuracy tradeoff. In classical pipelines, more sophisticated models usually have a higher accuracy ceiling but require greater expertise and compute, and are harder to tune. Teams spend significant time on feature engineering, hyperparameter search, architecture selection, and retraining schedules, often maintaining ensembles to handle heterogeneous patterns. This raises infrastructure spend and lengthens iteration cycles. TimeGPT delivers strong accuracy with minimal tuning, which lowers engineering effort and compute while providing consistent performance across use cases and scales.

TimeGPT is accessible through our public SDK in Python and R, the most commonly used languages among practitioners. With a few lines of code, teams can produce forecasts, fine tune on their data, and run backtesting for evaluation. Inputs and outputs mirror our open source ecosystem to facilitate a smooth transition from existing workflows.

TimeGPT is available as a packaged, fully self hosted solution that keeps data within the customer's environment. It is compatible with all major cloud providers and can also run on local machines. Installation is a single command that handles dependencies and automatically detects available hardware.

Value Proposition

Our enterprise solution modernizes forecasting pipelines with state-of-the-art methods, delivering higher quality outputs with only a few lines of code. Customers report measurable gains across accuracy, efficiency, and time to value, increasing throughput while maintaining or improving service levels to stakeholders.

Forecasting Outcomes

• Accuracy: Higher average accuracy, up to 50% over baselines and popular models.
• Ease of use: Install, fine tune, and run inference in a few lines of code. Codebases have been reduced by as much as 95%.
• Lower compute: Zero shot capabilities cut computational cost by up to 80%. Clients have reduced runtime from 6 hours to 5 minutes for thousands of series.
• Time to value: Teams have delivered new use cases from installation to production in days rather than months, reducing time to value by about 85%. Solutions are reusable across use cases.

Trusted by Industry Leaders

Organizations across industries trust TimeGPT to power their supply chain forecasting operations.