How Vertex AI Optimizes Inventory Management and Demand Forecasting

Consumer demand is more unpredictable than ever.

Seasonal spikes, promotional campaigns, or viral social media trends can cause sudden surges or drops in demand, while these consumers often expect fast, seamless, even same-day or next-day delivery. If a product isn’t available where and when they want it, they’ll quickly turn to a competitor.

These put a chain of stakeholders—namely retailers, manufacturers, and logistics providers—under constant pressure to deliver the right product, at the right time. They have to be lean, precise, and cost-effective while still meeting service-level expectations.

Yet, stockouts, overstocking, and missed forecasts continue to plague operations—driving up costs and eroding customer satisfaction. In fact, retailers alone lose over a trillion dollars a year due to mismanaged inventory, highlighting the urgent need for AI-powered demand planning. Studies show that even a 10–20% improvement in forecast accuracy can reduce inventory costs by 5% and increase revenues by 2–3%.

Vertex AI, Google Cloud's unified machine learning (ML) platform, offers a powerful suite of tools to transform how businesses tackle these critical challenges. For companies seeking production-ready AI expertise, partners like Bitstrapped specialize in bridging the gap between cutting-edge technology and measurable business outcomes.

Navigating Consumer Demand with Vertex AI

Vertex AI empowers businesses to move beyond static rules-based inventory systems by applying advanced machine learning techniques to large-scale, real-time data. Organizations can ingest both structured and unstructured data—including point-of-sale (POS) transactions, supplier lead times, customer demographics, e-commerce signals, and external factors like weather—into scalable ML pipelines. By analyzing this diverse set of inputs, Vertex AI builds predictive models that proactively optimize inventory levels and enhance supply chain agility.

One of the standout capabilities of Vertex AI is its dual support for custom model development and AutoML workflows. With AutoML Forecast, teams can automate data preprocessing, feature engineering, and model selection using techniques such as Neural Architecture Search (NAS). This lowers the barrier to entry for teams without deep ML expertise. Meanwhile, advanced users can develop and deploy tailored models using frameworks like TensorFlow, PyTorch, and XGBoost—ensuring adaptability to specific business requirements.

Vertex AI also includes Vertex AI Search for Commerce, which enhances digital retail experiences by offering integrated product search, recommendation, and browse capabilities. Businesses can upload catalog data and user interaction logs to train and refine models that power smarter, more personalized customer interactions across web and mobile platforms.

Perhaps most importantly, Vertex AI excels at modeling complex, high-dimensional variables and delivering real-time insights. This makes it particularly valuable in volatile markets where sudden changes in demand, shipping delays, or promotional campaigns can dramatically impact inventory needs. With Vertex AI, businesses gain the flexibility to adjust their inventory strategies dynamically, reduce risks, and capture new growth opportunities.

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Demand Forecasting with Vertex AI: Predicting the Unpredictables

Vertex AI Forecast provides an end-to-end framework to tackle this, combining scalable infrastructure with state-of-the-art ML architectures to build reliable, production-grade forecasts.

At its core, Vertex AI Forecast enables users to incorporate a wide range of demand signals—from historical sales and seasonal trends to macroeconomic indicators, competitor actions, local events, and unstructured sources like weather and customer reviews. This diversity of inputs helps businesses capture complex demand drivers in their forecasts.

Among its most advanced capabilities is hierarchical forecasting, which allows businesses to model demand across multiple levels—SKU, store, regional, and global—simultaneously. This granularity enables optimized inventory allocation and replenishment tailored to each location and product.

Vertex AI supports several leading forecasting architectures:

• Time Series Dense Encoder (TiDE): A DNN-based encoder-decoder optimized for high-throughput training and rapid retraining cycles. Well-suited for real-time, scalable retail use cases.
• Temporal Fusion Transformer (TFT): An attention-based model that provides both high accuracy and interpretability. Users can visualize feature importance, making it easier to trust and act on model outputs.
• AutoML Forecasting: A low-code/no-code solution that empowers non-technical users to generate robust models with minimal effort, powered by automated data preprocessing, feature engineering, and hyperparameter tuning.

All models leverage probabilistic forecasting to generate prediction intervals—giving decision-makers a range of likely outcomes rather than a single point estimate. This allows teams to plan for uncertainty and reduce risks associated with under- or over-estimating demand.

Vertex AI employs a variety of algorithms and techniques for inventory management and demand forecasting:

• Time Series Dense Encoder (TiDE): This optimized dense DNN-based encoder-decoder model provides fast training times and competitive accuracy for time series forecasting.
• Temporal Fusion Transformer: This attention-based DNN model is designed to produce high accuracy and interpretability for demand forecasting.
• AutoML: This no-code and web UI-based offering empowers users of all skill levels to quickly train accurate and sophisticated forecasting models.
• Seq2Seq+: This algorithm is likely to converge faster than AutoML due to its simpler architecture and smaller search space.

Vertex AI also utilizes rolling window strategies for generating forecast windows, allowing businesses to adjust the context window and forecast horizon to optimize their predictions.

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Workflow for Creating a Forecast Model

Vertex AI supports an MLOps-based approach to model development. The forecast model creation workflow includes:

1. Prepare tabular training data for forecast models: This involves gathering and preparing the necessary data for training the model.
2. Create a dataset for training forecast models: A new dataset is created in Vertex AI, and the prepared training data is associated with it.
3. Train a forecast model: Using the created dataset, a forecast model is trained in Vertex AI.
4. Evaluate your model: The newly trained forecast model is evaluated for prediction accuracy using various metrics.
5. Get predictions for a forecast model: Batch predictions are requested from the forecast model to generate forecasts.

All of this is orchestrated using Vertex AI Pipelines, which offer reproducibility, lineage tracking, and CI/CD integration for ML workflows, enabling businesses to quickly leverage the power of AI for improved decision-making.

Google Cloud Customer Success Stories

The effectiveness of Vertex AI in optimizing inventory management and demand forecasting is evident in its real-world applications. Groupe Casino, a leading French retailer, has successfully used Vertex AI for demand forecasting across its vast network of over 450 hypermarkets. By developing highly accurate, location and product-specific forecasting models, Groupe Casino achieved a 30% improvement in forecast accuracy and a 4x reduction in model training and experimentation time. Moreover, Groupe Casino was able to reduce perishable goods wastage by optimizing inventory planning with Vertex AI forecasting. This resulted in increased revenue and an improved customer shopping experience due to a reduction in missing products.

The Home Depot, a major home improvement retailer, provides another compelling example of Vertex AI's impact. The company has implemented an application called Sidekick, which leverages Vertex AI to help store associates manage inventory and keep shelves stocked. This application uses machine learning and computer vision to analyze customer behavior and improve the efficiency of online product searches, resulting in higher conversion rates and more relevant search results for customers.

Similarly, Lowe's, a major home improvement retailer, has leveraged Vertex AI Forecast to create accurate hierarchical models that balance SKU-level and store-level forecasts. These models take into account various factors, including store-level inventory, SKU-level inventory, region-level inventory, promotions data, and multiple other signals, resulting in more accurate forecasts and improved inventory allocation.

Benefits and Challenges of Using Vertex AI

Vertex AI offers numerous benefits for businesses looking to optimize their inventory management and demand forecasting processes:

Key Business Benefits

Improved Forecast Accuracy through Advanced Machine Learning
Vertex AI employs cutting-edge modeling techniques—including time series forecasting and deep learning architectures—that allow businesses to capture and respond to complex demand signals. By incorporating a range of inputs such as pricing, promotional activity, macroeconomic indicators, and seasonality, the platform supports multivariate forecasting that reduces prediction error and enables just-in-time inventory strategies. This results in tighter alignment between supply and demand and reduces the need for costly safety stock.

Reduced Costs Through Inventory Optimization
Enhanced forecast precision translates directly into cost savings. Vertex AI enables businesses to minimize overstocking, reduce storage and spoilage costs, and prevent revenue loss from stockouts. With global retailers losing over $1 trillion annually due to inventory mismanagement, the case for AI-driven optimization is both urgent and economically significant.

Operational Efficiency via Automation
Vertex AI automates key components of the machine learning lifecycle, including model training, evaluation, deployment, and ongoing performance monitoring. This streamlines internal operations and frees data science and supply chain teams to focus on strategic innovation rather than manual forecasting tasks. By embedding agility into forecasting workflows, organizations are better equipped to respond to real-time demand shifts.

Enhanced Customer Experience
Availability of the right product at the right time is foundational to customer satisfaction. Vertex AI empowers organizations to proactively manage inventory, reduce the incidence of stockouts, and deliver superior fulfillment outcomes. These operational improvements have downstream effects on Net Promoter Scores (NPS), customer retention, and brand loyalty.

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Challenges & Considerations

Data Quality and Infrastructure Readiness
The effectiveness of any AI model hinges on the quality and completeness of the data it is trained on. Vertex AI relies on well-structured, high-frequency historical and real-time data inputs. Organizations must invest in the foundational data infrastructure—leveraging tools like BigQuery, Dataflow, and Pub/Sub—to ensure data pipelines are robust, scalable, and production-ready.

Talent and Capability Gaps
Despite the abstraction of many technical complexities, deploying Vertex AI at scale still demands proficiency in model tuning, feature engineering, and machine learning operations (MLOps). This often necessitates partnerships with firms like Bitstrapped, which specialize in building enterprise-grade AI systems and embedding them within existing operational frameworks.

Systems Integration Complexity
To generate business value, AI insights must be tightly integrated into existing ERP, warehouse management, and order fulfillment systems. This level of integration typically requires API development, orchestration logic, and cross-functional coordination—a nontrivial endeavor that demands both technical acumen and strategic foresight.

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‍Integration with Other GCP Services

If you're already using other Google Cloud Platform services, or considering using them, it is good to know that Vertex AI can integrate seamlessly with other GCP services, enhancing its capabilities for inventory management and demand forecasting. These integrations streamline data management and analysis, making it easier for businesses to build and deploy effective AI solutions. Here are some of the examples:

• BigQuery: Vertex AI can directly ingest data from BigQuery, making it easy to utilize large datasets for training and analysis.
• Cloud Storage: Vertex AI can store and access data from Cloud Storage, providing a scalable and reliable storage solution.
• Cloud SQL: Vertex AI can connect to Cloud SQL databases, enabling businesses to leverage their existing data for inventory management and demand forecasting.

Vertex AI serves as strategic enabler for data-driven operations. For enterprises facing growing pressure to respond to volatile demand, supply chain disruptions, and rising customer expectations, it offers a scalable and intelligent framework to make faster, more precise decisions. By embedding AI into the core of inventory and demand planning processes, businesses can shift from reactive management to predictive orchestration—unlocking improvements in efficiency, responsiveness, and margin control.

The broader implications of AI in the retail and supply chain industries are significant. Vertex AI exemplifies how cloud-native, enterprise-grade platforms are redefining the speed and sophistication with which companies operate. As the technology matures and integration patterns become more standardized, its role in powering real-time, autonomous supply chains will only expand. Forward-looking organizations that invest today in AI readiness—data infrastructure, talent, and strategic partnerships—will be better positioned not only to navigate uncertainty, but to outpace it.

For businesses aiming to optimize inventory management and demand forecasting with advanced AI tools like Vertex AI, partnering with a trusted expert can make all the difference. Bitstrapped specializes in helping organizations develop and implement AI strategies tailored to their unique needs.

Interested in discussing how AI can transform your business? Book a complimentary consultation with our CTO to explore your AI roadmap and discover actionable insights tailored to your organization’s goals.