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Demand Sensing Techniques: 5 Essential Google Cloud Tools to Make Your Life Easier

June 12, 2024 | Premangsu Bhattacharya

Blog / Demand Sensing Techniques: 5 Essential Google Cloud Tools to Make Your Life Easier

Google predicts 2024 will be the year of data modernization. Earlier this year, a report from IBM highlighted a growing trend: big companies are not just experimenting with AI—they’re weaving it into the very fabric of their operations. This shift makes having a flexible data platform more crucial than ever.

In the supply chain world, many companies start their AI journey with demand sensing. Google Cloud, armed with deep insights into consumer behavior, plays a pivotal role in this innovation. In this blog, we will explore some of the most popular tools for demand sensing on Google Cloud and how a unified data platform can enhance its effectiveness.

1. Machine Learning Forecasting with Vertex AI

Vertex AI is Google’s powerful AI platform, designed to build, deploy, and scale machine learning models. When it comes to demand sensing, Vertex AI allows you to utilize pre-built models or develop custom ones tailored to your needs.

Key Features:

  • AutoML Tables: Automatically selects the best model based on your data.
  • Custom Models: Provides flexibility to create and train models specific to your business requirements.

Imagine a fashion retailer trying to prepare for the holiday season. By integrating their sales data, social media trends, and weather forecasts into Vertex AI through Pluto7’s Planning in a Box, they can generate highly accurate demand forecasts. This integration allows them to predict which items will be hot sellers, adjust inventory levels in real-time, and ensure they have enough stock to meet the demand without overstocking.

2. Probabilistic Forecasting with Vertex AI

Vertex AI also offers probabilistic forecasting, which goes beyond predicting a single outcome by providing a range of possible future scenarios. This approach is invaluable for managing uncertainty in demand forecasting.

Key Features:

  • Quantile Regression: Helps understand the range of possible demand levels.
  • Risk Management: Assists in planning for various scenarios by providing a spectrum of potential outcomes.

Consider a retail company launching a new product. They can use probabilistic forecasting to predict different sales outcomes based on factors like marketing spend, competitor actions, and economic conditions. Planning in a Box helps integrate these varied datasets, providing a comprehensive view of potential outcomes. This enables the company to create multiple inventory and production plans, mitigating risks associated with unexpected demand spikes or drops.

3. BigQuery for Data Analysis

Overview: BigQuery is Google Cloud’s fully-managed, serverless data warehouse. It allows you to store and analyze large datasets, forming the backbone for many demand sensing activities.

Key Features:

  • Scalability: Efficiently handles large volumes of data.
  • Integration: Seamlessly connects with other Google Cloud services for comprehensive analysis.

With BigQuery and Planning in a Box, a company can centralize its sales, marketing, and supply chain data. For example, a consumer goods manufacturer can analyze data from multiple regions to identify demand patterns and trends. By integrating this data into BigQuery through Planning in a Box, the manufacturer gains a unified view, enabling them to adjust production schedules and distribution plans proactively.

4. Cortex Demand Sensing 

Cortex Demand Sensing combines BigQuery, Vertex AI, and Looker. It simplifies demand sensing, especially for companies using SAP ERP, by integrating SAP data with external sources like Google Trends.

Key Features:

  • SAP Integration: Direct integration with SAP ERP systems.
  • External Data Sources: Utilizes data from various sources for a holistic analysis.

Cortex Demand Sensing, when paired with Planning in a Box, enhances the ability to generate actionable demand forecasts. A retailer using SAP ERP can integrate in-store sales data with online search trends and social media activity. This holistic view, facilitated by Planning in a Box, allows the retailer to predict product demand accurately, optimizing inventory levels and reducing excess stock.

5. Looker for Visualization

Overview: Looker is a business intelligence tool that creates interactive dashboards and reports. It helps visualize demand forecasts and identify trends, making data-driven decision-making more accessible.

Key Features:

  • Interactive Dashboards: Offers real-time insights through customizable dashboards.
  • Data Exploration: Enables deep dives into data for thorough analysis.

Looker, integrated with Planning in a Box, brings data to life. For example, a logistics company can use Looker to visualize shipping data, customer orders, and delivery times on interactive dashboards. By integrating these datasets through Planning in a Box, the company can quickly identify bottlenecks and optimize routes, ensuring timely deliveries and satisfied customers.

Think of demand sensing as the nervous system of your supply chain, constantly receiving and responding to signals from the market. With Google Cloud and Pluto7, you can enhance this system, ensuring your business stays agile and responsive. Join us in building a smarter, more connected supply chain. 


Premangsu B, is a digital marketer with a knack for crafting engaging B2B content. His writings are focused on data analytics, marketing, emerging tech, and cloud computing. Driven by his passion for storytelling, he consistently simplifies complex topics for his readers, creating narratives that resonate with diverse audiences.

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