Supply chain management has been at the forefront in the past few years. Shortages, delays, and bottlenecks during a global pandemic laid bare the complex interdependencies of many companies’ supply chains. The silver lining, however, has been the acceleration of the digital supply chain, which incorporates data touch points—such as weather patterns—and uses machine learning for better demand forecasting.
If you work in manufacturing or supply chain management, you likely have a ton of data and data points—but just because you have data doesn’t mean you can forecast properly. From internal data points on historical sales and customer information to third-party data like weather, demographics, and population, it can be hard to build analytical models that pull all this data together to give you an accurate view of what may happen.
That’s where Google BigQuery can help. By unlocking insights from your existing environment and pairing them with other data points, you can properly forecast demand, manage supplies and optimize production schedules. Plus, you can leverage machine learning and modernize your processes, giving you more opportunities to transform data to analyze trends and improve performance.
Google Cloud is becoming a key player in supply chain solutions, with the goal of making supply chains more resilient and sustainable. By processing and analyzing real-time events, businesses can predict and respond to risks—be they natural, financial, or operational.
With Google BigQuery, a serverless, multi-cloud data warehouse designed to turn big data into valuable business insights, supply chain professionals can anticipate issues, optimize demand and inventory, and innovate their logistics processes.
For example, demand forecasting models can improve supply chain planning, predicting how much inventory is needed before a major holiday. This can help organizations plan ahead, optimize resources, reduce costs, and even look for new opportunities, thanks to better predictability throughout the supply chain.
You can also better analyze and share your predictions with data visualizations in Looker Studio. Or take it a step further with BigQuery ML and deploy machine learning models using standard SQL. Google Cloud is also delivering new supply chain tools to the market based on its strength in cloud and logistics data. These include:
Supply chain data segments: Brings together three segments of data (private, community, and public) that enable users to model the supply chain holistically.
Supply Chain Twin: Provides ready-to-deploy connectors and transformation pipelines to bring data from ERP systems like SAP into the BigQuery data platform.
Supply Chain Pulse: A user engagement module for the Supply Chain Twin that offers visualizations into performance with drill-down capabilities of key operational metrics.
Implementing a new system, especially one as robust as BigQuery, requires a significant amount of time and expertise. In-house IT teams may already be working at maximum capacity to maintain the existing infrastructure. A partner can take the pressure off your in-house team, and their expertise ensures everything will be completed correctly and efficiently.
As a Google Cloud Partner, Pythian has deep expertise and experience in Cloud, infrastructure, big data, and advanced analytics. With Pythian’s guidance, companies can use BigQuery to better predict demand and fine-tune their production and supply chain management processes.
With our Enterprise Data Platform (EDP) QuickStart for Google Cloud, and many other advanced analytics services, including Google Cortex for SAP and Vertex AI, we can also integrate, clean, and organize data into Google BigQuery and quickly make datasets available for analysis. So if you need an efficient solution for storing and querying your data to get insights faster, our Google BigQuery services can get you there. From planning to implementation to 24/7 management, get in touch with a Pythian Google Cloud expert to see how our team can help.