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Why SAP Users Should Consider BigQuery for Supply Chain Data Analytics

December 7, 2023 | Asheesh Gupta

Blog / Why SAP Users Should Consider BigQuery for Supply Chain Data Analytics

Supply chain management, a critical component of modern business operations, especially in sectors like manufacturing, retail, and logistics, has always been data-intensive. Traditionally, organizations have relied on systems like SAP Business Warehouse (SAP BW) to handle this data. However, as the complexity and volume of supply chain data grow exponentially, SAP users face new challenges. These challenges include integrating disparate data sources, scaling data infrastructure to meet increasing demands, and leveraging advanced analytics for deeper insights. This is where Google Cloud’s BigQuery presents itself as a transformative solution, offering a robust, scalable, and efficient data warehousing platform tailored for the intricate needs of supply chain management.

Addressing Supply Chain Analytical Challenges with BigQuery

The complexity of supply chain management today demands not only the collection of vast amounts of data but also the ability to quickly analyze and act upon this data. Here’s a closer look at key supply chain analytics challenges and how BigQuery’s capabilities offer significant improvements over traditional data analytics tools:

Supply Chain Analytical Needs Limitations of Traditional Data Analytics How BigQuery Addresses These Challenges
Real-Time Inventory Management Slower data processing leads to outdated inventory insights. Provides real-time data processing for up-to-the-minute inventory tracking.
Demand Forecasting Accuracy Limited in handling large datasets, affecting forecast precision. Utilizes machine learning for more accurate and granular demand predictions.
Supplier Performance Analysis Difficulty integrating external data sources for a complete supplier view. Seamlessly integrates diverse data sources for comprehensive supplier evaluations
Distribution Network Optimization Struggles with complex data sets needed for optimizing distribution routes. Offers advanced analytics to optimize routes and reduce logistics costs.
Risk Mitigation in Supply Chain Disruptions Inadequate in predictive capabilities to foresee and plan for disruptions. Employs predictive analytics to identify potential risks and suggest proactive measures.
Compliance and Regulatory Reporting Time-consuming report generation, leading to delays in compliance. Accelerates reporting processes, ensuring timely compliance with regulations.
Customer Satisfaction and Feedback Analysis Limited in analyzing unstructured customer feedback data. Analyzes large volumes of unstructured data to derive customer insights and improve satisfaction.
Cost Reduction and Efficiency Improvement Inefficient in identifying areas of cost savings and operational inefficiencies. Leverages AI to pinpoint inefficiencies and suggest cost-saving strategies.

Is BigQuery a better alternative to SAP BW? 

  • BigQuery excels in handling diverse data types common in supply chain scenarios, including unstructured data from IoT devices, which SAP BW might find challenging due to its structured nature.
  • BigQuery’s ML capabilities are integrated into its SQL environment, enabling complex analytical tasks like predictive maintenance and demand forecasting without needing separate ML tools, a contrast to SAP BW’s more segmented approach.
  • BigQuery offers extensive support for geospatial data processing, essential for optimizing logistics and distribution networks, a feature not natively present in SAP BW.
  • BigQuery’s serverless architecture allows for faster query execution, even on large datasets, providing quicker insights for supply chain decisions compared to SAP BW’s limited query capacity.
  • With its ability to automatically scale resources, BigQuery ensures consistent performance during high-demand periods, a challenge for SAP BW which may require manual scaling.
  • BigQuery’s real-time analytics capability enables immediate insights into supply chain operations, a vital feature for dynamic decision-making, which SAP BW’s batch-oriented architecture might struggle to deliver.
  • Unlike SAP BW, which may involve significant upfront costs for infrastructure, BigQuery’s pricing model allows for more flexible and cost-effective data management.
  • The combination of serverless architecture, no upfront costs, and efficient data management leads to a lower TCO with BigQuery.
  • BigQuery facilitates a more collaborative environment for data analysis, enabling teams to share insights and work together efficiently.

Optimizing Supply Chain Planning with BigQuery: An Example 

ACME Retail, a leader in the handbag industry, faces significant challenges in adapting its supply chain to rapidly changing market conditions and consumer sentiments. Despite utilizing SAP’s robust systems for supply chain management, ACME struggles with gaining real-time supply chain visibility, quickly adjusting inventory to shifting consumer demands, and tapping into rising consumer sentiments effectively.

Demand Planning

ACME Retail uses SAP Advanced Planner and Optimizer (SAP APO) for demand planning.

SAP APO forecasts are based on historical sales data, promotional calendars, and market analysis.

BigQuery can integrate with SAP APO to further refine demand forecasts by incorporating broader market trends, online consumer sentiment analysis, and real-time sales data from ACME’s e-commerce platforms.

Demand Sensing

While SAP APO provides a baseline for demand planning, it may not quickly adapt to sudden market shifts.

By analyzing current data trends, including social media buzz specific to handbag styles and influencer impacts, BigQuery can offer more agile demand sensing, leading to more responsive inventory management for popular handbag models.

Inventory Positioning

ACME relies on SAP ERP Central Component (ECC) for inventory management and day-to-day transaction processing. 

SAP ECC struggles with real-time inventory adjustments, resulting in either overstocking or stock shortages, especially during sudden demand shifts.

BigQuery enhances SAP ECC’s inventory data by providing real-time visibility and predictive analytics for inventory levels, identifying potential stockouts or excesses before they occur. It also aids in determining optimal stock levels for each handbag model, considering factors like seasonal demand and fashion trends.

Bridging the Gap Between Data and Decision-Making with Planning in a Box

Planning in a Box is a decision intelligence platform developed by Pluto7, designed to make data analytics simpler and transform the way businesses make decisions. It leverages the power of Generative AI to analyze, interpret, and provide insights from vast arrays of data in an intuitive, user-friendly manner. 

We assist in harnessing BigQuery’s advanced features like ML integration and real-time analytics, ensuring they are effectively applied to supply chain scenarios.  With Planning in a Box, you get custom AI and ML models that work seamlessly with BigQuery, offering predictive insights tailored to the nuances of your supply chain. These models enable more accurate forecasting, risk mitigation, and efficiency improvements, directly impacting your bottom line.

By choosing Pluto7, SAP users can leverage the power of BigQuery not just as a standalone solution but as part of a comprehensive, business-focused strategy. Join us to transform your supply chain analytics and make the leap into a future where data drives not just insights, but actionable, impactful business decisions.



Asheesh Gupta is the Head of Enterprise Architecture at Pluto7, bringing over 20 years of experience in technology consulting, system integration, and service delivery. He excels in architecting and delivering data-driven solutions on hyperscalar cloud platforms like GCP and SAP BTP. With a focus on supply chain optimization through digital transformation and AI, Asheesh’s work at Pluto7 continues to drive value and innovation in enterprise technology.

Connect with Asheesh on LinkedIn