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Business Impact

  • Accurate Demand Forecasting With Advanced Analytics

  • Reimagined Supply Chain Precision

Customer Key Facts

  • Location : North America
  • Industry : Manufacturing
  • Size : ~6000
  • Core Product : Semiconductor Materials and Specialty Chemicals

Problem Context 

The client is a leader in specialty chemicals and advanced materials solutions for semiconductors and other high-tech industries. They were looking for a Supply Chain rejig with advanced Machine Learning and boost the demand forecasting accuracy to augment their planning process. They aimed at incorporating the below workloads:

  • Demand Forecasting using Google Cloud’s Vertex AI
  • Create a data lake for the high-value SAP supply chain and planning data, making conforming business data available into a single platform, building data foundations for AI/ML workloads, and rendering the predictions with visualization tools
  • Automate the collection, ingestion, and data analysis to support faster response to process variability

Challenges

  • The current demand forecasting process would not consider external variables in difficult times
  • Unavailability of a centralized platform/access point of data for advanced analytics and applied ML
  • Cloud-based data lake needed data pipelines from the SAP ecosystem

Technologies Used

Google BigQuery
Google Cloud Storage
Microsoft Power BI
SAP ECC, IBP, BW, Data Services
Google Vertex AI

Solution

Quantiphi created a business conforming Data foundation in BigQuery; integrated with various SAP sources crafting a range of advanced analytics use cases. We developed data pipelines to seamlessly move their existing High-value SAP Data to Google Cloud.

Results

Realizing the swift business impact, the client entered into a strategic partnership with Quantiphi to execute 50+ impactful business use cases, leveraging our expertise in SAP, AIML, and Google Cloud.

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