BI platform for data analytics for the brewery industry
Food & Beverages
Data engineering on unstructured data sets, capturing data from multiple sources and running data analytics to provide meaningful insights to the business.
The client is a leading brewer company based in Europe, having a vibrant ecosystem with more than 300 breweries, and an unparalleled distribution route to reach more than 1 billion consumers and 3 million customers.
Data was scattered in multiple systems like Blackline, SAP, Salesforce, Internal Systems, and Excel, which caused inconsistency in data between the systems.
Sending invoices and follow-ups were done manually, leading to delays and human errors.
Collecting data from various sources for audit purposes was challenging and time-consuming.
Seaflux created a system that fetches more than 500 MB of data from various sources at regular intervals and transforms the data as per business rules.
Sending 10K+ automated invoices daily to vendors as per the due date and follow-up emails automatically as per the frequency defined by the admin.
The system automatically escalates to the next level if there is no response in the defined timeframe.
Real-time business insights are displayed on the dashboard to take appropriate action immediately.
KPI tracking automation has been implemented to understand the role of employees and alter them according to the business goals.
The solution captures all the activity logs initiated by the system and actions taken by the required vendor for audit purposes.
Implemented the Import/Export feature for data comparison from the system to verify its authenticity with various third-party verified systems.
OpEx was reduced by 50% for the accounting department with various automation in place.
Real-time information also allowed upper management to access the company’s insights based on data accumulated, shown on their dashboard.
KPI tracking automation improved the business operations efficiency by 31%, which resulted in the faster achievement of the business goals.
Drill down of data to provide useful insights like credit risk modeling for vendor management, identifying customer lifetime value, and customer segmentation.