Verified recordChemical Manufacturing

Rahul Behre

Senior Business Information Analyst at Ashland

Based in Hyderabad, India

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Seniority

Staff

Department

Information Technology

Location

Hyderabad

Industry

Chemical Manufacturing

Company size

5.9K

Contact information

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Email

1 credit

r•••••••@ashland.com

Phone

5 credits

+91 ••• •••• ••••

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Background

About Rahul Behre

SKILLS: Power BI, DAX, QlikView, Qlik Sense, Dashboards, Python, Data Modeling, Snowflake, Data Warehousing, Data Architecture, AI & ML Concepts, Excel, MS O365/Nintex, MS SQL Server, Visual Studio, Data Structures, OOPS, Lotus Notes Domino, Lotus Notes Scripts and Formula Language, Lean Six Sigma Yellow Belt Certified, Oracle Certified Java Programmer. PROFESSIONAL SUMMARY: • 13 years of experience in IT industry in all aspects of Business intelligence applications with a strong background in Data Modeling, Data Warehousing/ Decision support systems and Business Intelligence (BI). • Expertise in Designing and Development of Business intelligence applications with Power BI, Power Automate and QlikView 12/11 as BI tool and DAX/Scripting, developing and designing Dashboards by extracting data from multiple sources (SAP R3, SAP BW, SQL Server, Flat files, Excel, QVDs, QVW etc.). • QlikView to PowerBI Migration. • Automated the Business Planning and Forecasting process by 80%. • Created the Manufacturing matrix which consolidates manufacturing spend, absorption, volume, forecasting and business plan, forecast trend, volume trend from 23 different sources. This saved more than 70% of analysis time. • Automated SQL dataset preparation and created Sales GP report which summarizes the different aspect of the Sales data. This decreased the refresh time by 30%. • Global innovation data automation provides an accuracy of more than 95%. 10 hours of manual work is getting published in less than 5 minutes. • Consolidated and automated logistics dataset for Actual, Forecast, Business Plan and Last Year using SQL server stored procedures. This provides a detailed picture of the data which saved more than 40% of analysis efforts. • Increased the data granularity based on % allocation method using Plan and Forecast numbers.

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