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Automation Lead,Global Supply Chain Finance at Ford Global Career Site

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

29 Apr, 26

Salary

0.0

Posted On

29 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Automation, Data Mining, Predictive Analytics, Statistical Techniques, Machine Learning, Forecasting, Cost Analysis, Workflow Streamlining, UIPath, GenAI, NLP, Powerapps, Powerautomate, Dashboard Creation, Team Training

Industry

Motor Vehicle Manufacturing

Description

Lead automation initiatives in Raw Material team Understand the databases available in Raw material space and identify potential opportunities to Simplify the process and make it more efficient and effective Identify clawback opportunities from suppliers Improve Forecasting Develop Business plan Perform Data Mining of huge data – redefine the Volume, Velocity, Variety, Veracity and Value of the data attributes in such a way that enables management to take quick decisions Use Predictive analytics, Statistical techniques, Machine Learning algorithms and historical data to project forecast of Raw material cost, claims and Indices in future Identify cost opportunities from suppliers using relevant techniques to analyse the existing data from various sources, understand the patterns, outliers, issues etc Streamline workflows, automate repetitive tasks using UIPath or any other relevant automation techniques to improve operational efficiency Automate Audit process using GenAI, NLP and any other relevant techniques so that the manual intervention is reduced to minimal Explore feasibility of Virtual assistant to handle queries and provide solutions across various aspects of Raw materials Enhance the existing E2E claim process tools (Powerapps, Powerautomate) Create comprehensive, real time dashboards across various parameters for better decision making Train the team on automation tools and techniques

Responsibilities

Lead automation initiatives in the Raw Material team and identify opportunities to simplify processes for efficiency. Utilize data mining and predictive analytics to improve forecasting and streamline workflows.