Chief Supply Chain Data Strategist

Job Title

Lead - Supply Chain Digital Transformation

Role Overview

This position plays a pivotal role in spearheading the development and implementation of data-driven strategies to enhance procurement, planning, third-party manufacturing, and distribution processes.

• This leader aims to leverage advanced analytics and Artificial Intelligence/Machine Learning to optimize operations, drive efficiencies, and support delivering high-quality healthcare solutions.

Key Responsibilities

1. Data Strategy Development: Develop and execute a comprehensive Supply Chain data science and AI strategy that optimizes supply chain operations aligning with procurement and planning/distribution objectives, focusing on service levels and cost-to-serve models.
2. Procurement and Planning/Distribution Enhancement: Identify and integrate key data sources (internal and external) to enhance visibility and accuracy in procurement decisions and planning/distribution processes.
3. Evaluation and Implementation of Emerging Technologies: Evaluate and implement emerging technologies and tools (e.g., GenAI, IoT, digital twins) that enhance data collection, analysis, and visualization capabilities.
4. Collaboration with Cross-functional Teams: Collaborate with procurement and planning/distribution teams to prioritize data science initiatives driving operational efficiencies and cost savings.

Advanced Analytics and Modeling for Supply Chain Optimization

1. Predictive Analytics and Replenishment Strategies: Lead the initiative to deploy scientific replenishment strategies based on secondary stock and sales data in Trade (India and Global markets).
2. Demand Forecasting and Inventory Management: Design and implement predictive AI/ML models to enhance demand forecasting accuracy, optimize inventory levels, distribution planning, and improve supplier performance evaluation.
3. Machine Learning Algorithm Application: Drive innovation by exploring and implementing advanced analytics techniques, machine learning models, and AI algorithms to derive insights and create value from large, complex datasets.
4. Trend Analysis and Procurement Strategies: Utilize machine learning algorithms to analyze historical data and identify trends informing procurement strategies and planning cycles.
5. Supply Chain Performance Assessment: Develop advanced analytics solutions to assess the performance of third-party manufacturers and optimize distribution logistics.

Collaboration and Stakeholder Engagement Across Functions

1. Cross-functional Team Collaboration: Work closely with cross-functional teams, including procurement, supply chain, planning, and IT, to identify data needs and ensure alignment on data initiatives.
2. Data Science Methodology Education: Facilitate workshops and training sessions to educate stakeholders on data science methodologies and the value of data-driven decision-making in procurement and supply chain operations.
3. Stakeholder Liaison: Act as a liaison between data science team and business units to ensure that data solutions meet operational requirements and enhance overall performance.
4. Communication and Project Updates: Communicate findings, recommendations, and project updates to senior management and key stakeholders as required.

Master Data Governance and Quality Assurance

1. Data Governance Framework Development: Develop and implement master data governance frameworks ensuring data integrity, quality, and security across all data science initiatives related to procurement, planning, and distribution.
2. Data Quality Checks and Validation: Design and oversee data quality checks and validation processes maintaining high standards of data accuracy and reliability in analytics outputs.
3. Regulatory Compliance: Ensure that all data science and AI initiatives comply with industry regulations, data privacy laws, and internal policies safeguarding sensitive procurement and supply chain information.
4. Data Protection Strategies: Develop strategies to protect sensitive data and ensure compliance with data protection regulations, including monitoring and responding to potential data breaches.
5. Rigorous Documentation and Audit-readiness: Ensure robust documentation, model governance, and audit-readiness of all analytics solutions.

Team Leadership and Development in Data Science

1. Data Science Team Leadership: Lead and mentor a team of data scientists and analysts, machine learning engineers, and AI experts fostering a culture of innovation, collaboration, and continuous learning focused on procurement and supply chain challenges.
2. Performance Expectations and Feedback: Set clear performance expectations and provide regular feedback to team members supporting their professional development in data science applications.
3. Knowledge Sharing and Best Practices: Encourage knowledge sharing and best practices within the team to enhance overall capabilities and effectiveness in addressing procurement and supply chain needs.
4. Data Literacy and Analytics Capability Building: Work closely with the SCM Academy to build data literacy and analytics capability across the function.

Represent the Function in External Forums, Awards, and Academic CollaborationsPerformance Measurement and Reporting for Continuous Improvement

1. KPI Development: Develop key performance indicators (KPIs) measuring the impact of data science initiatives on procurement, planning, third-party manufacturing, and distribution performance.

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