Demand Planner Eurasia

About the role

Mondelēz is hiring a Demand Planner for the Eurasia region, reporting to the DP&F Manager. The role owns the accuracy of sell-in plans through the IBP process and coordinates a team of IMS planners covering Uzbekistan, Tajikistan, Kyrgyzstan, Turkmenistan and Mongolia, with possible involvement in Kazakhstan, Georgia, Armenia, Azerbaijan and Belarus. It sits within the Customer Service & Logistics function.

Key responsibilities

• Develop the 0-24-month IBP forecast by brand and SKU, coordinating and challenging the forecast with input from sales, marketing and other partners
• Review the short-term demand plan (SKU/location mix and weekly pacing) and provide the final detailed plan to supply planners, adjusting it on their feedback
• Manage the forecast compilation process, including the short-term weekly plan and the long-term forecast of three or more months
• Work with the modelling team to generate statistical forecasts and run root-cause analysis on forecast errors and bias
• Monitor shipment plans, reflect code changes for the Biscuits & SS category, lead the LDR meeting for Stans&MNG, check SFA and bias performance, and compare sell-in reflection against production availability
• Coordinate a team of IMS planners responsible for sell-out plans and finished goods inventory levels

Required profile

• Bachelor's degree in business administration, supply chain management, operations management or a related field
• 3 years of proven experience in demand planning, forecasting, supply chain analysis, inventory management or a similar analytical role
• Experience in a fast-moving consumer goods or consumer packaged goods environment, preferably food, where shelf life is a constraint
• Experience within an IBP/S&OP environment and ability to influence cross-functional teams

Required skills

• Proficiency with demand planning and forecasting tools, preferably SAP APO
• Advanced Microsoft Excel (pivot tables, VLOOKUPs, data modeling)
• Data visualization tools such as Tableau or Power BI preferred
• Familiarity with statistical software such as R or Python is a plus