Demand Planner
Pubblicato il 14-09-2026 - Moncler in Pavia
Overview
In this role you will support demand forecasting and supply planning for carry-over products, ensuring product availability and optimal inventory. You will work with production, planning and merchandising to align supply with market demand and cross-functional insights to refine forecasts. You will apply statistical models to generate forecasts and monitor performance, driving informed production capacity decisions. This is an analytical, collaboration‑driven role at a global fashion group with a focus on efficient stock and strategic market insight.
Retribuzione / Benefits
- compensation range 35,000€ to 40,000€ gross
- corporate welfare services
- equal opportunity employer
- inclusive work environment
Responsabilità
- Plan and analyze demand forecasts to optimize sourcing and distribution of carry-over products
- Collaborate with production, planning, and merchandising to ensure smooth supply flow
- Analyze historical sales to develop accurate future demand forecasts
- Gather market insights from regional teams on trends and planned promotions
- Use statistical models to generate short-term and long-term forecasts
- Monitor forecast accuracy and adjust forecasts as needed
- Coordinate with production to plan capacity and allocate resources
- Analyze inventory data to determine optimal stock levels and reduce excess/shortages
- Generate regular reports on forecast performance, stock turnover, and key metrics
- Conduct market and consumer trend analyses to inform strategic decisions
Requisiti fondamentali
- 3+ years in a similar role
- Bachelor's degree in economics, statistics, supply chain management, or related field
- Ability to work independently in a dynamic environment
- Excellent analytical and problem-solving skills
- Proven ability to interpret business trends and derive actionable insights
- Proven ability to influence and negotiate with cross-functional partners
- analytical
- organized
- results-oriented
- demand forecasting
- statistical models
- data analysis