Demand Planning Analyst

What matters most

• Statistical Forecasting Accuracy

  The core mandate is to improve demand forecast accuracy over an 18-month horizon. You must demonstrate proficiency in univariate, multivariate, and ensemble modeling techniques using Python and R to deliver measurable improvements in planning effectiveness.

• SAP IBP Data Management

  You are responsible for owning and maintaining forecast data and pricing within SAP IBP. This requires ensuring high data integrity and alignment with business requirements, serving as the primary bridge between technical models and the enterprise planning system.

• Cross-Functional Stakeholder Alignment

  Success depends on partnering with Marketing, Supply Chain, and Commercial teams to refine demand models. You must translate complex analytical outputs into actionable insights that non-technical stakeholders can use for business decision-making.

• Automation via Machine Learning

  The role requires designing and deploying machine learning solutions to automate manual planning processes. You must focus on building scalable, robust, and maintainable analytical solutions within a cloud environment like GCP.

Likely challenges to think through

• Bridging Technical Models and Business Reality

  A common challenge is that sophisticated statistical models may not always align with market realities or commercial strategy. Prepare by demonstrating how you have previously adjusted technical outputs based on qualitative feedback from Marketing or Sales teams.

• Managing Data Complexity in SAP IBP

  Integrating custom Python/GCP-based models with a structured system like SAP IBP can be technically difficult. Prepare by discussing your experience with data mapping, ensuring consistency between external analytical environments and enterprise planning software.

• Communicating Technical Insights to Non-Technical Users

  You will need to present complex forecasting methodologies to diverse teams. Prepare by practicing how to explain the 'why' behind your model's predictions and how to use Power BI visualizations to simplify complex trends for business leaders.

Topics to prepare

• Time Series Forecasting Methodologies

  Review the theoretical differences and practical applications of univariate, multivariate, and ensemble forecasting models. Exercise: Compare the performance of an ARIMA model against a Random Forest regressor on a sample supply chain dataset to understand when to apply statistical versus machine learning approaches.

• Supply Chain Demand Planning Lifecycle

  Understand the integration of statistical forecasts with business inputs like pricing and marketing data. Exercise: Map out how an 18-month forecast horizon is adjusted by external market signals and internal constraints, focusing on how to maintain data integrity within a system like SAP IBP.

Skills to revise

• Python for Predictive Analytics

  Focus on libraries such as pandas, scikit-learn, and statsmodels for data manipulation and model deployment. Exercise: Build a script that cleans a raw demand dataset, performs feature engineering, and generates a forecast, ensuring the code is modular and scalable for a production environment.

• SQL for Data Management

  Practice complex querying and data modeling to support analytical solutions. Exercise: Write SQL queries to join disparate tables (e.g., historical sales, pricing, and inventory levels) to create a unified dataset suitable for training a forecasting model.

• Power BI Dashboard Development

  Develop skills in creating actionable visualizations that communicate complex trends to non-technical stakeholders. Exercise: Design a dashboard that tracks forecast accuracy metrics (e.g., MAPE or WMAPE) over time and allows users to filter by product category or region.

• Cloud-Based Analytical Deployment

  Gain familiarity with deploying analytical solutions in a cloud environment like GCP. Exercise: Practice containerizing a Python forecasting script using Docker and deploying it to a cloud environment to understand the basics of scalable, automated model execution.

• Prepare a Time Series Forecasting Case Study

  Develop a brief presentation or code walkthrough demonstrating your experience with univariate, multivariate, and ensemble forecasting models. Highlight how you handled data cleaning, feature engineering, and model validation to improve accuracy over a specific planning horizon.

• Demonstrate Cloud-Based Analytical Solutions

  Prepare to discuss a project where you deployed a scalable analytical or machine learning solution in a cloud environment (GCP, AWS, or Azure). Focus on how you ensured the solution was robust, maintainable, and integrated with existing data pipelines.

• Showcase Power BI Dashboarding Skills

  Prepare examples of dashboards you have built that translate complex data into actionable business insights. Be ready to explain how you tailored these visualizations for non-technical stakeholders to support data-driven decision-making.

• Formulate Questions on SAP IBP and Cross-Functional Collaboration

  Prepare thoughtful questions regarding how the Demand Planning team currently integrates SAP IBP with Python-based models and how you would collaborate with Marketing and Supply Chain teams to refine market demand models using external data.