Key Responsibilities and Required Skills for Demand Analyst

🎯 Role Definition

A Demand Analyst is responsible for building, maintaining and improving the demand forecasting and planning processes that drive inventory, replenishment and commercial decision-making across the business. This role combines statistical forecasting and data engineering skills with commercial judgment to produce accurate, consensus forecasts that minimize stockouts and overstock while supporting sales, marketing and supply chain. The Demand Analyst partners with Sales, Marketing, Finance, Supply Chain and Merchandising to translate business signals (promotions, new product launches, seasonality and market trends) into robust quantitative forecasts and actionable planning outputs.

Key keywords: demand forecasting, time-series models, S&OP, inventory optimization, forecast accuracy (MAPE), bias, ERP (SAP/Oracle), SQL, Python/R, Power BI/Tableau, promotional uplift, scenario planning.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Demand Planner / Demand Planning Coordinator
Business Analyst with supply chain exposure
Data Analyst focused on commercial or retail data

Advancement To:

Senior Demand Planner / Lead Demand Analyst
Demand Planning Manager / Forecasting Manager
Supply Chain Planning Manager or S&OP Manager
Head of Demand Planning / Director of Supply Chain Analytics

Lateral Moves:

Commercial/Category Analyst
Inventory Optimization Specialist
Supply Chain Data Scientist / Analytics Engineer

Core Responsibilities

Primary Functions

Develop, maintain and continuously improve statistical demand forecasting models (time-series, exponential smoothing, ARIMA, Prophet, XGBoost or other machine learning approaches) across SKUs and channels to drive accurate short-, mid- and long-term forecasts.
Own the end-to-end forecasting process for assigned product families or regions, including data ingestion, model selection, parameter tuning, forecast generation and automated deployment into the planning system (ERP/IBP/S&OP tool).
Lead the monthly and weekly demand review and consensus forecasting meetings with Sales, Marketing, Finance and Supply Chain stakeholders; synthesize inputs, adjudicate differences and finalize the consensus plan for S&OP.
Calculate and monitor forecast accuracy and bias (MAPE, RMSE, MAD), generate root-cause analysis for significant variances, and implement corrective actions to improve forecast performance.
Translate promotional calendars, marketing plans, trade promotions and new product introductions into uplift assumptions and scenario forecasts; quantify promotion lift and post-event measurement to refine future promotion models.
Build demand segmentation and clustering logic to separate causal vs. baseline demand, improving model fit and enabling differentiated inventory policies by segment (e.g., fast movers, lumpy SKUs, lifecycle-stage).
Define and maintain safety stock and reorder point calculations using service-level targets, lead-time variability and forecast uncertainty to optimize inventory investment and reduce stockouts.
Create automated, reproducible forecasting pipelines using SQL, Python or R and integrate with visualization tools (Power BI, Tableau) and ERP systems (SAP, Oracle) for real-time planning consumption.
Design and maintain dashboards and KPI packs for forecast accuracy, bias, fill rate, days of inventory, stockouts and obsolescence trends to provide transparency to leadership and cross-functional teams.
Conduct scenario modeling and “what-if” analyses to assess the impact of demand shocks, supplier constraints, lead-time changes or promotional plans on inventory and service levels.
Implement uplift and cannibalization models to quantify cross-SKU and cross-channel promotional impacts; use uplift estimates to inform pricing and assortment decisions in collaboration with commercial teams.
Partner with inventory planners and procurement to convert the consensus demand plan into replenishment orders and ensure alignment on capacity, lead times and supplier constraints.
Document forecasting assumptions, model logic and data sources; maintain governance over forecast versions, master data and model artifacts to ensure traceability and auditability.
Lead continuous improvement initiatives to reduce forecast cycle time and manual effort by automating data extraction, model refreshes and report generation.
Provide training and enablement to planners and commercial stakeholders on forecast interpretation, demand drivers and planning tool best practices to create a culture of data-driven planning.
Evaluate and deploy third-party forecasting tools, machine learning platforms or add-on modules; manage vendor relationships, pilot programs and business case analysis.
Integrate external data signals (macroeconomic indicators, weather, web/app traffic, syndicated data, POS) into forecasting models to capture demand drivers beyond internal transactional history.
Perform SKU rationalization analysis and lifecycle forecasting for new product launches, phase-outs and SKU consolidations to minimize excess inventory and alignment with merchandising strategies.
Monitor supply chain constraints and provide proactive demand shaping recommendations (allocation, prioritization, order limits) when supply is constrained.
Partner with Finance to align demand plans with revenue and margin targets and support demand-driven financial forecasting and budgeting cycles.
Conduct ad hoc analytical projects (elasticity analysis, price sensitivity, regional demand comparisons) to support cross-functional initiatives and strategic decisions.
Ensure data quality and master data hygiene: identify gaps, lead clean-up projects and establish controls to improve model inputs and downstream planning reliability.
Establish KPIs and SLAs for demand planning processes and drive performance reviews against targets, escalating risks and opportunities to senior management.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide periodic training sessions and documentation for new forecasting tools and processes.
Support cross-functional pilots to test new forecasting methods or external data integrations.
Assist with master data maintenance and periodic reconciliation of sales, shipments and inventory feeds.

Required Skills & Competencies

Hard Skills (Technical)

Advanced demand forecasting and time-series modeling (exponential smoothing, ARIMA, SARIMA, Prophet).
Experience with machine learning approaches for demand prediction (XGBoost, random forest, neural networks).
Strong SQL skills for data extraction, transformation and reporting from transactional/ERP systems.
Proficiency in Python or R for statistical analysis, model development and automation (pandas, scikit-learn, statsmodels, prophet).
Advanced Excel modeling skills including pivot tables, Power Query and VBA (where applicable).
Dashboarding and data visualization with Power BI, Tableau or Looker; ability to build interactive forecast and inventory dashboards.
Hands-on experience with ERP and planning systems (SAP APO/IBP, Oracle Demantra, Kinaxis, Blue Yonder/JDA) or similar demand planning tools.
Inventory optimization methods (safety stock, reorder point, EOQ) and understanding of supply chain KPIs (fill rate, days of inventory).
Familiarity with promotion modelling, uplift analysis and A/B test evaluation for marketing effectiveness.
Experience with version control and reproducible analytics pipelines (Git, CI/CD for data workflows).
Ability to integrate external data (POS, syndicated, weather, web analytics) into forecast models and pipelines.
Understanding of statistical accuracy metrics, forecast bias, and performance improvement frameworks.

Soft Skills

Excellent stakeholder management and cross-functional communication; ability to facilitate consensus across commercial and supply chain teams.
Strong analytical mindset and structured problem-solving; able to translate ambiguous business questions into analytic approaches.
Business acumen and commercial orientation — understands P&L impacts and trade-offs between service levels and inventory cost.
Attention to detail and commitment to data quality and documentation.
Influencing and negotiation skills to align competing priorities in S&OP forums.
Time management and the ability to prioritize multiple planning cycles and ad-hoc requests.
Presentation skills — can distill complex model outputs into clear recommendations for executive audiences.
Adaptability and continuous learning mindset — keeping up with new forecasting techniques and tools.
Collaborative team player with an ability to mentor junior analysts.
Project management capabilities for leading process improvements and tool implementations.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Supply Chain Management, Operations Research, Statistics, Data Science, Economics, Business Analytics, Industrial Engineering or related quantitative field.

Preferred Education:

Master’s degree in Supply Chain, Data Science, Statistics, Business Analytics or MBA with a strong analytical focus; relevant certifications such as APICS CPIM/CSCP, IBF Certification, or Coursera/edX specialization in forecasting/data science are a plus.

Relevant Fields of Study:

Supply Chain Management / Logistics
Data Science / Statistics / Applied Mathematics
Economics / Business Analytics
Industrial Engineering / Operations Research

Experience Requirements

Typical Experience Range:

2–7 years of progressive experience in demand planning, forecasting, supply chain analytics or commercial analytics.

Preferred:

4+ years in a Demand Analyst, Demand Planner, Forecasting Analyst or similar role within retail, CPG, manufacturing, e-commerce or distribution. Proven track record improving forecast accuracy, driving inventory reduction and enabling S&OP processes. Experience with ERP/IBP systems and advanced analytics toolsets (SQL, Python/R, Power BI/Tableau).