Demand Planning Skills for Supply Chain Analysts Training Course

Course overview

Supply chain analysts are expected to convert volatile demand signals into plans that operations, procurement, finance and commercial teams can act on. Yet many organisations still rely on spreadsheet forecasts that are not segmented, not measured against the right error metrics, and not reconciled with promotions, customer intelligence or supply constraints. This creates stockouts in priority lines, excess and obsolete inventory in slow movers, unstable production schedules and contentious S&OP meetings. This course equips analysts to diagnose these issues and establish a disciplined demand-planning process.

Participants learn to build demand plans from historical transactions, cleanse and structure demand data, segment products by demand behaviour and select appropriate statistical forecasting methods. They apply moving averages, exponential smoothing, seasonal indices, causal inputs and judgemental overrides; measure forecast accuracy using bias, MAD, MAPE and WAPE; and distinguish demand variation from forecast failure. The course also addresses forecast value add, demand sensing, lifecycle planning, promotion planning, consensus forecasting and the governance required to run an effective demand review.

Teaching combines instructor-led explanation with Excel-based modelling, realistic planning scenarios and cross-functional decision exercises. Participants work through a multi-product demand-planning case, progressing from raw sales history to an approved consensus forecast and supply-facing demand plan. They leave with a completed demand-planning workbook, forecast-accuracy dashboard, segmentation logic, exception-management rules and a practical 90-day implementation plan for their own planning environment.

The programme is designed for analysts who already work with sales, inventory, ERP or planning-system data and need to produce forecasts that can withstand operational and financial scrutiny. It is equally valuable for managers seeking analysts who can explain not only what the forecast is, but why it changed, where uncertainty sits and what action the business should take.

Course objectives

By the end of this course, participants will be able to:

• Clean and structure historical demand data into an analysis-ready forecasting dataset
• Segment SKUs using volume, variability, lifecycle and demand-pattern criteria
• Select and calculate moving-average, exponential-smoothing and seasonal forecasting models
• Measure forecast performance using bias, MAD, MAPE, WAPE and forecast-value-add analysis
• Build an Excel demand-planning workbook with forecast, override and exception-management controls
• Incorporate promotions, customer intelligence and causal drivers into a consensus demand forecast
• Facilitate a demand-review discussion that documents assumptions, risks, decisions and owners
• Produce a 90-day demand-planning improvement plan linked to S&OP and inventory decisions

Benefits of attending

Target competencies

Who should attend

• Supply Chain Analysts — who translate demand, inventory and operational data into actionable plans
• Demand Planners — who need to improve forecast accuracy, bias control and forecast governance
• Supply Planners — who require a more reliable demand signal for capacity, replenishment and inventory decisions
• S&OP Analysts — who prepare demand-review materials and reconcile commercial and operational assumptions
• Inventory Analysts — who need to distinguish forecast error from replenishment and stock-policy issues
• Commercial Planning Analysts — who quantify the demand effect of promotions, launches and customer events

Requirements and prerequisites

Participants should have practical experience working with sales history, inventory, orders or forecast data in a supply chain, planning or analytics role. They should understand basic supply chain terms such as SKU, lead time, service level, inventory and S&OP, and be comfortable using Excel formulas, filters, pivot tables and charts. Familiarity with an ERP or planning system is helpful but not essential. This is not a statistics-degree course: advanced mathematics, coding, data-science software and prior use of SAP IBP or Kinaxis RapidResponse are not required. Participants should bring a laptop with Excel available for workshop exercises.

Training methodology

The course uses short instructor-led modules followed by hands-on planning work in Excel. Participants analyse a realistic SKU-level dataset, clean demand history, classify demand patterns, test competing forecast models and interpret forecast-error results. Case discussions recreate the tension between sales intelligence, promotional commitments, supply constraints and statistical output in a demand review. Small groups prepare forecast recommendations and defend their assumptions using evidence. On the final day, each participant adapts the course templates into an implementation plan for a live or representative planning challenge.

Course outline

Day 1: Demand planning foundations and data diagnosis

• Demand planning roles within integrated business planning and S&OP
• The demand-planning cycle from data collection to approved demand plan
• Demand versus sales history, shipments, orders, returns and lost sales
• Time-series data structures, planning horizons and forecast buckets
• Data cleansing for outliers, missing values, discontinuities and master-data changes
• Demand-pattern classification: level, trend, seasonality, intermittency and volatility
• SKU segmentation using ABC value, XYZ variability and lifecycle status

Workshop: Participants cleanse and profile a raw SKU-location demand dataset, then produce a segmentation matrix and data-quality issue log.

Day 2: Statistical forecasting methods and model selection

• Naive and seasonal-naive forecasts as performance benchmarks
• Simple moving averages and weighted moving averages
• Simple exponential smoothing and smoothing-constant selection
• Trend-adjusted exponential smoothing for growing and declining demand
• Seasonal indices and seasonal forecast construction
• Intermittent-demand methods and limits of conventional averages
• Model selection by demand pattern, data availability and business use

Workshop: Participants calculate and compare multiple forecast models for selected SKU segments and recommend a baseline model for each.

Day 3: Accuracy, bias and forecast value add

• Forecast error definitions and period-by-period error calculation
• MAD, MAPE, WAPE and RMSE: appropriate use and limitations
• Forecast bias, tracking signals and systematic over-forecasting
• Accuracy measurement by SKU, product family, customer and planning horizon
• Weighted accuracy reporting for high-value and high-volume items
• Forecast value add analysis for statistical forecasts and manual overrides
• Exception thresholds, alert design and root-cause investigation

Workshop: Participants build a forecast-accuracy dashboard and use bias and value-add results to identify priority planning exceptions.

Day 4: Consensus forecasting and demand review governance

• Combining statistical baseline forecasts with market intelligence
• Promotion uplifts, cannibalisation, substitution and post-promotion effects
• New-product introduction, phase-out and lifecycle forecasting
• Causal drivers including price, distribution, events and customer programmes
• Judgemental overrides, assumption registers and override approval rules
• Demand-review meeting inputs, agenda, decisions and escalation paths
• Demand risks, scenarios and communication to supply and finance teams

Workshop: In a cross-functional simulation, participants negotiate a promotion-adjusted consensus forecast and complete an assumptions-and-risks register.

Day 5: Operationalising the demand plan

• Translating the approved forecast into supply, inventory and capacity implications
• Forecast horizons, frozen periods and handoffs to supply planning
• Planning-system workflows in SAP Integrated Business Planning and Kinaxis RapidResponse
• Excel controls for versioning, approval status and forecast overrides
• Demand-planning KPIs and a monthly performance review cadence
• Data ownership, master-data governance and auditability requirements
• 90-day roadmap for improving demand-planning maturity

Workshop: Participants complete a demand-planning playbook and 90-day implementation roadmap for their own organisation or the course case company.

Tools & standards covered

Microsoft Excel, SAP Integrated Business Planning, Kinaxis RapidResponse, Microsoft Power BI

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions