Supply Chain Demand Planning Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Demand Planners, Supply Chain Analysts and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-SCM-055

Before you join: Participants should have practical exposure to supply chain, inventory, sales operations, procurement or production planning.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Demand planners must translate incomplete sales signals, customer intelligence, product changes and operational constraints into credible demand plans. Weak forecasting creates stockouts, excess inventory, unstable production schedules, rushed transport and unproductive commercial discussions. The work is not limited to selecting a statistical forecast: planners must identify demand drivers, challenge assumptions, measure error correctly and gain agreement on an actionable plan.

This course develops the analytical and cross-functional skills required to manage a demand planning cycle. Participants build demand histories, clean and classify data, select forecasting methods, calculate forecast accuracy and bias, and prepare rolling forecasts at suitable product, customer and location levels. They examine seasonality, promotions, new products, intermittent demand, customer collaboration and exception management. The programme also covers demand review meetings, consensus forecasting, S&OP inputs, forecast value added and communication with sales, finance, supply planning and procurement teams.

Teaching combines concise technical briefings with supply chain cases, data-based group work, forecasting simulations and structured application planning. Participants work progressively on a demand-planning pack that mirrors the materials used in a monthly planning cycle. They leave with a documented demand-planning model, forecast accuracy scorecard, exception-management rules, meeting agenda and improvement plan that can be adapted to their own product portfolio.

The course is suited to analysts and planners who already work with supply, inventory, sales or operational data and need to produce more reliable demand plans. It is also relevant to managers responsible for improving forecast discipline and cross-functional planning decisions.

What you will be able to do

  • Build a time-phased demand history from sales, shipment, return and master-data records
  • Apply moving average, exponential smoothing and seasonal forecasting methods to demand data
  • Calculate MAPE, WAPE, bias and tracking signal measures for forecast performance
  • Segment demand using ABC-XYZ and demand-pattern classifications to select planning policies
  • Produce a rolling baseline forecast with documented assumptions and forecast-version controls
  • Facilitate a demand review using exception reports, commercial inputs and decision records
  • Assess forecast value added across statistical, sales and management forecast adjustments
  • Present a complete demand plan linking forecast, risks, actions and supply implications

Benefits of attending

For you

  • Produce defensible forecasts rather than relying on unsupported sales estimates
  • Diagnose whether forecast error arises from data, method, bias or demand change
  • Lead more focused demand-review conversations with sales and supply stakeholders
  • Build a credible planning portfolio for analyst, planner and S&OP roles
  • Explain forecast uncertainty and inventory implications clearly to senior managers

For your organisation

  • Reduce avoidable stockouts and excess inventory through better demand visibility
  • Improve production, procurement and transport planning with stable forecast inputs
  • Expose systematic commercial bias before it distorts supply commitments
  • Create consistent accuracy measures across products, customers and planning teams
  • Strengthen S&OP decisions through documented assumptions, risks and actions

Course modules

20 modules over 10 days

Participants can define the demand planner's responsibilities, decisions and interfaces within an integrated planning process.

  • Demand planning decisions linked to service, inventory and capacity outcomes
  • Planner interfaces with sales, marketing, finance, supply and procurement
  • Push, pull and hybrid replenishment models compared for planning implications
  • Monthly planning cadence from data refresh through executive decisions
  • Planning horizons matched to supplier lead times and product lifecycles
  • RACI matrix for forecast ownership, challenge, approval and communication

Participants can prepare a usable demand history and identify data conditions that undermine forecasting.

  • Sales orders, shipments, invoices and consumption data distinguished carefully
  • Time buckets selected for daily, weekly and monthly planning
  • Master-data hierarchy covering SKU, customer, channel, location and family
  • Returns, cancellations and backorders treated within demand history
  • Missing values, duplicates and outliers identified using data checks
  • Data lineage documented from source transaction through planning report

Participants can cleanse historical demand data while preserving an audit trail of planning adjustments.

  • Stockout periods separated from genuine zero-demand observations
  • One-off projects and tender wins flagged within transaction histories
  • Outlier treatment using business evidence and documented adjustment rules
  • Lost sales estimated where constrained supply masked customer demand
  • Calendar alignment corrects trading days, holidays and fiscal periods
  • Cleaned demand history retained alongside original transactional values

Participants can segment portfolios and assign appropriate forecasting and review policies.

  • ABC analysis ranks items by revenue, margin or volume contribution
  • XYZ classification separates stable, variable and erratic demand patterns
  • ABC-XYZ matrix assigns review frequency and planning attention
  • Product lifecycle stages determine suitable history and forecast treatment
  • Customer segmentation distinguishes contractual, retail and spot demand
  • Planning policy specifies forecast horizon, granularity and ownership

Participants can select an appropriate baseline forecasting method for different demand patterns.

  • Naive forecast establishes a transparent benchmark for comparison
  • Simple moving averages smooth short-term random demand variation
  • Weighted moving averages prioritise recent demand observations deliberately
  • Simple exponential smoothing updates levels using an alpha parameter
  • Trend models project sustained directional movement in demand history
  • Method selection considers data volume, volatility and interpretability

Participants can calculate seasonal forecasts and distinguish recurring patterns from temporary changes.

  • Seasonal indices calculated from comparable historical demand periods
  • Deseasonalised data reveals underlying level and trend movement
  • Holt-Winters forecasting combines level, trend and seasonal components
  • Moving holidays and trading calendars adjusted within seasonal profiles
  • Seasonality reviewed at product, channel and geographic levels
  • Seasonal forecast assumptions recorded for future performance reviews

Participants can choose planning approaches for low-volume, intermittent and project-driven demand.

  • Intermittent demand distinguished from low but regularly recurring demand
  • Croston's method estimates size and interval between demand occurrences
  • SBA correction reduces bias in Croston-based intermittent forecasts
  • Project demand planned from milestones, probabilities and customer commitments
  • Service parts demand linked to installed base and failure patterns
  • Manual review thresholds prevent false precision for sparse histories

Participants can calculate forecast performance measures and explain what each measure reveals.

  • Forecast error calculated as actual demand minus forecast demand
  • MAD measures average absolute forecast deviation across reporting periods
  • MAPE limitations examined where actual demand approaches zero
  • WAPE provides volume-weighted accuracy across product portfolios
  • Forecast bias exposes persistent over-forecasting or under-forecasting tendencies
  • Tracking signal identifies forecasts requiring management attention

Participants can design a forecast scorecard that supports accountability and useful management action.

  • Accuracy measures reported by SKU, family, channel and location
  • Volume weighting prevents low-volume items distorting portfolio performance
  • Forecast horizons separated to reveal near-term and long-term quality
  • Bias trend charts distinguish persistent error from random variation
  • Targets set by demand segment rather than one universal percentage
  • Scorecard commentary records causes, actions, owners and due dates

Participants can produce a controlled baseline forecast using selected methods and planning hierarchies.

  • Forecast granularity selected from decision needs and data availability
  • Bottom-up forecasts aggregated through product and customer hierarchies
  • Top-down forecasts apportioned using documented allocation drivers
  • Baseline forecast generated before commercial or management adjustments
  • Version control distinguishes history, baseline, consensus and approved forecasts
  • Forecast freeze dates protect supply planning from uncontrolled changes

Participants can evaluate commercial inputs and incorporate credible short-term demand signals.

  • Sales pipeline probabilities converted into explicit demand assumptions
  • Point-of-sale data assessed for timeliness, coverage and signal quality
  • Customer forecasts challenged against orders, consumption and contractual commitments
  • Market intelligence captured using structured assumption registers
  • Demand sensing differs from statistical forecasting and long-range planning
  • Signal reliability scored before changing the baseline forecast

Participants can model promotion and event effects without confusing them with underlying demand.

  • Promotion uplift separated from baseline demand using comparable periods
  • Cannibalisation assessed across products, packs, channels and customer groups
  • Price changes tested against historical volume and margin responses
  • Event calendars capture launches, campaigns, tenders and planned closures
  • Causal assumptions documented with source, owner and confidence level
  • Post-event reviews compare expected uplift against actual incremental demand

Participants can forecast new, replacement and end-of-life products with explicit uncertainty controls.

  • Analogous-product forecasting uses comparable launches and adoption curves
  • Attribute-based analogues selected by channel, price, function and customer
  • Cannibalisation assumptions incorporated into launch volume estimates
  • Phase-in and phase-out plans prevent duplicate demand commitments
  • Supersession rules transfer demand between old and replacement SKUs
  • Launch forecast reviews tighten assumptions as actual demand arrives

Participants can prepare and conduct a structured demand review that produces clear decisions.

  • Demand review agenda focuses on exceptions, assumptions and decisions
  • Pre-read packs present accuracy, bias, risks and opportunities
  • Consensus forecasting reconciles sales intelligence with statistical baseline evidence
  • Decision logs record forecast changes, rationale, owner and expiry
  • Escalation thresholds identify risks requiring supply or executive intervention
  • Meeting facilitation separates evidence-based challenge from personal opinion

Participants can assess whether forecast interventions improve accuracy beyond a baseline method.

  • Forecast Value Add compares each adjustment against baseline performance
  • Statistical baseline established before evaluating sales forecast changes
  • Override accuracy measured by planner, sales team and management layer
  • Value-destroying adjustments identified through repeated comparative results
  • FVA findings guide training, governance and forecast-override permissions
  • Performance reviews distinguish useful judgment from unsupported optimism

Participants can explain how demand uncertainty affects inventory, replenishment and supply decisions.

  • Forecast error translated into safety stock and service-level implications
  • Lead-time demand combines forecast horizon with replenishment exposure
  • Demand variability compared with supplier variability for risk prioritisation
  • Reorder point assumptions reviewed against updated demand forecasts
  • Capacity constraints require feasible demand scenarios and allocation decisions
  • Procurement commitments assessed against forecast confidence and obsolescence risk

Participants can prepare demand-planning inputs for S&OP decisions and executive escalation.

  • Demand plan reconciled with financial plan and volume assumptions
  • S&OP demand review outputs framed as decisions, risks and scenarios
  • Scenario planning tests upside, downside and constrained-demand cases
  • Gap analysis identifies imbalance between demand, supply and inventory
  • Executive summaries quantify revenue, service and working-capital exposure
  • Decision governance assigns approval authority for material demand changes

Participants can specify practical system controls and automation requirements for demand planning.

  • ERP demand data extracts reconciled with source transaction totals
  • APS forecasting parameters governed through documented change controls
  • Exception-based planning prioritises material deviations from defined thresholds
  • Workflow notifications assign review tasks and overdue action reminders
  • Master-data changes assessed before they affect forecast continuity
  • Spreadsheet models controlled through permissions, formulas and version histories

Participants can establish governance routines that sustain measurable demand-planning improvement.

  • Planning calendar defines data deadlines, reviews and approval milestones
  • Control checklist verifies data quality, assumptions and version completion
  • Root-cause analysis classifies error sources using five-whys questioning
  • Improvement backlog prioritises data, process, capability and system actions
  • KPI review links forecast measures to service, inventory and expedites
  • Maturity assessment identifies next controls for planning process development

Participants can build, review and present a complete demand-planning pack containing a forecast, performance analysis, assumptions, risks, actions and supply implications.

  • Demand history cleansed, segmented and documented for selected portfolio
  • Baseline forecast produced using justified methods and planning hierarchy
  • Accuracy, bias and Forecast Value Add analysed across forecast versions
  • Commercial assumptions assessed with confidence ratings and expiry dates
  • Demand review pack prepared with scenarios, risks and required decisions
  • Supply implications quantified for inventory, capacity and procurement commitments

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Demand-history cleansing checklist
  2. ABC-XYZ segmentation model
  3. Rolling baseline forecast template
  4. Forecast accuracy and bias scorecard
  5. Demand review meeting pack
  6. Demand-planning improvement plan

Who should attend

  • Demand Planners — improve forecast construction, accuracy analysis and review discipline
  • Supply Chain Analysts — convert operational data into usable demand-planning decisions
  • Inventory Planners — align stock policies with demand variability and forecast reliability
  • Supply Planners — interpret demand signals before committing capacity and replenishment plans
  • Sales Operations Analysts — structure commercial inputs for consensus forecasting discussions
  • Planning Managers — establish consistent forecast governance, measures and escalation routines

Skills and tools covered

Statistical forecastingDemand segmentationForecast accuracy analysisConsensus demand planningS&OP preparationPlanning data governance

Tools and standards

S&OP, ABC-XYZ analysis, Forecast Value Add, Holt-Winters forecasting

How the course runs

Training methodology

The course uses short instructor briefings to introduce forecasting logic, planning measures and governance methods. Participants analyse realistic sales histories, promotion records, product portfolios and forecast reports through individual and group exercises. Cases reflect consumer goods, manufacturing, retail, pharmaceutical and service supply chains. Forecasting simulations show the effects of bias, lead time, demand variability and late commercial information. Application planning sessions help participants adapt templates, measures and review routines to their own planning responsibilities.

Requirements and prerequisites

Participants should have practical exposure to supply chain, inventory, sales operations, procurement or production planning. They should be comfortable reading spreadsheets, working with percentages and interpreting simple charts and tables. Familiarity with their organisation's products, customers and sales history will help participants apply the methods quickly. Prior use of ERP, APS or specialist forecasting software is useful but not required. This course does not require programming, advanced statistics, calculus, data-science qualifications or prior responsibility for an S&OP process.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

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

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants should understand basic supply chain or commercial operations and be comfortable using spreadsheets, percentages and charts. Advanced statistics, coding and forecasting-system experience are not required.

Participants should bring a laptop if attending in person and any non-confidential examples of their demand-planning reports, product hierarchy or forecast measures. Course materials and case data are provided.

It is designed for demand planners, supply chain analysts, inventory planners, sales operations staff and planning managers. Participants gain the most value when they influence forecasts, replenishment, stock or S&OP inputs.

This course concentrates on estimating and governing future demand before supply commitments are made. Inventory and supply implications are covered, but the main focus is forecast construction, accuracy, commercial inputs and demand-review decisions.

Participants leave with reusable templates for demand history, segmentation, forecast scorecards and review meetings. These can be adapted to existing ERP, APS or spreadsheet-based planning processes.

The first week establishes data, segmentation, forecasting methods and accuracy measurement. The second week covers commercial inputs, consensus planning, S&OP, governance and a capstone demand-planning pack.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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