Agile Planning and Estimation Training Course

Participants can translate strategic intent into measurable product goals and planning boundaries.

• Product goals connect strategic intent, customer outcomes and delivery choices
• OKRs distinguish outcome measures from output-based activity measures
• Frame planning assumptions through problem statements and opportunity hypotheses
• Map stakeholders by influence, interest, decision rights and information needs
• Define planning boundaries for budget, dates, scope and regulatory obligations
• Create outcome measures using leading indicators and lagging indicators

Participants can create a structured backlog containing clear, testable and estimable work items.

• User stories express user, need and value using standard syntax
• INVEST criteria assess backlog items before estimation and selection
• Acceptance criteria define observable conditions of satisfaction for stories
• Split oversized stories by workflow, business rule, data and interface
• Epics, capabilities and features create backlog hierarchy without false precision
• Non-functional requirements capture performance, security, accessibility and resilience needs

Participants can use story maps to sequence customer journeys and define viable release scope.

• User Story Mapping visualises activities, tasks and release slices
• Backbone activities organise user journeys into coherent planning views
• Slice minimum viable releases through value, risk and learning criteria
• Sequence stories using workflow order, dependencies and customer impact
• Identify thin vertical slices that deliver observable user outcomes
• Release boundaries distinguish mandatory scope from desirable future enhancements

Participants can prioritise backlog items using explicit value, urgency, risk and effort criteria.

• MoSCoW prioritisation separates must-have, should-have, could-have and won't-have work
• WSJF ranks work using cost of delay and job size
• Cost of delay combines user value, time criticality and risk reduction
• Kano analysis distinguishes basic, performance and attractive product features
• Prioritisation policies prevent urgent requests displacing strategic delivery work
• Document trade-offs between scope, timing, cost, quality and risk

Participants can apply relative estimation principles and establish a shared sizing reference.

• Relative estimation compares uncertainty, complexity, effort and technical risk
• Story points avoid misleading conversion into individual delivery hours
• Reference stories establish shared anchors for consistent comparative sizing
• Fibonacci scales reflect increasing uncertainty across larger backlog items
• T-shirt sizing supports early portfolio and roadmap estimation discussions
• Separate effort estimates from value assessments and delivery commitments

Participants can facilitate estimation sessions that produce usable estimates and expose uncertainty.

• Planning Poker combines independent judgement, discussion and consensus-based sizing
• Affinity estimation rapidly groups backlog items by relative size
• Bucket System estimation handles large backlogs through comparative placement
• Wideband Delphi records assumptions before converging expert estimates
• Outlier discussions reveal hidden complexity, ambiguity and delivery risk
• Estimation agreements define participation, evidence, scale and escalation rules

Participants can run structured refinement that prepares work for reliable planning and selection.

• Refinement cadences maintain a ready pipeline of prioritised work
• Definition of Ready clarifies information needed before sprint consideration
• Clarify business rules, data needs, interfaces and acceptance criteria
• Spikes reduce uncertainty where research or technical investigation is required
• Record estimation assumptions alongside stories requiring future validation
• Remove blocked items from near-term forecasts until dependencies are resolved

Participants can create achievable sprint plans using capacity, availability and delivery evidence.

• Sprint goals focus selected work around a meaningful delivery objective
• Capacity planning accounts for leave, training, support and ceremonies
• Calculate available person-days before selecting candidate backlog items
• Skill matrices identify specialist bottlenecks and pairing opportunities
• Unplanned work allowances protect sprint forecasts from operational disruption
• Sprint backlogs connect selected stories, tasks, risks and daily inspection

Participants can interpret flow and velocity measures without misusing them as performance targets.

• Velocity measures completed relative size across comparable sprint conditions
• Throughput counts completed work items over a defined period
• Use completed work only when calculating historical delivery performance
• Control charts show cycle-time variation and work-item completion patterns
• Avoid velocity targets that distort estimation and encourage oversized stories
• Segment data when team composition, workflow or item types change

Participants can use Kanban flow data to identify bottlenecks and forecast work completion.

• Kanban boards make workflow states, policies and blocked work visible
• Work-in-progress limits expose bottlenecks and reduce multitasking delays
• Cumulative flow diagrams reveal queues, flow stability and constraint locations
• Little's Law relates work-in-progress, throughput and cycle time
• Service level expectations define probability-based completion time targets
• Replenishment meetings select work according to capacity and service policies

Participants can construct outcome-based roadmaps and release plans using realistic delivery evidence.

• Outcome-based roadmaps communicate direction without fixed feature promises
• Release plans combine prioritised scope, capacity, sequencing and confidence
• Plan release increments around validated outcomes and operational readiness
• Use milestone maps for external deadlines, governance gates and dependencies
• Date-driven planning identifies scope options before making delivery commitments
• Scope-driven planning forecasts probable completion windows from delivery data

Participants can produce probability-based delivery forecasts from historical throughput or cycle-time data.

• Monte Carlo simulation generates forecast ranges from historical delivery samples
• Throughput-based simulations estimate completion dates for fixed scope backlogs
• Cycle-time simulations estimate completion likelihood for individual work items
• Percentile forecasts express confidence levels without claiming certainty
• Validate historical data for representativeness, stability and sufficient sample size
• Present P50, P70 and P85 forecasts for different risk appetites

Participants can assess forecast risk and show how critical assumptions affect delivery options.

• Risk registers capture probability, impact, owner, response and trigger
• Assumption logs distinguish validated evidence from planning beliefs and estimates
• Sensitivity analysis tests effects of capacity, scope and dependency changes
• Three-point estimates represent optimistic, likely and pessimistic delivery conditions
• Risk-adjusted forecasts include contingency based on quantified uncertainty sources
• Escalation thresholds identify when forecast changes require governance decisions

Participants can identify and manage delivery dependencies across teams, suppliers and governance groups.

• Dependency maps show predecessor, successor, interface and handoff relationships
• Classify dependencies as knowledge, resource, technical, approval or supplier constraints
• Programme boards coordinate cross-team milestones, risks and integration dates
• Manage external dependencies through service agreements and explicit lead times
• Dependency owners maintain actions, due dates and escalation routes
• Integration planning includes environments, data, testing and deployment readiness

Participants can align agile planning artefacts with proportionate PMO governance and reporting requirements.

• PMO governance distinguishes decision controls from unnecessary reporting activity
• Stage gates use evidence-based criteria rather than fixed documentation volumes
• Portfolio Kanban visualises demand, approval, delivery and benefit realisation
• Business cases express expected outcomes, costs, risks and confidence ranges
• Agile metrics support governance through trends, forecasts and exception reporting
• Align team cadences with portfolio reviews, funding cycles and audit needs

Participants can communicate delivery forecasts, uncertainty and trade-offs clearly to decision-makers.

• Forecast narratives explain scope, evidence, assumptions, range and confidence
• Use fan charts to communicate uncertainty across future delivery periods
• Decision logs record choices, rationale, owners and review dates
• Escalate trade-offs using options framed by value, cost and risk
• Executive dashboards separate leading indicators from lagging delivery measures
• Respond to fixed-date requests with scope scenarios and probability ranges

Participants can incorporate quality work, technical debt and operational readiness into agile plans.

• Definition of Done includes testing, documentation, security and deployment criteria
• Technical debt registers make remediation work visible within prioritisation decisions
• Quality costs compare prevention, appraisal, failure and rework impacts
• Operational readiness plans cover support, training, monitoring and rollback
• Non-functional work receives explicit backlog treatment and release acceptance criteria
• Release checklists confirm compliance, resilience, ownership and service readiness

Participants can improve estimation and planning reliability through structured feedback and data review.

• Retrospectives examine planning accuracy, flow constraints and collaboration patterns
• Five Whys identifies root causes behind repeated forecast variance
• Fishbone diagrams organise causes across people, process, technology and policy
• Track planned-versus-completed work to test selection and capacity assumptions
• Calibration reviews compare estimate ranges with actual delivery outcomes
• Improvement backlogs assign owners, measures and review dates for changes

Participants can build, review and present a complete agile planning and estimation pack for a realistic delivery initiative.

• Define product goals, stakeholders, assumptions and measurable outcome measures
• Construct a story map with prioritised release slices and acceptance criteria
• Estimate selected backlog items using relative sizing and documented assumptions
• Model team capacity, dependencies, risks and operational readiness activities
• Generate release forecasts using velocity, throughput or Monte Carlo evidence
• Present delivery options, confidence ranges, trade-offs and governance decisions