Production and Inventory Planning Training Course

Participants can prepare demand data and select appropriate baseline forecasting methods.

• Demand history cleansed for returns, promotions, stockouts and one-off orders
• Time-series patterns distinguished: level, trend, seasonality and random variation
• Moving averages and exponential smoothing calculated for stable-demand items
• Seasonal indices applied to monthly demand histories and future forecasts
• Forecast accuracy measured using MAD, MAPE, bias and tracking signal
• Segmentation separates forecastable products from intermittent-demand and project items

Participants can construct a demand review that documents assumptions, risks and ownership.

• Statistical baseline challenged using sales intelligence and market events
• Forecast value add identifies adjustments that improve or degrade accuracy
• Consensus demand plan records assumptions, scenarios, confidence and accountable owners
• Promotional uplifts separated from baseline demand for supply-planning visibility
• New-product forecasts use analogue products, adoption assumptions and review gates
• Demand review cadence aligned with S&OP milestones and decision deadlines

Participants can define inventory policies matched to product value, demand and supply characteristics.

• ABC analysis ranks items by annual consumption value and control priority
• XYZ classification groups demand by variability and forecastability characteristics
• Service measures distinguish cycle service level, fill rate and availability
• Inventory policies assigned by item class, supply risk and customer criticality
• Decoupling points positioned between forecast-driven and order-driven operations
• Cycle stock, safety stock, pipeline stock and dead stock distinguished

Participants can calculate and validate replenishment parameters for stocked items.

• Safety stock calculated from demand variability, lead-time variability and service factor
• Reorder points combine expected lead-time demand with safety stock
• Min-max settings tested against usage patterns, lead times and service targets
• Periodic review systems calculate target stock and review-period demand
• Parameter governance assigns review frequency, tolerances and data ownership
• Stockout root causes separated into demand, supply, master-data and execution failures

Participants can select lot-sizing rules that balance ordering, holding and operational costs.

• Economic order quantity balances annual ordering cost against holding cost
• Economic production quantity accounts for finite production rates and consumption
• Fixed-order quantity rules assessed against pack sizes, tooling and transport constraints
• Lot-for-lot planning reduces inventory where setup costs remain acceptable
• Period order quantity groups requirements across planned replenishment intervals
• Quantity discounts evaluated using total relevant cost and holding implications

Participants can build an aggregate plan that reconciles demand, output, inventory and workforce options.

• Product families created from shared capacity, demand patterns and planning decisions
• Aggregate demand translated into monthly volume requirements by product family
• Level, chase and mixed production strategies compared using total cost
• Inventory, backlog, overtime, subcontracting and workforce options modelled explicitly
• Production rates tested against available hours, yields and planned downtime
• Management trade-offs documented between margin, service, cash and operational risk

Participants can design an S&OP process that produces approved and accountable operating decisions.

• S&OP governance defines executive ownership, meeting sequence and decision thresholds
• Product review, demand review, supply review and executive review integrated
• Pre-S&OP scenarios quantify demand, capacity, inventory and financial consequences
• Decision log records approvals, actions, due dates and unresolved risks
• Financial reconciliation connects volume plans with revenue, margin and working capital
• S&OP maturity assessed through data quality, behaviours, process discipline and outcomes

Participants can create a time-phased master production schedule for finished goods or end items.

• Master scheduling translates aggregate family plans into end-item production commitments
• Planning time fences distinguish demand, slushy and frozen scheduling periods
• Available-to-promise calculated from supply, orders and uncommitted inventory
• Demand time fences protect schedules from uncontrolled customer-order changes
• MPS records include opening stock, forecasts, orders and planned receipts
• Schedule stability measured through changes, adherence and frozen-period exceptions

Participants can test master schedules against critical resource capacity before release.

• Bill of resources translates master schedule quantities into critical-resource loads
• Rough-cut capacity planning compares monthly loads with available capacity
• Capacity profiles expose timing mismatches hidden by aggregate monthly totals
• Bottleneck resources identified using utilisation, queue time and constraint evidence
• Overtime, shift changes and subcontracting evaluated as capacity response options
• Feasibility decisions documented before releasing unstable master schedules

Participants can explain MRP logic and interpret planned order proposals and exceptions.

• Dependent demand derived from parent schedules through multilevel bills of material
• MRP netting offsets gross requirements against on-hand and scheduled receipts
• Lead-time offsetting schedules planned order releases before required receipt dates
• Low-level codes determine correct sequencing of multilevel material calculations
• Pegging traces component requirements back to parent demand and orders
• MRP exception messages prioritized by shortage date, customer impact and recovery options

Participants can identify master-data errors and establish controls for planning parameters.

• Bills of material checked for quantities, alternates, scrap and effective dates
• Routing data validated for setup times, run rates and work-centre assignments
• Lead times separated into queue, setup, run, move and supplier components
• Yield and scrap factors incorporated into planned material and capacity requirements
• Parameter change controls prevent unapproved planning logic and data degradation
• Data-quality scorecards focus remediation on high-impact planning records

Participants can convert planned orders into feasible work-centre capacity actions.

• Capacity requirements planning loads work centres from planned and released orders
• Finite and infinite loading compared for different scheduling decisions
• Dispatching rules prioritise jobs using due date, critical ratio and setup
• Input-output control measures queue, throughput and work-centre backlog
• Theory of Constraints focuses improvement on system constraints and protective capacity
• Schedule adherence investigated through material, labour, machine and engineering causes

Participants can coordinate inbound supply and distribution replenishment with inventory policy.

• Supplier lead-time reliability measured using promised and actual receipt dates
• Purchase order horizons aligned with supplier flexibility and commitment windows
• Vendor-managed inventory roles defined through replenishment data and ownership rules
• Distribution requirements planning allocates supply across warehouses and customer locations
• Intercompany transfers planned using transit times, allocation rules and stock visibility
• Supply risk mitigations include dual sourcing, buffers and approved substitutions

Participants can use planning KPIs and exception routines to improve plan execution.

• Inventory turns, days of supply and ageing reveal stock-performance patterns
• OTIF, fill rate and backorder measures connect plans with customer outcomes
• Forecast bias and accuracy tracked by product family and planning horizon
• Schedule adherence separates planning quality from shop-floor execution discipline
• Exception dashboards rank shortages, excess, capacity overloads and overdue actions
• Root-cause analysis uses five whys, Pareto charts and corrective-action ownership

Participants can build, review and present a complete integrated production and inventory plan with decisions, risks and controls.

• Demand plan combines baseline forecast, commercial assumptions and scenario ranges
• Inventory policy sets service targets, safety stock and replenishment parameters
• Aggregate supply plan balances production, inventory, overtime and subcontracting choices
• Master schedule converts approved volumes into time-phased end-item commitments
• Material and capacity checks identify constraints, shortages and recovery actions
• S&OP decision pack presents KPIs, trade-offs, owners and escalation triggers