Plan-to-Produce (also Demand-to-Supply)

Plan-to-Produce — sometimes called Demand-to-Supply or Forecast-to-Stock — describes the end-to-end production process from demand forecasting through master scheduling, materials planning, production execution and finished-goods replenishment. For manufacturing-centric ERP organisations, Plan-to-Produce is the fifth classical end-to-end process alongside O2C, P2P, R2R and H2R, and the most complex of them all — spanning the longest time horizons, the most dependent systems, and the tightest operational constraints.

Plan-to-Produce stages

1. Demand forecasting — statistical forecast plus sales-force input for the planning horizon (typically 12-18 months)
2. S&OP (Sales and Operations Planning) — monthly cross-functional reconciliation of demand, supply, finance and capacity
3. Master Production Schedule (MPS) — weekly or daily plan of finished-goods production
4. Material Requirements Planning (MRP) — calculate component and raw-material requirements from MPS
5. Capacity planning — verify machine and labour capacity supports the plan
6. Detailed scheduling (APS) — sequence orders on specific resources respecting all constraints
7. Production order release — firm orders dispatched to the shop floor
8. Shop-floor execution — execution via MES or ERP-internal shop-floor data collection
9. Goods-receipt to stock — finished-goods posting, quality release, cost calculation

Push versus pull

Plan-to-Produce uses two production logics often combined. Push (MRP-driven): forecast-driven MPS explodes into MRP-generated production and purchase orders. Standard for long-lead-time, variable-demand parts. Pull (kanban-driven): actual consumption triggers replenishment. Standard for short-lead-time, stable-demand parts. Most real-world manufacturers run both depending on part characteristics: A-parts (high value, stable demand) often on kanban, B-parts (variable demand) on MRP, C-parts (low value, sporadic) on simple reorder-point. Modern ERPs support per-part planning-strategy assignment, allowing the right mechanism for each part category within a single planning run.

Advanced Planning and Scheduling

MRP assumes infinite capacity; APS adds capacity constraints to produce executable schedules. APS engines consider machine availability, setup times, labour skills, tooling availability, raw-material availability, preferences for campaign production, and customer commitments to generate optimised sequences. Leading APS platforms: SAP IBP (Integrated Business Planning), Siemens Opcenter APS (formerly Preactor), Asprova, FlexSim Planner, Demand Solutions, Plex DemandCaster. Mid-market ERPs increasingly include lightweight APS modules; complex manufacturers add specialist tools. Implementation effort for a mid-market APS project: 6-18 months and 300,000-1,500,000 EUR, with payback typically through 5-15% throughput improvement and 10-30% WIP reduction.

Practical guidance

Three patterns for successful Plan-to-Produce. (1) Invest in data quality first. Poor BOM accuracy, stale routings, wrong cycle times and missing material masters break all downstream planning. Master-data quality is the highest-leverage Plan-to-Produce investment. (2) Match planning frequency to operational reality. Daily MRP runs for high-velocity operations; weekly for stable manufacturing; longer for engineer-to-order. Excessive planning frequency burns capacity without improving outcomes. (3) Treat S&OP as a management process, not a system output. The real value is the cross-functional alignment at the monthly meeting, not the spreadsheet template. Companies running disciplined S&OP consistently outperform peers on inventory turnover, on-time delivery and forecast accuracy.

Related Topics

• MRP
• APS
• Kanban