Frequently Asked Questions Manufacturing

1. How can Connected Planning reduce production bottlenecks? 

Integrated scenario modelling lets planners surface bottlenecks early, test capacity trade-offs (overtime, shift changes, subcontracting) and prioritise orders to maximise throughput while minimising expediting costs.

2. Can Anaplan help with production scheduling and sequencing?

Yes — it supports high-level sequencing and feeds detailed schedules into execution systems. Use Anaplan for constraint-based aggregate scheduling and release rules, then export prioritised run-lists to MES/APS tools; this keeps planning aligned to shop-floor realities without replacing execution systems.

3. How do you handle multi-site master-data (BOMs, routings, units)?

We harmonise BOM/routing hierarchies and create governed master-data layers for consistent use across models. Early master-data standardisation and transformation pipelines ensure product definitions, units and cost structures are reconciled so variance analysis and consolidated planning are reliable.

4. What inventory KPIs improve first in manufacturing?

Days of inventory, takt adherence, backlog days and forecast error for make-to-stock SKUs. Improvements come from combining statistical demand, safety-stock policy optimisation and scenario-testing for supply disruptions — typically improving inventory turns and reducing expediting spend.

 5. How fast can we see ROI on shop-floor related use cases? 

Core planning wins (forecast accuracy, reduced expedite) often show measurable improvement within 3 months after go-live. Starting with an Minimal Viable Model (MVM) focused on top SKUs, or a high-cost plant, reduces lead-times to impact and proves broader rollouts will deliver multiplied returns.

 6. How do you integrate with ERP, MES and SCADA? 

We build resilient ETL/API pipelines and reconciliation routines to exchange masters, orders and production confirmations. Integrations handle BOM, inventory transactions, work orders and production yields, with reconciliations to surface data drift and automated alerts for failed feeds.

7. Can AI/ML improve short-term demand sensing for production?

Demand sensing reduces mismatch between production runs and actual demand by blending causal signals (promotions, lead indicators) with statistical models and human overrides.

 8. How do you manage change for plant teams and engineers? 

Use role-based training, local champions and pilot-to-scale approach, starting with one line or product family.