Supply chain & capacity planning model (starter)

Purpose and hunger

Match capacity and inventory to customer demand while minimizing the cost and risk of stockouts, excess inventory, and missed commitments. This starter model helps teams diagnose supply chain levers, run focused scenario tests, and convert rules-of-thumb into repeatable decision rules you can tune for your environment.

Core model components

• Demand profile and variability — average demand, seasonality, trend, and short-term variability (day-to-day or week-to-week).
• Lead times and variability — supplier lead time distribution, shipping variability, and internal replenishment or production lead time.
• Inventory policies — reorder point (ROP), order quantity (EOQ or lot sizing), safety stock rules tied to service level goals.
• Supplier reliability assumptions — on-time delivery rate, fill rate, quality defects, and escalation/penalty rules.
• Capacity definition — available production hours, throughput rates, changeover impact, skill pools, and outsourcing options.
• Buffers and flexibility — time buffers, capacity buffers, dual-sourcing, finished-goods safety stock, and cross-trained labor pools.

Key formulas & quick references

• Safety stock (basic) = Z * sigmaLT, where sigmaLT = sqrt(leadTime * varianceDemandPerPeriod + demandMean^2 * varianceLeadTime) (use simplified Z * sqrt(LT * varianceDemand) for many cases). Choose Z by target service level.
• Reorder point (ROP) = averageDemand * leadTime + safetyStock.
• Order quantity — use lot-sizing that fits your cost model: EOQ, fixed-period review, or min-max rules. Test impact of larger lots on working capital and lead-time variability.
• Capacity utilization rule — keep critical resources below 80–85% loading for responsive systems; above this, lead times and variability explode.

Practical checklist (use for a first 90-minute diagnostic)

1. Gather current metrics: average demand, demand CV (coefficient of variation), lead time mean & std, current service level, inventory days on hand, on-time supplier rate, and capacity utilization for key resources.
2. Map the replenishment lead-time chain for top SKUs: order placement → supplier lead time → inbound quality check → putaway → available to promise.
3. Identify the 20% of SKUs that drive 80% of stockout cost or customer complaints. Focus scenario tests on these first.
4. Run two quick scenarios per SKU/group: (a) increase service level by X% and calculate required safety stock; (b) reduce lead time by Y% and calculate inventory reduction. Compare working capital impact vs avoided stockout cost.
5. Review supplier performance and options: dual-sourcing, shorter lead-time lanes, local backup, or consignment stock.
6. Inspect capacity constraints: identify bottleneck operations, opportunities to shift load or add overtime, and feasible cross-training options to expand skilled pools.
7. Set decision rules: define who approves exception orders, reorder triggers, and emergency capacity activation thresholds.
8. Create an experiment: pick one SKU or product family, implement a tuned ROP and capacity buffer for 6 weeks, and measure fill rate, inventory days, and lead-time variability.

Scenario tests to run

For each scenario, record assumptions and expected outcomes:

• Reduce lead time by 20% (process or supplier improvement): expected inventory reduction and change in service level.
• Increase target service level from 95% to 98% for critical SKUs: incremental safety stock and capital cost.
• Add a secondary supplier with 90% on-time rate vs primary 95%: overall fill-rate change, complexity, and risk trade-offs.
• Increase capacity buffer (e.g., one extra shift per month) vs outsourcing a portion of demand: compare cost and responsiveness.

Common mistakes and how to avoid them

• Using average demand only — ignore variance. Always include variability when sizing safety stock.
• Targeting utilization near 100% — this amplifies delays. Keep critical resources off the limit or create quick surge options.
• Treating all SKUs the same — segment by value, lead-time risk, and customer impact and apply different policies.
• Neglecting supplier behavior — model supplier lead-time variability, not just mean lead time.

KPIs to monitor

• On-time in-full (OTIF) and fill rate
• Days inventory outstanding (DIO) or inventory turns
• Lead-time mean and standard deviation
• Capacity utilization on bottleneck resources
• Stockout frequency for prioritized SKUs
• Supplier on-time rate and quality defect rate

Quick diagnostic questions

• Which 10–20 SKUs cause the most customer impact when late?
• How much of current inventory is safety stock versus cycle stock?
• Where does most lead-time variability originate — supplier, transport, internal process, or quality checks?
• Are critical resources regularly above 85% utilization?

Next steps & adaptation guidance

Start small: pick a product family, apply the checklist, and run one controlled experiment. Use the results to refine assumptions, update decision rules, and scale improvements. Tailor safety-stock formulas and capacity buffers to your service expectations and financial constraints. For organizations with frequent changes or high variability, prefer shorter review cycles and more frequent re-calibration.

Where this model fits in a reusable toolkit

This starter model can be bundled into a Supply Chain Toolkit that includes SKU prioritization worksheets, an interactive safety-stock calculator, supplier performance audit templates, and a capacity readiness checklist. If you plan to copy or operationalize this across sites, create a baseline dataset and a standard experiment protocol so local teams can adapt without losing comparability.