Casefill Rate - bharath143775/Executive-Metrics GitHub Wiki
๐ Casefill Rate (%)
๐งพ Definition
Casefill Rate measures the percentage of customer orders fulfilled completely and on time. It evaluates how effectively the supply chain delivers the exact number of cases (units/boxes) as requested by customers.
A high Casefill Rate reflects strong service reliability and customer satisfaction.
โ Simple Explanation
It tells us how much of what the customer ordered was actually delivered on time and in full.
100%= Everything requested was delivered correctly and on time.< 100%= Some items were missing or delayed.
This is a key customer service KPI used in logistics and supply chain.
๐ Data Required
| Data Point | Description |
|---|---|
| Cases Delivered (on time, in full) | Number of cases shipped exactly as ordered |
| Cases Ordered | Total number of cases ordered by customers |
"Cases" refers to units or boxes of product, not legal cases.
๐งฎ Formula
Casefill Rate (%) = (Cases Delivered / Cases Ordered) ร 100
๐ง Example
- Cases Ordered =
10,000 - Cases Delivered =
9,700
Casefill Rate = (9,700 / 10,000) ร 100 = 97%
97% Casefill Rate means the company fulfilled 97% of what the customer ordered โ a strong performance.
๐ผ Business Context
The Casefill Rate is a customer-facing reliability metric used by logistics, customer service, and supply chain teams. It is a direct reflection of how well the company meets customer demand.
๐ Why It Matters:
- Ensures customer satisfaction
- Indicates supply chain efficiency
- Affects brand loyalty and repeat business
- Useful for root cause analysis (e.g., if certain products regularly fall short)
๐จ Thresholds and Alerts
| Level | Casefill Rate (%) | Alert Type |
|---|---|---|
| โ Good | 98 โ 100% | Excellent โ No Action Needed |
| โ ๏ธ Warning | 95 โ 97.9% | Investigate Gaps โ Take Action |
| โ Critical | < 95% | Immediate Review โ Supply Chain Issue |
Alerts should be sent if the Casefill Rate drops below 98% for two consecutive periods or hits critical levels in any location or channel.
๐ Historical & Comparative Analysis
- Trend Monitoring: Helps identify recurring product or plant issues.
- Customer Segmentation: Some customers or regions may have lower service levels.
- Actionable KPIs: Combine with root cause data (stockouts, production delays) to drive improvements.