# Fill Rate Calculator MCP

> Fill Rate Calculator determines Order, Line, and Unit Fill Rates using your fulfillment data. It also quantifies the financial loss associated with stockouts and compares your performance against industry benchmarks for sectors like electronics or pharma.

## Overview
- **Category:** supply-chain
- **Price:** Free
- **Tags:** fill-rate, stockout, nps, benchmarking, inventory

## Description

Supply chain efficiency isn't just about counting shipments; it’s about knowing what goes wrong and how much it costs. This MCP helps you move past simple metrics to understand true logistics health. You feed in raw fulfillment data, and the system immediately breaks down performance across three levels: Order (did we fulfill the whole order?), Line (did we get all items requested on a line?), and Unit (did we deliver every single piece?). Beyond that, it gives you two critical perspectives. First, how does your operation stack up against industry peers? Second, if failure happens, what's the actual dollar impact and potential damage to brand reputation? By connecting this MCP through Vinkius, your AI client gives you a comprehensive picture of risk, turning complex logistics data into clear action points.

## Tools

### calculate_fill_rate_metrics
Calculates Order, Line, and Unit Fill Rates using raw fulfillment data.

### compare_to_benchmarks
Evaluates your current performance metrics against established industry targets for specific sectors.

### estimate_stockout_impact
Quantifies the total financial loss and potential NPS degradation from unfulfilled customer demand.

## Prompt Examples

**Prompt:** 
```
Calculate my fill rates for 100 total orders, 85 completed orders, 120 total lines, 100 completed lines, 500 units requested, and 450 units delivered.
```

**Response:** 
```
The calculation results are: Order Fill Rate: 85.0%, Line Fill Rate: 83.3%, and Unit Fill Rate: 90.0%.
```

**Prompt:** 
```
What is the financial impact of having 10 unfilled orders at a cost of $50 each with a high severity level?
```

**Response:** 
```
The total estimated stockout cost is $500, and the potential NPS impact is classified as 'high', indicating a significant risk of brand damage.
```

**Prompt:** 
```
Compare my current unit fill rate of 85% against the pharmaceutical sector benchmark.
```

**Response:** 
```
For the pharmaceutical sector, the benchmark is higher than your current 85%. Your performance status is 'Critical Failure'.
```

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## Use Cases

### Auditing poor seasonal performance
A regional manager notices that unit fill rates dipped sharply last quarter. They run `calculate_fill_rate_metrics` to confirm the drop, then use `compare_to_benchmarks` to see if this dip is normal for their region or indicates a systemic failure.

### Justifying new safety stock investment
The operations team needs budget approval. They run `estimate_stockout_impact` using high-severity data points, generating a precise dollar figure of potential lost revenue that directly supports their request for more inventory.

### Onboarding new product lines
A company launching electronics needs to know if its current logistics structure can handle the volume. They compare their projected fill rates using `calculate_fill_rate_metrics` against industry benchmarks via `compare_to_benchmarks` before placing large orders.

### Reacting to a major customer complaint
After receiving complaints about long delays, the supply chain team runs `estimate_stockout_impact`. This doesn't just flag 'low stock'; it flags potential NPS damage and assigns a severity risk rating.

## Benefits

- You get three levels of detail, not one. The `calculate_fill_rate_metrics` tool separates Order, Line, and Unit rates, so you know *exactly* where the bottleneck is in your process.
- Stop treating stockouts as a simple percentage problem. Using `estimate_stockout_impact`, you get a dollar figure for lost sales and a risk score that matters to executives.
- You don't have to guess if you're doing okay. The MCP allows you to run the `compare_to_benchmarks` tool, instantly seeing how your performance stacks up against pharmaceutical or retail averages.
- It gives you immediate context. Running benchmarks alongside raw metrics means you know if a low fill rate is normal for your industry or if it's critical failure.
- The three tools work together to give you a full picture: measure the problem, benchmark the ideal, and quantify the risk of waiting.

## How It Works

The bottom line is that it takes raw fulfillment spreadsheets and outputs three distinct, actionable reports: metrics, comparisons, and risk assessment.

1. You feed the MCP raw records detailing total items requested versus total items delivered, along with relevant financial costs and order counts.
2. The system runs three concurrent analyses: calculating fill rates, comparing data against industry targets, and modeling potential stockout consequences.
3. You get back a single dashboard view showing your current performance score, the specific failure metric, and an estimated cost of inaction.

## Frequently Asked Questions

**How do I use `calculate_fill_rate_metrics`?**
You pass it your raw fulfillment data, including total requests and total deliveries. It immediately breaks down the efficiency into separate Order, Line, and Unit rates for you.

**What does `compare_to_benchmarks` actually look at?**
It compares your current performance against industry averages based on sector type (e.g., food & beverage vs. electronics). It tells you if your 85% rate is 'Critical Failure' or just 'Needs Attention'.

**Is the financial loss calculated by `estimate_stockout_impact` accurate?**
It quantifies both revenue loss and potential brand damage (NPS). It’s designed to give a holistic, risk-weighted estimate that goes beyond simple cost accounting.

**Can I use all three tools at once?**
Yes. The MCP is built to run them together. This allows you to start with the raw metrics, contextualize it with benchmarks, and finish by seeing the financial consequence of everything combined.

**What data granularity does `calculate_fill_rate_metrics` require for accurate results?**
The tool requires raw fulfillment records, including total requested units, delivered units, and line item counts. It processes structured data that covers the necessary inputs across all three levels: Order, Line, and Unit.

**Are there usage limits when I run `estimate_stockout_impact` for massive datasets?**
No, the tool handles high-volume requests efficiently. We recommend batch processing large data dumps to ensure quick turnaround times and reliable cost calculations without hitting any service limits.

**Can I use `compare_to_benchmarks` if my industry isn't in the standard list?**
Yes, while the tool supports major sectors, you can input custom benchmark data. This lets you accurately compare your performance even for highly niche or proprietary industries.

**What kind of security is involved when connecting my client to `calculate_fill_rate_metrics`?**
The MCP uses standard OAuth protocols for secure connection management. Your AI client handles the necessary authentication tokens, guaranteeing that your fulfillment data remains private and protected during every calculation.

**What is the difference between Order, Line, and Unit Fill Rate?**
Order Fill Rate tracks complete orders; Line Fill Rate measures accuracy at the SKU level; and Unit Fill Rate calculates the total volume of items delivered versus requested.

**How can I estimate the cost of unfulfilled orders?**
Use the `estimate_stockout_impact` tool by providing the number of unfilled orders, the estimated cost per failed order, and the severity level of the service failure.

**Does this tool support industry benchmarking?**
Yes, the `compare_to_benchmarks` tool compares your current unit fill rate against standard targets for sectors such as retail, pharmaceutical, electronics, and food & beverage.