Supply Chain
Analytics Glossary

When it comes to Supply Chain Analytics, it's important to know a few different things: 

This glossary is designed to give you the answers to the third question. How do you calculate your analytics? For each of these supply chain focused metrics, we'll discuss our way of calculating and some potential nuances of that metric.

Weighted Payment Terms

Definition: The Average time it takes to pay your suppliers, weighted based on spend.

Use Case: In the world of Procurement Analytics, payment terms often reigns 2nd only to cost savings. This is the number that Supply Chain teams use to impact Free Cash Flow, with the goal of creating a negative cash conversion cycle

Beware: Suppliers usually hate extending terms. And it costs you money indirectly becuase they have to pay In order to extend terms, you need to build a ton of trust and ensure that the business relationship is beneficial to both parties.

How WPT is calculated: 

Download all your invoice data. Make sure it has Spend, Invoice Date, Pay Date

Calculate the days between Pay Date and Invoice Date. This is Days to Pay

Spend * Days to Pay = Weighted Spend

WPT = SUM(Weighted Spend)/Sum(Total Spend) 

Bonus: How to calculate Weighted Payment Terms, grouped by Supplier: 

Spend * Days to Pay = Weighted Spend

WPT for one supplier = SUM(Weighted Spend for one supplier) / SUM (Total Spend for one supplier)

Extra Bonus: If you're a supplier, I highly recommend diversifying your business so that you have a portion of revenue at Net 0 days, likely for small and new customers, and a portion of revenue at the extended terms with your larger customers.

Cycle Count Accuracy

Definition: The accuracy of your warehouse processes, comparing system to reality. The inverse is essentially "inventory loss"

Use Case: This is typically the most influential metric with a direct financial impact for Warehouse Analytics.

Beware: The scale for "pass/fail" is very different than the American school system. If you're at 93% accuracy, that's a failure. In some instances, 97% is failure. We don't personally like the 0-100 scale because it distorts expectations. The REAL range should be 90-100.

Decisions before the calculation: 

Do you measure accuracy by # of SKU's that are wrong, total $ lost, or total Quantity lost?
Do your operations completely stop when you cycle count?
It should really be "Accuracy over [x] amount of time"
If you have MORE quantity in stock, how do you incorporate that?

How to calculate total $ lost without a stop in operations, losses only:

For each part number, record system quantity vs actual quantity

System Quantity - Actual Quantity = Quantity Lost (Gained)

Lost Inventory = Quantity Lost * Unit Price <-- note, if your inventory is serialized against PO transactions, you may not need this step. You can use SUM(Received Value for all the serial numbers that were lost)

SUM(Lost Inventory) / SUM (Total Inventory) = % lost

1 - % Lost = Cycle Count Accuracy

% of Revenue tied to a supplier

Definition: The % of revenue (or profit) tied to a specific supplier, via the bill of material. 

Use Case: This is used to measure risk. If one supplier is tied to 100% of your revenue, you should ensure that you have a ton of contingencies. You can also modify this to be "country of origin" to measure reliance on one country. 

Beware: Two suppliers could both be at 100%. Let's pretend that all your revenue has a cardboard box and a shipping label, and you have one supplier for boxes and one for labels. Both of those suppliers would be 100% of your revenue. This isn't necessarily bad because there's plenty of cardboard box suppliers. However, this should be clearly known and contingencies should be built. 

How to calculate total % of revenue with a supplier:

Prep work: 

We're going to pretend that each part number has one supplier. There are ways to calculate multiple, but for the sake of this exercise, we'll keep it simple. 



Shipping Accuracy

Definition: The percentage of orders that are shipped without error, compared to the total number of orders shipped.

Use Case: Shipping accuracy is a crucial metric for any company that fulfills orders. It measures how effectively a company is fulfilling customer orders, and can have a direct impact on customer satisfaction and retention.

Beware: In addition to measuring the percentage of orders shipped without error, it's important to also track the reasons for errors, such as incorrect address or missing items. This can help identify areas for improvement and reduce the likelihood of future errors.


Total number of orders shipped without error / Total number of orders shipped = Shipping accuracy

Example calculation:

Let's say a company shipped 100 orders in a given period, but 5 of those orders were shipped with errors, such as incorrect items or missing items.

Total number of orders shipped without error = 100 - 5 = 95

Total number of orders shipped = 100

Shipping accuracy = 95/100 = 0.95 or 95%

This company has a shipping accuracy rate of 95%, meaning that 95 out of every 100 orders are shipped without error.

In addition to calculating the overall shipping accuracy rate, it may also be helpful to break down the accuracy rate by order type, customer type, or product type. This can help identify any patterns or trends in accuracy rates, and allow for targeted improvements in specific areas.

To track the reasons for errors, a company could also implement a system for tracking and categorizing errors, such as a root cause analysis. This could involve tracking the reason for the error, such as incorrect address or incorrect item, and analyzing the data to identify areas for improvement.

Overall, shipping accuracy is a key metric for any company that fulfills orders, and can have a direct impact on customer satisfaction and retention. By tracking and analyzing shipping accuracy rates, companies can identify areas for improvement and work to continuously improve their fulfillment processes.

Want to learn about something else? Check out our LEARN page

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