Key Industry Metrics
Keeping an Eye on Success
Are your inventory levels optimized to service your customers?
Are your orders shipped quickly and accurately?
Below is a list of key industry metrics or measurements. This is a partial list - listed here as an aid to our clients.
| Corporate Metrics | |
| Overall Management | |
Economic Value Add (EVA) Economic Value Add Momentum |
|
| Industry Metrics | |
| Cycle Times | |
Customer Order Promised Cycle Time Customer
Order Actual Cycle Time Manufacturing
Cycle Time Inventory Replenishment Cycle Time Cash-to-Cash
Cycle Time Supply-Chain
Cycle Time |
|
| Fill Rates | |
| Line Count
Fill Rate The number of order lines shipped on the initial shipment versus the number of lines ordered -- This measure may or may not take into consideration the requested delivery date (see On Time Delivery) Example: ABC Company orders 10 products (one order line each) on its Purchase Order #1234. The manufacturer ships out 7 line items on March 1, and the remaining 3 items on March 10. The Fill Rate for this Purchase Order is 70%. It is calculated once the initial shipment takes place. Calculation: Number of Order Lines Shipped on the Initial Order* / Total Number of Order Lines Ordered (7/10 = 70%) SKU Fill Rate Case Fill Rate Value Fill Rate *NOTE: "Shipped on the
Initial Order" -
This usually refers to the first shipment out of the primary
warehouse. Therefore,
if an order line ships out of an alternate shipping facility
and it ships out on/before the first shipment out of the
primary warehouse, then it is considered an addition to the
Fill Rate. |
|
| Inventory Record Accuracy | |
| Inventory Record Accuracy A common calculation is: Stratify SKUs: (annual usage X standard cost) A Items= items representing the top 80% of total dollars B Items= items representing the next 15% of dollars C Items= items representing the bottom 5% of dollars Cycle count items (usually daily) using a random sample, within the following groupings: A Items = 4 times per year B Items = 2 times per year C Items = 1 time per year Items considered accurate if the actual on-hand quantity matches the perpetual inventory quantity, within the following tolerances: A Items = plus or minus 1% quantity variance from perpetual balance B Items = plus or minus 3% quantity variance from perpetual balance C Items = plus or minus 5% quantity variance from perpetual balance Target should be an absolute minimum of 95% for MRP/DRP to function effectively; 99% for best-in-class |
|
| Inventory Turns (or Turnover) | |
| Inventory
Turns The number of times that a companies inventory cycles or turns over per year Calculation: A frequently used method is to divide the Annual Cost of Sales by the Average Inventory Level. Example: Cost of Sales = $36,000,000. Average Inventory = $6,000,000. $36,000,000 / $6,000,000 = 6 Inventory Turns OR
Inventory Turns Can be a Moving Number. Projected
Inventory Turns |
|
| On-Time Shipping Performance | |
On-Time Shipping Performance is a calculation of the number of Order Lines shipped on or before the Requested Ship Date verses the total number of Order Lines. Throughout the following text, I refer to "shipped" On-Time. But, if actual "delivery" data is available, it may be substituted and compared to the Requested Delivery Date. (such as with an EDI#214 ). *On-Time: Shipped on or before the requested ship date (except if the receiving party does not accept early shipments). Sample On-Time Metrics: On-Time SKU
Count |
|
| Perfect Order Measurement | |
| The Perfect Order Measure calculates the error-free rate of each
stage of a Purchase Order. This measure should capture every
step in the life of an order. It measures the errors per order
line. But how do you capture errors? Let's look at what happens when an error occurs. Say for example, your warehouse picks and ships the wrong item. Once the customer receives the order and notices the error, they contact the manufacturer and notify them of the mistake. The manufacturer then enters a credit for the item not shipped and an invoice for the item shipped in its place. For almost all errors that occur, a corrective credit is issued. It is through an analysis of these credits that you derive your metric. Most systems require a "reason code" to be used when entering a credit. Tracking these reason codes and assigning them to a category allows you to group them for the Perfect Order Measure. Example: Order Entry Accuracy: 99.95% Correct (5 errors per 1000 order lines) Warehouse Pick Accuracy: 99.2% Delivered on Time: 96% Shipped without Damage: 99% Invoiced Correctly: 99.8% Therefore, the Perfect Order Measure is 99.95% * 99.2% * 96% * 99% * 99.8% = 94.04% |
|
| Performance to Promise Dates | |
| When a Distributor
places a Purchase Order with a Manufacturer, he has certain
expectations about when
he will receive the items ordered. His original expectation is
the On-Time Delivery Metric. However, the manufacturer may give
him a revised estimate as to when they expect to fill the order.
The manufacturer's promise is called the "Performance-to-Promise
Date Metric". Example: ABC Company Orders 2 Products on Purchase Order #1234, with a Requested Ship Date of June 10. The first item is in stock and ships on June 10th.. The second item is on backorder. The manufacturer estimates that the 2nd item will ship by July 1. The item is manufactured and ships out on June 28. The Performance-to-Promise Date is 100% (items ship on-time or early) *However, if the 2nd item does not ship till July 2nd, then it's late. The Performance to Promise Date is 50%. |
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| Transportation Metrics | |
|
Freight Cost
Per Unit Shipped Outbound Freight
Costs as Percentage of Net Sales Inbound Freight
Costs as Percentage of Purchases Transit Time Claims as
Percent of Freight Costs Freight Bill
Accuracy Accessorials
as Percent of Total Freight Percent of
Truckload Capacity Utilized Truck Turnaround
Time Shipment Visibility/Traceability
Percentage Number of
Carriers Per Mode On-time
Pick-ups |
|
| Service Metrics | |
|
Utilization
Rate Realization
Rate Labor Multiplier Revenue Per
Head |
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