This cost breakdown can be used for an initial assessment. Since each company allocates costs differently, these breakdowns should be used with care. If costs in an area are 30% or 40% more than the norm shown below, than that indicates that the area deserves a closer look.
| Category | % of Sales |
| Transport | 2.7% |
| Warehousing | 1.5% |
| Inventory Charge | 2.5% |
| Customer Service and Administration | 0.9% |
| Total | 7.6% |
One effective measure of inventory health is the percentage of existing inventory that will be consumed by the forecast, orders, and planned production in the next 30 days. This metric can account for product substitutions, and the ability to move material between one location and another.World class supply chains operate at an inventory velocity that is in excess of 75%
Forecast errors generally increases further out into the future. The table below provides some clues as to the forecast error experienced at the SKU level. If your forecast errors are much greater than these at the SKU level, there is significant opportunity for improvement.
| SKU FORECASTING ERRORS AT 4 DIFFERENT FORECAST HORIZONS |
| 1 Year Ahead | 39% |
| 1 Quarter Ahead | 34% |
| 2 Months Ahead | 30% |
| 1 Month Ahead | 28% |
Table extracted from Benchmarking Forecasting Errors; by Chaman L Jain - The Journal of Business Forecasting, Winter 2005 -2006.
An improvement in forecast accuracy can reduce inventories and costs dramatically. Dr. Chaman L. Jain of the Institute of Business Forecasting has developed a simple and effective way of measuring the impact of forecast improvements (Dr. Chaman L Jain, St. John's University, Jainc@Stjohns.edu). Based on his ideas, we have created a spreadsheet that can be used to gauge the impact of forecast improvements. The following table details savings for some sample companies:
|
LOSS/GAIN RESULTING FROM 1% REDUCTION IN FORECASTING ERROR (By Industry) |
|||
|
Company |
Sales Volume (Millions of $) |
Loss/Gain Resulting from Under-forecasting (Millions of $) |
Loss/Gain Resulting from Over-forecasting (Millions of $) |
|
Computer/Technology |
|||
1 |
1,800 |
1.05 |
1.76 |
2 |
270 |
0.41 |
0.38 |
Weighted Average |
0.97 |
1.58 |
|
|
Consumer Products |
|||
3 |
3,000 |
5.08 |
2.06 |
4 |
2,400 |
6.18 |
1.21 |
5 |
1,300 |
0.59 |
1.46 |
6 |
900 |
1.24 |
0.92 |
7 |
800 |
1.04 |
1.10 |
8 |
800 |
0.9 |
0.85 |
9 |
307 |
1.01 |
0.98 |
10 |
100 |
0.3 |
0.90 |
Weighted Average |
0.97 |
1.58 |
|
© Copyright 2005 by Institute of Business Forecasting (www.ibf.org). All rights reserved.