Supply chain managers can thank thinkers like Ian Glenday for providing tools like the Glenday Sieve analysis. The Glenday Sieve can help you and your enterprise begin to identify and separate out the stability hidden beneath the chaos and firefighting that seems to dominate daily activity.
The chart below shows a Glenday Sieve analysis of three actual working warehouses. This analysis is by volume (usage quantities for SKUs). (We will discuss other options later.)
What strikes most folks when they look at this graphical presentation of the data is the fact that for all three warehouses, 50 percent of the unit volume is reached with fewer than twelve percent of the SKUs.
Actually, Warehouse W100, in our sample set, is a very small warehouse—carrying only 30 SKUs in total, so it is slightly outside the norm. The larger warehouses (i.e., W200 and W300) are more typical, reaching 50 percent of their unit volume with fewer than six percent of their total SKUs.
To the astonishment of most who see their company’s data in this way for the first time, typically 95 percent of the typical warehouse volume will involve fewer than half of their total SKUs.
More importantly, these results are typical—not only of inventory transaction, but of all sorts of business processes, when the correct metric is identified.
Consider this: In most warehouses, the last one percent of unit volume is spread across 30 percent or more of SKUs.
This is the long tail that “wags the dog.”
It is, generally, the low-volume SKUs with, more often than not, relatively high demand volatility, that consume most of the time and energy in operations management.
Sadly, that time and energy is not, most often, spent in production and improvements. Rather, it is all too frequently time and energy wasted in firefighting and attempts to minimize the impact of chaos across the rest of the organization—or supply chain.
By the way, the same situation probably exists all across your supply chain. Segmented by supply chain—rather than warehouse—a Glenday Sieve analysis would probably give you very similar results.
If we want “the tail” to stop “wagging the dog,” it should become readily apparent that we need to not attempt to manage our inventory, our operations or our supply chain the same way across the full range of SKUs.
Ian Glenday and other have suggested that products and processes can be broken down conveniently in four “streams” or flows:
When your management team looks at all of the daily firefighting, expediting and energy it takes to get through a typical day or week, it may seem overwhelming to even think about spending even more time and energy on lasting improvements.
However, when seen in the light of the Glenday Sieve, it no longer seems so overwhelming to spend some time focusing on just six percent of products or processes to begin a process of ongoing improvement (POOGI).
This (typically) six percent of products constitutes an underlying flow of stability in the midst of what otherwise might appear to be a sea of turmoil in your supply chain or on the production flow.
Focusing on just six percent seems like a reasonable goal, especially when the reward for the effort—any improvement gained—will affect half or more of your volume.
Seeing things in this way may help your executive and management team to discover that there is light at the end of the tunnel. And, improvements in “the green stream” will free up time, energy (and, perhaps, money) to begin improvement efforts on “the yellow stream,” as well.
This kind of analysis and the adoption of a POOGI (as an executive-sponsored program, not a one-time effort) has led to dramatic improvement in a great many organizations.
In order to stop the tail from wagging the dog, some enterprises have segmented their processes based on “the streams.” For example, green and yellow stream products are run on separate lines or managed in separate supply chains from products in the blue and red streams.
This kind of arrangement helps quiet the chaos and stop the firefighting over upwards of 90 percent of the volume. It also liberates the blue and red lines to make more time available for changeovers and setups that may be required to handle the broader range of SKUs in smaller volumes.
There is no one answer, but recognizing the cause of the present chaos is a great place to start for dealing with the subject in a rational way.
The Glenday Sieve analysis presented above is based on product volume (number of units sold, for example). This would be very helpful in prioritizing POOGI efforts in the areas of inventory management, production management or supply chain execution.
But consider some other potential applications for the Glenday Sieve:
| Analysis by “Hits” (i.e., number of distinct orders for a SKU) | Example application: Use this analysis to organize your warehouse, placing the items with the most “hits” in the spots most convenient for picking |
| Analysis by Revenues | Example application: Use the Glenday Sieve by revenues to help sales and marketing segment the market; Partition the sieve on geography, on the demographics of the customers, or even by salesperson and try to identify why some products sell better (or more poorly) across geographic or demographic boundaries, or perform differently by salesperson |
| Example application: Partition the sieve by product line to help product development see patterns of behavior based on product characteristics, customer groups, or marketing support |
We are confident that the light gained from such an analysis can be leveraged effectively for ongoing improvement in dozens—perhaps, hundreds—of ways.
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