Traditional approaches to inventory management and replenishment divide inventory stocks into two portions:
- Working stock – the inventories designed to cover daily demand
- Safety stock – the inventory quantities designed to cover variation in supply or demand or both
Years of statistical analytics and software development have been focused on improving the ways in which lead-time, demand and safety stock values are calculated. So much, in fact, that most of the people who use supply chain management, inventory management, or replenishment software frequently do not even understand what the software is doing, how it is doing it, or why it works or does not work.
Some years ago I was consulting a firm and, in the course of the business, reviewing how they went about their inventory management and replenishment. They had software that did inventory management and that included replenishment calculations.
So, we were sitting together and he was describing to me what he was doing on his computer. He said, “Here’s the ordering screen. It shows historical demand here [pointing], and the recommended order quantity here [again, pointing]. And, I don’t know exactly what this number is for [pointing], but if I think the system is suggesting that I buy too much or two little, I can adjust this number until the suggested order quantity lines up with what I think it ought to be.”
Well, of course, what the system was doing was exponential-smoothing of demand and the value he was adjusting was the value of alpha in the formula.
What I refrained from asking him (only by biting my tongue) was, “If you are going to simply adjust the system’s findings to your intuition, why use the system at all?”
The moral is: Systems that are not understood—and most complex systems are not understood—are also not trusted. Especially if they frequently—or even, regularly—produce what are perceived to be unreliable results.
The artificial divide
The artificial subdividing of stock quantities into “working stock” versus “safety stock,” and adding complexities around the factors used to calculate the one value versus the other provides no added value. In fact, the complexity actually leads to less reliability because the users frequently do not know how to set the input parameters effectively. Not to mention the fact that the parameters that are effective today may not—in fact, likely will not—be effective tomorrow or next week.
The fact of the matter is, in most cases, the only awareness of the division between “working stock” and “safety stock” quantities is found in the software itself and those that may be intimately acquainted with the software and its configuration. The people on the warehouse floor typically do not know when they have made an incursion into “safety stock.” They don’t know that the first 41 units they picked for order number 8789089 were from “working stock,” and the last nine units were taken from “safety stock.” And, they should not care.
Even the managers frequently have no visual signal that an incursion has been made into “safety stock.”
Inherent simplicity
Employing Theory of Constraints (ToC) Dynamic Buffer Management (DBM) makes life easier to understand for those responsible for inventory management and replenishment (read: supply chain managers). The buffer size (for any given item in any given stocking location) is a single number. (Let’s say, 1,000 units.)
The formula for setting the initial buffer size is simple and easily understood. Typically that formula is something like this:
Initial Buffer Qty = [Average Daily Demand] * [ToC Replenishment Days] * [2] * [Paranoia Factor]
The only factor that really needs any kind of explanation is the “Paranoia Factor.” This is merely a multiplier selected by intuition and based on senses of the criticality of an item. An item might be critical because it is used in the production of 800 other items; or because the majority of your customers all buy this item; or because one hugely important customer relies upon you for this item; or dozens of other reasons.
Once the initial buffer size has been calculated and set, the buffer is divided (mathematically) into three “zones.” The top third is called the green zone, the middle third is called the yellow zone, and the bottom third is called the red zone.
Going forward, the DBM system simply monitors for conditions at each replenishment cycle and adjusts the buffer size according to rules. The rules are typically:
- Too Much Green – The item has been found in the green zone on three consecutive replenishment cycles; therefore, reduce the buffer size by one-third.
- Too Much Red – The item has been found in the red zone on two consecutive replenishment cycles; therefore, increase the buffer size by one-third.
It’s that simple. No complex formulas for calculating and managing variability in demand or supply.
On top of that, supply chain managers can have simple visual signals as to the status of their buffers. A simple view of the inventory data (by location) can readily provide red light, yellow light, and green light indicators for the buffer status in any stocking location for any item. No math and easy to equate to action:
- Green light – no action required
- Yellow light – take note, perhaps investigate critical factors like larger-than-normal orders or orders pending for critical customers
- Red light – consider expediting measures, if necessary
NOTE: There are more options available with DBM, such as identifying and managing SDCs (sudden demand change items—like seasonality), managing Virtual Buffers (between stocking locations, such as warehouse-to-warehouse replenishment, or broader supply chain visibility and collaboration). It is not the intent of this article to exhaust the applicability of DBM.
RKL eSolutions, LLC is in the process building a cloud-based solution to help you manage your inventory in just such a way—using Dynamic Buffer Management and the Theory of Constraints. Contact me or fill out the contact form here if you would like more information.
