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- Backorders
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- Phantom inventory
- Prioritized ordering
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- Service level
- Service level (optimization)
- Stock-Keeping Unit (SKU)
- Stockout

ABC XYZ analysis, much like its predecessor ABC analysis, is a categorization tool aimed at identifying the best performing products in one’s catalog so one can determine appropriate service and safety stock levels. Unlike ABC analysis, which focuses exclusively on a single criterion (typically sales volume or revenue), ABC XYZ analysis attempts to also quantify a second dimension (demand uncertainty or volatility). Despite perhaps providing a slightly higher resolution snapshot of performance, ABC XYZ analysis is still a naïve application of the underlying mathematical principles and merely serves to amplify bureaucracy and instability. It also retains all the limitations of a classic ABC analysis but arguably provides an even greater sense of false security through mathematical flimflam.

Under this new nine-category rubric, X-class SKUs are the most stable (experience the least demand variance), Y-classes somewhat stable (experience moderate demand variance), and Z-classes the most unstable (experience the most demand variance). Building on the classic ABC analysis, a supply chain practitioner is presented with a seemingly more nuanced breakdown of one’s catalog across the time frame, where SKUs are analyzed across twice as many dimensions.

In order to process the new classification, a supply chain practitioner follows the same initial steps as the classic ABC analysis. Once this stage is completed, one progresses to the XYZ portion of the analysis, where one requires:

*The desired number of demand variance classes*: usually limited to 3, though this is flexible.*A threshold for separating each class*: entirely at the discretion of the supply chain practitioner. An example might be <=10% for X-class, >10-25% for Y-class, and >25% for Z-class.*The average for each SKU across the observed period*: easily calculated in any spreadsheet.*The standard deviation and coefficient of variance for each SKU*: also easily calculated in any spreadsheet.

The standard deviation, in the context of a year’s worth of data, is usually how much sales in any given month differed from the overall monthly average for the year. Once a supply chain practitioner has this information, they can calculate the

Once the CV is computed, the supply chain practitioner sorts the SKUs into their respective X, Y and Z-classes in line with their predetermined thresholds. This results in a nine-category matrix where SKUs are sorted in terms of their revenue and demand variance.

Figure 1. A model ABC XYZ analysis, as featured in the downloadable Excel spreadsheet. For explicit calculations, please consult the formulas within relevant columns.

**AX**: These SKUs generate high revenue and experience low variance. As such, a supply chain practitioner might decide that lower levels of safety stock are needed than the other A-class SKUs, in order to achieve high service level targets.**AZ**: These SKUs may generate equally high revenue as AX and AY ones, but experience significantly more demand variance. As a result, higher levels of safety stock might be deemed prudent.**CX**: These SKUs generate low profit and experience low variance. Low levels of safety stock would likely be the chosen course of action (relative to AX, AY, AZ, BX, BY and BZ).**CZ**: These SKUs not only generate low profit, but experience elevated levels of demand variance. From a supply chain perspective, these SKUs represent the worst of both worlds. Such SKUs would, theoretically, have low safety stock levels and be prime candidates for possible discontinuation.

As a general rule of thumb, ABC XYZ analysis indicates that SKUs require more safety stock as one moves along the x-axis, commensurate with the increased difficulty to predict demand (with CZ SKUs being a notable exception, as described above).

Figure 2. A model ABC XYZ matrix with *revenue* on the y-axis and *demand variance* on the X-axis. This matrix displays potential service level targets for each designation, with levels depreciating as revenue drops and demand variance rises.

**Low resolution**: Exactly as per ABC analysis, the nine categories of an ABC XYZ matrix miss demand patterns such as rising versus falling trends (see*Harry Potter*and*Tesla*t-shirts in Figure 3), limited offerings (see*Suez Canal*t-shirt), and seasonality (see*winter shoes*). As a result, the impact these can have on one’s inventory policies goes completely uncharted. This limitation also presupposes the supply chain practitioner has not arbitrarily opted for even more classes along each axis, which is entirely possible given the*laissez-faire*nature of the approach.

Figure 3. The line graph demonstrates the edge cases that ABC XYZ analysis missed in the model data set. For example, both Harry Potter and Tesla t-shirts finished as BY-class SKUs and would receive the same service level and safety stock targets. This ignores the fact that the SKUs are demonstrably trending in completely opposite directions.

**Increases instability**: ABC XYZ analysis extends the arbitrary and unstable categorization created by ABC analysis. The real*dollars and cents*difference between CZ and CY, or BZ and even BY, could be vanishingly small, if not almost financially unnoticeable. Not only that, just like in an ABC analysis, these practically imperceptible differences could shift depending on the selected time horizons. For example, a SKU could oscillate between AZ and CZ simply by expanding or contracting the selected time frame (e.g., monthly versus quarterly versus yearly horizons). Much like the selection of nine categories described above, there is no more or less sense in choosing a greater or smaller time frame.^{[2]}As such, setting service level and safety stock targets based on such unstable inputs is deeply flawed.

**Increases bureaucracy**: By definition, the unstable categories described above require management to intervene and establish distinct policies for each one. This, unfortunately, results in even more bureaucracy generated and bandwidth squandered. Just as the difference between an A and B-SKU might be a single percentage point (or mere handful of dollars), the CV differential between Y and Z-class SKUs might be faint at best. These parameters are completely arbitrary and ultimately determined by committee, hence they are of questionable provenance. Bearing in mind that SKUs can easily shift between the nine categories*throughout*the observed period (regardless of where they may*finish*it), setting arbitrary service levels based on this information not only creates unnecessary administration and meetings, but also increases the likelihood of costly stockout events.

Furthermore, many, if not most, of the chefs involved in setting these arbitrary parameters will lack the requisite mathematical training to parse the approach, let alone be able to meaningfully contribute to the numerical recipes. This criticism is expanded in*Theoretical objections to ABC XYZ*. It should also be highlighted that, despite the increased categorization and bureaucracy, ABC XYZ analysis does not actually identify*why*certain products are difficult to forecast – such as CZ SKUs. Rather, it simply determines*that*they are difficult to forecast, and management is left to quibble over which safety stock formulas to arbitrarily apply to these chance categorizations.

**Lacks financial perspective**: At its core, ABC XYZ analysis is predicated upon a*first order*approach to economic drivers. In short, this mindset considers SKUs only in terms of their*direct*margin contributions. Though ABC XYZ appears to also consider demand variance, its foundation is still based on how much each SKU contributes in an individual, direct sense (e.g., revenue). This approach views SKUs in*isolation*rather than*combination*. This nuance is the hallmark of a*second order*approach, where the value of a CX SKU, for example, is considered in relation to an AX one. Though the former may not contribute significant revenue, having it in stock may facilitate the sale of the latter, thus the CX’s*indirect*value may vastly outweigh its*direct*one. Therefore, an already arbitrary categorization process, which results in equally arbitrary inventory policies, is completely blind to these subtle economic drivers. This will almost certainly result in instances of stockout events for SKUs whose true worth has not been realized.^{[3]}

The third moment,

As a result, the validity of the statistical investigation in an ABC XYZ analysis is unfinished at best and misleading at worst. In fact, the nature of modern computing and statistical techniques is such that there is no need to limit one’s scope to only four moments, thus even a theoretical future iteration of ABC XYZ that incorporates these moments would still be underpowered by comparison.

ABC XYZ aims to help practitioners identify appropriate inventory policies for difficult-to-forecast SKUs (e.g., AZ or CZ) without identifying why these SKUs might be difficult to forecast. Moreover, it fails to provide any granular perspective on how SKUs interact (their

In terms of underlying tools, the approach doubles the arbitrary parameters of its predecessor, and triples the number of classes, while incorporating a partially literate grasp of statistics. This transgression cannot be ignored, however well-intentioned ABC XYZ proponents may be. The potential peril lies in the patina of rigor the XYZ calculations present readers. Unlike ABC analysis, which is accessible to just about anyone with a working computer and functioning brain, ABC XYZ purports to leverage a few statistical principles that, to the uninitiated, can appear quite advanced and impressive. This, however, is a buzzword-crutch that does not support its own weight. A proper statistical analysis of sales data

In fine, ABC XYZ analysis sacrifices statistical robustness to remain accessible to the general supply chain practitioner. This tradeoff results in a process that amplifies instability and distracts users from the underlying issues of interest. Practitioners whose businesses have outgrown such practices are welcome to email contact@lokad.com to arrange a demonstration of a production grade PIR solution – Lokad’s answer to the problems ABC XYZ attempts to solve.

1. Typically, A, B and C-class SKUs, where A represents the most profitable, C the least, with B somewhere between. The time frame is ordinarily a calendar year, but this can vary.

2. Granted, there is a lower limit to utility; selecting a single week’s worth of data would be of almost no probative value. However, once one has determined a data set of sufficient historical depth (say, 3 months of sales), there is almost no logical objection to the suggestion it could stand to be increased by another month. The result of this would, as mentioned above, almost certainly shift some SKUs’ placements in the ABC XYZ matrix. This serves to underline another problem with the ABC XYZ process: once one has reached a probative mass of data, the process is immediately vulnerable to further noodling. This stands in opposition to what a categorization is intended to do: provide robust and meaningful boundaries between entries.

3. This is a very brief summary of Lokad’s perspective and foreshadows stockout cover as a crucial economic driver. Both of these concepts are expanded upon in our prioritized inventory replenishment tutorial.

4. Or “SKU-ness” if you prefer.

5. Just as *pi* contains an infinite number of digits, a probability density function has an infinite number of moments of different order. However, in practice, generally only the first four are used.