XYZ Analysis: Goals, Advantages, Calculation Technique

Having previously covered the ABC classification in one of our earlier articles, we highlighted it as a commendable starting point for inventory management. However, it only scratches the surface of efficient stock level control. In this article, we'll unpack why and how to add more depth to your analysis.

In essence, the primary advantage of ABC analysis is its ability to identify those products that generate the highest sales for your company, thereby allowing you to focus your time and energy on them.

To better gauge service levels and more accurately forecast inventory targets, it’s essential to incorporate another dimension into our analysis: the XYZ categories.

It's no secret that different products experience varying demand uncertainties. Some products have a consistent demand, like toilet paper, while others, such as umbrellas, see fluctuations depending primarily on weather patterns.

Logically, the forecasting accuracy for a stable product like toilet paper (an X item) should outshine that of umbrellas (a Z item). And then there are products that lie somewhere in between, like mayonnaise, which generally has consistent sales throughout the year, but spikes during holiday seasons.

In essence, the XYZ analysis is a classification method for inventory based on the variability of their demand. Here's a breakdown:

X-Items: These are characterized by a consistent turnover over time. The future demand for this product group can be reliably forecasted to ensure sales aren't impacted by stockouts, and excess stock doesn't clog up the warehouse. These items typically have a variability coefficient of 0-10%.

Y-Items: While the demand for Y-items isn't as steady as X-items, their variability can somewhat be anticipated. This variability often arises due to known factors like seasonality, product life cycles, competitors' actions, or economic conditions. Accurate demand forecasting for these products can be more challenging. Their variability coefficient lies between 10-25%.

Z-Items: These have a highly unpredictable or sporadic demand. The absence of discernible trends or predictable causative factors makes their demand forecasting nearly impossible. Typically, such items are minimized or even eliminated from the inventory. However, some businesses, if they have the means and storage capacity, stock up on these, anticipating a sales surge. These products have a variability coefficient of over 25%.

In statistics, variability denotes the differences in a given metric's levels among various units of an analyzed composition during a specific research period or point.

Lastly, the variability coefficient is often expressed as a percentage and is determined by the ratio of the standard deviation (σ) to the mean (μ).

Below are diagrams illustrating the characteristics of the three categories.

Using the XYZ classification, businesses can create demand forecasts to determine optimal ordering schedules. X-category products should be ordered most frequently, as their steady demand allows for accurate predictions and timely order placements. Y-category products should be ordered less frequently, taking into account seasonal and other expected variations. Lastly, Z-category items should be ordered the least often since their demand is irregular and often unpredictable.

The frequency of conducting XYZ analysis varies depending on the industry. For instance, in the food sector, it's optimal to analyze sales weekly to understand on which days of the week specific product groups are purchased more frequently. For example, we might find that alcohol and confectionery items are often bought on Fridays and weekends, while bread and milk maintain steady sales every day.

It's essential to recognize that a one-time snapshot will not provide a comprehensive picture. To discern patterns in demand changes, it's better to evaluate at least three to four periods. Decisions can then be made based on this accumulated data, ensuring they are well-informed.

The variability coefficient is a relative metric that doesn't have specific measurement units but is still quite informative.

Significant seasonality can increase the variability coefficient, subsequently reducing its predictability factor. Calculation errors might lead to incorrect decisions, which is a notable drawback of the XYZ method. However, it's still preferable to have some form of structure rather than operating without any systematic approach.

1. Selection of Product Range for Analysis
- Depending on the purpose, the analysis can span the entire product assortment of a store, a retail chain, or specific product groups.

2. Determine the Time Interval for Analysis
- This is the time frame during which we'll assess the stability of each product's behavior. The interval can range from a single day, to a week, or even a month.
- The chosen analysis interval should exceed the sales frequency of most of the products under consideration. For grocery retail, a week is a logical choice due to cyclical consumer behavior.

3. Set the Time Period for the Analysis
- The longer the selected period and the more intervals it contains, the clearer the derived data will be.

4. Calculate the Coefficient of Variation for Each Product Category
- At this stage, evaluate the percentage deviation of sales volume from its average.
- The coefficient of variation is determined by a specific formula:

- For practical calculations, using Excel is more convenient. In Excel, the formula to calculate the coefficient of variation is: `= STDEV.P(range) / AVERAGE(range)`.
- For instance: `= STDEV.P(B3:H3) / AVERAGE(B3:H3)`.

- This formula evaluates the average value of the metric for the chosen period and assesses its fluctuation in percentages.

5. Classify the Product Assortment into Three Categories – X, Y, or Z:
- X - Products with a coefficient of variation between 0-10% represent items with the most consistent demand.
- Y - Products with a coefficient of variation between 10-25% denote items with variable sales volumes.
- Z - Products with a coefficient of variation exceeding 25% are items with sporadic demand.
- To facilitate this classification, use Excel's built-in "IF" function:
`=IF(I3<=10%,"X",IF(I3<=25%,"Y","Z"))`.

Enhanced Forecasting Accuracy:
- The method offers a more precise prediction capability.

Inventory Reduction, which:
- Improves the stability and efficiency of production processes.
- Boosts customer satisfaction.

Minimizes Inventory Obsolescence:
- Products are less likely to sit on shelves and become outdated.

Refines Service Levels for Products with Fluctuating Demand:
- Allows businesses to better cater to products that don't have consistent sales.

1. Speed:
- If you possess sales data, conducting the analysis only requires inputting the data into a table and applying the mentioned formulas.

2. Reliability:
- The calculation algorithm is straightforward, reducing the potential for errors and incorrect outcomes.

3. Versatility:
- The method can be applied to almost any business metric.

1. Rapid Demand Shift:
- If demand drastically changes, the variability coefficient might increase, possibly relegating a promising product to the Z category. To minimize this risk, it's recommended to analyze high and low sales seasons separately.

2. Short Data Accumulation:
- If data is only gathered over a short period, the analysis might not provide a comprehensive view; a more extensive time slice of statistics is preferable.

3. Profitability Oversight:
- The XYZ analysis reflects demand but doesn't account for profitability. Therefore, it's frequently combined with ABC analysis to identify not just well-selling products but also those that generate substantial profit.

A combined ABC/XYZ analysis is recognized as a powerful and versatile tool. The method involves creating a unified table where analysis objects are sorted into nine categories based on the results of both ABC and XYZ analysis.

This combined approach aids in managing both product resources and customer bases. The tool assists in fine-tuning product policies, amplifying the share of in-demand products and solvent clients.

Its strength lies in its universality, making it suitable for assessing various business aspects, from products to staff performance evaluation.

- Marketing/Sales: Explains why different attention and promotional costs are necessary for distinct product groups.
- Finance: Elucidates why it's challenging to reduce inventory and storage costs uniformly for all products.
- Manufacturing: Provides insight into why demand uncertainty and diverse service level targets prevent production schedules from remaining stable and consistent throughout the year.
- Suppliers/Clients: Assists in planning supplies and shipments while also fostering compromises.

The XYZ analysis assists in evaluating demand fluctuations for individual items or product groups, optimizing assortment, and warehouse inventories. It can be implemented independently but is best paired with the ABC method. This combination ensures a comprehensive overview, highlighting the most promising and profitable products.