In a food business, most employees are familiar with the term ‘FIFO’: first in, first out. It means that whatever products you have in stock need to be tracked in order of purchase so that you can prioritize selling the oldest batches.
FIFO is essential whenever food is involved because you’re dealing with products that have a shelf life. Some, like aseptic pureed fruit, can last up to one year. Others need to be shifted or consumed within weeks or days. Figuring out which goes first means better hygiene and less spoilage.
However, this isn’t the only application of FIFO. As a general system of product tracking and management, FIFO tends to be the preferred method when the time comes for declaring your income statements. It’s a reasonably simple and accurate means of inventory valuation. But will the advent of big data change that?
Any business that involves selling products will have to manage its inventory and come up with a cost of goods sold (COGS) over its fiscal year. Failure to do so risks having the IRS audit your books, and the easy solution is to outsource this task to a CPA or hire your own. But hands-on entrepreneurs and those with limited resources may want to tackle this task themselves.
The effort of calculating COGS would be minimal if prices remained constant throughout the year. For most materials and finished items, though, that’s not the case. And few businesses, if any, purchase and sell all their items in a single transaction per year.
A simple method of inventory valuation would be to average the cost of each batch of purchases. You don’t need to keep track of the price of each batch instead of maintaining a running average cost of the pool of products you have in stock. However, because it blends product costs, averaging is less accurate, especially if the prices you deal with tend to fluctuate.
Two other common methods for inventory valuation are FIFO and LIFO (last in, first out). Both methods require more effort to keep track of batch costs. The difference is that under FIFO, the assumption is that the oldest goods are sold first, while LIFO assumes that the newest units are sold first.
In an ideal comparison, prices don’t fluctuate, businesses don’t deal in different products, and there’s no inflation. Then, all methods of inventory valuation will yield the same COGS result, and the correct one to choose would always be averaging, which is the simplest. But that scenario never happens in the real world.
Due to inflation, prices tend to rise over time. This means that companies using FIFO valuation will report higher gross margins since it’s assumed that older units are sold first, and their cost is lower. LIFO valuation, on the other hand, makes your costs increase with inflation, reporting lower gross margins. The averaging method yields result somewhere in between.
The difference becomes relevant when you consider tax liabilities. Under the inflation scenario, companies using LIFO tend to get more favorable tax breaks than those using FIFO.
However, it’s not a one-sided advantage because, in the event of economic contraction, the tax liabilities are reversed. And the method of valuation must be the same one used to report to shareholders. Lower margins may result in reduced tax liability, but you’re also declaring your income to be less.
Ultimately, getting a tax break isn’t the main objective of most businesses when choosing how to manage and cost their inventory. What you really want is an accurate assessment of the value of your merchandise. Using a method that strays farther from the true costs or the order in which products are sold will undermine this goal.
The impact of big data can be a game-changer in this area. In previous years, only giant operations like Amazon could justify the effort involved in tracking the vast volume of products they handle. It would mean investing significantly in automated inventory management and data entry.
Now, however, traditional sales-based data isn’t the only source of information companies can harness. Putting IoT-enabled products in the hands of consumers multiplies the number of data points you can feed into an algorithm. This lets you train the AI to find and exploit correlations that lead to better material management and more accurate forecasts of supply chain efficiency.
As it becomes easier to harvest this non-traditional data, the difficulty associated with tracking a large, diverse inventory and constant fluctuations in cost will be less prohibitive. Companies can make a data-driven decision on anything inventory-related, including the costing method itself. Even perpetual inventory tracking based on specific unit identification could be feasible.
The IRS allows companies to apply for a change in costing method. Depending on what the data tells you, it might be time to do so.