Updated: Jul 1, 2022
Inventory turns are a poor indicator of corporate performance
The 2002 Oakland A’s won 20 straight games during a season in which they won a total of 103 games and finished first in their division. They did this despite having a budget that ranked 28th out of 30 teams. (Their highest paid player ranked 71st in salary among all players). This achievement is chronicled in the book and subsequent movie titled Moneyball. Moneyball tells the story of how this low-budget team exceeded expectations using insights gained from statistical analysis. The A’s chose players and adopted policies based on statistical analysis, not on historical rules of thumb, or the opinions of experts. A fundamental thrust behind this approach is that historical measurement approaches for determining outcomes were not just limited, but wrong.
It’s hard to believe but these insights were available for decades to anyone. All it took was interest in data and finding correlations between data and how teams win.
I have seen software professionals use Moneyball as an example to help audiences understand AI. However, the insights used by the Oakland A’s and subsequently by virtually all baseball teams are derived from simple statistics. There is no machine learning, neural networks, or even Bayesian analysis involved. In fact, a lot of the statistical analysis movement in baseball was started by a security guard-turned-baseball writer named Bill James. One of Mr. James’ biggest challenges was not the statistics but capturing 100 years of data from every game ever played.
For example, if you read the Moneyball book or see the movie, you know that the Oakland A’s eschewed stealing bases, even though stealing bases had been a critical part of the game of baseball for 100 years. The simple reason they arrived at this policy is that stealing a base is statistically a net positive towards winning (i.e. scoring more runs) only if your base stealing has a success rate of 70% or more. For decades, the average base stealing success rate across all of baseball was significantly less than 70%. (Interestingly since the widespread use of statistics in determining when to steal, the gap in the past 20 years has narrowed to zero).
What does all of this have to do with inventory turns? After all, this is an article presumably about inventory turns.
For starters, inventory turns is a widely-used measurement for supply chain management excellence. For example, Gartner uses it as one of the factors in their widely watched annual supply chain rankings. But do higher inventory turns contribute to corporate success? Just as the Oakland A’s asked the question “does stealing bases contribute to scoring runs and winning games?,” I decided to put the inventory turns question to the test vis a vis corporate success.
But what is corporate success? Higher profit margins? Higher growth? Perhaps the most independent and objective measure of corporate success is the value that the public markets give to a company, in other words, the company’s market capitalization. Shareholders consider many factors when placing value on a company and many executives are measured based on how much shareholder value they create relative to a peer group.
I recently concluded an analysis of more than 2300 public companies across 19 industries that participate in global supply chains. A summary of the results of an earlier version of this analysis was published in Supply Chain Quarterly in February (What type of supply chain strategy drives market cap leadership?). A summary report (PDF) for all industries can be downloaded here. Individual industry reports are also available for download. An example (PDF) for the consumer goods industry can be downloaded here.
The thrust of this analysis and related ongoing work is to determine the supply chain factors that contribute to market leadership. The analysis uses market capitalization multiple (market cap multiple) as a key measure of corporate success. Market cap multiple is the market cap of a company divided by its revenue. This has the effect of normalizing for revenue when comparing companies with different revenues.
This is particularly important since across all industries, the top quartile of market cap leaders have market cap multiples that are 2.4 times their industry average, and 12.7 times industry laggards (defined as the bottom quartile). In other words, industry market cap leaders are creating 140% more value for their shareholders than their respective industry averages (per dollar of revenue).
So, what does this analysis conclude regarding inventory turns? The clear conclusion is that inventory turnover is a poor indicator of corporate performance, at least insofar as market performance is concerned. 82% of the companies that fall into the top quartile of market cap multiple leadership in their respective industry perform worse than their industry average in inventory turns. Furthermore, there is a near zero statistical correlation between inventory turns and market cap multiple across the entire data set of 2300+ companies.
Another interesting finding is that – in general – aggregate-level industry inventory turns are lower today than they were ten years ago (the same was true in 2019; current inventory levels may have some dislocations caused by the pandemic).
So what are we to make of this? Just as the Moneyball guys determined that certain long-held beliefs for success measurements were simply not true, the same goes for inventory turns. The net upshot is that inventory turnover – in and of itself – is meaningless when comparing performance between two companies. It must be combined with other factors and measurements to find meaning.
First and foremost, how is inventory contributing to operating profit? One would think that the widely-used GMROI (gross margin return on investment) and turn-and-earn measurements (Gross margin multiplied by inventory turns) offer some answers. However, the analysis shows that these too are not good indicators of corporate performance. There is a slight negative correlation between inventory turns and gross margin within most industries; however, joining the two measurements together also does not show a correlation to corporate performance. That said, some companies have low gross margins to intentionally provide cost leadership to customers; they must drive high inventory turns to make money (Costco is an example of this).
The answer to this conundrum likely lies somewhere in a mix of the following:
1. Serving the customer
2. Product variety
3. Cost of money
In the past ten years, the focus of supply chains has shifted significantly downstream towards the customer. Leading companies have shifted their supply chains from a cost focus to provide unique, differentiated value propositions for their customers. As a result of this, SKU counts, product variants, and delivery options have grown. At the same time, lead times and time windows for delivery and production have shrunk. Leading companies have done this by carrying the same or more inventory. Furthermore, over the past ten years (and beyond that), the cost-of-money component of inventory carrying costs has been reduced significantly (this is expressed as the weighted average cost of capital or WACC).
Simply put, supply chain management has evolved to a complex multi-variate science. Single operational measurement approaches – particularly inventory turns – are not useful for discerning excellence. The past decade has been about satisfying customers with increasing nice-to-haves in terms of product and delivery innovation. Nice-to-haves quickly become must-haves. The time duration between when something is introduced as a nice-to-have and when it becomes a competitive must-have is shrinking to zero. Supply chain management today is about delivering this to the customer while maintaining operating margin and return-on-investment. Those companies that align to this will be rewarded by investors. You must decide what inventory level is necessary to drive these customer strategies, not the other way around.