What Manufacturers Can Learn from Wal-Mart
According to Wikipedia, a company’s “Return on Assets (ROA) percentage shows how profitable a company's assets are in generating revenue” and “is an indicator of how profitable a company is.” Regardless of what type of company you are, ROA is calculated the same way:
(Sales/Total Assets) (Net Profits/Sales)
Total assets is a key component in the equation, so obviously asset structure plays a vital role in how ROA is measured and controlled -- but asset configurations can be very different from industry to industry, which leads to interesting problems in how companies can proactively monitor ROA. The equation, after all, is based on historical performance – so how can it be applied to your business while you are negotiating contracts or making scheduling decisions?
Major retailers like Wal-Mart have this problem licked. 80% or more of their assets can be tied up in the inventory sitting on the shelves in their stores, and the rest could be classified as plant, property and equipment. The faster that inventory moves, the higher the ROA percentage. They aren’t making their money off of the 100 plasma TV sets they sold last month – they take up too much shelf space and have a razor thin profit margin. The batteries, greeting card and the shaving cream you picked up when you bought the TV? Pure profit.
The bottom line is that retailers know this, and have it wired in to the way they do business. The systems are in place to monitor inventory turn, and corrections can be made on demand. But what about manufacturing? The asset structure is the opposite of a retailer – 80% or more of their asset base is plant, property and equipment while 20% (or less, hopefully) is tied up in inventory. So is thinking about “inventory turns” even applicable? Yes, but with a twist.
Take a look at your products from a profit-per-minute perspective, and not just margin. It’s like the “inventory turn” for Wal-Mart, but instead of basing your strategy on “inventory turn x margin” you base it on “production speed x margin.” Take a look at this example:
The decision to make Product A rather than Product B due to its higher margin results in over $1.5 million less profit over the course of a year! And don’t stop at the product level. What is the average profit-per-minute of the products that customer A bought from you last month? What about customer B? Without factoring in production velocity along with margin, you are very likely focusing on the wrong products and the wrong customers.
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