Dell Computers: A Case Study in Low Inventory When managers discuss low inventory levels, Dell is invariably discussed. Hell, even I've mentioned Dell on this site. So why all the commotion? Has their low inventory REALLY helped out that much? In short, yes. This article is primarily going to discuss how much it helped. This article will not discuss how they achieved such high inventory turns using a state of the art just in time inventory system. Reasoning behind need for lower inventory The first thing that needs to be discussed is why low inventory has such a great effect on Dell's overall performance. The reason is quite simple: computers depreciate at a very high rate. Sitting in inventory, a computer loses a ton of value. As Dell's CEO, Kevin Rollins, put it in an interview with Fast Company: "The longer you keep it the faster it deteriorates -- you can literally see the stuff rot," he says. "Because of their short product lifecycles, computer components depreciate anywhere from a half to a full point a week. Cutting inventory is not just a nice thing to do. It's a financial imperative." We're going to assume that the depreciation is a full point per week (1%/week) and use that to determine how much money high inventory turns can save Dell. This means that for every 7 days a computer sits in Dell's warehouses, the computer loses 1% of its value. Ok, now that we know how much Dell loses for each day, let's take a look at some of Dell's data over the past 10 years that I pulled from www.themanufacturer.com What I got from this was the inventory turns. An inventory turn, as this website successfully describes it, is "cost of goods sold from the income statement divided by value of inventory from the balance sheet". Typically, this is turned into a value showing how many days worth of inventory a firm has by dividing inventory turnover by 365. I divided the inventory turnover by 52 in order to show how many weeks worth of inventory Dell holds. Here are the results: Dell’s Inventory Turnover Data Year Inventory Turnover Week's Inventory 1992 4.79 10.856 Week/day’s Inventory = number of 1993 5.16 10.078 weeks or days the company hold its 1994 9.4 5.532 inventory before selling it = number of 1995 9.8 5.306 weeks/days per year divided by 1996 24.2 2.149 inventory turnover 1997 41.7 1.247 Week’s inventory = 52/Inv. turnover 1998 52.40 0.992 1999 52.40 0.992 Day’s inventory = 356/Inv. turnover 2000 51.4 1.012 2001 63.50 0.819 Key point to notice here is that Dell was carrying over 10 weeks worth of inventory in 1993. By 2001, Dell was carrying less than 1 week's worth of inventory. This essentially means that inventory used to sit around for 11 weeks and now it sits around for less than 1 week. So what does this mean for Dell? Remember, computers lose 1 percent of their value per week. This isn't like the canned food industry where managers can let their supplies sit around for months before anyone bats an eye. Computers aren’t canned goods, and as Kevin Rollins of Dell put it, computers “rot”. The longer a computer sits around, the less it is worth. That said, due to depreciation alone, in 1993 Dell was losing roughly 10% per computer just by allowing computers to sit around before they were sold. In 2001, Dell was losing less than a percent. Based on holding costs alone, Dell reduced costs by nearly 9%. Since 2001, Dell has continued to lower inventory. Looking at their latest annual reports, day's inventory has dropped by approximately a day. Hopefully this article provided you with a practical example that demonstrates the positive effects lower inventory can have on a firm's overall costs. For more information regarding lawyers in the Texas area, check out Dallas Fort Worth trucking accident attorney. For more basic information regarding holding costs, please read A Simplified Look at the Pros and Cons of Inventory. A Case Study in Software Implementation Optiant is a supply chain software solutions provider for companies including Gillette and HP. The discussion described how the implementation of Optiant's PowerChain® software suite helped to dramatically decrease inventory and increase service for a well-known manufacturer of consumer adhesives. Supply Chain: The manufacturer being discussed operates primarily in North America. Raw Materials First, they procure over 3000 raw materials from multiple sources and hold the raw materials at one of their two manufacturing sites until they are processed. This is the first stage (echelon) where inventory is held. After the raw materials are turned into finished goods, they are immediately shipped to a distribution center (DC). Finished Goods The firm has two DCs and over 1800 SKUs (stock keeping units. This is basically how many different products and package variations they have on those products). The DCs are the final stop for finished goods inventory before they are shipped to a myriad of retailers. Of those retailers, Wal-Mart, Staples, Home Depot, and other mass market stores constituted one third of their overall volume. The DCs also receive some finished goods which come from other manufacturers in the form of finished goods. The DCs are the second stage (second echelon, thus making more than one echelon, hence the name, multi-echelon) where safety stock is held. Old Inventory Policy Prior to Optiant's consulting work and software implementation, the manufacturer had no real method for determining their safety stock. Essentially, they used a trial and error method where they would set a level and if they were stocking out too often, they would increase inventory. When they stopped stocking out, they would scale back inventory. Their inventory was also high because of expansion in their product line and high service levels demanded by stores like Wal-Mart. The manufacturer lacked the expertise regarding how to set a safety stock level that optimized each local inventory stage, and additionally, they were without the experience necessary regarding how to optimize the overall system. As John Neale put it, this is a problem because while safety stock formulas can be useful for local optimization, one point he made was, "Don't just optimize things in isolation." Upon realizing that there were better ways of doing things, they contacted Optiant. Optiant What Optiant did for them was more than just selling them a software package. Optiant spent many weeks learning about their supply chain constraints and gathering data, and then used PowerChain® to optimize the safety stocks for each of the 1500 SKUs and each of their 3000 raw materials. Data Requirements Much of this data is demand data. In order to get a feel for demand, Optiant uses historical demand projections. In order to figure out what kind of deviation there is on these projections, they look at historical projections and compare them with historical demand realizations. As you can imagine, a lot of companies don't keep accurate records regarding this data. The less a company has in the way of records, the less effective software initially is. Keep this in mind before you bring in any consultants: start collecting data before they get there, so you're ready to roll once they're on the clock. Optiant also required supplier data, including lead times, costs, and a bill of materials (list of parts required for each SKU). Software Once they have the data, they can start to use their software model. I'm not clear on the math that runs the program other than that it uses algorithms to minimize holding costs while maintaining service level requirements. I didn't bother asking for more detail, because what I understand is this: the software they have works and is based on the kind of framework that you would expect to come from someone with a Ph.D. from MIT, which is precisely what Optiant co-founder, Sean Willems, has. The point is, the program is complex, but it is not baseless, and it is not a hoax. It is exactly the kind of complex software I was referring to when I wrote about the kind of advantage that professional software can offer that Excel can't even come close to providing. Results Before you refuse to believe the software works without understanding every detail behind it, consider that Optiant's solution allowed this adhesive manufacturer to raise their service level while lowering safety stock value by over 20%. First of all, 20% is a very large reduction in safety stock value on its own. In addition to this, they were able to raise their service level while lowering safety stock. At first glance, this seems too good to be true. Normally, the way to raise a service level is by raising safety stock, not by lowering it. Why is this case any different? Multi-Echelon Inventory Management This case is different because it is a multi-echelon inventory model. What this means in this case is that they had the opportunity to hold inventory at two stages: the raw materials stage and the finished goods stages. Balancing Raw Materials and Finished Goods Remember, holding costs are a function that involves the value of the inventory and at the raw materials stage the value is considerably less expensive. This means that if the adhesive manufacturer has short production lead times from raw materials to finished goods, which they do, then they can afford to hold large amounts of raw materials, small amounts of finished goods, and still be in a position to meet demand. Thus, by reducing finished goods inventory and increasing raw materials inventory, they can increase service level because of their ability to quickly turn raw materials into finished goods, and they can reduce inventory costs because they are holding less finished goods. Risk Pooling The other reason they are able to reduce safety stock value while increasing service is because they have so many SKUs that all use the same basic raw materials. The importance here is the inherent flexibility that raw materials when they can become a variety of different finished goods. This allows them to keep materials raw for as long as possible, which reduces their vulnerability to fluctuations in demand. The vulnerability to these fluctuations is limited because many of their glue product SKUs are essentially pooled as one product with an overall demand that is less likely to fluctuate as long as products are kept as raw materials that can be turned into any product once demand projections are closer to demand realizations. To further illustrate this is an example from the MITSloan Management Review about apparel manufacturer Bennetton Group SpA and how they delay final goods production by keeping raw materials in a position ready to be turned into finished goods: An inventory of undyed sweaters gets stockpiled in one location; coloring takes place only after specific orders have been received. This pooling of demand across geographical areas, and across colors, helps Benneton greatly reduce inventory risk while more effectively meeting customer demand.1 Another example cited in the article is how the house paint industry holds only base paints which colors are added into instead of holding onto hundreds of different colors at each retail location. The effects of this are incredible because for the adhesive manufacturer, paint companies, and Benneton, the risk of each individual product in the product line can be vastly reduced by simply keeping finished goods as raw materials for as long as possible. Paint companies no longer have to worry about having too much blue paint and not enough red paint. Statistically, the variations in each type of paint will even out. So if yellow doesn't sell as much as expected and green sells twice as much as expected, paint companies are still ok as long as they have the right amount of base paint. Unfortunately for the consumer this makes it difficult to return paint. Luckily for the adhesive manufacturer, risk pooling works. So does the software Optiant creates.
