Goosing The Bottom Line

Analytical software tools help manufacturers squeeze out higher profits in tight times.

Just in case you were nodding off during accounting 101, here's a quick review: Profit = Revenue-Expenses. It's laid out in black and white (and hopefully not red) on your company's monthly financial statement. If sales are up, everything's hunky-dory. If sales are down, it's time to cut costs. That's the extent to which some companies go to manage their profits. Rick Lijana, division vice president global manufacturing at Ondeo Nalco, tells a different story. For its 52 plants worldwide, the Naperville, Ill.-based maker of water-treatment and process chemicals is using software to calculate and report what Lijana calls "profit velocity." By tracking the velocity that product flows through key production and supply-chain bottlenecks, and aligning that with sales, Ondeo Nalco can identify critical production processes. Because everyone knows how performance in these areas impacts company profits, the company can continually optimize performance before the month-end results hit the financial statements. For example, for a particular batch chemical process, Lijana recalls that the company had always focused on managing the evaporator stage. Looking at it in terms of profit velocity, managers realized they actually needed to manage the reaction process, and were able to translate this variability into a dollar value for the operator. "Literally, there's an adjustable screw on the variable feed pump. Our operator knew that if you turn it to the right, [we'd] be making $780 per hour. If he had to back off to the left, it would go down to $650 per hour," Lijana says. Not surprisingly, such profit visibility on the plant floor has extended a sense of ownership throughout the organization. "The concept that we use is dollars per hour, or margin per unit of time going through a constraint. You [might] actually make more money on a lower-margin customer when you're thinking about the utilization of your assets," explains Ron Shulman, CEO of pVelocity Inc., the Toronto-based company that developed the software used by Ondeo Nalco. As Shulman implies, within most sales organizations there's an unquestioned push to move high-margin products, the higher the volume the better. While gross margin generally offers an accurate picture of profit potential, sometimes a higher-margin product might require four times more time to make, or yield twice as much waste. Such costs aren't always well captured by traditional accounting methods. Pushing such products could, in fact, erode profitability. "We've had products at 55% gross margin that we were losing our shirts on," Lijana confirms. "Versus something we're running at 20% that we're making cash hand over fist." Such analytical tools use data that most companies are already capturing through ERP systems (product identification, customer identification, price information, cost information, ship date, ship quantity, invoice number) and process-control systems (machine start and stop time, quantity in/out, good units, scrap rates). Armed with this information, a company's sales and marketing team can make tactical decisions that maximize profit, shifting the product mix as opportunities arise. This is exactly what Pittsburgh-based U.S. Steel's commercial sheet division is doing. The group's 150 external and internal sales people are routinely analyzing the impact on the operation's bottleneck units if they sell one gauge and width product versus another. The analytical software that U.S. Steel is using, developed in this case by Maxager Technology Inc., San Rafael, Calif., refines the negotiating process one step further according to Jim Kutka, vice president, commercial sheet products. He describes a common situation in which a customer, balancing raw material needs, wants to give only 20% to 25% of its order volume to any one supplier. "If I have enough good information, I know which of his 5,000 tons I should be driving for," Kutka explains. "The important thing from a salesman's standpoint is to get the right 25% for my facilities, so he can get the most throughput, the most profitability, out of his order book." Accordingly, sales incentives for the organization have been realigned. If a strip mill is the bottleneck unit for example, with a certain average cash-contribution per minute today, the sales team's objective is to exceed that by 10% next year. As U.S. Steel's understanding of costs improves, its pricing strategy continues to evolve. Kutka notes that it's only logical that a sheet steel product that's half as thick as another, rolled at the same speed, but that takes twice as long to process, should cost more. Looking at profit velocity has had the added effect of aligning the steel company's sales and production people around a common, impartial metric. "The plant wants to get a lot through, and sales wants to sell things for higher revenues which might go through more slowly," Kutka says, describing a frequent point of friction among manufacturing organizations. Looking at profit per unit of time "challenges the plants to make things that are more difficult, and it challenges the sales folks to collect a higher price for something that's difficult [to make]." Of course prices are ultimately set by the market. But this is another area -- looking at the revenue component of the basic profit equation -- where some companies are applying new analytical tools to enhance earnings. Tuning In To the Market A lot of people presume that pricing is an art and not a science," says Foster Finley, an Atlanta-based vice president for the management-consulting firm A.T. Kearney Inc. "The data shows that it is, in fact, a science -- and a pretty robust science." It's a science that's currently being perfected by grocery, mass merchant and department stores. Companies in these industries are just beginning to report how they're able to optimize prices based on geography, climate, time-of-year and particular customer segments. Historically, Finley traces such price strategies to yield management practices in the airline industry, where variable pricing based on ticket purchase date has a long history. Within the retail industry, the computing power needed to handle the transaction data necessary for such analysis has become available only in the past two years. "The stars are in alignment for retail. They've got the data. They've got the bandwidth," Finley observes. Armed with ample point-of-purchase information, these companies are using sophisticated models to better understand the elasticity of supply and demand, predict how customers will respond when prices are nudged up or down and balance the tradeoff between profit margin and volume. Such an approach may be a sign of what's to come for manufacturers. "Manufacturing is the next domino to fall in terms of pricing," predicts Larry Warnock, vice president of marketing at Zilliant, an Austin, Texas, software developer focused on price and revenue optimization. More specifically, he believes that the next group to benefit from such market analysis will be "those companies that have flexible or dynamic purchase offers, a quote [is given], and it's taken or passed." In these and other businesses considering this type of strategy, one of the key challenges will be the availability of historical win-loss data. Many sales people and companies are unwilling or unable to track lost deals; it's only human nature, after all, not to dwell on losses. Warnock says companies need to start measuring loss not as a "chronicle of failure," but as a valuable indicator of customer demand. By analyzing a robust history of such data, companies can better differentiate behavior, zero in on customer propensities to close a deal, and segment products and customers accordingly. A simple example: A business assumes that all customers respond similarly to a discount, but historical analysis reveals that West Coast customers are more sensitive to price than Midwest customers. That company might be able to increase yields following a more geographically focused discount strategy. Of course location is only one of many factors influencing price sensitivity that more sophisticated market-response models are able to uncover. New product releases are another area where prices could be set more intelligently. "A lot of people set prices based on competitors, 5% above or 8% below, depending on what they think the product image or brand image is," observes Kevin Scott, a senior research analyst in the customer management strategy group of AMR Research. Such assumptions are often misguided. Scott says companies need to step back and perform a deeper assessment that actually determines what the market will bear for a particular product, and set prices accordingly. On the issue of price manipulation, the experts offer some final words of caution. First, companies need to wholly anticipate and prepare for the impact of any price changes on volume, which has a direct impact on a company's supply chain. Dramatic fluctuations could have some untended consequences. Second, airlines may be able to change their prices every day, but most companies cannot. Companies need to be careful not to be seen by their customers as being too fickle when it comes to price. Software can create proposals and run what-if scenarios, but it's up to someone who understands a company's brand and general business objectives to determine how such analysis is applied. "At the end of the day, software is a decision support tool," notes A.T. Kearney's Finley. As with any analytical tool, executives cannot expect a piece of software to run their business. "You can support a lot of the decisions you make on a day-to-day basis, but the decisions will be no better than the data and the robustness of the thought that went into the decisions." Profit Per Unit Or Profit Per Minute?

Standard Unit Cost, Unit Price, Unit Profit Approach
Selling price per ton Standard cost per ton Margin per ton
Product1 $366 $330 $36
Product2 $417 $408 $9
Product3 $354 $299 $55
Profit Velocity Approach
Raw material cost per ton Cash over raw material per ton Tons per mill minute at measurement point Cash contribution per minute
Product1 $221 $145 4.62 $669
Product2 $270 $147 8.00 $1,176
Product3 $224 $130 7.92 $1,029
Out of the three listed, Product 3 initially appears to be the most profitable, and Product 2 the least profitable. But because it flows through this operation's bottleneck point at the highest velocity, Product 2 actually generates the most cash for the company, and overhead costs should be reallocated accordingly. Source: Maxager Technology Inc.
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