What You Don’t Know Can Hurt You
Many factors impacted Cisco Systems’ bottom line in its recently reported $2.7 billion loss; however, those losses would have been much smaller if management had recognized double orders being made and adjusted its sales forecast accordingly, says Stanford Graduate School of Business faculty member Erica Plambeck. “Cisco failed to account for duplicates in the order backlog and, therefore, although the tech economy had begun to slow down, Cisco anticipated continued high demand for its products,” says the assistant professor of operations, information and technology.
A report from the Stanford Graduate School of Business briefly outlined Cisco’s dilemma: Sophisticated information systems gave its managers real-time data, allowing them to detect the slightest change in current market conditions and to forecast with precision.” But the highly hyped systems failed to account for frustrated customers and resellers, tired of long waits for products, who began to order from multiple distributors.
Cisco began to stockpile components, added workers, and helped contract manufacturers buy more parts. The backlog evaporated as customers canceled duplicate orders and new orders failed to materialize.
An examination of what happens when management fails to correctly estimate demand for a product or customers’ sensitivity to delay is the crux of a recent paper by Plambeck and co-author Mor Armony, assistant professor at Stern School of Business, New York University. They show how these factors can cause companies to build too much capacity or not enough. “Cisco is not the only company with difficulties in estimating demand because of duplicate orders,” Plambeck says. “Intel and other semiconductor companies believe that data on bookings is irrelevant because it is too difficult to distinguish between duplicate orders and true demand.”
Yet it is important to account for double orders and the reasons for them, she says. “Otherwise, by counting duplicate orders as true demand, you overestimate the demand rate, and by counting the cancellations of duplicate orders as lost sales, you overestimate customers’ sensitivity to delay, and then you wind up with excess capacity.”
Plambeck says she became interested in the subject of double ordering after reading about Cisco’s problems. “If you read the business press you hear only about overestimating demand. I had not read anything in the business press about overestimating customers’ sensitivity to delay, the rate at which sales are lost when customers are forced to wait for the product. The optimal level of capacity increases with customers’ sensitivity to delay, so estimating customers’ sensitivity to delay is a very important part of the puzzle.”
She and Armony showed how to tackle the problem using observable data that varies over time, such as the stock distributors have on hand, the number of outstanding orders, the number of orders placed and the number of cancellations per day per distributor, and the length of time that customers wait for the product. In a simple model with one manufacturer and just two distributors, they calculated the most likely values, in technical terms the “maximum likelihood estimates,” of the true demand rate, the average amount of time that a customer will wait before canceling his order, and the rate at which customers can be expected to double order when forced to wait for the item they want.
What Plambeck and Armony found is that errors in estimating duplicate orders and cancellations are common even in stable supply-and-demand environments. “Even if customers are rarely back-ordered, the manufacturer will make a significant error in estimating the reneging rate unless it accounts for double orders,” the paper concludes. “Typically this results in excess production capacity.” In one of the modeled examples, the manufacturer’s capacity is 20 percent greater than optimal because of overestimated demand. Conversely, the researchers say, a manufacturer can invest too little in capacity, again by miscalculating the key factors. “Our analysis serves to warn manufacturers: Watch out for double orders or you might make a grave mistake,” the paper says.
But Plambeck cautions that the model is “not very realistic — it’s stylized and stripped down to teach a lesson.” In a setting with many distributors, and with buyers seeking out alternative distributors in response to long lead times, so that the incidence of duplicate orders evolves over time, the estimation problem becomes more complex, requiring substantially more computing power, and maximum likelihood estimation becomes no longer effective. Plambeck notes, however, that Stanford professors J. Darrell Duffie and Peter Glynn have developed efficient estimators for complex financial applications. “Ongoing research with Peter Glynn will develop similar estimators to handle industrial-sized problems with duplicate orders,” she says.
Report from the Stanford Business School
How To Appraise an Automation Supplier
Automation Trends, an Omron Electronics publication, says that early in the 1990s, machine designers and end users watched in horror as their long-term automation suppliers merged, trashing their now-familiar control platforms and abandoning users to a short period for upgrading hardware and software. When the dust cleared, many discovered there was no way to migrate old programming to the new hardware and software. A decade later, it’s happening again. If you are re-appraising major automation suppliers, here are some tips and questions to ask so you avoid technological dead ends or one-way streets with your next automation partner.
When a controls manufacturer puts up a brick wall so there is no migration path from an established platform to newer models, we call that a dead end. To make any change generally requires scrapping the entire investment in hardware, training and programming. This resets the meter on cost-of-ownership. Regardless of brand loyalty or a sense of low risk involved in staying with the same supplier, you are now faced with starting over from scratch. With a cold, objective attitude, pose these questions:
• Does the supplier have a history of ending one product line abruptly, then starting over with a whole new one?
• Does the supplier design and manufacture newer systems?
• Was the new system built to supplier spec, a private-label project, or acquisition of another company’s system?
• How long will they offer technical support?
• How has the company handled support for past product retirements?
• Do you pay a premium for support compared to newer models?
• How long will they continue to supply replacement parts?
• How many different software platforms are required to support old and new systems?
• Have they got software to convert old programs to operate the new hardware?
Questions to ask yourself:
• How long can your company afford to support multiple controller platforms?
• Are software/programming specialists required for each system?
• Is an inventory of parts required for each system type?
• Is downtime or long lead times required for delivery of replacement parts?
• What’s the cost of not upgrading to new capabilities?
• Will production still be competitive?
And watch out for one-way streets. The controls manufacturer has a migration path, but it may not be as simple to implement upgrades to the controller as it seems. The main problem users encounter is that the upgrade is not backward compatible with legacy systems. In order to maintain productivity, several platforms and software packages must be supported.
Steer clear of one-way street upgrade scenarios by getting straight answers to these questions:
• Are upgrades of capabilities available on all series models? If not, are they available for the models you have installed?
• What steps are involved in upgrading the firmware and software?
• Is there more than one software package required to complete the upgrade?
• What additional programming is required?
• Are there any software utilities that can convert my current program code for use with new controllers?
• How much of my original hardware investment can be reused?
• Is programming similar enough that additional training is minimal?
• How long does it take to complete the overall transition?
• Can you afford to have your production stand idle for that long?
Become a skeptical, objective shopper when you reconsider controller suppliers. Remember, old loyalties must be based on realistic performance and service. If either of those has faltered recently, it is more risky to your company’s future to stick with a partner with weaker performance.