Forecasts Demand Change

April 11, 2010
The economy may have forever altered the way manufacturers think about sales and demand forecasts. Here's a look at demand-planning strategies that have improved forward-looking visibility for several companies.

Billoo Rataul has made more than a few wake-up calls to customers during the so-called Great Recession. The CEO of Silicon Valley-area electronics contract manufacturer Paramit Corp. wants his customers to know that lead times for materials have changed dramatically during the economic downturn of the past two years as its supply base shrinks. The company, with approximately $100 million in sales, builds medical devices, aerospace and military equipment and other low-volume/high-mix commercial products and is dependent on the semiconductor industry for its materials. In 2009 alone, 27 semiconductor fabrication plants closed worldwide, and another 21 are expected to shutter this year, according to industry trade group Semiconductor Equipment and Materials International.

The changing landscape has pushed lead times on primary materials from about one month to 12 to 20 weeks and as much as 30 to 40 weeks on specialized items, says Rataul. Without customer forecasts, Paramit is left scrambling to meet customer demand. The company has responded by revamping its own forecasting strategy and requesting that customers do the same. "In my 20 years, never before has it been more important for OEMs to have accurate forecasting to about 12 months -- six months minimum," Rataul says. "They cannot expect to place orders at about six weeks or eight weeks lead time and expect to get product."

Paramit's demand-planning issues have been common across many industrial sectors as manufacturers struggle to achieve accurate forecasts amid sharp market declines. These challenges have some people questioning traditional forecasting strategies that result in set targets that many manufacturers discovered were unattainable when the economy soured.

Customer Participation

At Paramit, Rataul isn't pulling any punches with customers about supply chain issues in the electronics industry. He speaks frankly of the lead-time challenges his privately held company faces in a 2010 business outlook video on the company's Web site. In the nearly three-minute long video, Rataul directs customers to a newsletter entitled "Don't Get Caught With Your Pants Down." The article features seven recommended best practices for supply chain management (see sidebar "Paramit's Top Demand-Planning Steps"). The first best-practice suggestion is the implementation of a six-month rolling forecast.

Billoo Rataul, CEO, Paramit Corp.

For Paramit's customers, the stark reality is they don't have many other options. If they don't provide Paramit with some forecasting data, meeting demand will be difficult. Rataul says this is where showing customers hard numbers comes in handy. "Frankly, you scare them a little bit because it is scary," he says. "They've been insulated from it so long, the first thing you have to do is get in front of them and say, 'This is real; this is what's happening, and your future revenues are going to, frankly, be in jeopardy if you do not forecast. You are not going to be getting the components, and we're not going to be able to build product for you, and you're not going to be able to fill your orders.'"

San Francisco-based processed foods manufacturer Del Monte Foods Co. involves its retail customers in the sales and operations planning (S&OP) process to make its entire supply chain demand driven, says Chief Information Officer Marc Brown. Beginning in 2006, the $3.6 billion company wanted to utilize data from customers as an additional demand signal. Del Monte began tapping into retailers databases and storing that information into its own repository. Wal-Mart, for instance, has a data program called Retail Link that it requires all suppliers to participate in. From there, Del Monte can feed demand data from the retailer's supply network into its own system. That information is eventually fed into a demand-planning system that provides statistical modeling based on the retailer forecast and Del Monte's marketing consumption forecast. "That gives us the ability to look at all of those, have a discussion across the business and determine what our view of the future is going to look like," Brown says.

The system has helped Del Monte improve its forecasting accuracy to the high 80% level compared with the 50% to low 60% range previously recorded, according to Brown. The company also lowered its inventory level by approximately 27% in two years driven by more visibility into its distribution centers. "We know our restocking points, we know what the demand looks like, and we can project where we're going to have outages, how much demand we're going to have in the future," Brown says.

Del Monte's ability to get customers on board with the program varies. The company utilizes demand-planning data from the retail level for more than 35% of its total volume, Brown says. Some retailers, such as Wal-Mart, not only make the data available, but they want suppliers to have the information. Other customers either don't have the capability or don't see the value, Brown says. Part of the trick is convincing them that it's necessary. Del Monte tries to communicate to customers the gains they can realize through more data transparency.

"We can help them to understand where they can improve the cycle time of our replenishment and in-stock level at the shelf and get some of the same type of inventory reductions for them that we've been seeing with equal or better on-shelf availability, which is pretty powerful," Brown says.

Aggregate Forecasts

The Dow Chemical Co. has implemented a program that's a variation of S&OP called executive sales and operations planning (ES&OP). Traditional S&OP typically involves enterprisewide stakeholders who come to a consensus on the company's demand plan. S&OP gained steam during the recession because the process gives companies better sensing capabilities, says Jane Barrett, research director for industry value chain strategies at AMR Research Inc. "You can better sense supplier failure, the need to shut down manufacturing plants or to reduce capacity or increase capacity," she says.

Dow's ES&OP process involves changing the way higher-level organizational members think about forecasting using an aggregate market-based process. These grouped forecasts are inherently more accurate than disaggregate projections because of lower statistical fluctuations, says Yossi Sheffi, director of the Massachusetts Institute of Technology Center for Transportation and Logistics.

Using ES&OP, Dow forecasts three to 36 months out and focuses on overall market performance rather than a detail-level look at specific products or customers, says Jacqueline Faseler, supply chain planning expertise leader for Dow. For instance, if Dow wants to create a forecast for a chemical sold into the paints market, the company will compile historical data for the paints segment then create a statistical forecast projection based on that information, Faseler says.

Dow presents the data in a control chart along with market intelligence so marketing managers have a better idea of industrywide trends. "What we've tried to do is align the forecasting process with how the commercial and marketing organization thinks, whereas before we may have had businesses that tried to forecast what's going to be made on an asset or a product," Faseler says. "We try to look more at market trends and really bring the capability to look at that compared to external factors."

The company has adopted the ES&OP strategy for 32% of its sales volume, Faseler says. The process has improved accuracy and helped businesses within Dow look at the bigger-picture market trends. "Businesses are not getting caught up in the details of what customer A, B or C is doing next week or next month. Rather, it paints that higher-level picture, which forces businesses to take a step back and analyze what's going on and make some decisions around balancing demand and supply that maybe before they never got to because they were so focused on trying to collect all of this very detailed data," Faseler explains.

Manufacturers can use a variety of industry-specific tools to track market trends. Companies can utilize information from marketing data firms such as J.D. Power and Associates for the auto industry or Information Resources Inc. for the consumer packaged goods market, Barrett says. Manufactures also can examine macroeconomic indicators such as housing starts and demographics to gauge market changes.

Sometimes supply chain planning changes are necessary to facilitate aggregate forecasts. As an example MIT's Sheffi cites forecasting challenges Hewlett-Packard faced when selling printers in Europe that were customized for specific countries. Some printers had country-specific power cords and language-specific manuals. The company forecasted demand for each country. If projections were off, the company was sometimes left without enough printers for one country and too many for another.

The company decided to ship printers without the power cord and user manual to a central distribution center in Holland. Once the printers arrive at the distribution center the company can then customize each item for individual countries once HP knows the local demand.

The change means the company can forecast demand across Europe rather than by country, leading to more accurate projections and lower inventory, says Sheffi, who wrote about
HP's planning efforts in his 2005 book "The Resilient Enterprise."

"Inherently there will be deviations from what we expect," Sheffi says. "The trick is not the fact that there will be mistakes, but the things you can do in order to mitigate the effect of having a wrong forecast."

See Also

About the Author

Jonathan Katz | Former Managing Editor

Former Managing Editor Jon Katz covered leadership and strategy, tackling subjects such as lean manufacturing leadership, strategy development and deployment, corporate culture, corporate social responsibility, and growth strategies. As well, he provided news and analysis of successful companies in the chemical and energy industries, including oil and gas, renewable and alternative.

Jon worked as an intern for IndustryWeek before serving as a reporter for The Morning Journal and then as an associate editor for Penton Media’s Supply Chain Technology News.

Jon received his bachelor’s degree in Journalism from Kent State University and is a die-hard Cleveland sports fan.

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