Moving to reduce inventory while maintaining customer service levels, agribusiness Syngenta needed accurate demand forecasts. But first the company had to put a forecasting process in place.
[From Supply & Demand Chain Executive, August/September 2004] Forecasting demand, as much an art as a science, is never easy in the best of circumstances. But Greensboro, N.C.-based Syngenta Crop Protection faced at least one major hurdle in forecasting North American demand for its fungicides, herbicides and insecticides, according to Richard Herrin, manager for NAFTA planning and forecasting at the company: "We didn't have a forecasting process."
Mostly Seasonal, With Variable Demand
Syngenta Crop Protection is a $6.2 billion business globally. With 19,000 employees in 90 countries, including 4,300 in the North American Free Trade Agreement (NAFTA) region, the company is headquartered in Basel, Switzerland. Syngenta sells its fungicides, herbicides and insecticides to distributors and dealers, who then sell to growers.
The agribusiness market, and Syngenta's segment of that market in particular, offers a challenging environment for demand forecasters. Syngenta produces the active ingredients (AIs) with a two-year lead-time and distributes them to various regional divisions, which formulate and package local variations of its products (stock-keeping units SKUs) in accordance with the local requirements and regulations. The challenge for the local units has been to manufacture enough of each particular product to meet demand, even though, absent a formal forecasting process, the production side of the business did not have a clear picture of actual demand.
In the past, Syngenta employed a "top down" business planning process for generating sales goals and production targets whereby the company's finance group would generate quarterly sales estimates and set targets for the sales staff. On the manufacturing side, the company's production planners would determine how much of each SKU to produce. The catch, of course, was that without accurate demand forecasts, the company found itself loading up on inventory to ensure that it could meet fluctuations in customer demand and minimize orders lost due to out-of-stock product.
On top of these challenges, Syngenta faced the imperative to minimize its working capital needs while maintaining its high customer service levels. To get better control of its inventories, Syngenta elected to undertake a major sales and operations planning (S&OP) initiative. Trouble was, S&OP uses inventory control models to affect working capital requirements, and the basic inputs into the inventory control models are accurate demand forecasts, which Syngenta did not have.
Spreadsheets Offer Clouded Visibility
The company made an initial move to get at forecast numbers using Excel spreadsheets. Once a month, Syngenta's central office would create a spreadsheet for each business unit, with volume and price forecasts, and e-mail the document to a contact within the unit who had been tapped to do the forecasting. The business units would revise the figures and e-mail them back to the central office, where another staffer combined all the numbers for manual uploading into the company's enterprise resource planning (ERP) system.
This approach had several shortcomings: First, the company was updating its forecasts only once a month, but changes in the market might require daily revisions to production plans. In addition, the spreadsheet forecasts reflected sales estimates made by business unit heads and excluded insights that could be provided by field reps and their local district managers. And finally, this manual process did not allow the company to identify and manage gaps between the forecast and sales plan, and forecasters did not have ready access to such information as sales to date, current inventories or marketing plans.