Forecasting Processes from the Ground Up

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.

Around the beginning of 2002, Syngenta began looking for a more effective tool for forecasting that would overcome these shortcomings. The company's requirements included, among others, that the tool support Syngenta's S&OP process cycle at the business unit, country and NAFTA levels; allow gap, exception and assumption management; allow for multiple languages and currencies; and meet the needs of the various functions that would be using the forecast.

Identifying the Right Tool for the Job

With that broad mandate, Herrin and his team surveyed the market for collaborative forecasting engines and eventually settled on a solution from Chicago-based John Galt Solutions, a privately held company that the Syngenta group had met at a conference of the Institute of Business Forecasting. Herrin says that Galt's ForecastX solution was particularly appealing for its adaptability.

That was important because, as Syngenta began implementing Galt's solution in mid-2002, Herrin and his team wanted to make the transition to the new tool as painless as possible for staff in the field. "So we actually designed it to look exactly like the spreadsheets that the guys had been e-mailing back and forth." Herrin says.

Galt also scored by coming in with a project of a scale and price that Syngenta found acceptable. "Everyone else we talked to wanted to come in with a big package ... and we told them, 'We just need a collaborator, something where everyone can put in their opinion and we can aggregate it up,'" Herrin explains.

Currently Syngenta has about 250 users working with the forecasting tool, although the company uses a solution from Cognos as the reporting tool for Collaborator, and essentially the entire company can access the reports that the forecasting tool generates. Collaborator also feeds Syngenta's ERP system with sales plan and supply requirements.

A New Process

Syngenta's formal S&OP process, as it stands now, works like this: At the beginning of the month, the company's crop managers (CMs) enter their sales forecasts for each SKU. Those forecasts get rolled up to the business unit level, and by the end of the first week of the month, the business units develop, and commit to, their own forecasts, which may or may not conform to the CMs' forecasts (both are shown in the Collaborator report). Once all those data are entered, in the second week of the month the brand managers review the sales and demand forecasts, and then they input their product supply requirements, which are fed into the ERP system and used by the company's supply chain function to plan production.

In the third week of the month, the company's S&OP meeting brings top executives together to review the current supply and demand plan against the annual budget. Based on any trends occurring with supply and demand, or in the marketplace, the S&OP meeting could produce changes in the sales plan, which can then be entered into Collaborator.

Finally, toward the end of the month, district managers enter their seasonalized invoiced sales forecasts into the system, in part based on feedback from sales reps out in the field. The district managers' input feeds up the chain to the CMs, and the process begins again.

Putting the Process Before the Tool

Implementing the new process, and the new tool, has not been entirely without challenges, both technical and cultural in nature, according to Herrin. On the technical side, the issue was data accuracy in the various systems from which the forecasting engine pulls information, including the financial, ERP, manufacturing resource planning (MRP) and supply chain planning systems.

On the cultural side, the challenge has been ensuring that all interested parties within the company buy into the new process and adopt the new tool. To ensure adoption, Herrin says that his team has come to take a process-based approach rather than focusing solely on the new solution. Over time Herrin's team also has had to take into account changes in business units or in the sales force's structure, requiring that they readjust the process or forecasting tool to keep up with the changes.

Despite the challenges, Herrin says that the forecasting initiative appears to be paying off already in that, combined with other project initiatives, the company has been able to achieve inventory reduction targets and has focused attention on tracking and reducing top-line and product mix forecast error.

Looking down the road, Herrin says that the company is exploring using the new process and Galt's Collaborator solution to support the entire S&OP process as a sort of executive information system, to give the participants in the S&OP meeting all the data they need when they need it. In addition, Syngenta is looking at implementing Galt's ForecastX SDK solution for statistical forecasting to provide baseline performance metrics. Explains Herrin: "Once we have a baseline, we can use that to drive assumption management."

Elsewhere, Syngenta is looking to use Collaborator to drive distribution and logistics planning. And finally, the company is looking at the possibility of extending the collaboration aspects of the new forecasting process out to both its customers and its suppliers. "I want to eventually take the Collaborator to our customers and say, 'Give us a forecast,'" Herrin says. "And also to share the variability with our vendors. I want to say, 'Look, here's our variability around the market, what can you do to help us?'"

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