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.