The company made an initial move to get at forecast numbers using the favorite tool of many a planner: 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, that is, those employees who were closest to the customers and who could offer valuable perspective on market trends. 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, including supply, purchasing, sales, marketing, finance, development and strategic planning.
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. "Everybody else we talked to was going to come in and tell us, 'You have to forecast in this way,' because that was the way that their software worked," Herrin says. On the other hand, "Galt was very flexible, and we could do a lot of front-end customization with their front-end engine."
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. "We had to come out with something that would say, 'Hey, just tell me what you think you're going to sell,'" Herrin says. "So we actually designed it to look exactly like the spreadsheets that the guys had been e-mailing back and forth."
Galt also scored by coming in with a project of a scale and price that Syngenta found acceptable. "Cost was a big issue, and Galt came in lower than anyone else," Herrin says flatly. "Everyone else we talked to wanted to come in with a big package, a full-blown forecasting tool. And we told them, 'We just need a collaborator, something where everyone can put in their opinion and we can aggregate it up.'"
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. "So if you think about it, the whole company is working off numbers that are fed directly from Collaborator," Herrin says.
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) — one step up the organizational chain from the local district managers — 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 — the next step up the organizational chain — 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.