Finding a Cure for Forecasting Problems

Grandi Salumifici Italiani, a packaged meat business headquartered in Modena, Italy, needed to integrate its forecasts with its budgeting processes. In 2007, the firm created an internal task force that included IT and marketing people to define improvement targets and key performance indicators. Based on the task force’s recommendation, Armentano Raco, trade marketing manager, chose a financial management and forecasting system. With the new system and process, forecasting is more standardized, the staff is more focused on meeting deadlines and the margin of error has been reduced.

When dealing with a perishable product, it’s absolutely critical to forecast demand accurately. At Grandi Salumifici Italiani, we can’t afford to be wrong about projected demand because the steps needed to make renowned products such as Parma or Prosciutto ham involve sizable costs, so we must have accurate forecasts.

We also need to integrate our forecasts with our budgeting processes to operate efficiently.

The packaged meat business in Europe is highly fragmented. Tastes and preferences vary from region to region, and Italians, in particular, appreciate foods prepared using traditional ingredients and “slow cooking” methods.

To continue offering these traditional tastes while taking advantage of the efficiencies of a large company, Unibon and Senfter merged in 2000 and became Grandi Salumifici Italiani (GSI). Business has grown by double digits each year, and, in the most recent year, we produced nearly 110,000 metric tons, or 242 million pounds, of product, which were sold in 30 countries. We now have 1,350 products, eight sales channels, 16,000 retail customers, 15 factories, 300 sales agents and 40 direct sales representatives.

As we’ve grown, adding other small manufacturers, we decided to invest in both people and technology. We consider technology critical to giving our managers the knowledge they need to make decisions, since inaccurate or incomplete data can paralyze our decision making and jeopardize our bottom line.

In 2007, we created an internal task force consisting of IT and marketing people to define improvement targets and key performance indicators. We brought in several vendors to show us their demand planning, forecasting and financial integration solutions. In 2008, based on the task force’s recommendation, I chose SAS Financial Management and SAS Forecast Server, to run on two dual-core IBM servers. Combining these separate applications was challenging, but we were able to do it with help from SAS, and we completed the implementation in five months.

We selected this system based on cost and its robust forecasting capabilities, which are critical for our business. For example, if we incorrectly predict in September that the demand for our precooked products will be low during the peak-season Christmas holidays, we could face major losses.

Before we used SAS for forecasting, our sales force created forecasts using spreadsheets. Information was entered by multiple parties and, as we tried to create forecasts, information was sometimes manipulated in a way that created inaccuracies.