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.
Developing the Process
Our new forecasting process was developed over the course of a year. We first selected several company estimators, trade marketing experts and other professional staffers to form a team of 15 people who are now called forecasters. The team defined the forecasting model, which the IT staff then implemented in SAS. In the past, upward of 340 people had been involved at some level in forecasting sales. By limiting input to a group of people with specific skill sets, we were able to greatly improve our forecasts.
Data entry has been streamlined to prevent “multiple versions of the truth.” And having a smaller group of forecasting experts prevents confusion caused by too many staffers using different spreadsheets with differing estimates.
Our forecasters now work with monthly sales information to create forecasts three months out. The SAS forecasting tool works with Excel spreadsheets, and the navigation system is similar to a Pivot table, so it is familiar to users.
The process begins with estimators who work on forecasts for the following one to three months during the first nine to 12 days of the preceding month. The evaluation matrix is structured so that each estimator can find information for a specific group of customers with similar purchasing behaviors and food assortments. The estimators can look at the prior year’s data for that month or data from the past three months. All the data is linked to both total volume and promotional volume.
When the estimators finish this process, project leaders adjust and analyze the results with the help of trade marketing directors and sales directors. The validated results are sent directly to stock managers responsible for production planning. Our error rate is currently about 15 percent, down from 18 percent, and we want to reduce it to around 5 percent.
With the new process, forecasting is more standardized, the staff is more focused on meeting deadlines and our margin of error has been reduced. Whoever makes the estimate now sees new items automatically highlighted or can view items listed in decreasing order of importance. This is critical because newly introduced items might need more attention than traditional sellers. In addition, items that consume a larger percentage of the marketing budget, cost more to manufacture or have slimmer margins may need more attention than other products.
We are also able to create what-if scenarios. For instance, if the price of a key raw ingredient goes up, we can mine our data to determine how much product will sell at a higher price—ensuring that we don’t under- or overproduce. We can also look at how different product sizes sell in existing markets to get a better idea of what size package to recommend to retailers who are adding one of our products.
One of the goals we’ve set for our forecasting is to reduce the incidence of slow-moving products from 2 percent to 1 percent, since having to sell products at low prices when they get near their expiration date results in losses. We are making progress on this, having reduced slow-moving products from 2 percent to 1.7 percent in just the first six months of using the solution.
At the same time, we also don’t want to understock and be “out of stock” more than 3 percent of the time. With our forecasting solution, we’re working to reduce that to 2 percent or 1 percent.
We use the Financial Management tool in conjunction with the forecasting tool. While the forecasting tool builds from the bottom up, the Financial Management tool can build a budget from the top down—helping us make decisions about which products to support with additional funds.
The forecasters use the budget to help the sales team understand how much they have to sell—and at what price—to make budget. Information becomes transparent, to the point that a self-teaching process is created, and the forecaster continuously sharpens his or her forecasting skills.
Although we haven’t yet completed our full investment return analysis of this project, it appears to be successful. Since a major purpose was to save money on missed deliveries and wasted foods, the fact that we were able to reduce costs in those areas is a distinct advantage. In addition, indicators such as reduced errors and fewer out-of-stock situations show that the project has been beneficial for us.
We think these solutions will prove just as useful in the future. Whether we’re expanding into the overseas market or purchasing other small companies, we are well-positioned to grow into the next decade.
Armentano Raco is the trade marketing manager for Grandi Salumifici Italiani, a packaged meat company based in Modena, Italy.