Nestlé enhances demand forecast with SAS analysis solutions
The company improved the sales precision in one of their business units in 9 percent, creating direct impact in the level of customer service, reducing inventories and improving product freshness.
SAS, the global leader in analytics solutions, was the partner chosen by Nestlé, the largest food and beverage company in the world, to fulfill its business needs, focusing in sales demand forecast calculations that directs decisions in a number of company areas. In Brazil since 1921, the company has 31 factories around the country and more than 22.000 employees, all of them committed to Nestlé’s purpose of improving quality of life and contributing to a healthier future.
Nestlé and SAS have a global contract for the acquisition of new analytics solutions. The idea is to improve the effectiveness of demand planning. In Brazil, the project was started in 2015.
The Brazilian subsidiary adopted the SAS Enterprise Guide to associate the data that will be inserted in demand planning. They also adopted the SAS Forecast Studio, used in the creation of the analytical models of demand forecasting. Both are used in a complementary manner and are part of DDPO (SAS Demand-Driven Planning and Optimization), which is an integrate package of forecasting modules, analysis, visualization, report and data optimization.
To measure the results, Nestlé defined four pillars: improvements in customer service (in retail), inventory reduction and better freshness of products, which contributes to loss reduction.
SAS solutions are implemented in different areas and business units, like Chocolates Garoto, NESCAFÉ Dolce Gusto, Liquids, Child Nourishment and Dairy products. According to Marcos Borges, manager at Nestlé’s Planning Process, NESCAFÉ Dolce Gusto is a positive example of statistical analysis use, in which
is possible to observe favorable contributions in their forecast indicators, with benefits already identified. “SAS use allowed better decision making in demand planning meetings”, says Pedro Feliu, director of Nescafé Dolce Gusto.
“In times of constant change we need flexible tools that allows us to test and associate new variables while the competitive environment changes, a solution that we found in SAS”, says Reinaldo Monma, manager of Planning at Nestlé Dolce Gusto.
“Comparing semestral measures it is possible to notice an improvement of 9 percent in effectiveness of demand forecast. Consequently, it's already possible to note a 1 percent improvement in service customer levels, with less disruptive issues. These percentages represent a significant contribution to our business, compared to the previous scenario”, says the Coordinator of Planning at Nestlé Dolce Gusto, Sérgio Garnica.
Nestlé reinforced it's structure to work with SAS solutions, including a professional with experience in tools and processes of demand forecast. Today, there are 9 SAS users working directly with the solution.
Key factor for sales management
Demand forecast is essential for the processes of supply chain, as well as for determining income expectations of a company. Demand forecast is important for defining production plans for each one of their factories, specially when you have a clear notion of how much manpower will be needed, how many working lines will be filled, definitions about the purchase of supplies and etcetera. In this case, the better the forecast, the better will be the planning of the production, including the possibility of working with smaller inventories.
“The first challenge was to access the scenario to have a complete view of demand, considering all possible events, like price compared to competitors, special dates, as mother's day or a quarterly closing. The second challenge was to evaluate the scenario based on the business goals of the company and discuss which decisions would be necessary,” explains Marcos Borges.
Before migrating to SAS, the company's statistical forecast was based only in historical sales records, without taking into consideration other factors that could influence demand planning - the causal variables, like price index. This demanded the adoption of more robust tools that could work with a larger volume of data and, at the same time, could offer the needed flexibility for the data insertion in other statistical models, including macroeconomic factors, like exchange variation, PIB forecast and others.
By generating the forecast, Nestlé uses different data sources, with the history already used in the old solution being among them. “We also use data from Nielsen and a series of data we collected internally in each of the business units, like old pricings,” says Borges. The company even uses economical variables and seasonal information related to commemorative dates like Christmas, Easter or Mother's day.
Each one of these fonts is inserted in the SAS solution as an specific event, which is used in predictive analysis. The diferencial of the solution is that since each demand has its own variables, the tool is able to identify everything that is or isn't related to a particular business unit and, if necessary, part of the information is used in demand forecast.
Even with the positive results, Marcos Borges can still see other challenges in a near future, like the demand forecast by distribution center. Another challenge is to understand statistically which variables are affecting demand.
According to him, a number of areas are involved: finances, marketing and supply purchase. “We have been learning a lot since we adopted SAS tools, what represents continuous learning and the need to explore the solution more and more. It is an exercise that forces us to a robust integration in the compan
to achieve our goals,” concludes Borges.
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