General Mills, a global manufacturer and marketer of branded consumer foods sold through retail stores, approached SCMO2 nearly a year ago to as they began strategizing for implementation of an upgrade to their APO infrastructure. After performing a system assessment, SCMO2 recommended a suite of specific APO enhancements that would be most effective to their operation and provide the best return on the investment of capital and time.

Identifying a Weakness in Forecasting

During the process of implementation, a new potential for system improvements was identified by SCMO2, and General Mills agreed to include the Stat Pack forecasting service in the full scope. The recommendation of SCMO2 was to consolidate their organization-wide statistical forecasting functions into a dedicated center of excellence. By adopting better methods and stronger statistical models, and tasking fewer but more specialized human resources to fulfill the role, metrics and triggers could be put in place to ensure the success of their statistical modeling efforts for the long term.

The Original Model

General Mills had been previously assigning all their statistical forecasting to a team of individual demand planners, all of whom were in overseas locations, away from their domestic headquarters in Minneapolis, Minnesota. Each planner was responsible for generating a portion of potentially thousands of various planning combinations, none of which was automated or governed by triggers or threshold flags. Resultantly, forecast accuracy was unacceptable, and the turnover rate among their overseas demand planners was more than 50 percent annually which compounded the challenge because no knowledge base could be built up and retained.

The New Solution

Heeding the advice, recommendation and expertise provided by SCMO2, General Mills built a forecasting center of excellence and reassigned the statistical modeling function and responsibility to this new team. Additionally, new metrics and triggers were designed into the process to create more of an exception-based system that allows specific needs to bubble up to attention as needed. Because fewer statistical models are now applied to the entire data set, and effective triggers and metrics are in place to spotlight any potential misalignments, General Mills’ demand planning has become much more proactive and less reactionary. The accuracy and efficiency gained from this transformation has improved operations in numerous ways.

By focusing on design, process and results, General Mills was able to repurpose many of their existing demand planning resources into a system that reduced turnover, retained institutional knowledge, introduced new analysis and modeling, and allowed the company to gain better control over production. This outcome demonstrates how SCMO2 expertise can be leveraged to deliver an innovative procedural solution to a problem, rather than relying on technology alone.