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ConAgra Foods, Inc., is one of North America’s largest packaged food companies with branded and private branded food found in 99 percent of America’s households, as well as a strong commercial foods business serving restaurants and foodservice operations globally.

 

Within the food manufacturing industry, increasing competitive pressure is causing companies to need a more accurate forecast of product demand in order to better understand future costs to maximize profit.  During the forecasting process, large amounts of data are produced that need detailed analysis to understand where the opportunities and risks exist.  Analyzing this data can be difficult and time consuming which poses a significant challenge when business decisions need to be made under a tight time line. Businesses need to understand the input costs, when the product needs to be made, and costs to move the product throughout the supply chain to its end destination.

 

To better understand these costs, ConAgra Foods needed a solution that allowed deeper analysis on its businesses, while shaping future outcomes by more accurately predicting future events. These modeling capabilities would allow general managers to see the impact of business drivers on the margin and profitability of a change prior to implementing. Not only would this help the general manager, but it also provides other business areas a better understanding of the total delivered costs. To allow for this, the end solution needed to be able to process large amounts of data at detailed and aggregated levels, while still allowing for visualization of the data and exception indicators when the unexpected happens.

 

To accomplish this ConAgra partnered with SAP to co-innovate on a Total Margin Management (TMM) solution that provided costs at the lowest level of granularity, and the forecasting capabilities to model scenarios to better predict the future. This solution comprised of two components, TDCA and MMA.

   

Total Delivered Cost Analytics (TDCA) allows general managers to do detailed analysis on cost inputs and visualize the information to make informed, responsive decisions. Margin Management and Analytics (MMA), provides the ability to then take this broad cost decomposition, along with other detailed information for volume, trade and A&P, and run analytics to model future costs and revenues, ultimately allowing for the modeling of profitability at the product/customer level. With this information, general managers can generate a more accurate forecast in less time with less people. This enables ConAgra Foods to meet changing business needs in a flexible manner.

To analyze the huge amount of data needed to make decisions in near real time, ConAgra Foods looked to SAP and HANA. HANA provided the high-speed in-memory platform needed to process and visualize this amount of information. With HANA, calculations that used to be run as batch, could be returned instantly and data analytics became faster than ever thought possible. Thanks to the speed and storage features of HANA, the user can quickly perform iterative scenarios and “what if” forecasting on large amounts of data.  This increases the reliability of the margin forecast and produces a single forecast leveraged across all segments, while simultaneously creating a forecast that is risk adjusted and applicable to multiple business areas.

Business Impact: 

  • Improved processes and shortened forecast cycle times.
  • Leveraging technology for direct source data feeds, flexibility/usability through SAP HANA and analyzing Product & Customer profitability
  • Increased speed of decision making through customized analytics and modeling business scenarios

Technical Impact:

  • Data Compression — Prior to implementing HANA, data needed to support TMM was 6TB.  With HANA and TMM, we have been able to reduce the data size to 800MB.
  • Performance — Prior to TMM, running a detailed forecast bridge was impossible without letting the overnight processes pre-calc all scenarios.  With TMM, we can now run detailed bridges dynamically in under 60 seconds.
  • Level of Detail — Prior to TMM, system generated P&L’s were generated at a higher level of the product and customer hierarchies and needed manual analysis to get to low level details.  With TMM, the system will allow General managers to quickly get to detailed P&L information in a matter of seconds.
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  1. Dan Everett

    Congratulations on winning.

    I’m curious what you use to get data into HANA. SLT, Data Services, Replication Server, HANA Smart Data Access/Integration, something else?

    It’s interesting to me that very few submission even mention data integration/cleansing aspects of the solutions.

    Kind regards,

    Dan

    (0) 

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