Predicting & Reducing Production Down Time for Rich Products

Posted by Sample HubSpot User on Dec 27, 2019 1:17:33 PM
Sample HubSpot User


Rich Products is a foodservice and retail provider in business since 1945, with  annual sales over $3.5 billion while employing more than 10,000 people worldwide.  

Business Challenge

Rich Products is a leading supplier and solutions provider to the foodservice, in-store basket, and retail marketplaces. They have been in business since 1945, and post annual sales of more than $3.5 billion while employing more than 10,000 people worldwide over 6 continents and 112 countries. Rich’s is the world leader in nondairy toppings, icings and other emulsions, and is committed to developing food solutions that raise the bar on quality, convenience and efficiency for its customers.  

Rich Products found themselves dealing with unplanned equipment downtime on the product manufacturing line, which was leading to increased costs. Rich was looking for a system that would allow engineers to proactively predict unplanned maintenance events, and minimize reliability issues as much as possible. They needed a way to detect patterns in their data and find indicators to help predict future outcomes that would save them time and money.


  • A standardized budgeting and forecasting platform that can easily integrate new locations as the company continues to grow
  • A best-practices approach to planning and budgeting, integrated into the tool so ongoing governance is simpler
  • The ability to produce dynamic, detailed reporting and analysis quickly and efficiently
  • Streamlined and real time forecast process across all locations with actuals updated automatically using one standard template
  • Information that gives stakeholders up-to-date information and the ability to spot problems in advance providing fast, accurate and flexible reporting
  • Vast reduction in the manual steps required for reporting by automating the data integration, calculations and report creation
  • Immediate access for stakeholders to updated financials on a daily basis








LPA built association and classification models to predict unexpected production line downtime. Using SPSS modeler, LPA was able to help Rich Products use their data much more efficiently to understand their business and reduce equipment downtime.

Combining a high volume disparate sensor, product data, and plant data, in order to develop our solution for the company, LPA developed models that could predict an average of 6 hours of unexpected downtime per month with over 80% accuracy. Per the production line, cost savings are projected to exceed $100,000 annually.

The IBM predictive maintenance products LPA implemented will use powerful analytics in combination with data integration and management to help Rich Products reduce operational costs and improve asset performance in the coming years.

Topics: Retail Market

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