IDS Case Study: Predicting Farming Equipment Failures

By Keiter Technologies

IDS Case Study: Predicting Farming Equipment Failures

Can I Predict and Avoid Equipment Failure?

Data Science can help you achieve your business goals with tailored advice specific to your organization. Learn more about how Keiter Data Solutions solved a similar problem for one of our clients in the below case study.


Challenge and Opportunity

A Keiter Technologies client and farming cooperative wanted to develop a model that could predict when its equipment needed maintenance service to avoid lost revenues from breakdowns or failure.


Our Approach

  • The team carefully analyzed the maintenance schedules, recent repairs, brands, and usage hours from all existing equipment.
  • Using Python, the team developed a predictive model which specifically outlined when equipment would need repair or maintenance based on the EC unit.
  • The development of the model required extensive collaboration between the company’s field crews and our data scientist.


The model successfully predicted construction equipment maintenance needs and reduced unpredictable equipment failure (like onsite ones) rates by 90 percent. This resulted in decreased cost for the company.


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About the Author

Keiter Technologies

Keiter Technologies

Keiter Technologies focuses on serving businesses with their strategic technology needs through data science, cybersecurity, and IT audit and consulting.

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The information contained within this article is provided for informational purposes only and is current as of the date published. Online readers are advised not to act upon this information without seeking the service of a professional accountant, as this article is not a substitute for obtaining accounting, tax, or financial advice from a professional accountant.


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