How NatureSweet improved yield predictions by 7%

Summary

NatureSweet accelerated its yield forecasting from 88% to 95% accuracy—resulting in millions of dollars in savings and preventing over 900 tons of tomato waste per quarter—by deploying Calibo’s Data Fabric Studio to automate its data pipelines, consolidate siloed operations into a single source of truth, and leverage AI/ML forecasting. What’s more, development time dropped from an estimated 12–18 months to just 8 weeks.

Action points: audit your current forecasting workflows for manual, error-prone steps; build a centralized, self-service data pipeline; apply machine learning for forecasting; and deploy user-friendly dashboards to enable scenario modeling—this approach helps you forecast more accurately and act proactively.

“Instead of building our own digital ecosystem, which would have taken 12-18 months – before we could solve business problems – we could start within 8 weeks thanks to Calibo’s platform. For one application, we were able to improve yield forecasting by 7%, resulting in millions of USD of savings the first three quarters after implementation.”

Noé Angel, Global Head, Chief Information Officer, NatureSweet

NatureSweet is widely known for growing some of North America’s finest fresh tomatoes. With over 5000 employees, and their greenhouse facilities spanning a robust 1,000 acres, they’re committed to year-round excellence in tomato production.

Challenges

NatureSweet’s approach to forecasting was largely manual, relying heavily on the acumen of their harvest planners. This method didn’t embrace the wide array of data potentially at their fingertips, and this oversight led to an accuracy rate of below 88%.

NatureSweet faced additional challenges, including relying on outdated legacy tech ecosystems which were difficult to maintain and scale, and using too

many siloed technologies altogether which ultimately slowed down their development work.

What’s more, they had limited business and IT resources, leading to a skillset deficit and a shortage of specialized knowledge. These limitations significantly hindered their ability to accurately forecast yields and maintain optimal productivity levels.

Objectives

Customer satisfaction

Overall, NatureSweet needed to accurately improve their yield forecasting, productivity and customer satisfaction.

Anticipate yield

NatureSweet’s manufacturing and planning team aimed to gain the capability to peer six weeks into their crop yields’ future. They needed to be more savvy with how they handled the surpluses or shortages of their yields within the critical six-week window, minimizing those unexpected fluctuations that could throw a wrench in the works.

Enhance productivity

NatureSweet sought to enhance productivity by creating a comprehensive machine learning model using data from all greenhouse operations, making it simpler for all staff levels to manage this data effectively. They intended to build a Single Source of Truth (SSOT), pulling in and merging data from a variety of relevant sources to forecast the yield more accurately.

Precise output

The company aimed to identify key data factors, train the machine learning model for precise output, and provide user-friendly tools for visualizing data insights to support informed decision-making.

High-accuracy forecasting

NatureSweet aimed to achieve high-accuracy forecasting, regularly measuring and comparing these results at various operational levels to maintain a superior standard of data precision.

Modernize technology

The company aimed to modernize their technology ecosystem through the adoption of a self-service platform with integrated Cloud, DevOps and Agile best practices to accelerate data solutions development.

Rationale for using Calibo

NatureSweet were looking for a business enabling platform that provided an end-to-end solution including a complete best-of-breed tech stack integration in a multi-cloud environment.

Calibo was also chosen because the platform helped facilitate the AI/ML capabilities NatureSweet needed to power their yield forecasting and planning simulator solutions.

Solution

The solution, crafted by Calibo using their Data Fabric Studio, leveraged advanced data science and forecasting methodology. It automated the data pipeline, which greatly minimized manual labor and cranked up the accuracy of forecasts.

With data from a multitude of channels being funneled and processed through an automated data pipeline, users on the other end enjoyed a centralized self-service platform equipped with smart algorithms and user interfaces that made yield analysis a breeze.

NatureSweet + Calibo tech stack

Outcomes

By using Calibo, NatureSweet has saved several months of development time. The accuracy of yield predictions increased by leveraging robust and customizable AI/ML models within the platform.

The introduction of automated systems allows a more insightful correlation of multiple variables, paving the way for dependable and consistently precise predictions. Such an increase in accuracy not only arms NatureSweet with better insights but also enables them to take proactive steps in honing their operations.

Results highlights

• Yield forecasting improved by 7%, from 88% to 95%, resulting in savings of millions of USD in the first three quarters after implementation.
• The increased accuracy also saved 900+ tons of tomatoes from potential wastage per quarter.
• Instead of building its own best-of-breed tech stack in 12-18 months, NatureSweet could start work within 8 weeks to realize its data and digital initiatives.
• Business-impacting applications can now be built within half of the time with Calibo, as opposed to previously.

The farming data, sourced from diverse channels, is now seamlessly collected, and channeled through an automated data pipeline right into a handy self-service platform. This innovation is a game-changer for users, empowering them to run data science algorithms without a hitch.

What’s more, it delivers user-friendly dashboards and insights for the team to parse through and understand

yield patterns, all thanks to smart, intuitive interfaces that lay out information across different parameters for easy review and in-depth analysis.

With the platform, they can now reuse components, automate certain capabilities and go from ideation to deployment in half of the time. bringing down the dev time considerably.

Specific improvements experienced

USER EXPERIENCE

• The platform incorporates user experience (UX) design based on journey maps, specifically targeting, and addressing pain points faced by planners.

• Users can model multiple scenarios based on production assumptions and historical data.

• Alert notifications are implemented to inform users upon the completion or failure of a simulation.

OPERATIONAL EFFICIENCY

• The solution optimizes operational efficiency by reducing manual work and streamlining the data ingestion process through automation.

• Different planting cycles can be strategically planned to align with supply and demand, resulting in optimized utilization.

• The yield forecast solution offers recommendations to address capacity constraints and maximize operational output.

Thanks to the proactive solutions provided by Calibo, NatureSweet saw enhancements in productivity, accuracy, waste management, and efficiency – vital factors in the success of their vast agricultural operations.

“With Calibo, we were able to create business impacting appli– cations which would have taken us more than 9 months to de– velop in just 4 to 5 months, resulting in faster time to value.”

Alberto Langle Bornacelli, Global Head of IT Business Relationship, NatureSweet

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FAQ

What challenge was NatureSweet trying to solve?
They needed more accurate crop yield predictions to reduce waste, improve efficiency, and avoid costly forecasting errors.

How did NatureSweet improve forecasting accuracy?
By using Calibo’s Data Fabric Studio to automate pipelines, unify siloed data, and apply AI/ML models, they increased prediction accuracy from 88% to 95%.

What benefits did this approach deliver?
It saved millions of dollars, prevented over 900 tons of tomato waste per quarter, and reduced development time from 12–18 months to just 8 weeks.