Harvest scheduling: Unique challenges for the produce industry

By - February 25, 2022

Today, an effective supply chain has never been more important. Growing transportation costs, evolving weather patterns, and worker shortages can have massive impacts on a company’s ability to plan and execute their supply chain. In addition to these factors, the produce industry faces a unique challenge with a complex supply forecast process. Produce suppliers need to grow or procure their supply amidst hard to predict factors such as weather, pests, etc. It is essential to plan planting operations based on expected future demand. Calculating this includes how much to plant, when to plant, and where to plant. This all drives when that crop is ready to harvest and can be sold to customers.

The biggest customer service challenge when considering all these supply and production factors is maintaining visibility. This can be a labor-intensive process. Currently, producers need to record each time a planting is made, where it is located, when it should be ready to harvest, and more.

The way that farmers and producers alike have projected harvest yields has evolved over the years. Initially, it began with tribal knowledge from generations of farmers who had worked in the industry for years. It then turned into grower reported yields and sampling to determine the expected harvest volume, and now if we look forward, machine learning opens a new field of model driven projections based upon historical data.

This brings a huge opportunity to apply technology to streamline this process.

Opportunities for technology

Utilizing a technology solution to accurately forecast, record real time data at the source/field, and plan operations can reduce the uncertainty involved in harvest scheduling. These solutions can be extended to include other aspects of the harvest process as well. A prime example would be to monitor spray and pesticide tracking to manage field readiness for harvesting.

When providing visibility across a produce organization, there are a few key components to consider:

Sales forecasting

As the driving force behind any scheduling solution set, sales forecasts should be entered to begin the scheduling process, and these forecasts should be continuously updated in a centralized location based on ever-changing customer demand.

Planting projections

Turning accurate sales forecasts into a planting schedule ensures that the right product will be available in the forecasted quantity at the right time. Based on the maturity days for a certain crop (planting type, region, and season), planting dates can be calculated to support the planting processes. This initial calculation will also provide a harvest projection to support future customer demand.

Tracking plantings

After planting the product, it is extremely beneficial to have the ability to track the product growth and ultimately to be able to forecast the expected harvest dates and yield. Several factors e.g., temperature, precipitation, season can have an impact on the duration between planting and harvesting produce. This can help drive the expected harvest dates and labor needs so that companies can appropriately plan for a seasonal workforce.

Field spray applications tracker

In addition to normal crop tracking, another challenge that arises in produce is tracking when pesticides and other sprays can be applied to crops and reporting those applications to the appropriate state/county organizations. There are certain periods of time after application where workers are unable to enter the field again. This information can be used to protect workers and make sure clients can safely monitor crop growth. It also provides a history to ensure that future plantings would not be affected by previous spray applications that could be harmful to some vegetables but not others.

Future technology applications

The wealth of technology solutions continues to grow and evolve on a global scale. Producers and harvesters are now utilizing drones and robotics to record crop growth, quality, and harvest counts. Machine learning and Artificial Intelligence (AI) can be integrated to combine historical data with real time inputs (e.g., weather today and in the future) to produce the most accurate forecasting models to date. Leveraging new and emerging technologies to model harvest projections can help produce organizations to more accurately plan labor, equipment, production, and logistics. These technologies, when paired with an ERP solution set, can provide produce companies with a single dynamic solution that addresses the challenge of harvest scheduling.

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