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Why Agriculture Should Come Together to Form a Digital Twin

Digital twins are gaining traction as tech giants like Google develop virtual models to track logistics and manufacturing processes. Paired with AI, digital twin transforms into a tool that can help prepare for future risks. The impact of recent factory closures and product delivery delays due to truck driver shortages across multiple markets could have been improved with digital twins. It seems likely it’s a technology that will be embraced by supply chain leaders across all industries.  

It should be a priority for the ag tech sector. 

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Although agriculture has historically been able to weather incoming difficulties – even of the magnitude of a global pandemic – we can’t afford to rest on our laurels as other challenges such as climate change look set to step disruptions up several notches.   

87% of ag businesses now use AI in some capacity so clearly there’s already an appetite to future-proof the sector. Implementing digital twins at speed is a logical next step as these offer the potential to reduce inefficiencies across the supply chain, like improving workflow solutions to plant seeds and preventing food waste before it reaches the consumer.

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Most players across our sector have developed a bad habit of creating silos of information. Value between active players in the Ag industry has been known as knowledge one party has independent of the other.  But in an industry largely built on different commodities moving at speed, such a limited view causes major headaches and limits what agriculture is capable of.  

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From consumer safety issues to simple good practice, being able (and willing to) to be transparent is a significant problem. Like an online banking app that allows customers to track standing orders and bank transfers in a few taps, stakeholders should have ubiquitous access to live updates like crop yields and inventory levels, as well as ensuring goods meet the required standards. 

Achieving this vision requires industry-wide data sharing and collaboration, and it’s the visibility through said data that should be the lever for digital twin initiatives to create solutions to common challenges. 

It would enable the tracking of delivery output and stock volumes internationally, even integrating AI to assess how long the shortages could last, meaning growers and trusted advisors would be best positioned to pre-empt issues like this and mitigate by ordering earlier or investing in cover crops or other alternative products.   

And this won’t just benefit those who work in the fields. Manufacturers and distributors who have visibility of a digital replica can ensure labels are updated with any herbicide switches and that these are in line with industry regulation. 

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Unfortunately, a lack of trust amongst means keeping your cards close to your chest is often seen as offering a competitive advantage, but data needs to be shared from all pockets of the supply chain to make a digital twin model accurate.  

Therefore, the industry needs an unbiased provider that looks holistically and addresses these qualms. One that identifies where the most data is generated, where it’s limited and assesses what parts of the supply chain are adapting fastest to automation to ensure the sector moves in unison. 

The initial investment of shifting to a digital twin model seems sizable, but it’s a major cost-cutting tool in the long term. If all players input their data to make this work, they’ll make smarter decisions based on real-time updates from their suppliers and can consider necessary pivots for their businesses, which will ultimately be of benefit to the end-user. 

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