Many logistics professionals are excited about artificial intelligence’s potential. After all, they could merge countless AI and food technologies since these models are so versatile — the possibilities are virtually endless. However, others are wondering whether they should take algorithms seriously. Will leveraging AI in the supply chain permanently change how it functions, or is this uptick in integration merely a passing trend?

Combining AI and Food Technology Will Impact Logistics

Many cold chain logistics professionals struggle to maintain strict quality control, mitigate in-transit temperature fluctuations and manage their massive operational datasets. While deploying various platforms, devices and apps to counteract these pain points would technically work, it would exponentially expand the number of disparate systems they need to manage.

However, that doesn’t mean a solution isn’t necessary — administrative inefficiencies and cold chain breaks must be addressed to meet increasing demand. According to the United States Department of Agriculture, the food-away-from-home sectors supplied $2.39 trillion worth of food in 2022, up from $2.11 trillion the year prior. This growing market needs support.

AI has already entered transportation, warehousing and supply chains, so its integration into cold chain logistics is natural. Thanks to its unparalleled analysis and automation capabilities, it is gaining traction fast — one company’s chief innovation officer estimates it will have some direct connection to around 90% of the food in the U.S. by 2027.

While there’s no telling how prevalent this technology will become within the next few years, indicators suggest implementation will continue rising as early adopters uncover new use cases. In this likely future, combining AI and food technologies could redefine what it means to manage inventory, fleets and supply chains.

Existing AI Technologies Reshaping the Cold Chain

Early adopters are already utilizing standard algorithms for various cold chain applications. Some even leverage advanced subsets like machine learning, natural language processing and neural networks.

1. Inventory Management

Timeliness is critical when storing perishables for distribution. Since warehouses are only considered food-grade safe if they have efficient inventory management systems and consistently control facility temperatures, they would benefit from automation. AI can optimize processes to prevent overstocking and increase energy efficiency.

2. Process Automation

Even basic AI supply chain systems can automate administrative tasks because they can be purpose-built for specific roles. They can process orders, compile compliance documents, answer calls, schedule logistics workers or send invoices. Advanced models — like neural networks embedded into robotics, for example — can automate aspects of picking and packing.

3. Demand Forecasting

Will recent supply chain disruptions affect order volume? Is paying for a less-than-truckload shipment cost-effective? AI can answer questions like these by forecasting demand for the type and volume of perishable goods, maximizing capacity and minimizing trip frequency. Companies can reduce supply chain errors by 20%-50% with this approach. They may even save on fuel.

4. Data Transformation

There are bound to be duplicates, missing fields and outliers when aggregating information streams from dozens of fleet vehicles, suppliers, distributors and retailers. AI can automatically correct those errors and fill gaps with synthetic data, reducing noise. This way, decision-makers get a massive, clean dataset for generating quality insights.

Emerging AI Solutions with Considerable Potential

AI’s forecasting, automation and analysis capabilities are well-known because it has always been able to uncover hidden patterns and operate autonomously. With the fundamentals covered, companies are testing boundaries to pioneer new innovative technologies.

1. Risk Management

An AI supply chain isn’t complete without a risk management system. If business leaders aggregate weather, equipment and route data streams, their model can produce a score that indicates how likely their perishables are to spoil. With this insight, they can proactively adjust their plans or contact contractors to mitigate any issues.

2. Condition Monitoring

Generally, refrigerated trucks should be one degree Celsius above the point of cold damage to maximize perishables’ shelf lives. However, ventilation, cooling system or vehicle malfunctions may cause fluctuations that swing temperatures too low or too high. Even if they stabilize upon arrival, the conditions may have been ideal for bacterial growth — meaning foodborne illnesses.

Combining AI and food packaging technology enables professionals to remotely monitor refrigerated trucks’ temperature, vibration and humidity levels, mitigating this issue. While the sensors collect and aggregate data, the algorithm can send detailed reports, automating recordkeeping and compliance reporting.

3. Fleet Management

Linking AI and food transportation centralizes data aggregation, processing and analysis, giving management greater insight into fleets’ maintenance needs and drivers’ on-road behaviors. Since refrigeration systems account for 80% of total energy consumption in the cold chain, leveraging this technology to optimize temperatures in transit by considering weather, regional and distance factors would be ideal.

4. Quality Assurance

Spoilage is one of the most significant pain points in cold chain logistics. Roughly 33.3% of food produced for human consumption — around 1.3 billion tons — is wasted. While harvesting and manufacturing defects are common, losses often happen in warehousing and transportation. During storage, 10%-20% of fruits and vegetables are lost. In transit, another 5%-10% spoil.

Fortunately, joining AI and food packaging can help preserve perishables. Unlike modified atmosphere preservation or specialized coatings, this novel technology can adapt in real-time. As freshness sensors generate new information, a model can control connected systems to regulate the environment.

Can AI Permanently Redefine Cold Chain Logistics?

Initial indicators suggest incorporating a model into logistics processes is a sound business decision. Early adopters of an AI supply chain increased service levels by 65%, improved inventory levels by 35% and reduced logistics expenses by 15%. Demand forecasting, dynamic planning, automation and end-to-end traceability are robust — especially when combined.

While manufacturers can incorporate AI into computer vision systems to detect quality changes, fleet managers can embed it into sensors to track time-temperature indicators. Suppliers, producers, distributors and retailers can each use this technology in novel, business-specific ways to address pain points and resolve long-standing issues.

In other words, even if AI isn’t present in the entire cold chain, its influence will still be felt throughout each process. Even if it doesn’t redefine inventory, fleet and supply chain management permanently, it will undoubtedly leave its mark. Either way, for now, professionals should prioritize exploring this new technology instead of worrying about the big picture.

What the Future of Cold Chain AI Will Hold

As algorithm-based technology advances, industry professionals will find even more novel use cases to optimize. If development continues at today’s pace, numerous exciting innovations will likely be uncovered within the coming years. Even if adoption remains relatively low in the cold chain, interested parties can still benefit.