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Are Data Analytics and Data Prediction Boosting the Food Industry?
The food industry has the highest consumer demand for quality, and at the same time, no other industry requires a more significant cost control than the food industry. Therefore, safety, wholesomeness, and affordability are expected from our food.
It is a hard task for the food industry to keep the balance and prioritize the expectation of the consumers. Data analytics and data prediction technologies provide a robust solution for food producers, grocers, transporters, and restaurants.
Data analytics employs the insight given by artificial intelligence (AI) to benefit the lifecycle of the food, from farm to plate. As a result, the food quality can be determined by gathering data from various sources and helps the software applications spot issues that can affect safety, food quality, and freshness.
Role of Data Analytics in the Food Industry
Data analytics benefits the transporters, processors, growers, and food retailers with the help of the database. For example, farmers can enter soil testing, harvesting, and planting data into the database used by the software program; even weather information for the whole growth cycle of the crop can be entered into the database.
Moreover, the logistics team can input the starting and ending times for the trip so that the refrigerator's temperature can be monitored, and food processors enter the start and end timings for various stages of the process so that all the procedures can be tracked. Finally, customer feedback on social media can be accumulated into aggregate data and used to generate more insights into the food supply chain.
The software analyzes the information and provides intelligent insights to the parties in the supply chain. The accumulation of unstructured and structured data used for data analytics is known as big data, and it can also benefit the food industry in various other ways.
Predictive Analytics in the Food Industry
Being a cutting-edge technology, data prediction or predictive analytics taps the power of AI to pinpoint patterns and predict outcomes. For instance, traffic conditions, detours, road construction, and adverse weather are the reasons behind determining how quickly the food products can get to the market. Big data can inform AI about these issues and enable the software program to expect the freshness of the food before it reaches the destination.
Predictive analysis is a strong tool for forecasting supply chain issues and customer behavior.
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