Application of Internet of Things (IoT) to Food Supply Chain Under Uncertainty-Case: Traditional Dairy Products

Abstract

Implementing information technology in production has triggered an increase in Industry 4.0. Owing to this technological advancement, manufacturing tools that can communicate with each other through the Internet of Things are now able to collect real-time data. Under dynamic production conditions, real-time information can enhance production control and supply chain efficiency. To optimize the production process, the food industry must utilize new technologies and the Internet of Things (IoT) to reduce costs, increase productivity, and eliminate waste. This is particularly true with the growing trend in this area. The study created and implemented a manufacturing production planning system that utilized Internet of Things technology, with specialized equipment used in dairy factory operations as the case study. This system collects information on the dairy production process, including material and product inventory, in real-time on Internet of Things devices, then analyzes it with the help of a neural network, and predicts the demand for the next 3 days. The dynamic schedule optimization, optimal timing of milk production, product quantity, and raw materials are determined using two heuristic algorithm methods. Additionally, there are multiple algorithms available for further processing. The optimization results indicate that implementing dynamic scheduling via the Internet of Things can mitigate uncertainty and boost income by 10 to 15%, profit by 13 to 18%, and job shop-level productivity by 13%.

Keywords

• Food supply chain
• Internet of Things
• dynamic scheduling
• heuristic algorithms
• dairy products
• uncertainty