In today’s world of digitalization, the milling industry requires innovation to face dynamic risks and certain environmental challenges. The milling industry requires idea generation (creativity) and its implementation (innovation) to revolutionize the future of wheat production for the upcoming years. Large process industries including; car manufacturing & assembly lining, and oil refining have adopted certain technological advancements for smart production which includes “Internet of Things” (IoT), and “smart manufacturing” (SM). However, due to the lack of professional workers and high cost, IoT and SM have failed to make a mark in the milling industry. As per the reports, Milan Shah, the technical director of Henry Simon Milling in India, gave insights to the future vision of 2030 in the milling industry. He indicated that milling industry technological advancements will include machine learning, IoT and applications for milling automation. He further indicated artificial intelligence to also take part in the best future of the milling industry.
According to Prof. Gustavo Sosa, who is a licensed grain inspector and industrial mechanical engineer, the key towards the future of milling industry, is the rapid designing, customization and adaptability towards change. He further emphasized that customization of small flour batches as per grain quality is only possible if high automated milling is available. The modern mills use “Supervisory Control and Data Acquisition” (SCADA) technique to control production and processes within a factory, through individual machine adjustment as per variations within the product. The product can be either a raw material, processed form of goods or a finished product.
Artificial intelligence can help in understanding certain changes occurring within the factory by learning the overall milling process along with understanding how a human reacts towards certain things. Instead of providing it specifically, it would use patterns and would interfere like humans in case of any problem. This method is known as machine learning. Also, it is closely related to “data mining” which is the usage of algorithms to discover patterns in computer information. The patterns help in understanding quality fluctuations, causing problems in product manufacturing, and reasons for machine failure. SM also helps in defining and eliminating workplace inefficiencies for improved performance. For example, roll pressure for grain can be increased, sifting time can increase, grain mixtures can get some adjustments etc. This will save time, as manually adjustment won’t be required. Manual adjustment system will replace with actuators which will control machinery operations.
Another revolution in milling industry is “blockchain technology.” This technology traces the development process from farm to finished product. For example, you would know the quality of the wheat grain, under what conditions it grew and from which farm it came. The information is then passed through elevator, mixing certain batches depending upon quality and demand and making another blockchain involving farmers. Afterwards, flour is manufactured, keeping similar weight e.g. 1kg, and know the entire process from farm till it reaches the supermarket. Big data technology can also help in tracking either large or smaller grain packages. This helps consumer earn the desired amount of grain required. However, the system requires large computers as smaller computers cannot perform huge functions and calculations.