At the ongoing Virtual AI Summit in Hong Kong, there was a session titled ‘Supply chain predictive AI and improved inter-connectivity optimising commodities supply chain’. The speaker, M. Tanvir Siddique, Head of Operations, B-Trac Solutions Ltd, said that predictive and proactive systems, driven by AI, dramatically improve network planning and demand forecasting. The combination of ML, IoT and sensor data enables businesses to avoid risks, anticipate events and come up with solutions.
An estimated 1.3 billion tonnes of food is wasted globally, each year. The impact of AI in marketing is estimated to reach $40 billion by 2025. There are several key stakeholders in the commodities supply chain, such as farmers, traders, food processing, logistics, etc. Some foods are lost before crops leave the farm. Food/agro processing have problems such as product expiry. Logistics/service providers face fleet planning and storage facility planning. Retailers face problems such as inventory management and reaching out to the consumers. Consumers have problems such as product availability and pricing.
Data analytics is descriptive and based on past events. It doesn’t prevent the impact of a change in the variable. Predictive analytics relies on human intervention to query data, validate patterns, create, and then, test assumptions. AI machine learning makes assumptions, re-assesses the model and re-evaluates the intervention of a human. AI does it at scale and depth of the detailed information.
AI-powered predictive analysis is necessary. Data is generated by the primary raw materials producers. There is raw data from the agricultural equipment IoT platform. Trader information is obtained from the trade associations. Logistics service providers have vehicle tracking systems and service platforms.
There is storage facility management and monitoring system. For food and agro processors, there is the FMCG and agro processing companies. Retailers have an inventory and SCM system. Finally, from consumers, we get end consumer information, and from hotels, etc.
The AI-driven analytics platform helps in giving us actionable insights. The actionable insight map allows product planning, and later, consumption pattern. There can be 1% reduced wastage that can save $26 billion each year. 8.15 million people can be fed at this optimized cost. Bringing in AI can lead to new business planning and opportunities. That will also provide us the opportunity to scale up our business.