Using Artificial Intelligence in Supply Chains
Advances in storage, computing power, algorithms, and the advent of the Internet to Things (IoT) have given rise to different forms of Artificial Intelligence being applied to address challenges posed by businesses, organizations and customers.
Supply chains are one such area. Supply chains are evolving from linear supply chains to digital supply networks, where numerous automated systems, such as sales, inventory, production and logistics are integrated with each other. This means that logistic systems now have access to much more enterprise data and business and customer patterns such as buying behaviour, seasonal order placement, fast moving stock, average time for delivery according to geography and so on. Business owners potentially, can use make sense of this data and turn into business intelligence i.e. insight that can drive business decisions and actions.
In the case of supply chains this would mean better anticipation of orders, better planning of logistic, developing ‘cognitive’ capability in logistic systems i.e. a logistics system that can not only detect patterns and make useful knowledge constructs, but also learn from the application of this knowledge and understand business and customer situations better. This could also result in lowering of costs and overheads and improve the agility, responsiveness of logistics, improve turnaround time, and greatly improve customer satisfaction. Already, autonomous vehicles and robots are operational in large supply chains around the world.
Without taking advantage of the benefits that technology affords them, supply chains can become inefficient, out-dated and unable to keep up with the pressures of competition and demands of the customer.
A McKinsey study estimates that businesses could earn anything from 1.3 to 2 trillion dollars a year by using artificial intelligence based logistic systems.
Restaurants were the first businesses to embrace AI tools. They began by analysing Point of Sale data. From this data, they were able anticipate and forecast customer demands and plan better for it. This benefit cascaded throughout the supply chain to suppliers, and vendors who delivered quickly and ‘in time’.
Another illustrative example is that of a telecom manufacturer. The manufacturer analysed historical data of its production, sales and logistics and customer feedback, along data about season and weather. With this intelligence it was better able to tell its channel partners what products were available and when they could be delivered, at the earliest. This made for a more integrated and responsive supply chain, translating into better customer satisfaction and improved profitability.
Another area pertaining to supply chain, where Artificial Intelligence proves very useful is when it comes to preventive maintenance of equipment. Data generated by sensors on mission critical equipment along with maintenance reports can be interpreted by artificial intelligence to predict when it would be a good time to do a maintenance ‘check in’ and when it would be a good time to do preventive maintenance and when it would be a good time to repair. Such intelligence has shown to improve the productivity of the equipment and improve maintenance costs by nearly 10%. For mission critical equipment, this could mean significant savings, reduced down time, satisfied customers and even competitive advantage.
Overall the benefits of using Artificial Intelligence in supply chains are many. In the sum, they have the potential to make the supply chain more responsive, more integrated with the demands of the customer and the objectives of the business, and finally more productive and profitable.