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The saying “time is of the essence” doesn’t get any more real than when you’re waiting anxiously for the product you ordered on Amazon exactly two days ago. Two days? Unbelievable! With the inception of one-day and same-day delivery, customer standards on punctuality and speed have risen to levels unlike ever before. It’s difficult to recall a time when consumers had to wait two weeks for an ordered product to be delivered. That said, while a delayed delivery will at most be a nuisance to the individual consumer, demands for speed ultimately flow upstream into the supply chain where retailers and manufacturers are constantly being pressed on time. For these organizations, on-time performance is a matter of millions of dollars of customer orders or contractual obligations. Unfortunately, with the unavoidable challenges that come with managing variability in the supply chain, even the most well known logistics carriers such as FedEx and UPS saw a 6.9 percent average delay across shipments made by 100 e-commerce retailers who collectively delivered more than 500,000 packages in the first quarter of 2019.
While delays may be unavoidable, retailers and manufacturers have the ability to manage any negative impact that delays have on their supply chain, by foreseeing and mitigating potential disruptions. The difficulty in doing so today is that retailers and manufacturers are ill equipped with a lack of forward looking information. However, through the use of AI, supply chain managers can proactively anticipate irregularities in the supply chain by predicting whether deliveries will arrive on time for both outbound and inbound shipments. Using historical shipment data and features associated with deliveries such as weather and port traffic, AI learns patterns associated with on-time and late deliveries to accurately classify future shipments into either bucket and offers the top statistical reasons why. Based on this information, supply chain managers are able to implement changes that prevent avoidable late deliveries, and to mitigate the risks that stem from unavoidable late deliveries.
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Retailers deliver better demand forecasting, marketing efficiency, transparency in the supply chain, and profitability through advanced AI applications and a myriad of AI use cases throughout the value chain: from store-level demand and out-of-stock (OOS) predictions to marketing channel modeling and customer LTV predictions. With the abundance of consumer data, changing consumption patterns, global supply chain shakeups, and increased pressure to drive better forecasting, retail businesses can no longer ignore the potential of AI in their industry.