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Supply chains have frequently been a black box that organizations try to interact with as infrequently as possible. To many companies, “the supply chain” is an email thread with a manufacturer, an invoice, and a tracking number for a shipment. The complexity of supply chain management has meant that understanding, let alone acting, on data has been outside the reach of most SMEs.

So how are some companies able to crack the supply chain code to offer better service to their customers and increase their bottom lines, without raising their prices? Chances are they are leveraging artificial intelligence and machine learning to take control of their data. This allows them to decrease shipping costs, mitigate risks and delays, and increase customer satisfaction with predictive analytics.

In the last article of this series, we examined use cases that demonstrate the effectiveness of Artificial Intelligence (AI) and Machine Learning (ML) in marketing. In this article, we’ll explore how AI and ML can be used to augment your supply chain and operations.

Logistics marketplaces: Instant shipping profits

We all know that shipping products doesn’t have a one-size-fits-all solution. Everything from package weight, size, destination, carrier, and delivery speed play a role in the quality of service and the cost of shipping goods. A single carrier may optimize their shipping processes to offer the best delivery coverage in rural areas while another carrier may optimize their shipping processes to offer the fastest and cheapest delivery to metropolitan areas. Which are you supposed to use? The number of variables in the shipping equation have always been too high for companies to make sense of without hiring expensive consultants, who often have a dubious track record.

AI and ML provide a new outlook on the shipping problem. The vast amounts of data collected by carriers, customers, suppliers, and sellers can now be analyzed in real-time to determine the optimal shipping decision for not only every individual products, but for every possible combination of product that may be shipped together. Digital brokerage platforms such as ShipHawk comb through all the shipping variables to provide an immediate savings of 10% to 30% over current parcel spend as well as the ability to repurpose employees to other higher-value tasks through automation. Shipping is one of the few under-optimized areas where immediate savings can be created for your organization.

Supply chain 2.0: Mitigate risk before it happens

In a 2016 survey by SCM World, 47% of supply chain leaders responded that AI and ML are disruptive in supply chain management. The supply chain has innumerable nodes and routes, each with their own inherent level of risk: shipments can be held up in a port; a natural disaster can halt a delivery route; or a strike could stall a supplier. How impactful these risks are to your customer’s experience and your bottom line largely depend on how quickly and reliably you’re able to respond.

AI and ML have positioned themselves as strong contenders to predict and minimize risks along the supply chain. Companies like TransVoyant collect and analyze one trillion events each day from sensors, satellites, radar, video camera, smartphones, and Internet of Things (IoT) devices. These events are distilled into risk prediction models and prescriptive recommendations that can be taken to minimize or completely avoid potential negative outcomes. This kind of risk mitigation across the whole supply chain can simultaneously reduce inventories, cut supply chain costs, and increase revenues through better customer service.

Wrap up

Next week, we’ll cover more examples of how AI and ML are putting control over the supply chain in your hands. Get in touch with the Hanu Rock Stars to discuss the possibilities of using artificial intelligence and machine learning in the cloud to reclaim lost profit in your supply chain management and business operations.