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Logistics

AI in the Supply Chain: Uses and Benefits for Businesses of All Sizes

Logistics
September 27, 2022
6 min read
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Artificial Intelligence (AI) and machine learning are more than just buzz words. This guide contains real-world applications that can improve operations and increase margin up and down the supply chain.

What Is AI and How Is It Used in the Supply Chain?

Artificial Intelligence (or AI) enables a machine to respond in real-time to a challenge, request, or question in the way that a human would. AI in the supply chain can be used to make digital twins and play out scenarios to make more informed decisions and build resilience into the supply chain

Machine learning (a subset of AI) identifies patterns in historical data to make predictions. It can be used to predict consumer demand for more efficient end-to-end operations.

Demand planning and scenario mapping are more important than ever for companies looking to build a more resilient supply chain. Supply chain disruptions are becoming more and more frequent. According to McKinsey, we can expect a disruption to the supply chain lasting more than one month every 3.7 years.

Consumer behavior is also becoming increasingly more difficult to predict. Shoppers have endless options for product discovery – from ecommerce marketplaces to social shopping to brick and mortar. Today’s merchants must adopt an omnichannel approach to get in front of the right customers at the right time, and their supply chains must incorporate digital solutions like AI to meet the demands of omnichannel fulfillment.

Real-World Applications

There are three main applications of AI in the supply chain that can benefit businesses of all sizes. These applications help merchants make smarter decisions around procurement, transportation, and final mile delivery.

  • Demand Forecasting enables efficiency across the supply chain – from procurement to delivery. A robust demand forecasting model will leverage not only a merchant’s own historical sales data but will also include industry-wide trends, weather patterns, and promotional calendars.
  • Network Optimization helps merchants move beyond knowing how much inventory to order and when to order it. Applying machine learning and AI can help merchants also know exactly where to store their inventory in order to improve delivery speed without increasing final mile delivery costs.
  • On-time Delivery Predictions help merchants get in front of communications with their customers to let them know when a delivery is at risk of being delayed. Today’s consumers have high expectations for tracking and delivery updates, and transparency is key to building trust and creating an excellent customer experience.

Challenges to Implementing AI in the Supply Chain

Ultimately, AI is only as good as the data you feed it. Many small to mid-sized businesses (SMBs) work with small data sets or may not have enough historical sales data to create an accurate demand forecast or scenario.

However, merchants who outsource their supply chain can gain access to larger data sets across their industry and beyond. The longer a merchant works with a single supply chain partner, the smarter and more accurate machine learning algorithms become. Over time, the algorithms will learn that merchant’s particular business patterns, becoming even more efficient.

Benefits of Leveraging AI in the Supply Chain

According to a report by McKinsey, early adopters of AI-enabled supply-chain management saw logistics costs improved by 15 percent, inventory levels optimized by 35 percent, and service levels improved by 65 percent. Here’s how implementing AI in the supply chain helps improve each of these areas of a merchant’s business:

  • Cost Efficiency: According to a recent merchant survey, rising supply chain costs have affected 80% of US merchants. Leveraging AI for scenario mapping enables merchants to look at every possible scenario to compare costs to make the best decision for their business. Scenarios might include: importing to an East Coast port vs. a West Coast port, storing inventory close to expense port cities vs. trucking it cross-country, or storing inventory in two warehouses vs. three or more. 
  • Higher Margins: Having a robust demand forecast enables merchants to make smarter decisions around procurement, all the way down to the SKU level. When they know not only which product lines, but which individual SKUs are going to be their best sellers, they can really dial in on their procurement strategy. Demand forecasting based on machine learning will also optimize inventory carry cost – striking a balance between reducing the risk of stockouts on one end of the spectrum and carrying too much inventory on the other.
  • Faster Delivery: Building an optimized fulfillment network enables merchants to stock their inventory closer to the end customers. This lowers time in transit (TNT), allowing them to offer 1- to 2-day ground shipping to meet consumer expectations for fast and affordable shipping.

How Ware2Go Leverages Machine Learning and AI

Ware2Go’s free network planning tool, NetworkVu, uses machine learning and AI to show merchants where they should be storing their inventory to offer faster delivery to their best customers. Merchants are given a two-warehouse scenario and a three warehouse scenario with cost comparisons and the percentage of customers that fall within a 1- or 2-day ground delivery footprint.

Curious what your ideal fulfillment network looks like? Connect your ecommerce shopping cart or upload your own sales data to NetworkVu for a free network analysis, delivered to your inbox within minutes.

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