To create intelligent supply chains, ones that can adapt to changing customer demands and increase efficiency and profits, enterprises are increasingly leveraging artificial intelligence (AI) and machine learning (ML).
In this blog, we’ll examine some of the ways AI and ML are revolutionizing supply chains, while also getting into their potential usages in the retail, consumer packaged goods and manufacturing industries.
IoT networks will drive networks & supply chains
The Internet of Things (IoT) refers to the use of network-connected devices that are embedded in the physical environment to improve existing processes, or enable new, formerly futile ones.
These devices connect to a centralized hub and, more notably, communicate with one another to convey valuable information from the natural world and convert it into digital data, thereby enhancing visibility into how users interact with a product, service or application. These choice tools are worthwhile for any organization engaged in complex processes and supply chains.
Enterprises use IoT devices to send status and location updates on packages in-transit. When there’s a potentially at-risk delivery, the network-connected devices alert staff at downstream distribution centers, so that appropriate actions may be taken. IoT sensors also give real-time status updates on machines in need of maintenance, thus improving efficiency and safety, and enterprises can employ IoT to better learn about product origination and quality, which helps them assess the value of their of partnerships.
Augmented reality can aid in decision-making
Efficient supply chains result from an assemblage of decisions based on analyses of varied factors, thus available carriers, routes and schedules are reviewed at the outset so that the ideal carrier is chosen.
Traditionally, workers can spend as much as 10 minutes analyzing these variables, but through AI, this analysis can be automated to deliver the top results to employees who then make the final selection. This process is referred to as augmented reality; rather than replacing the employee, these tools enhance their efficiency and decision-making. Plus, with the time saved, employees can focus on tasks that are more impactful to the business.
Let’s briefly return to the aforementioned optimal carrier selection example. Say AI presents an employee with the three best options for getting a package from Seattle to a customer’s home in Boston in time for the holidays. The employee’s ultimate selection should also take into account their intuition and experience, and they might pick the southernmost route as a means to avoid potential winter weather delays.
One of the most significant advantages AI and ML affords is predictive analytics. Previous forecasting models relied on historical data to spot patterns and changes, assuming they’d recur in the future. But historical trends don’t always persist, and predictive analytics uses real-time information to predict an organization’s future needs.
The benefits of predictive analytics are extremely evident in sales and marketing, wherein a greater understanding of customer behavior can lead to more effective campaigns, but their capabilities extend much further, including alerting stores of needed inventory and carrying this messaging up the supply chain.
Say a retailer sees an unexpected spike in a specific style of women’s jeans, a trend that deviates from the historical data. Predictive analytics can flag this store as one that needs more inventory than expected. Fulfillment can then increase the store’s jean delivery, possibly by moving product from other stores where it isn’t selling as expected.
Leaders can make better business decisions with AI and ML. Consider a CPG company with a relatively comprehensive understanding of their supply chain. They already know the most cost-effective routes for each of their ingredients, and they also recognize why common shipment delays happen. Still, this company could benefit from the insights AI and ML yields to:
- Determine their most efficient supply chain partners and reevaluate those that are not meeting expectations
- Learn which products have the greatest demand growth potential, while also seeing how meeting those demands would affect the supply chain in its entirety
- Hire more workers in distribution centers that are struggling to keep pace with current demands, thus improving job satisfaction
- Identify future distribution center or processing plant locations, by taking into account route optimization, lower costs and more
With AI and ML, this and so much more is possible. By analyzing all available data, these tools present a variety of scenarios that leaders can then choose from.
AI and ML are already revolutionizing the supply chain
AI and ML grant an unprecedented understanding of operations, ROI and customer satisfaction, and the above examples are just a few of the many ways these tools are improving supply chains across industry.
Knowledgeable enterprises are already reaping the benefits of AI and ML. In fact, one McKinsey study found that 42% of business surveyed reported AI driven revenue from supply chain and operational improvements, and more specifically, AI adopters in the transportation and logistics industries saw 5%+ profit margins.
For many organizations, the supply chain revolution is here, so it’s high time that everyone else get onboard.