Say “Hi” to AI in Supply Chain
If you’ve been hearing a lot about artificial intelligence (AI) lately, you’re not alone. It’s one of several disruptive technologies being discussed as a harbinger of significant change in almost every industry. And although it can replace human involvement in some cases, it can also accomplish laborious and time-intensive tasks we could never dream of undertaking.
A Definition of AI
Computerworld defines AI as “a sub-field of computer science. Its goal is to enable the development of computers that are able to do things normally done by people—in particular, things associated with people acting intelligently.” AI is often associated with Machine Learning, particularly for business applications.
This technology is excellent at making correlations within large amounts of data much faster and more accurately than humans are capable of doing. Examples include self-driving cars, IBM Watson, voice assistants like Alexa and Siri, and predictive preference analytics such as recommendations from Amazon and Netflix.
What Makes AI Significant in Supply Chain?
There are numerous applications of AI both within the four walls of the warehouse and across the wider supply chain spanning procurement, logistics, and inventory planning. Much of the interest in this technology centers around its ability to interpret vast data streams related to the movement of goods that would take numerous people days or weeks to analyze.
What components can improve with AI?
Throughput
From forecasting demand to order procurement and transportation, there are many opportunities to introduce AI in day-to-day work that affects throughput. Decision-making can be enhanced and simplified with meaningful correlation of existing data run through what-if and planning scenarios.
Improving forecasts for ordering behaviors can inform changes in required inventory levels in the warehouse, potentially reducing the need for safety/buffer stock. This level of sophistication can push throughput numbers much higher than is often possible via traditional methods of forecasting.
Cost-Efficiency
Use of material handling equipment (MHE) is another way AI can be found in the supply chain, especially when an operation is dealing with the movement of large product volumes. A job that might typically take five associates can be optimized with a single machine (perhaps an AGV) that interfaces with a Warehouse Execution System (WES). The WES can pull data from the Warehouse Management System (WMS) and Warehouse Control System (WCS) and use AI t
o correlate this data in new ways that advance the capabilities of the AGV. The initial investment in technology can be hefty, but oftentimes the business case can justify the expense.
For example, an office supply retailer in Europe has replaced active locations in their warehouse with an area completely automated by machines. In fact, humans aren’t even allowed to enter for safety reasons. All replenishment and picking activities are performed by these robots based on an AI algorithm. The initiative has resulted in notable reductions in space and labor utilization which have translated into significant cost savings.
Customer Satisfaction
In a world where the customer is king, any incremental improvements in buyer satisfaction are a boon to any business. E-commerce has put massive pressure on supply chains that now have to meet expectations for expedited fulfillment and delivery. Implementing new technologies like AI can have a significant impact on improving many aspects of the supply chain, particularly for large-scale operations. For example, one global apparel company uses AI to generate the information required to run a routing wave ahead of a picking wave to streamline workloads and fulfill orders faster.
The Future of AI
AI is finding its footing as companies begin to understand both its promise and its drawbacks. At 4SiGHT, we’re following along closely with its applications in all aspects of the supply chain.