This article will discuss how supply chains are being improved through the use of innovative technologies before highlighting five uses of artificial intelligence and machine learning in supply chains. When you finish reading, you’ll understand why many industry analysts have described A.I. technologies as disruptive innovations that have the potential to alter and improve operations across entire supply chains.
Supply chains across the globe are facing increased pressures due to a whole host of factors. The globalization of manufacturing coupled with high customer demand driven by strong performing economies are putting strain on supply chains. Furthermore, trucking shortages are amping up the pressure in the U.S., the world’s largest economy, and the shortfall in available drivers reached a record 296,311 in 2018.
Adopting the latest technological solutions has become a prerequisite to the operation of a successful supply chain. Efficiency, transparency, and predictive analytics in supply chains are all driven by using technological solutions, such as Big Data, Internet of Things, Radio Frequency Identification (RFID) technology, and artificial intelligence.
A.I. has huge potential in supply chains, and machine learning has arguably the most diverse range of use cases out of all the fields of A.I. for supply chains. As a brief reminder, machine learning is a domain of A.I. that encompasses the use of statistical techniques to give computers the ability to learn and improve performance at tasks without needing to explicitly program them.
Improved Last Mile Delivery
Last mile delivery refers to an incredibly important stage in the journey of a product from its fulfillment center to a final destination, which is most often the chosen delivery location of the customer. Given the high cost of last mile delivery in proportion to the total cost of getting products to customers, visibility into last mile delivery is taking on increasing importance.
Dedicated last mile software platforms are helping to optimize last mile operations, however, A.I. can provide further benefits by ensuring more consistency and accuracy in delivery. Geocoding algorithms can automatically map addresses into coordinates, enabling optimization of last mile delivery routes and schedules. This type of automated geocoding has previously proven difficult to implement due to the inconsistent ways in which people input addresses in ordering systems.
Machine Vision & Quality Inspections
Using human labor to conduct quality inspections across warehouses and other logistics hubs is inefficient. These types of quality inspections can become time-consuming and cause downstream effects on the operation of the supply chain. High customer demand is driving a need for more automated quality inspections with greater production throughput from manufacturers while at the same time maintaining a consistently high level of quality.
Automated quality inspections are possible through machine vision, which is a set of techniques and disparate technologies that provide image-based automatic quality inspections. Machine learning algorithms can improve the accuracy of machine vision-based inspections by learning what a normal product without any defects looks like through the use of high volumes of training data.
Autonomous Vehicles In Shipping and Logistics
Autonomous vehicle technology has made incredible progress over the last few years. Companies like Amazon use drones in some areas to get goods to customers. Self-driving trucks, powered by A.I. could also be on the horizon, and they could solve the truck driver shortage that continues to plague supply chains across the U.S.
The idea of a fully autonomous truck is still more conceptual than nearing real-world implementation, however, even semi-autonomous trucking could have dramatic and positive effects on supply chains. For example, trucks that can drive themselves under certain conditions could give drivers the opportunity to take breaks from driving while not slowing down shipping times.
NLP For Intelligent Supply Chains
Enormous amounts of natural language data are generated within supply chains, and with the globalization of supply chain management, this data might arise from multiple languages at different stages, from supply to procurement to retail to end customers. Sources include texts, tweets, news websites, and weather data.
For example, a manufacturer might depend on a key supplier for its product, but unbeknownst to that manufacturer, the supplier recently lost a key contract that puts its liquidity at risk, and the story was reported on an industry website in another language.
Natural language processing, which blends A.I. and computer science, can help tap into all this natural language data and derive insights from it, including information on suppliers’ abilities to meet order fulfillment needs.
Modern manufacturers tend to use a lot of sensor-based equipment which generates tons of data about the usage of that equipment. Machine learning models can tap into this data and predict the failure of machine assets before they even happen. The machine learning models achieve this capability by being able to analyze time series data.
Predicting failures before they happen allows you to take proactive, corrective measures and/or conduct planned downtime for repairs instead of the type of chaos that ensues when machines fail unexpectedly. This type of predictive maintenance drastically reduces downtime and lowers costs, leading to smoother and more stable supply chains.
The five use cases here show just what artificial intelligence and machine learning can bring to supply chains. Expect to see technologies like these become staples of supply chain management over the coming years as the use cases become more widely known and lower technological costs drive wider adoption.