Artificial intelligence is in the news a lot these days. Though it used to be that AI was only found in science fiction films like Star Wars or Terminator; today it’s wound its way into everyday life. From Siri to Netflix we’ve become so used to AI experiences that we’re no longer amazed by them.
So, what is AI really? In 1956 John McCarthy created the term ‘artificial intelligence’ based on the assumption that human intelligence can be so precisely described that a machine (AI) could perform tasks normally requiring human intelligence. In essence, he believed that computers and machines could be programmed to mimic cognitive function so they can “learn” from input and problem solve without constant human intervention. Although AI continues to be a developing technology, many companies use AI to assist in their operations.
Advances in AI have spurred a number of industries such as social media, retailers, stock traders and healthcare providers to begin rolling out AI-assisted products and services. Twitter and Facebook use AI to help moderate and filter content on their sites, including searching for troll accounts and pages. Target and Amazon, among other retailers, use AI to predict what customers will buy based upon past purchases. One early success that made headlines was when Target was able to predict a teen’s pregnancy before her father even knew using an algorithm that reviewed purchases against items known to be purchased by customers who were pregnant.
Many stockbrokers use AI to manage and analyze data which helps them make decisions on when to buy and sell. And some of the most prominent hedge funds in the world have been using machine learning for algorithmic trading, reaping big returns for their investors. Healthcare providers use AI to help speed diagnosis and to suggest medications and treatment plans for patients. It’s clear that AI can help many industries, but one industry where AI can drive significant efficiency improvement is with supply chains.
How AI and Machine Learning (ML) Impact Supply Chain
Though the supply chain industry, in general, has been slower to adopt new technologies as a whole, the logisticians and supply chain professionals that are using AI have found that it can save massive amounts of time and money. To date, its primary use has been for predictive and prescriptive purposes. Organizations are also using AI to power chatbots. A chatbot is a program that responds to verbal or written human input. When properly written, a chatbot can use data gathered from tags to respond to customer’s questions and requests. For example, a customer may send an email asking why a shipment is late. Taking company-generated tags as well as data from other sources it can determine that a bridge closure caused the shipment delay and relay that information to the customer.
Leveraging ‘Trustworthy ETAs’
Machine learning, a subset of AI, evolved from the study of pattern recognition and computational learning theory in artificial intelligence. ML involves the creation of algorithms that can learn from and make predictions using big data. One critical advantage machine learning can offer supply chain operations teams is the ability to generate accurate shipment ETAs, a long-standing challenge. Companies that have consistent accurate shipment ETAs have a significant advantage as it has cascading positive impacts on supply chain operations and planning. One of the biggest opportunities accurate ETAs provide managers is the ability to safely remove slack (the cushion added by each link in the supply chain journey). And when you have confidence in how long goods take to move in your supply chain, you can also safely reduce safety stock without increasing your chances of stock outs.
Facilitating Earlier Contingency Plan Execution
Another valuable benefit AI offers supply chain managers is the capacity to highlight where they need to focus their time to resolve potential disruptions. ML culls through all the shipment data inside an in-transit visibility solution to isolate and flag which shipments need attention to avoid or mitigate delays. Using algorithms to detect patterns and rules to scenarios, AI can also exist within analytics programs and be used to help prescribe solutions in real-time to shipments in need of contingency planning. AI has already begun to transform the logistics industry and will continue to save those organizations that invest in it both time and money.
We’re Talking Big Savings
The logistics industry has the potential to be transformed more than any other industry using AI. The consulting firm McKinsey estimates that AI will add between $1.3 trillion and $2 trillion a year in value to the logistics industry. Businesses calculate that roughly 6500 hours a year are wasted due to processing papers, fixing purchase orders and replying to suppliers. Beyond labor inefficiencies, $470 billion was wasted on overstocking and $630 billion on understocking in 2015. The encouraging news is that AI can significantly lessen wasted time and money through chatbots, in-transit visibility software, prescriptive and predictive analytics, (analyzing historical and real-time data to predict more accurate ETAs.)
Risk management and loss prevention are other areas where AI can reap massive benefits. AI is amazing at analyzing and identifying patterns that humans may not notice. By looking at multiple data sets in a blink of an eye, AI can see how to optimize routes or identify routes that are prone to theft or inclement weather and reroute shipments to avoid those areas. When an additional input such as fuel optimization is added to their programming, AI could identify ‘greener’ route optimization suggestions—a fantastic use to help decrease fuel consumption.
AI decreases the manpower needed to solve complex issues, according to Goldman Sachs. In the next 10 years AI will bring down logistics cost by 5%, which will save an estimated $25 billion. All these benefits sound almost too good to be true. So why is it that only 21% of the firms surveyed by McKinsey have used AI beyond the testing phase? Perhaps because AI is still in its relative infancy, its rollout has been slower than predicted.
Obstacles to AI Adoption?
Despite the overwhelming benefits of AI use, there are some factors that can inhibit adoption. First, AI can be quite costly and time-consuming to implement due to difficulties with device and system integrations. The time and money needed to invest in AI integration can deter companies who otherwise would have upgraded their equipment and software. Second, companies also must spend significant time and money to retrain both employees and customers on the new systems. Employees may resist new systems because they do not know how to properly utilize them. This resistance can be overcome by investing in comprehensive training events and documentation, such as easy-to-understand videos. These obstacles notwithstanding, the benefits to implementing AI far outweigh the obstacles to adoption—which is why Savi Technology® has invested heavily in AI-enhanced supply chain solutions.
Savi’s in-transit visibility software, Savi Visibility™, analyzes historical and real-time data coupled with ML to provide accurate, continuously-updated ETAs of shipments to its customers. Visibility also provides smart, AI-based notifications that automatically notifies users when a shipment is likely to arrive late, even far in advance of the scheduled arrival date, while reducing late arrival “false alarms.” This allows logisticians to intervene early, if needed, to find alternate sources or transportation options in order to meet crucial deadlines. And our analytics product, Savi Insight™ applies AI to cleansed and ordered data, producing insights for supply chain managers to make data-informed decisions. Accurate, objective data reduces uncertainty which permits logisticians to safely remove slack from schedules. Together, these AI-assisted solutions give Savi’s customers the certainty of “knowing” so they are able to confidently streamline their supply chains, saving time and money. This is truly a competitive advantage.
