How can the cargo freighters use an AI? How can a well-trained algorithm define the next big thing on the market? How can you time the shipments so well, that the fish caught in North Pacific will be served fresh in Johannesburg’s restaurants? The technology has an answer already, and we’ll try to break it down, bit by bit.
How exactly can machine learning help?
ML is a complex subject, capable of solving issues in many industries. But what application does it have within the supply chain? We have determined one of the most important reasons for the efficacy of machine learning within this heavily data-reliant industry.
1. Machine learning can analyze a superior amount of information and come up with logical advice
By advice, we mean a structured result of all data analyzed using specific algorithms that one or another business requires. The industry used to rely on manual analysis and data collection — a time-consuming process that slows down the chain and drains more resources, including finances.
Machine learning allows analyzing data that was previously hard to use in the demand forecasting models due to a large number of variables, like:
- Historical data
- Market specifics
- Promotional activities throughout the market
- Presence on the web
Teaching the AI to understand all of these factors and to produce a forecast made supply chain management more responsive to the surroundings of each industry. The companies learn to create quality things and to cover the demand thoroughly, which results in better customer satisfaction and better revenue for the business.
2. Logistics benefit from an advanced form of last-mile tracking
According to Datafloq, 28% of all delivery costs belong to the last mile delivery. It is a critical part of the whole process, as the efficacy of it can have a direct result on sales, customer experience, and, in some cases, product quality.
The clients wish to know where the shipment is at the moment and how soon will it be in their hands. The tracking software is already capable of showing the approximate location of the shipment and the phase of delivery (e.g. “the shipment is in the post office”). Machine learning in logistics, however, is offering more opportunities.
Taking into account the wide variety of how people enter their addresses and how much time does it take to deliver the goods to specific locations, ML can provide valuable assistance in optimizing the process and offering the clients more accurate information on the shipment status.
Large enterprises are also interested in this branch of machine learning due to the vast demand for internal tracking.
3. Machine learning is capable of boosting supply chain management efficiency to a previously unavailable level
Machine learning algorithms offer immense help in managing supply chain operations. Utilizing the pros of each methodology allows a vast analysis and, later on, precise predictions of a broad variety of aspects.
Big data visualization, marketing, real-time monitoring and activity, all kinds of predictions (from demand forecasting and customer behavior to weather at any location globally). This toolkit is as vast as it looks, with much effort required to tune it properly. But once machine learning is done right for a business, it lets the company step up their game. The report of DHL and IBM offers more insight into ML and its methodologies, with a great visualization of the latter: