Widespread adoption of machine learning in the supply chain represents a quantum leap for optimization and efficiency analysis efforts. Powerful software can do the work of dozens of human analysts in a fraction of the time, freeing up your experts to focus on teasing out trends and often finding solutions for inefficiencies we didn’t know existed.
Machine learning covers operations related to data recognition, diagnosis, prediction, and planning. Supply chain companies are already using big data to find trends and anticipate fluctuations in demand, and powering use cases like advanced truck scheduling, ultra-optimized picking and packing, and ideal route finding (just to name a few).
Machine learning is set to transform the way the whole supply chain operates, from drayage to your customer’s welcome mat. Supply chain machine learning will positively impact fulfillment turnaround, reduce overhead costs, and help inspect cargo for damage in an instant. Using AI to drive predictive analytics could allow companies to schedule trucks to reduce carbon impact, anticipate and plan for inclement weather, and improve overall efficiency — allowing companies to reclaim considerable revenue lost to inefficiencies.
Optimizing software tools
The modern supply chain leverages powerful software to boost agility and streamline operations – two things that are necessary to remain competitive in today’s tight, ecommerce-driven fulfillment market. Used in concert with IoT devices, warehouse management systems generate incredible amounts of data that can be used to improve performance at points across the supply chain lifecycle.
Machine learning can continuously optimize the ways separate tool sets work with one another, and use the data they produce to improve overall productivity. For example, inventory management software takes data from your WMS to determine where cargo needs to go in order to meet demand. This can minimize the need for overstock — something that’s especially valuable for companies that operate small, regional warehouses rather than traditional DCs — and power omnichannel fulfillment strategies like in-store pickup.
From B2C to B2B
Compiling good supplier data is an increasingly important function in effective supplier relationship management. Smart companies are starting to use consumer technology to manage their vendor partnerships better and fortify their most valuable relationships.
Some companies already use machine learning to study seasonal trends, map product demand cycles, and track the daily actions of customers. That data processing can be used to draw valuable conclusions from qualitative supplier relationship data (assessments, audits and evaluations, performance trends, etc.) These conclusions inform larger decisions about business relationships with other supply chain partners.
Inspection and security
Pattern analysis is the bread and butter of machine learning. That’s why AI is especially well suited for inspecting products for minute details.
In time, machine learning will become a valuable tool for inspecting and maintaining cargo as it travels along the supply chain. Inspection technology can document the condition of cargo for insurance claims, finding small details that may get passed over by the human eye. Moreover, it can expedite the time-consuming process of deluxing furniture.
In addition, machine learning technology can improve security, especially at the port and in the yard (places that traditionally act as black sites for data collection). GPS container tracking, RFID product tracking, and IoT technology all document cargo throughout its fulfillment lifecycle, helping 3PL orgs avoid shrink, loss, and damage.
The sustainable supply chain is just around the corner and machine learning technology will play a pivotal role in its widespread adoption.
Currently, a global supply chain comes with an enormous carbon footprint. As more companies feel pressure from consumers and government regulators, their supply chain relationships will be heavily scrutinized — currently, an average company’s supply chain generates 5.5x more CO2 than all other operations.
Efficiency and sustainability go hand in hand. Time spent running trucks in the yard as they wait to be loaded, time spent in traffic getting out of the port — all this creates non-productive carbon that can be eliminated by greater efficiency.
The movement has begun in many small ways. Already, powerful route-setting software is finding the fastest way to run trucks based on traffic conditions, weather, and other key factors. Better scheduling limits the need for half-truck shipments, which also unnecessarily burn fuel. Even simple measures like partnering with a port-side warehousing auxiliary limits the need to order small quantities of product from overseas.