Imagine… you are preparing for an important event. To prepare, you have taken to Amazon to order some last-minute essentials for your occasion. You press order and boom, your shipments will arrive 5 days before the day you need them, everything is perfect. Two days later you get an Amazon update on your order, your shipment will be arriving later than originally expected, to be precise, approximately 3 days AFTER your event! Instant worry of where else you will get these essential items sets in. This scenario is the same frequent and devastating reality of the distribution industry. If you are a distributor, you most likely add a few days, maybe even weeks, to your expected date of delivery from a supplier. Why is your delivery late, and what factors contribute to a late delivery? The answer can range anywhere from where your delivery is coming from to conditions or technological issues. Regardless of the cause, you are spending money on working capital and valuable time. Through existing supply chain issues, it is pivotal to receive accurate lead times from suppliers but how can this be achieved with so many uncontrollable external factors? The simple answer is with machine learning.
What is a Supplier Lead Time?
A supplier’s lead time is the quoted time between when an order is received by the supplier and shipped or distributed. In simple terms, it is how long it takes for the supplier to have the order prepared for delivery. This lead time is vital in the overall concept of supply chain resiliency, especially within the distribution industry. Distributors rely upon this given lead time to plan and balance working capital and inventory. Inefficiencies can be seen within the ability for suppliers to accurately and timely provide distributors with the lead times they so heavily rely on. As a result, distributors are left with uncertainties such as a dangerous abundance of working capital, insufficient inventory, and even furthermore concerning, disappointed and unhappy customers.
Enhancing Suppler Lead Time Efficiency with Machine Learning
With the extensive consequences of inaccurate supply lead time so extreme, getting a better estimate than what the supplier is giving you is of great value to distributors. To accomplish this, suppliers and distributors alike have turned to one of the most trending technological imperatives, machine learning (ML). ML is an advanced mathematical model-based application of Artificial Intelligence that has the capability to learn and predict independently by means of experience. According to Jonathan Bein, Managing Partner at Distribution Strategy Group, using AI and ML techniques, suppliers can reduce their lead time by about 30%-60%. Using ML, factors such as product category and shipping distance can be learned and can aid in helping that supplier provide their distribution customers with as accurate of a lead time as possible at a far greater ability than humans alone. Suppliers need to maintain that relationship with distributors because they are the link to the customer, and ML can be that support.
DataXstream helps advance the supply chain by helping customers be resilient during supply chain disruption through OMS+ Task Management, which allows customer service representatives across multiple sites – supply centers, refining plants, and distribution centers – to communicate with each other. More than simply email, OMS+ Task Management increases process efficiency by raising awareness to sales documents (quotes, orders, deliveries) that need immediate action to keep the process moving. DataXstream’s OMS+ia combines the concepts of complex order management and end-to-end visibility with intelligent automation and ML capabilities. The ML component of OMS+ia allows customer service representatives to find materials and customers quicker, advancing the overall lead time process. This reimagined user interface streamlines complex sales processes, allows suppliers to view inventory across locations, and empowers end users with ML.