Simplistically, product is inbounded from a manufacturer to a distribution center, where it is moved into inventory, before being out-bounded to the customer. For example, if a product is not received at the distribution center, it is not available in inventory, and the company cannot fulfill an order to a customer.
As a product moves through the supply chain, there are various pieces of data captured so it can be tracked and accounted for, and often that data is siloed, is difficult to access, and/or cannot be aggregated with all the other products moving through the supply chain. While there is an emphasis within organizations to better understand the supply chain and identify opportunities for improved efficiency and cost-saving opportunities, running ad-hoc or scheduled queries against legacy source systems does not allow for teams to glean insights quickly or easily, limiting their agility.
Organizations are elevating their supply chain departments to become more data-driven with the introduction of the data lakehouse architecture and machine learning. While there are countless tools that enable businesses to create a data-centric environment, they are typically fragmented.