Find the Best Data Conversion Lead for Inventory with airSlate SignNow

How to create outlook signature

analyzing end-to-end lead times has never been achievable by any technology because of how many paths of finished good can take to be created and ultimately delivered to a customer this is because most supply chain processes are decoupled by Nature for example if it takes 30 days to create a car engine but deliveries need to be made within seven days of an order a material planner has to keep inventory on hand to meet those orders as a result there are multiple processes involved in providing a finished good to a customer perhaps a customer placed an order when there was sufficient material on hand or a finished good was created well before any customer orders came in other words these decoupled processes cannot be linked by mapping single cases like purchase orders or production orders which is how we visualize processes using traditional process mining but with object-centric process mining we can break that barrier and create a data model that follows and connects objects not single cases let's jump into the role of Emily a lean manager who uses the salonis end-to-end lead time app to identify and reduce access inventory throughout the entire value chain here Emily can see a snapshot of her finished goods she can see some contextual insights like where the finished good is located how complex it is to create that finished good and the end-to-end lead time to create that finished good as well as performance insights like the service level for that finished good and excess stock by combining these insights in one view Emily can address both service level and working capital objectives she sorts materials by excess stock and identifies that the finished good car engine has the highest excess stock she selects this material as it has the highest potential for working capital optimization shows us the sourcing and distribution network of this finished good every path to supply the raw materials required to make this finished good to produce the finished good and make it available for distribution salonis shows the end-to-end lead time of each path and the excess inventory within each path Emily sorts by highest excess stock and selects the stream at the top of the list as this is the path that has the highest access inventory in the path profile view Emily can evaluate the different inefficiencies within the procurement production inventory and distribution value streams in the access stock tab we can see all the materials in this stream with excess stock Emily sees that the minimum days of cover is significantly higher than the replenishment lead time this tells her that the Safety stock levels may be overstated Emily creates a task for the materials planner to evaluate if the safety stocks can be lowered based on the replenishment lead time insights she's seeing lowering Safety stock levels to reflect the latest consumption and replenishment Trends will help prevent access inventory within this critical value stream and free up working capital Emily also creates a review task for the procurement team to cancel any in-flight purchases for the materials in excess now let's recap what we just saw thanks to object-centric process mining we were able to provide a true view of this material's end-to-end lead time a view that would have been almost impossible to achieve with traditional process mining with just a few clicks Emily was able to identify and act on hidden value opportunities within her end-to-end supply chain to reduce excess inventory