Register to watch this webinar from Splice Machine and Intrigo that demos order promising and the creation of machine learning models to predict late shipments, and how a new data platform called online predictive processing (OLPP) can automate this insight.
Anything can happen in the supply chain – Customer drops a huge order, test equipment fails, Contract manufacturer runs into capacity constraint, supplier slips on shipment, parts are faulty, trucks break down - the list is endless.
Many companies handle supply chain anomalies with reactive planning systems. They quickly try to remediate problems with optimization algorithms once anomalies occur. But this is tantamount to looking in the rear-view mirror. Plus, lead times to ship products, procure new goods, or produce new goods are often lengthy, making it impossible to react to meet customer demand when supply suddenly changes.
One new approach to dealing with supply chain uncertainty is to use machine learning to predict what might go wrong and use that as the basis for the supply chain planning processes. This webinar takes you on a journey to the predictive supply chain with supply chain industry veterans, Monte Zweben, CEO and co-founder of Splice Machine, and Santhosh Kumar, CTO of Intrigo. The webinar will focus on true order promising that gives salespeople a real-time reservation tool to better serve customers and make reliable commitments on behalf of the enterprise.
The webinar will demo order promising and the creation of machine learning models to predict late shipments, and how a new data platform called online predictive processing (OLPP) can automate this insight.
It can create expected “scenarios” directly from machine learning models that predict which ASNs are likely to be late and estimate how late they will be. With OLPP, machine learning models can provide every ASN an “expected” date in addition to its scheduled date, creating a new “scenario” comprised of all live ASNs and their expected deliveries.
We will also talk about integrating this capability with SAP and enabling a synchronization of reservations with clean SAP sales orders.