Get to know JD Edwards EnterpriseOne Orchestrator

The early days of Internet of Things (IoT) piqued my interest, primarily due to the potential for equipment monitoring with properly installed sensors. But it wasn’t until this year when IoT was included with the latest Oracle JD Edwards (JDE) Tools Release that I got excited about the application and impact of IoT.

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One of the many conveniences that JDE has had for many years is the ever-increasing range of Interoperability tables. These facilities allow the mass input or output of data into almost all the master tables in JDE: Address Book, Sales Orders, Invoices, Checks, Purchase Orders, and more.

Where it has come short, however, is a frontend that is easy to use, easy to understand, and, perhaps the most notable pain point, is easy to load into the system. Over the last year alone, I’ve had five clients request some means of facilitating the loading of data into the Interoperability tables. This is such a hole in the whole process that I suggested that we make a free-standing product to perform this service.

Orchestrator to the Rescue

With Orchestrator, this free-standing product can be created more cost-effectively and be universal in its usage. Orchestrator has so much more flexibility to analyze incoming data that we can make a single Orchestrator instance do both the Item Master and the Item Branch/Plant in one pass. For example, using the standard functionality and our usual method of uploading a spreadsheet of information had to be run, changed, and run again. What a time saver!

Orchestration and Workflow

Additionally, the Orchestrator runs on the AIS server, thereby reducing the load on the Enterprise server. This opens up the possibility for the Orchestrator to perform “watching” programs – ones we used to call Never Ending Programs (NEP) on the IBM midrange machines. Orchestrator can watch for an incoming table from the bank, for example, and not only process it when it arrives, but send us an email if the bank sends us a duplicate table. It can even analyze the number of records, existence of a header and footer and their accuracy, and let us know if it’s a true duplicate or another table with the same date/time stamp but different characteristics. All of this wasn’t possible before without a great deal of code.

We’ve got a project to upgrade and migrate from World to E1 9.2, and I’m looking forward to putting this new functionality through its paces. Look forward to a case study Spring 2018 when I apprize you of our results.

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