X12 EDI Supply Chain Workflow Interface Configuration in the eiConsole
This is the PilotFish eiConsole. It’s the integrated development environment you’ll use to build, test, maintain and deploy all of your integration interfaces.
For this video today, we’ll be looking at the sample supply chain EDI workflow and doing some purchase order ingestion (EDI 850). We’ll be taking in some EDI, doing some field validation, a little bit of tweaking and then updating a database and then also sending an acknowledgment back to the sender (EDI 855). So let’s get into it.
Main Route Configuration
Opening up our route here you’ll see our main route grid and this is where you’ll do the majority of the construction of your route. So this is where you’ll define your source and target systems, you’ll set up your validation work in the mapper – all those sorts of things.
This is organized by having our source systems on the left (this is all the places that we’re taking data from) and we have our target systems on the right (these are all the places that we’re sending data to). The data enters the system and flows through the 7-stage assembly line process as it’s finally sent to its destination system.
X12 Purchase Order Route Overview
So for this route here, as we could see overlooking at our sources, we’re picking up some X12 EDI data from our SFTP and looking at our transport systems (our target systems). Here we’re updating our purchase order archive database. And then, in our second target system, we’re sending that 855 acknowledgment back to our sender.
Identify Source System in Route
So at this point, let’s look at each of the 7 stages and the role that they play in the integration process. The first stage on the far left, the Source System stage – this is mainly documentational. So for each of our different source systems, we can give it a name, some metadata (if desired) and also a customized icon. This is very useful when you’re working collaboratively on these different routes – to be able to quickly identify the source and target systems, what they’re doing, where the data is coming from – all those sorts of things.
Listener Configuration for Incoming Data
Next, we’ll move into the 1st functional stage of the integration pipeline and that’s our Listener stage. So if we wanted to integrate some data, the first thing we have to do is get that data and we use the Listener stage for that.
We have these pre-built listeners for all different kinds of ways that you could imagine to get data into the system. Whether it’s something like an FTP (as we’re using here), maybe a directory, an email database query, something from the cloud (like AWS or Google Cloud Engine) – any way that you can imagine getting data into the system, we’ve got a Listener for it.
How all of our different modules work is they take a base level of configuration (these are our required field values) and then there’s more advanced configuration (with same defaults if needed). So for our FTP listener here, all we really need to set up is our host port, our credentials, how often we want to poll and then it’s ready to go and receive files.
Data Processor Stage
Next, we move into the Processor Stage. The Processor Stage is where we perform any pre-transformative operations on this data. Let’s say we’re getting our EDI from our SFTP and it’s encrypted, it’s in some foreign character encoding or in a zip file that we need to decompress. We’ll chain processors to perform all those sorts of operations – so that when we get to our next stage, we know that the data is ready for transformation. Here in our purchase order workflow – we’re using an asymmetric decryption processor with some conditional execution to say “in case my EDI payload that I’m picking up is encrypted, I want to go ahead and decrypt that before I send it onto the next stage”.
Source Transformation into Common XML Structure
Next we’ll move into the Source Transform stage. The purpose of this stage is to take any non-XML formats and convert them into an XML structure that we understand. We do that in a two-part process.
First, we use a Transformation module to take any non-XML rep formats and give us kind of a baseline XML representation. Second, we’ll do a logical mapping – this is an XML to XML mapping using our data mapper. Since we’re ingesting some EDI here, we’re using our EDI transformation module. We’re supplying the X12 table data for the transaction that we’re trying to work with, in this case – EDI 850. And since we’re also dealing with EDI, we’re going to add some enhancements to the XML that we’re producing. We’re going to use some friendly naming here to add some more human-readable friendly names to that XML that we’re producing. To help us later on with our mapping, we’re going to also add some segment indexing.
Mapping the Data in Drag & Drop Mapping Tool
The second piece of our Transformation stage is this logical mapping using our data mapper, which I’ll show now.
Here we have our Data Mapper – our 3-pane graphical drag & drop mapping tool. This is organized by having our source format on the left – this is our incoming expected XML structure. We have our target format on the right – this is the XML structure that we’re trying to produce. You drag & drop from the left and the right (use the tool palettes at the top for more complicated operations) to construct your mapping. Since we’re taking in some EDI data, we’ve got our EDI XML format on the left – you should recognize our loops here (our PO1 & CTT from that incoming 850).
Field Validation in Data Mapper
What we’re really doing in this mapping is some field validation. We’re not changing the structure at all – we’re just adding onto that data structure, if any of our validation rules aren’t met. Our target format is also that EDI 850. We’re using an identity template to essentially copy over all of the XML – so we’re copying directly across. And then for any of our loops or defined elements that match, we’re going to perform this validation rule.
Here we’re doing a pretty simple validation rule to say “get the count of all of our PO1’s and make sure that they’re equal to our total”. We’re saying, “if my line item total doesn’t equal the count of actual line items that I have, then I want to go ahead and identify with this CTT versus PO1 mismatch text attribute”.
Data Mapper Generates XSLT
A little more about our data mapper – under the hood, this is all generating W3C compliant XSLT so there’s no binary formats – this will run in any XSLT engine. Here in the data mapper, we also have a built-in testing mode. So you can feed in a sample file (as I’ve done here) then you can push “go” and immediately see the results of that incoming data pushed through our mapping that we’ve put together. Taking a step back – we ingested our data via the Listener, we transformed it into that XML and the Source Transform did a little field validation. Next, we move into the Routing Stage.
Data Routing Rules in the Routing Stage
The Routing stage performs a couple different operations, but the main one that we’re going to focus on here is our routing rules. Routing rules are our primary way of taking our incoming source data and sending it only to the target systems that we want. We can define these arbitrarily complex routing rules to say, based on either some information in the transaction (maybe some metadata that we’ve set), follow these rules, flag the ones that match and then only send the data to those certain target systems.
Here I’ve got a couple of simple rules set up. My first rule here is looking at that EDI XML and saying if my BEG07 is AC and if my sender has requested an acknowledgment (EDI 855), then I want to send this message to my second target system here. Otherwise, I just want to go ahead and in all cases send it on to the database transport. So we’ll always be updating our database, but only if our sender wants an acknowledgment will we send the data on to the second target system.
Configuring the Target Transformation
Moving on to the target side, it operates basically the same as the Source side, but in reverse.
First, we’re doing our Target Transform using a logical mapping here and then we’re using our transformation module to produce any non-XML formats. For instance, here looking at our second target system, since we’re producing that 855 response, we’re using our logical mapping to map into that 855 XML and then we’re using our EDI transformation module to actually produce that final EDI.
Route Transport System Setup
Next, look at the Transport System – these operate analogous to the Listener, where we’re pushing data to a Target System. For instance, since we’re updating this purchase order archive database, we’re using our database sequel transport to perform these database inserts. And then in our second target system, we’re using our HTTP POST transport to send our 855 response back to this target URL. In our last stage (target system), this is identical to the source system where we can give our target systems a name, icon and some metadata.
Route Testing Stage
Now that we’ve got all of our pieces in place, let’s see this in action. Here in the eiConsole we also have a built-in testing mode. This runs byte for byte identical to the eiPlatform – the headless runtime execution engine – so there’s no compilation necessary to test your routes here. Here in the testing mode, you can start and stop your tests from any stage as well as feed in alternate data and transaction attributes to test those was crazy edge cases and you can also save your testing configurations for easy replayability later.
That’s what I’ll do here, using this sample 850 file that I’m feeding in. When I hit “execute tests”, we’ll see the data move left to right through the system. We’ll get green checkmarks for any successes and then we’ll get red X’s in the event that we have any failures on any stages.
So here, looking at the data quickly, we’re starting with just a basic single line item EDI 850. Next, we will move into our EDI transformation stage – the transformation module where we took that incoming EDI and transformed it into this XML structure. Here you can see the friendly names (mentioned earlier), where each of our segments and elements have this really easy to understand human-readable friendly name attached to it.
Route Validation Stage
Next, we move into our Route Validation stage. As we can see, all we really did was copy the data over. If any of our validation rules would have not been met, we would have had that extra element that we would inject into it. But since our number of total line items equals the number of purchased line items, we didn’t break our validation so there’s no change here.
Moving into the routing stage, we can see that since our 850 message included that AC – we did end up sending to both of our target systems. Looking at our database target, we’re using our XSLT to interact with and to produce some SQLXML – so this is an XML representation of a database operations. So we’ve taken that 850 and transformed it into these two insert statements, saying “I want to insert into this transaction table” and “here’s my summary purchase order information”.
Down here is my second insert. I’m going to insert into my order item table and use the identity from this first insert as a foreign key. Here is some more information that I want to insert into my order item table. Looking at our second target system, we’re using XSLT to generate our 855 message and then looking at the results of our EDI transformation. Here we see that we’ve produced that EDI 855 message that’s ready for sending.
It looks like our Transport here was successful, so our database operations worked. But it looks like we had a failure here on our post transport. For any places where we have errors, we can view the stage output and get a really detailed exception message to tell us exactly what went wrong – so that we can go back, fix the issues as needed and then run our test again. So that’s really all there is to it, with the eiConsole and this purchase order ingestion.
Summary of Purchase Order Ingestion Workflow
It’s really as simple as that! In just under 10 minutes, we were able to build, test and deploy this purchase order ingestion interface using the PilotFish Graphical Automated Assembly Line process.