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Automating purchase orders into Shopify for Righteous Felon Craft Jerky

Companies in the distribution sector know the headaches associated with receiving purchase orders from multiple sources all too well—each comes in its own format, and they often must be entered into a standard system by hand by a sales rep. 

Our client Righteous Felon Craft Jerky is a boutique distributor of unique, clean artisan snacks. They work with businesses like hotels, airlines, independent grocers, and more to offer their snacks to customers, and provide their clients with a customized experience that makes ordering, delivery, and inventory management a breeze.

The problem

While Righteous Felon has an online store, they receive a number of their POs through email as PDFs or Excel files, which ultimately need to be entered into Shopify to process the order and kick off the fulfillment process. 

Doing this manually was a painstaking process, and opened the door for human error—depending on the source of the emailed PO, the steps to extract and format the data for Shopify differ, and overall it was a very inefficient process. 

We like to use our ROI Calculator to figure out how much money can be saved by automating processes like these. In Righteous Felon’s case, it showed potential for considerable savings, and we worked together to build a workflow that would automate this process and help them realize these savings.

“We worked closely with Datos to get our arms around all the different types of channels/formats we receive POs from our various customers. This process, which a human was previously doing, took roughly 3.5 minutes per order, but was being done by highly paid sales managers who were collectively entering over 200 orders per week (about 12 hours per week).”

Brendan Cawley, Founder & CEO of Righteous Felon Craft Jerky

Our solution

Building a solution to this problem started with deeply understanding the various purchase orders that Righteous Felon was receiving. In order to create consistent orders in Shopify, we needed to understand the sources for these emails, what information was present in the emailed POs, what format it was in, what data was present on spreadsheets vs PDFs, etc.

To be as efficient as possible, we started with the formats they received most frequently and took them the most time to process. All else being equal, starting with big wins first is always a great approach.

Once we had a good handle on all of the different purchase order formats we could expect to receive and how they’d need to be adapted for entry into Shopify, we were able to design and deliver a comprehensive solution that automates the entire process, from the initial reception of the email to ultimately creating the order in Shopify. The solution we built includes:

  • PO Pre-Processing: When a new purchase order is received via email, it’s routed to the correct next step for further processing, based on the source of the email
  • Document Data Extraction: Docparser, a document parsing service, extracts relevant data from PDF files contained in the emails, so it can be passed further along the workflow
  • Data Processing: N8N then receives all of the relevant data from the pre-processing and extraction steps, formats it for appropriate entry into Shopify, and enriches the order with additional data from Righteous Felon’s systems, such as Salesforce
  • Automated Order Entry: Once all of the data processing is complete, the PO is entered into Shopify with the right customer, SKUs, metadata and more

This visual outlines the workflow and the various tools involved at each step:

Because the formats of the purchase order vary so much depending on who the customer is, we needed a dedicated workflow for each type of PO to prepare the data before sending it to this workflow. For example, orders placed with Righteous Felon’s Excel template don’t need to go through the Docparser step, because the data is already structured and doesn’t need to be extracted. 

To be efficient, we built one order entry workflow that handles putting the order into Shopify and the multiple steps required to do it. All of these disparate workflows process the data and send it to this centralized order entry workflow so we don’t have to reinvent the wheel if we want to add a new customer or PO format to the mix. 

One of the key elements of this workflow is not entering the order if anything is wrong with it. Just a few of the things that can go wrong:

  • The same exact PO has been entered into Shopify already
  • The customer doesn’t exist in Shopify
  • The SKUs mentioned in the PO don’t match our systems
  • The order total in Shopify doesn’t match the customer’s expectation
  • The PO data wasn’t parsed accurately in Docparser because the customer changed the format

Instead of simply failing, the workflow knows which issue caused the failure and alerts someone on the team to enter the order manually while explaining what went wrong so it can be solved for next time. 

Altogether, this workflow has yielded many benefits for Righteous Felon, such as improved efficiency, enhanced order entry accuracy, scalability, and cost savings. 

“Datos was able to create streamlined workflows to collect those POs as they come in, normalize the data within them, and automated our order entry process into our order management software & financial systems. The system Datos developed, once fully tested and fine-tuned, was able to reduce the amount of manually entered orders down to about 50 per week (75% reduction), savings us thousands of dollars a month in hard costs, but more importantly, giving our sales team back 8 hours a week of selling time – an even more impactful financial outcome (and morale booster for the sales team).”

Brendan Cawley, Founder & CEO of Righteous Felon Craft Jerky

Here’s an example of one of these workflow paths from start to finish.

PO receipt and beginning processing 

Data processing once Docparser extracts info from PDF:

Auto order entry workflow (that all orders go through)

We’re thankful Zapier isn’t our only option for building automations. The conditional logic and multiple paths that n8n provides, along with its ability to handle multiple line items and seamlessly merge them back together allows us to cover so much ground in one automation. 

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