Data Ingestion

Automate Complex Data Ingestion in Supply Chain & Logistics

Written by 
Naresh Venkat
December 14, 2023

Three key learnings from this article:

  1. Supply chain and logistics firms solve complex data ingestion challenges with flexible solutions like Osmos Pipelines.
  2. Osmos helps organizations accelerate workflows by removing touchpoints and eliminating manual processes.
  3. Improve end-to-end supply chain planning by automating data ingestion, streamlining processes, and increasing resource efficiency with Osmos.

Delivering on the promise of efficiency

Service sector: Manufacturing Supply Chain

Data ingestion challenge: Handling mismatched schema and streamlining complex processes

Customer need:  SDP Automation

Supplier Delivery Promises (SDP) are crucial to improving end-to-end planning, forecasting capabilities, and shortage detection. A business's commitment to deliver can be impacted by product output, lead times for acquiring raw materials or purchased parts, and operational performance issues.

A leading automobile parts manufacturer needed a solution to acquire, process, and upload to SAP delivery promises against their plant releases for direct material. At the time, they were limited to using advanced shipping notifications, which covered material in transit but offered no further visibility. They needed to increase resource efficiency by building a production plan considering restrictions and the ability to simulate scenarios with long-term visibility.

Graphic demonstrating the data ingestion process
Data Ingestion Process

Solution: Manufacturers trust Osmos to improve end-to-end supply chain planning. By leveraging Osmos in the first step of the automation process, they can now onboard vendor files into a standard template as a CSV file. Osmos ensures that the formats match the requirements for the upload.

Today, supplier delivery promises are sent in various forms to material planners at various manufacturing facilities. Rather than upload to SAP, a time-consuming manual task. Thanks to Osmos, material planners can now use the data to inform their short-term constraints, which equates to benefits across the entire data process.

Enhancing customer experience through data automation

Service sector: Supply Chain & Logistics

Data ingestion challenge: Streamlining data ingestion processes

Customer need: Purchase order and pricing automation

A data center supply chain and logistics solutions provider with dozens of offices worldwide offers a robust suite of services that spans infrastructure, cloud, communications, and cyber security. They work primarily with large, complex businesses in the process of scaling and are well-versed in data center infrastructure. 

The organization’s sales teams were tasked with receiving, verifying, and loading purchase order and vendor pricing data into local systems. Their very manual data ingestion process required two dedicated resources and consumed more than 60 hours each week. 

Frequent escalations plagued the team. With many ingestion points, they looked for the right tools to simplify the experience of manually coding data ingestion workflows. 

Graphic demonstrating an automated and easy to manage data ingestion process

The Goal: Make engagements faster, remove touchpoints, and let technology drive the movement of data so their people can focus on customer needs.

Solution: Supply chain and logistics solutions providers trust Osmos Pipelines to automate and manage complex data ingestion tasks so that their most valuable resources, their people, can be their eyes on the ground, ensuring seamless order management.

Today, this firm efficiently manages touchpoints with valuable suppliers, vendors, and partners. They keep total cost of ownership (TCO) low by uncoupling data ingestion from human resources. The time they give back to their frontline teams is spent focused on customer success.

Should You Build or Buy a Data Importer?

But before you jump headfirst into building your own solution make sure you consider these eleven often overlooked and underestimated variables.

view the GUIDE

Naresh Venkat

Co-founder and COO