Data Automation to accelerate eCommerce
One of the largest after-market Tire distributors in the United States is using automated data quality and data setup workflows to set-up new products in 7x faster in 4 days vs 4 weeks without the automation. With daily reconciliation of 16 million data points between their ERP and eCommerce systems, they are achieving 99.999% accuracy between the 2 systems ensuring accurate product catalog offered to distributors and consumers. This reduces the number of complaints, return items and increases overall customer satisfaction.
Our customer is one of North America’s largest marketers of automotive replacement tires. Through its multi-channel strategy, has been a tire company ahead of the curve.
A tire may look like molded rubber. But making a Tire is a sophisticated piece of engineering requiring 10s of raw material, recipes, and processes. These results in each tire product having 150+ attributes.
To set-up a tire in the ERP system, the customer used to go through a multi-step process that required collecting attributes from the manufacturer, from their buying team, their finance team and their marketing team.
Additionally, this product information has to be synchronized nightly with their eCommerce platform that served the company’s online eCommerce portal as well as the product catalog that is used by all its customers.
Challenge 1: Collecting 150+ attributes across suppliers and multiple internal departments were taking the Master Data team 4 weeks to set-up a new product. In many cases, this prevented them from receiving the product which was already shipped by the vendor and sitting outside their docks. The result is held-up inventory, and inability to ship the product to its customers.
Challenge 2: The ERP and eCommerce systems were built on completely different platforms. The attributes in the eCommerce system are labeled differently than the ERP system. And the rules governing those attribute values were different. As a result, on any given day, there would be 1000s of SKUs (~1%) which would have inconsistent data between the two systems. The process of identifying the inconsistency itself required a full-time business analyst to download data from both systems over a week. The impact is that the information on their eCommerce platform would not be correct, resulting in product purchases being returned due to incorrect information.
Using the Workflow tool on the DvSum platform, the customer set-up a new article creation workflow. In this workflow, the customer can load a raw excel file with collected information from the vendor and process it inside DvSum. DvSum Data Quality engine then validates the data type, format, uniqueness, consistency, and validity (list of values) of all the data. Once data is validated, the workflow routes it through other groups to collect additional attributes to enrich that information. After the data has been completely prepped, it is sent to data analysts to enter the data into their ERP and related systems. A final automated reconciliation test is performed between the ERP system and Excel file by DvSum to ensure the information entered matches what is in the final spreadsheet.
Using the X-system reconciliation validation in DvSum, the customer has set-up a rule to check all 160 attributes of each active Tire between its ERP and eCommerce system. DvSum process picks data from the eCommerce system and ERP system and compares ~16,000,000 data points and identifies which article and which data point does not match. It notifies the data analyst of the mismatches, allows her to fix the data in the tool and then downloads an update file which is then used to mass update the bad data into the eCommerce system.
Business Value Delivered
3 months after using DvSum, the customer was able to reduce the lead-time to set-up a new product from 4 weeks to 4 days. And maintain a higher level of 1st-time accuracy.
On the eCommerce side, the customer was able to completely automate the reconciliation process down from 1 week to 6 minutes every day which allows them to reduce and maintain inconsistencies to 100s down from 1000s.
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