Paperless Parts’ Quoting Platform Adds Advanced Analytics Feature
New functionality unlocks useful quote-to-order data and enables manufacturers to leverage information they have never had before to improve operations.
The Advanced Analytics feature is said to enable estimators and sales teams to prioritize requests for quotes (RFQs) that will increase their win rates. Photo Credit: Paperless Parts
Paperless Parts has added Advanced Analytics as an upgraded feature in its cloud-based sales and quoting platform for custom part manufacturers to use data and automation to drive efficiencies in manufacturing. To use data to optimize the front office, prioritize quotes and price work effectively, manufacturers need the ability to analyze the data they are collecting at every level, the company says.
The company says the Advanced Analytics feature is a modern business intelligence tool that enables manufacturers to leverage multiple layers of data to make impactful decisions. The feature is designed to help manufacturers understand when and why they are winning or losing jobs.
The tool is said to enable estimators and sales teams to prioritize requests for quotes (RFQs) that will increase their win rates. While many manufacturing systems may track surface-layer data (such as win rate by a specific customer), the platform’s Advanced Analytics is a solution that goes deeper, the company says.
The analytics feature enables manufacturers to query their own data and pull information such as win rates by part geometry, manufacturing process or material. While shops may already be collecting this data, many may not have had a simple way to analyze it. With Advanced Analytics, it is said manufacturers can now make sense of the information they have and act on it. The solution enables manufacturing companies to not only see how their business is performing at that moment but also how it’s performing over time to continually improve operations.
With the Advanced Analytics feature, manufacturers can build customized reports to evaluate past business performance and determine critical future decisions such as which quotes to prioritize responding to first; how quickly they need to respond to RFQs to win the job; which customers are just shopping around for pricing based on past trends; win rates based on complexity of parts being quoted; and when to buy a new piece of equipment to support winning new jobs.
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