Industrial Data Labs

Starting with business problems

By leveraging data and implementing machine learning, they sought to automate key steps, streamline workflows, and enhance productivity. This case study highlights the approach used and the resulting improvements in response times and efficiency.

Mapping Existing Workflows

Industrial Data Labs began by conducting a detailed assessment of the current workflow for responding to RFQs. This included observing the entire process from receiving a proposal to sending a quote, identifying manual steps, and noting bottlenecks. By mapping the existing workflows, they gained a clear understanding of areas where time and resources were being underutilized.

Quantify Potential Time Savings

The team then developed and tested new workflows incorporating the trained model to automatically fill in quote details and suggest responses. Early prototypes indicated a potential reduction in response time by up to 80%, enabling inside sales reps to focus more on high-priority tasks and relationship management.

Create a Proof of Concept

A proof of concept was created using a limited set of RFQs to demonstrate the effectiveness of the new system. The prototype integrated seamlessly into the existing sales platform and was able to handle a diverse set of RFQs, showing a significant reduction in time spent on repetitive tasks.

Results

By reengineering the RFQ response workflow, leveraging existing data, and implementing a machine learning model, Industrial Data Labs significantly reduced the time spent responding to RFQs. The automated system not only improved response efficiency but also empowered sales reps to focus on strategic tasks. With a continuous feedback loop and regular updates, Industrial Data Labs is well-positioned to maintain and enhance its competitive advantage in the industry.