In today's fast-paced industrial sector, responding swiftly and accurately to request-for-quotations (RFQs) can be a game-changer. Nakkila Works, a Finnish company specializing in the delivery of customized steel tanks for industrial use, recognized the need to accelerate and improve their proposal handling process. The ReBoot Satakunta project focuses on advancing digitalization (robotics, automation, artificial intelligence, IoT, cyber security and other ICT solutions) in the Satakunta region. Nakkila Works was one of three implementation trials of the digital solution. Under the ReBoot Satakunta project Nakkila Works They partnered with Navaia and Dyme solutions. Where Navaia created the AI solutions and Dyme solution was responsible for the software implementation to customers. Nakkila wanted to utilize AI-driven solutions to enhance their efficiency, reduce manual workload, and improve accuracy. This blog explores how Nakkila Works transformed their RFQ process using cutting-edge AI technology.
Nakkila Works: The Challenge
Nakkila Works manufactures tailor-made steel tanks for industries such as processing and storage. These tanks are designed to meet stringent standards and are either fully assembled in the factory or at customer sites. With operations spanning Finland, Sweden, and Norway, the company handles around 200 RFQs annually, each varying in complexity.
RFQs can range from a simple email to comprehensive requests containing numerous documents. Preparing a proposal typically involves gathering both commercial and technical information, a task that demands significant expertise and time. As the scale of RFQs increased, so did the need for a solution that could handle the growing volume and complexity more effectively.
The Goal: Boost Efficiency with AI
The objective was simple: reduce the time spent on RFQ processing. With each RFQ involving dozens of documents, sellers needed to sift through technical drawings and specifications to provide accurate proposals. This manual process is often delayed response times by weeks. Nakkila Works wanted a solution that would streamline this process, allowing their team to focus on more strategic tasks rather than document review.
Expectations from AI Technology
The AI-driven solution was expected to:
- Standardized processes for RFQ handling.
- Reduce errors in proposal preparation.
- Accelerate the process, freeing up time for additional sales opportunities.
- Alleviate the workload on employees, enhancing their well-being.
By integrating AI, Nakkila Works aimed to not only reduce time spent on RFQs but also lower overall costs, boost productivity, and minimize risks associated with human error.
AI-Driven Proposal Automation
To tackle this challenge, Nakkila Works teamed up with ReBoot Satakunta, Dyme solution and Navaia, a company specialized in AI solutions, to develop a tailored proof of concept (PoC). The project involved collecting past RFQ documents and training the AI to recognize and extract critical information using Microsoft Azure’s image and text recognition tools, combined with Navaia’s proprietary AI components.
How It Works
Nakkila Works provided key technical and commercial data points from past RFQs to train the AI model. With enough training data, the AI was able to analyze incoming RFQs, extracting and organizing essential information such as:
- Raw material requirements
- Standards
- Dimensions from technical drawings
- Commercial terms
With enough training data, the AI was able to analyze incoming RFQs, extracting and organizing essential information which drastically reduced the time required for manual data collection and review.
Results of the PoC
The PoC successfully demonstrated that AI could handle the complexity of RFQ processing. The tool was able to:
- Reduce processing time by cutting down manual work.
- Improve accuracy by minimizing human errors in analyzing technical specifications and drawings.
- Free up staff to focus on other critical areas like client engagement and strategic planning.
Nakkila Works also found the AI-powered solution to be highly adaptable. The more data it processed, the more efficient it became. Employees involved in RFQ handling appreciated the reduction in workload and the positive impact on their well-being.
Key Insights and Learnings
The development of the AI solution revealed several valuable insights for both Nakkila Works and Navaia:
- User Involvement Is Key: From the outset, end-users (the sales team) were actively involved in the development process, ensuring that the tool met their practical needs.
- AI Proved Reliable: Initial skepticism about AI’s ability to handle complex tasks was quickly dispelled as the technology consistently delivered accurate results during testing.
- Minimal Development Burden: The solution was relatively easy to integrate, requiring minimal time and effort from Nakkila Works' staff during development.
- Cross-Functional Collaboration: Success depended on the combined expertise of all.