DMSi Software - Item Standardization Using Artificial Intelligence
Team members:
Jason Niemi, Vice President of Product Strategy, with:
- DMSi Software Team:
- Jason Niemi
- Adam Schlichtmann
- Bailey Simoens
- Erika Haifley
- Katie Bruckman
- Kyle Conway
- Patrick Cropsey
- Paul Lichter
- Raikes Design Studio Team:
- Samuel Atkins
- Angeline Luther
- Justin Morrow
- Shivani Mudhelli
- Daniel Noon
Country: United States
Organization: DMSi Software in partnership with 2022-2023 UNL Raikes Design Studio
We're an independent family-owned business. We make digital tools for inventory and order management and we provide unparalleled service for building material suppliers.
Awards Categories:
- Best Moonshot Use Case
- Best Data Democratization Program
- Best Approach for Building Trust in AI
- Best ROI Story
Business Challenge:
DMSi is independently owned, privately held, and completely dedicated to the lumber and building materials industry. They put decades of experience to work building solutions that help customers better serve their customers. DMSI is inspired by technology. passionate about service, and excited about the future of this industry. DMSi has been in the enterprise resource planning business for building materials suppliers for decades with their flagship application called Agility. Their customer base includes a large portion of the building material supply chain in North America.
For companies in the industry to conduct business with each other electronically, they need to cross reference their products and their desired unit of measure. An item cross reference lets you associate on of your company's inventory items with a specific customer or supplier, or with another inventory item. When companies carry anywhere from 10-100 thousand products, cross referencing their entire catalog can be extremely time consuming and costly.
To address this, the Design Studio team created their vision statement: "To create an algorithm and user interface to classify construction materials in order to optimize the material classification process leading to saved time and money."
To execute this vision, the team created a new concept, a Standard Item Group (or SIG). Instead of a person making one-to-one connections with another company's products, the algorithm will now assign each of their items with a SIG. Once connected to a SIG, cross references to all other items in that SIG will be created in Agility. If our algorithm isn't confident in its analysis, it will send the item to the user interface for manual classification.
Business Solution:
Dataiku was essential in enabling the team to efficiently modify the data we had coming in and test algorithms on those data sets to build our solution. Dataiku was easy to use and provided a user friendly interface with no code required.
The team used Dataiku as a platform to create the groundwork for our solution. If we had an idea of something to add to the solution that dealt with data or algorithms, we tested those methods in Dataiku first to see how they would work. Setting up those methods and testing them in Dataiku was much easier and more efficient than developing the code in our application and trying it there.
The team consisted of students in the University of Nebraska-Lincoln Design Studio program in collaboration with executives, analysts, implementation and customer support individuals from DMSi Software.
Please review this video summarizing the project.
Business Area Enhanced: Supply Chain/Supplier Management/Service Delivery
Use Case Stage: In Progress
Value Generated:
The solution provided enables the end customer the ability to cross reference items much faster, saving time and resources. The accuracy of the cross reference is also considerably improved as the risk of human error is eliminated. Hundreds of man hours will be saved with this process.
Value Brought by Dataiku:
The ability to use Dataiku to streamline the development process through Dataiku's built in tools. This saved the team a considerable amount of time and effort. This project was about streamlining the future of the building material supply chain with artificial intelligence.
Value Type:
- Improve customer/employee satisfaction
- Reduce cost
- Save time
- Increase trust