Artificial Intelligence & ERP for La Bota Roja: Solving Today’s Business Challenges with Tomorrow’s Technology

Introduction

In the fast-paced world of retail business, efficient inventory management is the key to success. Following a thriving implementation of an omni-channel eCommerce retail solution including NetSuite and Shopify (see full details here), La Bota Roja was continuing to confront substantial obstacles in their product set-up process because of manual data entry and lag in product loading, their inventory counts and procurement processes were hindered, creating operational setbacks.

Eager to overcome these obstacles, they turned to Trajectory for operational improvement avenues.

About La Bota Roja

Founded in the early 1940s, La Bota Roja started as a family business with a flagship store in Parral, a historic town in the Linares Province of Maule, Chile. Over the years La Bota Roja developed a reputation for excellent customer service, leading them to open additional stores throughout Linares, and in 2020 they launched their online store fully integrated with NetSuite and haven’t looked back.

The Challenge

La Bota Roja has implemented a highly organized structure for their product catalog. They follow a parent/child hierarchical model and assign multiple attributes to each product. Additionally, they regularly update and add new products to their catalog in NetSuite to ensure that the inventory levels are accurately reflected. The existing process for setting up products involved manually populating csv templates with shoe stock data and then loading them into NetSuite. This highly manual and time-consuming process left much room for errors and caused delays in ensuring the accuracy of inventory counts. As a result, loading products and maintaining precise inventory levels in NetSuite often got delayed, which adversely affected both operational efficiency and productivity.

Objectives

The client sought a solution that would streamline their product set-up process, eliminating the need for manual data entry and reducing the chances of error and delay. They aimed to have timely and accurate inventory counts in NetSuite, facilitating smoother procurement processes. They also hoped this would alleviate the burden on staff, allowing them to focus on other essential responsibilities.

Project Highlights

Our in-house development team, having leaned into exploring AI (and more specifically Machine Learning) and its application to ERP infrastructures, saw an opportunity to assist. Leveraging Machine Learning (a subset of Artificial Intelligence that enables a machine or system to learn and improve from experience), supported by Trajectory’s in-house integration tool Initus, Trajectory built an automated process for setting up products based on product photos and automatically creating the related Purchase Orders in NetSuite.

Based on this process, NetSuite automatically identifies whether the item is an existing or new product. If existing, the product details related to the existing item are updated. If new, the new product is set-up with the appropriate attributes (i.e. brand, color, gender and category), resulting in less errors and faster load times. The new process also auto-generates codes and barcodes to identify the products for use throughout the ERP system (i.e. on Purchase Orders).

Approach

To train the Machine Learning models, the Trajectory Development team leveraged Initus to collect extensive data from the shoe database in NetSuite, comprising numerous attributes of each item. They augmented the data by extracting relevant information from public internet, such as brand details, color variations, material specifications, style characteristics and more. This comprehensive dataset served as the foundation for teaching the models to identify and classify shoes accurately.

Solution

When the shoes are received at the warehouse, the client team conducts a manual count and captures a picture of each product. They then upload the images and count data into the Initus/NetSuite interface. Initus then uses innovative Machine Learning Models to analyze patterns, shapes, colors, and textures, allowing the models to make precise predictions.

The implementation process is streamlined for NetSuite users, who only need to validate the data instead of manually inputting it. With a simple click, Initus automatically analyzes the shoe based on physical characteristics, classifies the product and creates it in NetSuite following proper accounting and operability configuration (e.g. Matrix items). In addition, the process generates codes, barcodes, configures the product in Shopify and associates the product in both systems to facilitate and streamline integration when the shoe is purchased online. Based on the quantity count, Initus also creates Purchase Orders for each vendor.

Solution Benefits

• Process Automation – Automating complex manual processes saves time and increases accuracy.
• Fast to Implement – The solution was implemented within a four-month timeframe. This period encompassed one month for solution design and three months for implementation, which involved building a custom user interface (UI) in NetSuite, setting-up of the automated product creation process, and providing training to support this new process.
• Perpetual Process Improvement – The automated solution continuously evolves and captures updates, evolving with La Bota Roja’s business.

Conclusion

Trajectory’s unique capability to utilize cutting-edge Artificial Intelligence and Machine Learning technologies to address our clients’ business challenges presents an opportunity for our clients’ continuous process improvement and operational efficiency.

Alex Olano

CEO & MANAGING PARTNER
TRAJECTORY GROUP