Country: United States
Organization: Advanced Analytics Office, Logistics COE & Western Digital
Advanced Analytics Office is missioned with accelerating Analytics solutions at scale across the enterprise to rapidly capture business value. These solutions target key business metrics, such as, reducing manufacturing costs, improving capital efficiency, reducing time-to-market to develop new products, improving operational efficiency, and improving customer experience. The Logistics Center of Excellence focuses on identifying complex supply chain problems and delivering value and scalable solutions across the supply chain by researching, innovating, prototyping, changing mindsets, and upgrading skills. The COE steps out of the operation role and works on prototyping new scalable solutions to support supply chain priorities and long-term business goals.
Western Digital’s Logistics Control Tower team uses a Global PDL email address for both internal and external communications. Everyone is triggering emails to this PDL for shipping reports, shipment location queries, loss and damage, delivery and invoicing issues, etc. The traffic gets up to 8k-10k emails every week and gets even higher during quarter-end. Such massive email traffic has created a lot of issues:
Previously, we tried to analyze those thousands of emails manually, taking two to three employees over two weeks to sort, categorize, annotate, and evaluate. Now we urgently need a solution that would work with such a large and complex data set and sort the emails by topics (category) with high accuracy and produce results promptly.
Once we understand the email category and sender profiles, we would identify hot/critical issues faster so that Corrective Actions can be developed in time, which would help reduce response time and raise customer satisfaction rate.
This solution was built in collaboration among data scientists from Western Digital’s Advanced Analytics and Logistics teams. The project has the following components built on Dataiku:
We built the project on a design node with the following benefits from Dataiku:
Business Area: Internal Operations
Use Case Stage: Built & Functional
With this all-in-one text analysis and data visualization studio, we can sort emails by topics, quantify the average response time spent on each category, and identify major internal and external service requestors per customer profile. Also, we can dig deeper into data with greater granularity, quickly re-label and continuously train the model for any new categories or updates, and create custom charts and visualization in a blazing-fast experience.
Eventually, actionable insights can be drawn from the data and team hours saved. Ultimately, automated email analysis has empowered WD’s Logistics team by:
We had a couple of new data scientists join the project. The Dataiku Academy, along with ease of use of visual flows and components, enabled us to quickly onboard new team members. Dataiku streamlined the processes to clean, label data, and visualize email categories — all in one place.
Prior to implementing the solution in Dataiku, we used csv files to label data. For this project, more than 2,000 emails had to be labeled. Using csv for labeling was very tedious and error-prone. ML Assisted labeling plugin was a very useful collaborative tool that allowed easy access and visualization of data and made the tedious task of labeling easy by reducing time taken to label the data by at least half of what it would otherwise be. Furthermore, we were able to consult multiple subject matter experts and label in a collaborative and iterative process.
The Quick lab section was quite handy in terms of feature handling as well as comparing various model architectures, making the development of models faster. Not only that, one of the important aspects of working on data science projects is to prepare a visual way to present the results. Dataiku has these features to export the confusion matrix, features used, training information, and so on, making it easy to present the solution.
The Dashboards and charts were also quite useful in this regard. Dataiku enabled the data scientists to build solutions quickly and let other non-technical users take over the project and maintain it without much coding experience, making it possible to hand off the solution to the users. Since NLP use cases are common across the board, the ability to re-use the project’s design to build another solution is an efficient way to utilize an existing architecture.
Value Range: Hundreds of thousands of $