Harsh Vora, Lead Data Scientist
Zachary Thorell, Data Analyst
Sandeep Punjari, Lead Data Analyst
Ajinkya Bankar, Data Scientist
Daniel Moseley, Data Scientist
Luis Becerra, Associate Data Scientist
Deepak Arora, Vice President Corporate Strategy
Sanjay Khobragade, MLOps Architect
Sudip Roy, Big Data Solutions Architect
Justin Swogger, Solutions Architect
Matthew Stewart, Vice President Logistics Operations
Danil Bogomazov, Director Data Analytics
Ty Seigler, Lead Business Data Analyst
Shannon Sarkees, Sustainability Manager
Federico Gervasio, Senior Industrial Engineer
Javier Diaz, Senior Analyst, Quality Assurance
Mohtat Ali, Sr. Naval Architect
Country: United States
Crowley, founded in 1892, is a privately-held, U.S.-owned and operated logistics, government, marine, and energy solutions company headquartered in Jacksonville, Florida. Services are provided worldwide by four primary business units – Crowley Logistics, Crowley (Government) Solutions, Crowley Shipping and Crowley Fuels. Crowley owns, operates, and/or manages a fleet of more than 200 vessels, consisting of RO/RO (roll-on-roll-off) vessels, LO/LO (lift-on-lift-off) vessels, articulated tug-barges (ATBs), LNG-powered container/roll-on, roll-off ships (ConRos) and multipurpose tugboats and barges. Land-based facilities and equipment include port terminals, warehouses, tank farms, gas stations, office buildings, trucks, trailers, containers, chassis, cranes, and other specialized vehicles.
Crowley is a data-driven organization. We seek to embed data in every decision, interaction, and process and embed AI teams with businesses to empower the development, deployment, and enhancement of AI-driven products.
To deliver this vision and generate superior value to employees and customers through data, Crowley wanted a centralized development platform to utilize data and machine learning for decision-making in our business.
This centralized development platform will enable the collaborative development between business analysts, data engineers, and data scientists on key machine learning use cases in the supply chain industry such as network optimization, real-time visibility, customer demand forecasting, predictive equipment maintenance, churn prediction, customer lifetime value prediction, carbon footprint forecasting, and many more.
As a 130-year-old company undergoing digital transformation, we seek to utilize predictive and prescriptive analytics in partnership with our business leadership to enhance our revenue, customer experience, employee experience, and sustainability efforts.
We pioneer digital transformation in the supply chain industry through (1) centralization of data from our operational, commercial, and sustainability data into a data warehouse, (2) utilization of a singular platform to develop predictive and prescriptive analytics that enables all personas through no-code, low-code, and full-code capabilities, and (3) democratization of data engineering and machine learning activities through employee upskilling programs.
We utilize Dataiku to centralize and develop predictive and prescriptive analytics use cases across our operational business units.
In our logistics business, Crowley’s data scientists utilize Dataiku to deploy algorithms to maximize utilization on our vessels, forecast container demand at each port and yard, predict on-time container delivery and documentation for international voyages, automate estimation of supply chain carbon footprint, predict ETA of orders, and for predictive maintenance of equipment used to load and discharge containers.
In our shipping business, we utilize Dataiku for streaming data for predictive maintenance of boat engines, flagging orders with a high risk of incidents onboard ships, and developing a web application that enables economic asset optimization.
In our corporate business, we utilize Dataiku for predictive and forecasting mariner demand, forecasting procurement demand for each node in our supply chain, flagging potentially fraudulent expense claims, enabling cybersecurity and risk compliance, predictive analytics for customer segmentation and churn prediction, and for predicting employee churn.
Business Area Enhanced: Supply Chain
Use Case Stage: In Production
At Crowley, we utilize predictive and prescriptive analytics to enable better decisions, improve the effectiveness of our processes, and deliver superior value to customers and employees.
Dataiku provides a centralized, end-to-end platform for business analysts, data engineers, and data scientists to work together on analytics use cases and enables centralization across processes such as ETL, data analysis, machine learning, and reporting.
Seamless integration of Dataiku with Snowflake also enables the democratization and development of our data warehouse and ETL capabilities.
Another significant value addition comes from the interactive visual interface and a great suite of AutoML models provided by Dataiku, enabling the democratization of predictive and prescriptive analytical models throughout the enterprise.
For the MLOps team, Dataiku provides a seamless manner of registering and deploying models to production. The deployer enables the necessary governance checkpoints and the inbuilt drift monitoring, metrics and checks enable the development of appropriate post-production alert systems.
Finally, Dataiku simplifies the infrastructure needs of a company growing in the analytics space. Ours compute needs are always changing/increasing, and Dataiku’s Fleet Manager enables seamless scaling of servers and Kubernetes clusters.
Value Range: Millions of dollars