Interpretation of Forecasting Models
Forecasting and Methodology: Improving the Accuracy of Predicted Ocean Travel Time
An American exercise equipment and media company had received a record-breaking order volume alongside a reduction in staff due to the COVID-19.
The supply chain was broken, and delivery times changed from 30 to 60 days due to anchoring and port congestion. Our objective was to:
The client presented their forecasting model, wherein ocean lead time was factored using simple exponential smoothing with no trend, seasonality and external factors.
Validation WAPE (Weighted Absolute Percentage Error) of 11.35% for data from week of 1st February 2021 to week of 17th May 2021 had been achieved till our team was brought in.
Our approach to problem solving involved the following steps:
Data Collection: Collect the Anchorage Time, Port Time, No. of Vessels, and No. of Calls at the port of arrival for each week.
Data Exploration: Remove “Calls” cluster of variables using correlation analysis.
Feature Engineering: Create lagged derivatives for Ocean, Anchor, Port & Vessels clusters.
Data Partition: Split the Train & Test data using rolling forecasting origin techniques.
Forecasting: Build regression, SVM, Random Forest, Gradient Boosting models.
We achieved a validation WAPE of 11.13% and were able to demonstrate that among the external factor clusters - Anchor, Port, Vessels and Lead Times; Anchor variables are not significant in the presence of the other three clusters.
Apart from the Historical Lead Times in the Ocean, the previous week’s Port Time & Number of vessels 2-weeks earlier are affecting the Lead Time Ocean for the current week.
Python & Tableau
Redesigning a Value Based Program
In the collaboration project with Accenture and Krenicki Center, students assisted in developing semantic layer for reports, developed report content, prepared test data, and assisted in building data extracts. The final deliverables were 5 reporting dashboards successfully integrated with the client’s database. The students undergo three main phases of the project to achieve the final deliverables:
Reducing the Uncertainty During a SAP Transition