Using a broad definition of ML/data science, here are a few ideas:
First, coding toy problems (related to shipping or not) that implement linear regression, genetic algorithms, or neural networks, etc. will be a useful start
Analyze shipping and tracking EDI data to predict whether a shipment will be late (0.0 to 1.0 output, 1.0 being it will be late for certain)
Predict the likelihood a customer will churn (stop using your services) based on changes in volume, billing amounts, and other characteristics
Predicting this year's peak season shipping volume based on past years' data. See if you can beat the marketing/sales folks' predictions
Identify factors correlated with the most profitable shippers
Predict the likelihood a package is damaged
Use a genetic algorithm to improve driver routing
Reconfigure pickup times / drop off times to improve profitability
Use EDI shipping data to build a network graph of who is shipping to whom, segmented by type of some sort. Say you find that many A-type firms are shipping to B-type firms; any B-type firms that are not already customers could be interesting targets.
Score prospects to estimate their profitability by comparing their characteristics to existing customers' profitabilities
Use a neural network (or something else) to analyze EDI shipping data, damage data, and make packaging recommendations to customers
Analyze tracking EDI data, segmented by delivery area (zip+4?) and see if there are areas where drivers are more efficient at delivering faster. Maybe start an initiative to look at what separates the most efficient drivers from the least.
Reporting: not sexy, but really useful in this space
Bona fides: I used to work in the supply chain consulting space and consulted at firms like yours. Things are surprisingly basic in the shipping space - less meaty data science than one might think.
First, coding toy problems (related to shipping or not) that implement linear regression, genetic algorithms, or neural networks, etc. will be a useful start
Analyze shipping and tracking EDI data to predict whether a shipment will be late (0.0 to 1.0 output, 1.0 being it will be late for certain)
Predict the likelihood a customer will churn (stop using your services) based on changes in volume, billing amounts, and other characteristics
Predicting this year's peak season shipping volume based on past years' data. See if you can beat the marketing/sales folks' predictions
Identify factors correlated with the most profitable shippers
Predict the likelihood a package is damaged
Use a genetic algorithm to improve driver routing
Reconfigure pickup times / drop off times to improve profitability
Use EDI shipping data to build a network graph of who is shipping to whom, segmented by type of some sort. Say you find that many A-type firms are shipping to B-type firms; any B-type firms that are not already customers could be interesting targets.
Score prospects to estimate their profitability by comparing their characteristics to existing customers' profitabilities
Use a neural network (or something else) to analyze EDI shipping data, damage data, and make packaging recommendations to customers
Analyze tracking EDI data, segmented by delivery area (zip+4?) and see if there are areas where drivers are more efficient at delivering faster. Maybe start an initiative to look at what separates the most efficient drivers from the least.
Reporting: not sexy, but really useful in this space
Bona fides: I used to work in the supply chain consulting space and consulted at firms like yours. Things are surprisingly basic in the shipping space - less meaty data science than one might think.
Edit: Formatting