As part of Amazon logistics analytics team, you are tasked with creating a new algorithm that optimizes package deliveries in NYC. Specifically, your goal is to predict how many Amazon's own delivery vehicles (i.e. not UPS/USPS/FedEx) Amazon will need to send to streets of NYC on any given day. Explain which factors you might consider including in your prediction model and why. Suggested options include shipping costs for customers (1day/2day/5day), number of warehouses in the area (affects distance driven by vehicles), pick-up options such as Amazon Lockers in Whole Foods stores, number of deliveries fulfilled by UPS (as opposed to Amazon's own fleet), all kinds of traffic fees (tolls/parking fees/etc.).
As part of Amazon logistics analytics team, you are tasked with creating a new algorithm that optimizes package deliveries in NYC. Specifically, your goal is to predict how many Amazon's own delivery vehicles (i.e. not UPS/USPS/FedEx) Amazon will need to send to streets of NYC on any given day. Explain which factors you might consider including in your prediction model and why. Suggested options include shipping costs for customers (1day/2day/5day), number of warehouses in the area (affects distance driven by vehicles), pick-up options such as Amazon Lockers in Whole Foods stores, number of deliveries fulfilled by UPS (as opposed to Amazon's own fleet), all kinds of traffic fees (tolls/parking fees/etc.).
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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As part of Amazon logistics analytics team, you are tasked with creating a new algorithm that optimizes package deliveries in NYC. Specifically, your goal is to predict how many Amazon's own delivery vehicles (i.e. not UPS/USPS/FedEx) Amazon will need to send to streets of NYC on any given day. Explain which factors you might consider including in your prediction model and why. Suggested options include shipping costs for customers (1day/2day/5day), number of warehouses in the area (affects distance driven by vehicles), pick-up options such as Amazon Lockers in Whole Foods stores, number of deliveries fulfilled by UPS (as opposed to Amazon's own fleet), all kinds of traffic fees (tolls/parking fees/etc.).
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