Predictive shipment refers to shipping products to your customers even before they have placed the order to you. I would say even the customers have not realised the need for the product or made any purchase decision. This is made possible with the advancement in the business analytics. 

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Let us explore this concept with an example using an imaginary eCommerce company known as ProCart. Say I am living in Chennai at a particular location. Today, I decided that I would like to buy an iPhone X. So I visit ProCart website, selects the appropriate model of iPhone X and places the order. Within 10 minutes, I get an email and phone call saying that my order is available for pick-up at the nearest pick-up centre. How is this possible?

ProCart may not know who are the exact customers who are going to buy iPhone X, but they know how many of iPhone X will be ordered from a particular geography. So there is some good forecasting and analytics involved. Since ProCart doesn’t know the exact customers, they will ship the required quantities of iPhones that will be ordered from a particular geography to the nearest pick-up centres. As soon as the customer places an order, the label with the customer details are printed at the pick-up centre and ready for handover.  Soon the customer will visit the pick-up center and collect the product.

Predictive shipment may not be suitable for all the products. Before adopting predictive shipment for a product we need to carefully consider the following questions:

One, which stage of the product life-cycle predictive shipment will be useful?

Two, what is the kind of volume demanded by customers for the product?

Three, what is the demand pattern of the product?

Four, how much time the customer is willing to wait for a product?