Demand Forecasting conventionally done on sizable population data collected via survey, past sales reports, market condition, projected GDP and many more PESTEL conditions. It is very old and conventional approach used in many industries including fashion. Earlier data of single customer such as purchases, daily searches, save for latter, add to wish list, were not available for a long duration of time. What if data of single customer online orders, daily searches, save for latter, add to wish list etc are available for past 10 years?
Consider an example a female customer profile that buy female dress, suddenly start buying baby diapers. This is very common practice as a girl become mother; she has to buy baby diapers. Now take a city where population is 1 million. In that this abrupt change was observed in 100 female ecommerce profiles. Additionally baby wears diaper from 0 to 4/5 years. Now the online company can ship diaper to this city without specifying exact address based on ordering frequency of these 100 female profiles data analysis. This is just a hypothetical scenario where lots of data is required.
Anticipatory Shipment get advantage of long duration analysis of a customer profile based on his/her activity on internet, ordering pattern luxury / middle class/lower middle class, daily searches, orders made in last 10 years. This kind of data is not available to physical stores.
Anticipatory Shipping Frequency system might include a package for shipment to a delivery address, Selecting a destination geographical area to which to ship the package, shipping the package to the destination geographical area without completely specifying the delivery address at time of shipment, and while the package is in transit, completely specifying the delivery address for the package. Data that could be analysed to determine customer demand for a particular pre-shipped package to help decide where to route it geographically could include historical buying patterns, preferences expressed explicitly via surveys/questionnaires, demographic data, browsing habits, wish-lists and so on.
MIFT ( Mysore Institute of Fashion Technology) students, trainee and scholars study and research about fashion, makeup, forecasting, e commerce, disruptive technologies in fashion & cosmetics industry. The primary research objective is to create open industry modular ecosystem platform for fashion designers and makeup artists to work and earn in hyper personal and local market using IR 4 and 5 ( industrial revolution) technologies to disrupt connected digitalization of mass production.