How Discovery’s solution help in predictive order fulfilment

 In Blockchain, Internet of things, Supply Chain

What could be more efficient and effective in the supply chain than predicting the demand and shipping products even before it is ordered? This is what predictive order fulfilment is all about. Unlike making guesswork or trying to be a psychic to predict future, forecasting the demand for products with the help of artificial intelligence can let businesses make informed decisions accurately enough that they can risk packing and shipping the products before getting an order. According to a report, it is estimated that the predictive analytics market will be worth $12.4 billion by 2022.  But why is predictive analytics important? There have been countless instances where smartphone makers have run out of stock within the first few days of the release of their smartphone rendering the prospective buyers to a frustrating period of wait until restocked. While for some, it could be a strategy to create hype but for many, it is certainly not the case. Running out of stock is an issue faced across many industries such as FMCG, fashion etc. This problem ultimately comes down to the inaccurate demand forecasting which is where predictive analytics can become the game changer. In addition to the efficient management of stocks, there are various issues experienced in the order fulfillment process and these issues can be related to billing, delivery, incomplete data, shipping and etc. Any issues experienced among these can lead to delay in delivery.  Order fulfillment process might vary depending on the type of order. For instance, wholesale procurement order for large-scale businesses varies from individual customer orders. Nevertheless, order fulfillment is roughly comprised of receiving items followed by warehousing and inventory management which is followed by processing the orders and finally shipping it to the customers.


How does Discovery’s IoT solution incorporate predictive analytics?


Discovery’s innovative IoT solution for supply chain management has already garnered significant interest. By bringing together IoT, blockchain, edge computing and artificial intelligence in an unprecedented feat, not only does Discovery’s solution puts an end to the issues of stock-outs and stock-obsolescence faced by various industries but it also makes way for the much-needed technology assisted the transformation of the supply chain. A three-layered network comprised of an IoT tag named Cliot, a community of smartphone users and a data analytics component, Discovery essentially provides enterprises with a one-stop solution that can shape their business with a competitive edge. By attaching the Cliot tag on products, Discovery can gather information on products such as its whereabouts and condition. This data is then transmitted to the nearby smartphones that are registered in the Discovery network. Upon receiving the data, it is then processed on those phones and the processed data is further sent to the cloud for performing analytics. While AI is employed in the network to automatically manage the devices registered in the network based on its geographical location, it is in the third layer where analytics is performed. Based on the enterprise requirement, any type of analytics such as predictive, descriptive, diagnostic and prescriptive analytics can be performed. Predictive analytics makes use of AI; specifically, machine learning, to gather insight into the data received. It does so by recognizing patterns in the data without requiring the system to be explicitly programmed for the purpose. As suggestive of its name, this method improves with time as more and more data is fed into the system. Considering a very simple scenario, if Cliot with appropriate sensors is attached to a bottle of juice, as its quantity decreases below a certain limit, the tag can transmit the information to the company. If it is a regular customer, the company can preemptively send another one. Although this scenario where companies attending to each individual seems like a farfetched idea, this can be a realistic application on a macro scale where individuals can be replaced with local retailers with the insight received from data being the stock levels.


Business is a numbers game; it is all about having the most accurate data or numbers and crunching those to get the best insight into the factors affecting a business. Today, while the majority of the businesses are conducted by answering the immediate demand for products, with the ever-expanding capability of technology, soon forecasting the demand through data and answering it in the present will be the defining factor for a successful business.


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