Using machine learning algorithms, we’ve built an efficient model which can forecast future demand so that you can prepare to fulfill the sales order in advance.<br/> We’ve combined mathematics, statistics and computing to build a model which has less errors and provides a more accurate forecast which gets better each passing week due to the capabilities of Machine Learning.
Inventory control and supply chain management is challenging work. Globally 8.9% sales are lost due to the OSS. It’s hard to catch the variability of market demand and the vendors behaviours. By using Multi-Echolen optimization technique we take care of both ever changing demand and variable lead time. We forecast demand and unleash the pattern of the vendor to help you decide how much safety stock to keep, at what point you have to raise the new purchase order(PO) and in how much economical quantity, all these are done with a single model.
Know which vendor is more consistent with your purchase order, and who is more likely to be irregular. Derived from the heuristic, vendor performance profile gives you the clear picture of how your business is going to be affected by the incerto of vendors behaviors. Such matrices helps you to decide in advance and plan the supply of goods.
Not properly raising the purchase orders may disrupt the harmony of good supply from the central warehouse and equally make the supply chain operation uncertain. There is either the over-availability of goods or shortage, so to mitigate both out of stock and over stock problem, Lumle has economic order quantity which automatically suggests how much order to raise for each product. The numbers are based on future uncertainties and risk of being understock as well as overstock.
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