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Inventory Optimisation and Management using Data Analytics and Artificial Intelligence

Author(s): Saurabh Singh Thakur


Inventory or stocks are the goods held by any business organisation for the purpose of consumption, production, or sale. These are the non-capitalized assets of the organisation. Inventory can be classified into four categories – finished goods, raw materials, work in progress inventory and spare parts inventory (also called as plant maintenance, repairs and operations (MRO) inventory). Spare parts inventory is leveraged in the running and maintenance of the plants or the manufacturing units. In general, inventory management is the process of balancing the inventory thereby – having the right amount of inventory in the right place at the right time. Though it looks easy, it is one of the most challenging problems that industries deal with. In this paper, we will discuss what are the different challenges organisations face during inventory management and how we solve them using artificial intelligence (AI). We have built an AI enabled dynamic inventory management application across all the inventory categories. Dynamic inventory for finished goods application optimises the inventory using stock, order, product, location and historical sales data across the business. The application can accurately forecast demand to get the right level of inventory, in the right locations, to meet service level targets. Similarly, dynamic inventory for raw materials application applies AI models to inventory, sales, bill of materials, supplier, work-in-progress and finished goods data from across your business – so you can optimise raw material inventory levels to meet demand and reduce costs. The dynamic inventory for spare parts inventory management application applies AI models to plant data like location and capacity, and inventory data like stores, spares and consumables across business. We predict demand across stores, spares, and consumables on a granular level. Then, we use forecasted demand to optimise inventory across each plant which will save cost while meeting the required service levels. We will also show how business can achieve a good ROI by deriving the value from our AI based inventory management applications using some use case examples.

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