Use Case

Inventory Optimization

Reduce inventory costs and free up working capital by lowering the risk of stockouts

  • Reduced cost of warehousing
  • Decrease in lost revenue due to out of stocks
  • More efficient supply chain planning

Challenge

Retailer and Manufacturers either pour too much capital to prevent stock outs or too little and this ends in either too much working capital tied in inventory or they end up with manufacturing and retail issues when the inventory levels are not right. Macro and Micro economic changes often result in lack of demand or too much demand and the inventory levels can’t cope with the increase or decline for this. 

Another problem is that different stock quantities end up in different stores and distribution centres. Most retailers fly blind with this and rely on past performance and human intuition to come up with future forecasts. 

This ultimately results in lost sales, markdowns, low profitability, profit shrinkage because of sub-optimal distribution to different stores or distribution centres. 

The Process

We work with retailer and manufacturers to understand their processes in detail and what methodologies and reasoning they have been using in the past for maintaining inventory levels. This also includes the different factors they take into account for optimizing inventory levels. This ensures that we take in affect all the critical data points that are important. Our AI system also takes previous historical inventory data, sales data, supplier data and then we train our machine learning models that can dynamically and continually optimize reorder parameters to minimize inventory holdings for different store, distribution centers and geographic locations. 

Our Inventory Optimization AI system can integrate with your existing inventory management softwares like SAP, Xero via their APIs and we also provide our own custom ML analytics dashboard for visualizations and recommendations.

Technical Bit

Our Machine Learning system uses a branch of Machine Learning called Reinforcement Learning along with Supervised Learning for training inventory optimization models. Below are some of the research papers that demonstrate how reinforcement learning algorithms outperform existing rule based techniques for optimizing inventory. 

Our RL models not just perform simple prediction and classification but recommend the best set of actions to take at any particular time given all the different market and business data. These models are continously learning from new data and human feedback as well so humans always stay in the loop. Reinforcement Learning systems are extremely hard to train but yield phenomenal results. But you don’t have to worry about the technical bit because we are here to help you with that. 

Results

Our AI system provider real time recommendations for optimal inventory levels. The models also provide a level of maximum acceptable risk of stock out for any part to optimize recommendations. Our Analytics dashboard provide live optimization with real time data integration. 

Implementing intelligent inventory management system not just reduces inventory holding costs and improves cashflow but also improves a business’s ability to manage and negotiate with suppliers. This ultimately results in organisational efficiency and higher profitability.