Effective inventory control centres on the fine balance of holding enough inventory to ensure the business operates effectively while avoiding the overstocking that ties up valuable cashflow and leads to waste.
Inventory control can benefit from artificial intelligence (AI) because AI provides powerful insights for companies, highlighting interesting trends from large volumes of data that help procurement and warehouse teams to better manage the daily tasks of inventory management.
Artificial intelligence
Artificial intelligence is an area of computer science that focusses on the creation of intelligent machines programmed to work and react like we humans.
AI computers are designed for learning, planning and problem-solving. With the use of such methodologies as time series prediction and reinforcement learning that can be applied AI helps companies to predict consumer demand, manage supplier backorders and to optimise inventory stock levels.
Inventory control and AI
The pairing of AI with stock control has generated significant improvements for those companies that have already implemented it. Through AI, machine learning technology or more complex artificial intelligence systems businesses can create smart data-driven manufacturing and distribution centres.
However, human oversight is necessary for the success of this pairing and AI implementation should be used as a complement to the inventory control system and not a replacement for it.
The capacity of AI to understand a multitude of real-time inventory control dynamics that affect inventory stock levels differentiates it from traditional tools. AI can predict scenarios, recommend actions and even act — independently or with human approval.
For example, AI-powered cognitive inventory control addresses such questions as what the companies optimal minimum and maximum safety levels are and how to reduce working capital tied up in excess stock. It can even recommend the most efficient way to move product from the factory to the warehouse to the store.
The Unleashed Inventory Management Guide
A comprehensive guide to the best inventory management techniques and tools Read moreThe technology consolidates, standardises and enriches data, providing the foundation for AI analytics to present data-driven recommendations that managers can either chose to accept, reject or revise. With its capacity to act independently, it can free up the time personnel spend on routine tasks and allows managers and business owners to focus on more strategic decision-making.
The ways in which inventory control can benefit from artificial intelligence are numerous and include advantages such as:
- Demand prediction for inventory control built around a time series prediction model that can estimate what demand will be like for the coming days across all items of inventory stock. External data sources that impact demand can be incorporated into the demand prediction system. You can start improving demand prediction by improving your inventory management.
- Making recommendations for optimum inventory stock levels for thousands of SKUs based on demand predictions. Optimising inventory control by removing the complexity of data volumes that were traditionally the torment of inventory managers.
- AI automatically orders the correct amount of raw materials to fulfil manufacturing orders and distributors can merge datasets to make predict future demand for products. Enabling these organisations to make well-informed business decisions, reduce waste and increase profit.
- Reinforcement learning systems are a more advanced AI approach that involves a model taking serious control of the inventory operations, with human checks and balances. A domain in AI where the models don’t simply make predictions but act on the predictions.
Reinforcement learning is about giving AI the option to act on what it is predicting however you need to consider how this will integrate with existing inventory management software and undertake considerable testing prior to implementing a reinforced learning system.
It’s important to remember when considering AI models for inventory stock that each item is different and needs to be treated differently. While some items are very predictable and regular in their movement, others can be unpredictable but are still essential items to keep in stock.
