Inventory efficiency is essential to the success of any manufacturing or retail enterprise and poor inventory control can have a dramatic impact on your business’s bottom line.
Effective inventory management is important to the health of your business but the main issue with achieving good inventory control is the ability to get the balance right. This is further complicated by other factors such as suppliers, service providers and other external factors.
In addition, picking, packing and shipping activities are both time-consuming and resource-intensive. Having reliable knowledge of your inventory types and volumes can improve the effectiveness of your inventory operations and will save you money by reducing shrinkage and waste.
Machine learning enhances inventory management through automation, improved service levels, increased inventory turnover and scaled optimisation across multi-channel distribution sites.
Machine Learning Explained
Machine learning at its most basic, is the practice of using algorithms to deconstruct and learn from data. Powered by large-scale data it is a form of artificial intelligence, that provides computers with the ability to learn without being explicitly programmed.
The artificial intelligence used in machine learning and modelling make it possible to consider external and independent variables that affect demand and time-to-customer inventory performance. Any data from point of origin, transit routes, location and status can be collected on inventory stock and reported by radio frequency identifiers.
Businesses can build a machine learning model to predict nearly any aspect of your operations based on the data it receives. Notably, with more feedback and data, machine learning has the potential to get better over time.
At its core, machine learning effectively provides a hugely scalable, tireless and consistent quality digital labour force able to perform tasks previously undertaken by employees.
Benefits of Machine Learning for Inventory Control
Manufacturers place great importance on finding new tactics for growth and delivery of a quality product while still capable of undertaking short lead-time production runs for their customers.
Real-time monitoring and machine learning algorithms will optimise processing operations and facilitate better decision-making to manage production runs. Machine learning can provide real-time knowledge and helpful insights into machine load levels, determine how the load levels of individual machines will impact overall production, scheduling and performance.
This information allows proactive actions to be taken when a shipment of inventory stock is likely to be delayed, enabling you to notify customers in advance of any disruption to supply. Customer are more likely to be forgiving if they are forewarned of possible scheduling issues, rather than if they are simply kept waiting.
In the retail environment, autonomous robots help customers to find products via a searchable computer display and advanced voice recognition. Robots can navigate the store using laser-based sensors, generating real-time information via machine learning and computer vision technology. Scanning inventory and seeking out patterns in product or price discrepancies.
With every part of the business connected and capable of collecting and sharing data, the advanced analytic tools of machine learning can measure and rigorously examine information, allowing it to determine how one component interacts with every other area of a business operation. For inventory control, this information is used to help predict future inventory stock needs.