Using Machine Learning in Inventory Management

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For businesses with growth ambitions, it’s important to get a handle on the key factors affecting inventory management. Any business dealing with stock will be aware of the difficulties surrounding managing stock levels, optimising inventory space, dealing with inaccurate forecasting, managing idle and redundant stock, and delivering to customers in a way that improves customer satisfaction. While these factors affecting inventory management seem to be unavoidable to an extent, using technology such as machine learning and artificial intelligence can minimise the risk of ineffective inventory management and allow your business to continue to thrive. Machine learning is effective because of the real-time information gathered and used to improve predictions, optimise assets, and reduce the risk of loss.

Machine learning to track stock

Using machine learning to minimise the factors affecting inventory management is a growing trend in many of today’s industries. Using it to improve stock tracking accuracy, optimise inventory storage, and offer transparent supply chain communications are just some of the many ways businesses can take advantage of this new technology.

With machine learning, up-to-date data input is used to adjust calculations and predictions made by software, meaning the software is customised to suit your business the more you use it. This optimises the performance of tracking technology in inventory management and offers more accurate data to assist in planning for the future.

Optimising inventory management

For most companies concerned with inventory management, plenty of time is put in to improving optimisation techniques. With the aid of artificial intelligence and machine learning, algorithms can be crafted to fit customised constraints that suit your business. This can be used to improve inventory optimisation, particularly in businesses with multiple distribution locations. These models can be adjusted to take into account independent variables that may delay product delivery. In terms of factors affecting inventory management, using machine learning to optimise inventory space is a more efficient way of managing stock. By diverting this work onto artificial intelligence, more focus can be put on product quality and customer experience, ultimately improving business performance.

Reducing forecasting errors

In manufacturing, sturdy supply chains are essential to keep constant product availability. Most industries heavily rely on forecasting to assess how much stock will be required in the near future. With forecasting errors, over- or under-stocking can cost growing businesses customers. Using machine learning technology, predictions can be made by continuously using data to adjust forecasts to suit companies and take into account more factors than typical forecasts. Machine learning can be used to reduce transport and warehousing costs by reducing inventory to a lean but comfortable level, and can predict demand in the near future, allowing for stock to be purchased in time for sales. This improves customer delivery times and ultimately improves customer satisfaction.

Minimising idle stock

One of the major factors affecting inventory management is the concern surrounding stock levels. Predictions to calculate how much stock to carry are often unpredictable when solely relying on outdated tracking models. Excess and idle stock essentially symbolises tied-up money that could be put to better use. Idle inventory is also highly likely to get damaged or be outdated by new stock. Shrinking stock levels requires accurate predictions of future demand, which is becoming more accessible due to machine learning technology. By using current data, inventory mismanagement can be reduced to ensure optimal business performance, ultimately leading to satisfied customers.

Improving customer satisfaction

One of the common uses of artificial intelligence, specifically in the online retail industry, is to use autonomous robots to interact with customers. This use of real-time data and machine learning technology can help customers by scanning inventory, searching for particular items, or identifying deals. Using bots to improve customer relations, while not specifically targeting inventory management, can still identify low stock levels and offer insightful analytics regarding product demand.

To combat negative factors affecting inventory management, artificial intelligence and machine learning technology can be used to optimise inventory levels to avoid wasted stock. By using data analytics to forecast more accurate future demand and to plan stock purchasing, machine learning can offer a business advantage by providing consistency for customers while also relieving management stress regarding fluctuating demand and stock management. By diverting inventory management to new technologies, more focus can be put on customer satisfaction and product quality, ultimately improving your business performance.

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Melanie - Unleashed Software

Article by Melanie Chan in collaboration with our team of Unleashed Software inventory and business specialists. Melanie has been writing about inventory management for the past three years. When not writing about inventory management, you can find her eating her way through Auckland.

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