The Inventory Management Challenge

Machine Learning manages thousands of lines of stock and significant resource constraints within the inventory management team.  Retail Arena research has shown retailers are failing to cope with the demands placed on their inventory teams: –

• Initial store allocations are often based on broad brush averages which fail to reflect a stores ability to sell.

• With stock levels reviewed weekly on a small percentage of lines, the warehouse continues to replenish to the original “sub-optimal” store allocation.

• At season end costly stock rebalancing between stores is required; but movements are based on anecdotal evidence and human intuition not capable of analysing and comprehending the myriad of variables that drive demand.

The result – lost sales, unnecessary markdowns, increased handling costs of the line, reduced revenue and margin.

Resolution – A “World First” Machine Learning Solution for Inventory Management

Retail Arena is excited to announce a “World First” machine learning module, purpose built to support retailers to optimise and maximise returns on their entire inventory range.

Initially focused on generating optimal store allocations, re-optimizing store stock levels and warehouse replenishments mid-season, and rebalancing stock between stores at season end; our machine learning module analyses vast quantities of complex data and presents an idealized proposal to the inventory manager. The proposal is reviewed, edited where appropriate and executed with minimum effort.

Manual time-consuming processes compiling and reviewing data are eliminated. Human intuition is enhanced by optimized recommendations based on accurate analysis of large data sets. Weekly processes traditionally focused on a small sub-set of inventory lines. You can now execute your processes at any time across the entire product range.

The result – optimal inventory levels across the store network, markdowns are minimized, revenue and margin is maximized.

How it Works

Machine learning uses pre-defined algorithms, purpose built by Retail Arena to solve each inventory management problem. Typically, a unique process or problem requires a unique new algorithm.

Once defined, the standard algorithms are applied. The retailer’s data is seamlessly imported via an integration interface to the designated algorithm and the model can “re-train itself” generating recommendations and validating them against real-world results. The algorithm is adjusted where required until the desired result is achieved.

Where you require an innovative new algorithm to solve a new problem within your business, Retail Arena will work with you to produce the best outcome.

About Machine Learning

Machine Learning is an application of AI where computing power is used to access and analyse large data sets, solve problems and make recommendations not supported by traditional manual or computing methods. Amazon for example uses machine learning to analyse customer buying patterns and offers suggestions for additional products you may want to buy.

Machine learning absorbs data –both structured such as searchable tabular data in databases; and unstructured such as photos and audio files –  and turns these into actionable insights. These insights and recommendations result in new offerings and services, to increase retail business performance, customer engagement and satisfaction.

Cognitive computing uses machine learning algorithms to make computers more user-friendly, with interfaces that are more focused on what users want. Cognitive computing uses self-learning systems that utilise data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing aims to create automated IT systems that can solve problems without human assistance.

Machine learning and cognitive computing is an efficient and advanced way for organisations to improve processes, save time, manpower and money; and improve outcomes. Traditional computing processes are programmed and operate based on rule sets. Cognitive systems learn continuously and are not dependent on rules and programming.

Talk to us today on how Retail Arena’s machine learning can work in your business to improve outcomes and profits and consumer experience.