CPO Strategy

Machine Learning Helps Businesses to Save Time and Money

By Dave Brittain, Head of Amazon Business UK

It is well-known that machine learning is used today in Amazon Alexa and other virtual consumer assistants, but it also plays an important role for businesses looking to save time and money when it comes to procurement. 

Procurement is a key player in the broader transformation and growth story for organisations, but it can be a costly exercise for both large and small companies. Companies of all sizes are looking for ways to drive cost and time savings, process efficiencies, and better control and visibility for procurement teams. For larger companies, this is particularly true in tail spend – this means all the unmanaged and seemingly insignificant purchases that often occur outside of the normal procurement processes. This can add up and accounts for 20 to 40 percent of the gross purchasing volume of a company. For smaller companies with tight budgets it’s important to find the best deals – mistakes can be costly, but the time it takes to find the best products can be costly too.

The benefit of machine learning is that it can improve efficiencies and help businesses make better procurement decisions. This is why more than 60 percent of purchasing managers and chief procurement officers are currently learning about artificial intelligence and machine learning or are currently implementing the technologies – according to a study published by Amazon Business and WBR Insights.

How machine learning can help

This is where digital technologies such as artificial intelligence and machine learning can help. The field of artificial intelligence refers to solving cognitive problems associated with human intelligence, such as learning, problem solving and pattern recognition. Machine learning is a subfield where data captured from past experiences enables learning to happen automatically. 

Amazon Business is constantly expanding the use of machine learning to automate manual and time-consuming tasks for its customers. Looking for ways to predict product trends and leverage these results to better forecast the required quantity of a certain product, which reduces storage costs in Amazon’s fulfilment centres, streamlines fulfilment processes and ultimately lowers the price of an item for customers. For consumers, but also for business customers. 

Constantly learning and improving 

Another example is that purchasing managers can submit their preferred products and machine learning can then assess this catalogue to automatically identify the same or similar items on Amazon Business and provide purchasing managers with cost effective alternatives. One more field is search. The search on Amazon Business is the starting point for most business customers on Amazon. Machine learning continually learns from search and purchasing behaviour and the provided information – and combines industry-specific parameters to identify products that may be interesting to customers. In understanding a search query, natural language processing algorithms can distil semantic information and present suitable products for the queries. In this way machine learning helps searchers obtain context-relevant results and suggests recommendations and products and suppliers that they might not have considered before. 

This ensures that products are ranked and optimised in a highly relevant way, which helps the business customer to find the item that they are looking for more quickly. 

Curated and supported shopping 

A further field for machine learning is “curated buying”. The technology helps businesses to drive process efficiencies by automatically prioritizing products that they do need and prefer based on their order history, the buyer’s budget, industry classification systems, and company-related buying guidelines provided by the business customer. In the future, machine learning could set up and apply purchasing guidelines on its own, based on the provided business goals of an organisation – and provide the required flexibility to continually adapt these guidelines to ensure that the goals will be achieved. Additionally, when it comes time to restock the products, automated repeat purchases could be made simpler by enabling a customer inventory demand to forecast and automatically reorder items on the buyer’s behalf.

Machine learning also helps to presume the needs of business customers and suggests features to help meet them – for example: considering Business Prime to save shipping costs; setting up the pay-by-invoice feature to streamline processes; or adding additional users to enable other departments to buy on their own. 

In the past, when it came to evaluating procurement data, companies would need to invest in experts such as business intelligence engineers, data scientists and IT professionals who would create complex analysis models from the data. Today thanks to machine learning procurement managers don’t need to be an expert to take complex data and build narratives – buyers can just evaluate the order history data of thousands of employees to make purchasing decisions.

At Amazon Business, we’re excited to see procurement teams enable more modern ways of working, drive greater employee and organisation productivity across the board, improve operating effectiveness, and play a key part in achieving greater business agility and velocity – from sole proprietors to small businesses, hospitals, universities, and even large enterprises with tens of thousands of employees.  

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