In the world of artificial intelligence (AI), machine learning (ML) is a powerful tool that allows analysts to learn patterns and forecast future information with minimal human intervention. By utilizing ML techniques, businesses can streamline their spend analytics process, freeing up time for more important tasks. In this blog, we'll explore how ML can be used to classify spend, leading to efficient spend strategy and increased profitability.
There were numerous, daily human interfered tasks done by the analysts that can now be done through the machines. If the work is done automatically by the system, then analysts can concentrate on more important tasks; having said that, there are a plethora of applications of machine learning – one of which is Spend Analytics
Leveraging Machine Learning Models to Classify Spend
Machine Learning techniques are utilized to classify the data efficiently according to the requirements which eventually helps the company to categorize the products/spend that they are buying as per the classification they want. ML can help categorize spend automatically or provide suggestions based on specific rules which can be improved with new data; to go beyond the same, this can also spot errors made by humans in classifying spend based certain rules and learnings from cumulative data
This will help the company to have adequate visibility across various organizational spend categories which, in turn, will help the company to design an efficient spend strategy; for instance:
Problem Statement:
An India-based information technology company faced challenges due to the use of multiple packaged ERPs (Enterprise Resource Planning Software) and legacy systems – across spend categories – to record their spend across business entities. This led to spend categorization in multiple standards, ultimately creating reporting and analysis challenges.
Solution and Impact:
The company leveraged Oracle Spend Classification module, and defined and configured a standard Spend Category Taxonomy to be used for global reporting. This helped company to further build upon the Knowledge Base using the Training set with standard hierarchical support vector machine algorithm or with a customized advanced knowledge base using other algorithms. Insights from the said analysis and classification helped the company in attaining supplier and spend rationalization to drive profitability
Machine Learning Techniques in Spend Classification
There are three types of ML techniques that can be used in spend classification:
Supervised Learning: Uses labeled data to train a model that can predict output based on input variables.
Unsupervised Learning: Uses unlabeled data to identify patterns and group data points based on similarities.
Reinforcement Learning: Uses feedback from actions taken to improve decision-making over time.
Final Words
While advancements in ML, neural networks, and natural language processing (NLP) are relatively new to the procurement and supply chain domain, exploring these technologies can help businesses achieve cost savings through better spend visibility, streamlined processes, reduced risks, supplier evaluation, and minimization of maverick spend. By leveraging the power of ML, businesses can make smarter decisions and optimize profitability.
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