Reasons Behind Machine Learning in Procurement

Machine Learning in Procurement

Machine learning is a subset of Artificial Intelligence (AI) that can give your system the ability to acquire knowledge and improve from the available user experience, without actually being told to do so. Basically, ML can access data (such as examples and instructions) to identify patterns and improve decision-making based on your system experience.

The main goal of Machine Learning is to improve automatically and accordingly, without human input.

Difference between RPA, NLP, ML & AI

RPA is a simpler type of software that imitates a task-related activity of a human within a process (e.g. log-in, copy/paste, field entry, etc.). NLP represents algorithms that can interpret, transform and generate human language. Machine Learning (ML) is more complex and its goal is to acquire knowledge (e.g. image/speech recognition, medical diagnosis, etc.). On the other hand, AI is driven by data, and it actually aims to become more intelligent, to simulate intelligence.

AI-ML-RPA

Although procurement has a known track record in constantly striving toward process automation (e.g. payroll, administration, invoicing, supply chain, calculation, or the material flow), the complexity of today’s procurement habitat, data volumes, and high expectations on both ends are extending the limits of sourcing professionals in reaching their main objectives – effective time management, cost savings, high volumes, networking, collaboration and a competitive edge.

Types of Machine Learning in Procurement

Because Machine Learning represents a natural upgrade to RPA (Robotic Process Automation), it has a significant potential for application within procurement and strategic sourcing.

ML

Procurement uses 4 types of Machine Learning, with different levels of human intervention required for each.

  1. Supervised Machine Learning – The algorithm learns from patterns of using past data, while supervision provides correct answers.
  2. Unsupervised Machine Learning – The algorithm is thought to find new patterns in new data. No supervision required.
  3. Reinforcement Machine Learning – The algorithm decides how to act in certain scenarios, with reward or punishment involved, depending on the outcome.
  4. Deep Machine Learning – The advanced algorithm, inspired by the biological processes of the human brain.

Advantages of Machine Learning in Procurement

Some of the Key Benefits of Machine Learning in Procurement include:

  1. Predictive analytics
  2. Inventory Management (e.g. image classification)
  3. Spend Management
  4. Supplier Management
  5. Automated Quality Insights
  6. Compliance
  7. Shipping
  8. Efficiency Tracking
  9. Contract Management
  10. Speed & Accuracy

ML-in-procurement

Conclusion

It’s quite obvious that the potential of Machine Learning in Procurement and Strategic Sourcing will provide added value for the entire customer-supplier loop in the future.

Having said that, organizations that are ready to embrace the digital transformation of their processes will most likely gain a competitive edge over those that are still having second thoughts about using cognitive models.

Do you agree?