What Powers Artificial Intelligence (AI)? A Guide to Procurement!

What powers Artificial Intelligence

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What powers Artificial Intelligence article and permission to publish here provided by Edward McGeachie at myseaforth.com.

Artificial Intelligence is an increasing part of our everyday lives, powering our smart phones and the internet of things.  But few people really understand what it is, how it works and more importantly, why it is so important to Procurement.  

This paper seeks to answer those questions and specifically address its benefits to the Procurement process by addressing the following checklist: 

Checklist

  • What is Artificial Intelligence or AI?
  • What type of AI is used – Supervised or Non-Supervised?
  • What AI model is powering the Application? 
  • What is the Accuracy of the Model?
  • What is the Accuracy of the Outcomes? 
  • How much time will it take to train?
  • How much SME resource is required?
  • What are the expected benefits?

What is Artificial Intelligence or Machine Learning

The Oxford English Dictionary defines Artificial Intelligence (AI) as the theory and development of computer systems able to perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages.

For many people in Procurement, the language used in data science can be confusing. It is far simpler to explain by simply saying, powered by AI.  

However, when investing in large scale technology transformation projects it is important to know what element of the process is powered by AI and how the AI itself works.  What powers Artificial Intelligence ?

Artificial Intelligence, or Machine Learning are typically classified into two broad categories:

Supervised learning

  • The computer is presented with example inputs and their desired outputs. “Teacher” or “Training set” data are used to establish a general rule that maps inputs to outputs. 

Unsupervised learning

  • No labels are given to the learning algorithm, allowing it to independently find structure in its input. Unsupervised learning can be an effective method of discovering hidden patterns in data.

Both Unsupervised and Supervised Learning can be used to establish baseline behavioural profiles for various entities which are then used to find meaningful insights & anomalies. Within the field of Procurement data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to the following tasks or processes:

  • Inventory Management
  • Invoice Payment – Invoice Fraud
  • Supplier Relationship Management
  • Sales Pipeline
  • Marketing Analysis
  • Customer Segmentation
  • On Time Delivery 
  • Operational KPIs
  • Supplier on-Boarding
  • Spend Analytics
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‘Powered by AI’ is a common feature or term used to sell the benefits and merits of digital transformation solutions.  The aim of these analytical models is to provide Procurement teams with reliable, repeatable decisions and learn from historical relationships and trends in the data. Therefore, it is important to have a general understanding of how it works and what is powering the AI.

What is the real power behind AI?

The power behind AI is a series of structured learning algorithms or code used to analyse input data.  Often open-source coding software like R or Python are used to develop the AI power that these software models use.  Within these applications there are several models or libraries that can be used to “power” the AI. 

Below is a list of the most popular decision libraries that Power AI

  • Decision tree learning
  • Association rule learning
  • Artificial neural networks
  • Deep learning
  • Inductive logic programming
  • Support vector machines
  • Clustering
  • Bayesian networks

AI Transparency

A criticism levelled at AI models is the term “Blackbox”, likening it to an airplane flight recorder. It is true that AI models are not always transparent.  It can be difficult to understand how they derived the outcomes and exactly how the calculation or prediction was made.

AI Model Construction & Accuracy

A model can have more than 95% accuracy and be optimised using several iterations called epoch(s), to produce stable functioning.  This does not mean the output from the model is accurate, simply that the model itself is stable and performs as it should.

Data Training & Validation Time

Completion of the model can take several hours to finalise. But it can take days or even months to achieve accurate outcomes and run the risk of the model over-fitting.  Additionally, it may require an expert subject matter resource to be set aside upfront to validate the outcomes. 

Accuracy is critical and, in many cases, dependent upon the training data set itself. The training set needs to be considered carefully to make sure there is no data bias. Bias occurs when one or more groups feature more often than others and distorts the outcomes, creating bias. 

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However, repetitive tasks that are predefined and use a flow process called robotic process automation that replicates manual tasks makes uploads faster, the process slicker and produces very accurate results. 

What Powers Artificial Intelligence In Summary

For most Procurement professionals these models seem complex. Models that initially require highly skilled data engineering and data science professionals to write the code is costly to produce.  For many, AI echoes the same sentiment which drove big data a decade ago, with its varied success use cases and Procurement benefits.

With the proliferation of marketing hype and veiled technology “Blackbox” solutions, it is more important than ever to fact-check and get clear answers about how the AI is powered. 

The aim of Procurement digital transformation is to perform tasks and process more efficiently using technology to deliver benefits seamlessly, without impacting its current operations. Like many sourcing decisions it is important to understand the benefits of AI, the Procurement case for it and the return on investment. 

About the Author

Edward McGeachie has over 25 years Procurement and Supply chain experience at Schlumberger, IBM & Lenovo PC division. He has managed Global Procurement, operations, and logistics teams, before establishing a centre of excellence for Lenovo GSC supply chain analytics. 

Co-Founder of Seaforth Analytical Services, Supply Chain Analytics Company based in the UK. He holds an MBA and B. Eng.(hons) in Industrial Engineering and is also a certified Black Belt in Lean Six Sigma.

He has developed innovative Spend Analytics platform using a mixture of the processes outlined above called Accelerated Insight ® .

Additional Resources

Microsoft Azure and Google TensorFlow are also great places to investigate how to code AI.

Originally written for Supply Chain Game Changer and published on February 11, 2021.
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