Machine learning, deep learning and AI are enabling transformational change in all fields from medicine to music. It is helping business from procuring to pay, plan to produce, order to cash and record to report, to uncover optimising opportunities and support decision making which was never possible earlier with tradition business analytics and information technology.
Machine learning and AI is a complex field and bit daunting for people from my generation who may not have come across computer before undergraduate level education. In this introductory article, I will illustrate a simplified example of machine learning implementation in procurement and quality management business process for optimisation. I will avoid complex and subtle details in this article and keep the example simple at this point. We will discuss complex scenarios in the next series in this article. I will be using the programming language Python to implement machine learning algorithms to solve supply chain business challenges in this series, and I am assuming that you have some basic understanding of Python language.
Business Case
A vendor supplies different materials with wide unit prices and total purchase order values. Sometimes a few parts from supplier fail to pass the quality checks, but the defect percentage doesn’t seem to be following a trend. For the sake of simplicity in the first blog of the series, I have assumed a linear relationship between the independent and dependent features.
Objective
The business would like to predict the defective piece percentage in delivery from the supplier. If the defective piece percentage predicted is below the threshold level, then a quality check will not be performed. Let us consider that if the defect piece percentage in the delivery is more than 0.4%, then explicit incoming inspection is needed to ensure the quality of the end product.
This will help to focus on quality check on only particular purchase orders delivery, to control the end-product quality, and optimise inspection costs. It will also enable to uncover the variables/parameters which are influencing the defective deliveries of few materials at times and work collaboratively with the supplier to address it.
Data Points and Information
Purchase orders information and respective incoming inspection results in the ERP provided the data points for all the order over three years period.
Sample Historical Data exported from ERP to train the machine learning algorithm
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