Machine learning—it’s a ubiquitous buzzword that’s used loosely but at the same time widely misunderstood. Like many powerful technologies, machine learning has the potential for great business benefit, but if wielded in the wrong way can result in wasted time and resources, and poor business decisions. Remember the saying, ‘a stitch in time saves nine’? The key to sustainable supply chain benefit from machine learning lies in taking a thoughtful approach at the outset: establishing goals, devising a strategy, testing results and refining your approach as you go.
We’ve compiled six important tips for businesses considering applying machine learning to supply chain planning problems, based on our nearly 10 years of experience building and delivering machine learning solutions.
First, let’s take a quick look at the machine learning process, and why it’s a great fit for demand planning challenges.
Acquisition and storage of relevant structured and unstructured data sets.
Exploratory data analysis, cleansing, transformation, feature engineering, and selection, training and test data set split.
Domain appropriate choice of supervised, unsupervised or reinforcementa learning algorithm(s) (e.g. K-means clustering, decision trees, neural networks, etc.).
Train the model with the training data set.
Measure the performance of the trained model on the test data set against a defined evaluation metric (e.g. achieve a forecast accuracy of at least 85%).
Empirical process of changing algorithm parameters to improve model performance.
Deploy the trained model in a production system environment
Sound a bit intimidating? Fortunately, the right machine learning technology partner can automate and simplify these steps with self-learning models that react to changes in your business. That means you don’t need an army of data scientists to incorporate machine learning into your demand planning.