DC Velocity - April 2015

46 DC VELOCITY APRIL 2015 www.dcvelocity.com
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3Machine learning has many potential supply chain applications. Supply chain managers need to possess a basic understanding not only of analytics but also of machine learning. Machine learning refers to the collection of algorithms that have been developed over the last several decades in a variety of fields, such as statistics, data mining, and artificial intelligence. These algorithms represent the “brains” behind a lot of what is new in predictive analytics.

In the simplest sense, machine-learning algorithms take a set of input data and then create a model based on the data that will either predict future outcomes or will uncover patterns in the data. It may seem less intimidating once you recognize that regression analysis (the statistical process for estimating relationships among variables) is classified as one type of machine-learning algorithm.

It may seem strange that supply chain managers should need to know about machine learning; it sounds like something that belongs in computer science or robotics. But just as supply chain managers today should know about regression analysis, they should also know about machine learning. Knowing how machine-learning algorithms work and what they can accomplish will help you better understand what is now possible with predictive analytics. That is, it will give you new ideas you can apply in your organization to get more value from your data.

Supply chain managers should be familiar with several of the more popular machine-learning algorithms. For example, with some data sets, these algorithms (like “k-nearest neighbor,” “decision trees,” or “random forests”) can out-predict traditional regression analysis by better teasing out patterns in the data or by allowing text-input values. Other algorithms are well suited for predicting whether an event will happen or not—for example, will the order be late, will the carrier accept the load, or will the machine break. This is typically done using logistic regression (a statistical technique used to predict the probability of possible outcomes). There are also algorithms for understanding text documents—like trying to determine whether an e-mail sent to customer service is negative or positive. This is done with naïve Bayes algorithms (a prob-abilistic algorithm used to classify inputs). Algorithms can help you determine which items are likely to be ordered or shipped together—very helpful for determining which items should be stored together. These are known as “ association rules” or “market-basket analysis.” And finally, there are algorithms for detecting clusters in your data.

Once you start to see the power of the various
machine-learning algorithms, you will realize that you can
combine them in interesting ways to solve complex supply
chain problems. Some companies’ transportation depart-
ments have built sophisticated models to first predict when
a carrier will accept a load and then use price optimization
to set the best price for that load. Some companies use the
algorithms to better determine which products their sales
teams should recommend to their customers (similar in
some ways to the recommendation engines that Amazon
and Netflix use). Others use the algorithms to predict when
inventory might go obsolete and to adjust prices
accordingly to move the stock.

Supply chain managers should be aware of the different ways these algorithms are being used to solve business problems. In the field of analytics, good ideas can start out in one organization and then be picked up and adapted by others. For example, association rules have long been used in the grocery industry to see what items consumers would buy together. One retailer’s e-commerce managers realized that they could use the same algorithms to determine which items they should place in the same warehouses because they tend to ship together. As this example suggests, the more you know about the different applications, the more likely you will be able to apply them to your business.

TIME TO ENHANCE YOUR SKILLS

The field of analytics is very exciting right now.

It is getting a lot of attention and is having a big impact on all types of businesses and organizations. Although it is more often associated with high-tech companies like Google or Amazon, This article should provide you with both a framework for thinking more clearly about analytics and a starting point for conducting more research on your own. There is a wealth of books and online resources about analytics and big data as well as commercial tools and open-source software available to help you get started.

As a supply chain manager, you will benefit from challenging yourself to come up with new ways to use the data in your possession. Be a leader in data and analytics; it will help propel your company—and your career—forward.

Notes:

1. This definition is adapted from the article “Competing on Analytics” by Thomas H. Davenport, which appeared in the January 2006 issue of Harvard Business Review.