{"id":836,"date":"2021-04-21T09:03:25","date_gmt":"2021-04-21T07:03:25","guid":{"rendered":"https:\/\/nexuslcm.com\/?p=836"},"modified":"2021-04-21T09:03:25","modified_gmt":"2021-04-21T07:03:25","slug":"demystifying-machine-learning-part-2","status":"publish","type":"post","link":"https:\/\/nexuslcm.com\/demystifying-machine-learning-part-2\/","title":{"rendered":"Demystifying Machine Learning \u2013 Part II: Decision vs. Insight in Logistics"},"content":{"rendered":"

This is the second article in a series on Demystifying Machine Learning. If you have not read the first article yet, you can check it out here:\u00a0Demystifying Machine Learning<\/a>.<\/p>\n

Reviewing Decision vs. Insight in Machine Learning<\/h2>\n

In our first article, we outlined the three main types of machine learning (supervised<\/strong>, unsupervised<\/strong>, and reinforcement learning<\/strong>) and identified what outputs could be expected from each. If you seek assistance or automation in making a decision that has a correct or incorrect answer, use a supervised machine learning model to aid in selecting the correct answer to new problems based on a training model using similar, past problems. If instead, you are searching for unknown insights in the existing data\/situation then you should turn to an unsupervised machine learning model to help you discover previously un-examined patterns and anomalies.<\/p>\n

Machine Learning Applications for Logistics Operations<\/h2>\n

In recent years, machine learning (ML) and artificial intelligence (AI) have been applied to a varied number of problems and industries ranging from health to entertainment to banking. One of the fields that stands to benefit significantly from the application of machine learning is logistics operations<\/strong>.<\/p>\n

For large commercial organizations, governments, and militaries, the movement of people, products, and equipment is complex. Assuring items arrive in the correct location, at the correct time, in the correct quantities, is challenging even at a small scale. Some organizations, such as shipping and delivery companies, have utilized emerging technologies to conquer the complexities of these activities and can reliably and consistently manage a massive network of products and transportation equipment. Other organizations have had less success in trying to overcome these challenges.<\/p>\n

Machine learning and AI can be used to predict transportation times, alert of equipment breakdown risk, or quantify the impact of changing weather on resource consumption rates, to name a few examples. This information can be the key to successful logistics operations activities. In the next sections, we will review some logistics scenarios where decision or insight support is achieved using ML techniques to improve the outcomes of logistics operations. These scenarios will focus on consumables management.<\/p>\n

Logistics Use Case 1: Consumption Rate Prediction<\/h3>\n

Consumables, no matter their class (e.g., food, water, fuel, or ammunition), are a major factor in the success and complexity of the planning, execution, and sustainment of a mission. While an excess of consumables can complexify planning, transportation, and storage, a shortage can derail execution and jeopardize a mission and the engaged personnel. Finding the right balance between what is needed and what is manageable is key.<\/p>\n

Supervised learning techniques applied to historical data from missions of a similar nature (e.g., same geographical operational area, weather conditions, objectives, etc.), can help decision makers to better predict future consumption and plan accordingly. For instance, historical water consumption data can be analyzed to identify trends and generate predictive formulas. These formulas can be applied to forecast data by extrapolating future consumption and support leaders in making educated decisions about resource requirements and sustainment operations when planning for future events.<\/p>\n

Logistics Use Case 2: Uncovering Insights<\/h3>\n

While predicting consumption rates is a direct way of optimizing consumables management, uncovering insights about those rates is just as important. This can be achieved using supervised or unsupervised learning methods, both methods providing different types of insight.<\/p>\n

On one hand, supervised learning can be used to analyze historical data to help understand the correlation between different independent variables (e.g., environmental factors) and an associated dependent variable (e.g., water consumption rates). For instance, when looking at past water consumption, this type of analysis can be used to identify and rank environmental factors (e.g., temperature, humidity, elevation) by the size of their impact on the resulting water consumption (this measure of impact size on the dependent variable is known as the correlation coefficient).<\/p>\n

Although, it is often the case that two independent variables may be strongly related (known as correlation), . Two variables can be strongly correlated to one another, leading to an incorrect belief of causation between the two variables, when the correlation is actually the result of a third, unexamined, variable which is impacting the two examined variables in a process known as confounding. For instance, while ice cream consumption and the number of shark attacks are strongly correlated (i.e., they tend to follow each other), they are both caused by rising temperatures (the confounding variable):<\/p>\n