The contributed article was authored by Jonah McIntire of TNX Logistics and Anna Shaposhnikova of Transmetrics. The opinions expressed here are those of the authors, and do not necessarily reflect the editorial policy or outlook of FreightWaves.com.
Artificial intelligence (AI) now powers many real-world applications, from facial recognition, fraud detection, language translators, to assistants like Siri and Alexa. Soon it will be applied to core logistics operations. This should be a golden era of practical AI, when algorithms give way to implementation.
For decision-makers, it is worth understanding some of the basics behind those algorithms to help ensure first experiences with AI in your workplace are likely to succeed. Therefore, the remainder of this article lays out our thinking about two approaches to AI in logistics and where they are most likely to bear fruit.
Two Approaches in Brief
The first approach is what is known as statistical AI, or more popularly as machine learning. It is premised on the idea that a large volume of historical, current and future data has patterns of importance. The software discovers those patterns, with varying degrees of feedback from humans. The patterns, and the models to represent them, act as predictors for data the business finds useful. It is called machine learning because additional experience improves the predictive power of the software.
The second approach is what is known as AI planning. Planning doesn’t necessarily require learning from experience. AI planning is premised on accurately describing the state of the world, the actions available, and goals we want to achieve. With that, AI planning acts as a rational agent trying to achieve goals with allowed decisions and with a model of how the world reacts to them.
Human involvement in AI planning is not to teach the the software, necessarily, but rather to act as a gatekeeper on final decisions. People are said to be in, on, or out of the decision-making loop depending on if they approve decisions, can only halt the process entirely, or are strictly observers.
For completeness, it is worth noting that the two approaches can be mixed. AI planning can include a means to learn from past experience.
In the Logistics Context
Logistics includes areas of application for machine learning and AI planning. To make this concrete, let’s look at two specific use cases in detail. The first is data cleansing. Data quality can be summarized by accuracy, completeness, timeliness and precision. The logistics sector has had mixed success in achieving a consistently high quality of data. That is an issue because so many downstream processes act as multipliers on data quality. Transport planning, customer service, seasonal staffing, inventory forecasts and even safety are impacted. The chronic lack of data quality is not an obstacle to solving an important problem. Sometimes it the most important problem.
Machine learning is a good tool to identify and even correct data quality errors early in the process. In this application, machine learning is predicting the actual data values (or corrections to them) and improves at this through a combination of gaining experience with large historical datasets, combined with human feedback. The result is an increasingly good data quality result despite poor data capture. The models for making these corrections depend on the interactions between data; machine learning makes up for occasional missing or inaccurate data points by learning how those figures should relate to other known data.
Planning is critical in many phases of logistics. From warehouse slotting, pick and pack strategies, dock door and staging usage, transport consolidation and routing, and procurement of all of the above. Given that planning is about decision-making in a known environment to achieve goals, almost all these areas have their own IT systems that just append “planning” to them – labor planning, inventory planning, and transport planning to name a few.