In the previous article, we considered the challenges that the logistics industry is facing with digitization. However, these challenges also present several opportunities for AI. Eventually, AI will optimize network orchestration and redefine the behaviors and practices prevalent in the industry today.
Several areas in logistics can benefit from AI and machine learning. Two approaches are currently being employed – statistical AI, which is also known as machine learning and AI planning. It is also important to note that these two approaches can be adopted simultaneously for completeness.
To consider how these approaches can be implemented, let’s consider two specific use cases – data cleansing and planning.
The logistics industry has always had mixed success in achieving high quality data consistently. This is because several downstream processes act as multipliers on the quality of data. Customer service, transport planning, inventory forecasts, seasonal staffing, and safety can be impacted by data quality.
Machine learning and statistical AI can be used to correct errors in data quality early on during the process. When combined with human feedback, AI and machine learning can be used to achieve a consistently high level of data quality. This, in turn, aids data analysis and produces more accurate results.
Planning is critical across many different stages in logistics. From slotting to pick and pack strategies, transport consolidation and routing to dock door and staging usage, planning happens everywhere.
Planning in logistics involves taking decisions across disparate systems. These include inventory planning, labor planning and transport planning, among many others. There is a significantly high level of complexity associated with this and optimum planning cannot be achieved through human thinking alone. By making use of data analysis and AI planning, companies can implement plans across disparate systems and achieve a clear goal.
Executives in the logistics industry have already started realizing these opportunities and are implementing AI solutions effectively. In our next article, we’ll take a look at how AI is successfully transforming logistics management.