This commentary appeared in the print edition of the Jan. 6, 2020, Journal of Commerce Annual Review and Outlook.
Since after World War II through to the late 1980s, the logistics industry grew to become highly containerized and increasingly commoditized.
Early efforts to digitize logistics entrapped a largely manual system reliant on people processes that was built from pre-internet, pre-cloud, and pre-data science knowledge. And while this early digitization allowed companies to scale, it also held the seeds of its limitations — the fractured, fragmented, and fragile system we see today.
If the global logistics system was redesigned from scratch, using today’s technology, knowledge, and experience, it would be completely different from today’s system of logistics processing.
In any well-designed major economic system, efficiencies of scale eventually drive consolidation to produce a smaller number of very large and highly sophisticated players. Yet, consolidation of the logistics industry is happening at a glacial pace and is riddled with complexity and risk. And while technology is key to driving both the need and the ability to consolidate and to drive increased efficiency and productivity in an industry as large and significant as logistics, it is not the entire solution.
Redesigning the aging legacy logistics model is one of the most critical issues facing the logistics industry today.
The industry is already seeing an increase in the use of machine learning, natural language processing, and other automations and integrations to remove valueless, simple, highly repetitive tasks like data entry from human hands, enabling supply chain professionals to perform higher-order, value-creating activities.
The implementation of these technologies and a redesign of the basic model of logistics, supported by the idea of creating “straight-through processing,” will mean fully digital data flows from end to end, without being interrupted or rekeyed, and that the physical systems will be much more predictable and lower cost than the current model.
A global system that enables optimal planning and execution driven by high-quality, real-time data flowing freely across borders, boundaries, and logistics providers, from the moment an order is conceived through to delivery to the final customer, is the goal.
They key challenge is one of changing mindset, breaking habits, and redesigning the way everything is done. If you’re going to make the most efficient and effective system for global logistics, you’re going to have to rethink the entire model from the ground up and be digital first.