Logistics and supply chain networks are hard to manage. The COVID-19 pandemic and other recent global geopolitical events have brought inherent logistics systems complexities and weaknesses to public attention. Almost daily, media reports tell us about the detrimental effects of supply shortages, logistical bottlenecks, industry disruptions, and rising costs of goods.
Logistics issues have grave effects on the national and global economies, disrupting basic, critical areas that depend on smoothly functioning logistics, including healthcare, food, commerce, manufacturing, energy, and agriculture.
The extent of logistic inefficiencies is well-known and well-documented. One example study estimated the extent of waste of temperature-sensitive pharmaceutics (vaccines) due to logistics problems to lead to $35 billion in direct loss of material and indirect disruption of service costs every single year.
The Dawn of Logistics AI
Many of the logistics sector’s fundamental ailments can be solved or at least greatly alleviated with advanced data-centric algorithm technologies such as augmented intelligence, artificial intelligence (AI) and Machine Learning (ML).
Still only embraced by early adopters, these technologies are already proving themselves, allowing logistics managers in organizations small to large, to become more resilient, work more efficiently, improve service levels, minimize waste, lower costs, and increase profitably.
Think about solving a super-huge sudoku puzzle: we, as humans, are limited in our ability to consider and analyze many interrelated variables, constraints, and contingencies. Therefore, the considerable time and resources needed to solve such a very big and complex puzzle are unreasonable, unfeasible, and, often unavailable, rendering the human-driven solution inaccessible. Logistics is far more complicated than Sudoku, with significantly more variables, uncertainties, and constraints. The result is that in order to cope with sudoku-like complexities, managers traditionally simplify assumptions about their reality and simply accept the inherent uncertainty and risk aspects of their tasks as a “fact of life”. This often leads to suboptimal operations. For years, many problems and inefficiencies have been deemed “unsolvable”, and their sub-optimal consequences were traditionally accepted as unavoidable costs of doing business.
But AI agents and ML algorithms can change that legacy way of thinking!
With these new technologies, human logistics managers can cost-effectively augment our (limited) capabilities and efficiently solve big, complex multi-variable operational logistics issues without forfeiting unfeasible costs or compromising on service levels.
Machine learning technology can help mitigate risks and uncertainties. Over time, ML can provide substantial improvements in performance, quality, and profitability, as is already being demonstrated by early adopting companies who are embracing these powerful technological tools. For example, initial implementations of AI for local “last-mile” deliveries are showing efficiency improvements of up to 30%!
One of many examples of the revolutionary advantages of using machine-learning algorithms in the logistics sector is route planning and route optimization. With AI algorithms processing thousands of parameters simultaneously, in real-time, many multiple route plans can be dynamically expedited at the push of a button, ensuring not only the timely arrival of the entire fleet but also ensuring organizational costs stay well within strict budget limits.
With such technology, the combined requirements, and constraints of all the various stakeholders – senders, carriers, and receivers – can now finally be taken into account without compromise, to deliver optimal operational insights and decision-making support for all parties along the supply chain. The optimization of routes not only takes the destinations, distance, and time constraints into account but also predicted traffic patterns, predicted weather conditions and service levels (SLAs), and cost structures of 3rd- or 4th- party service providers and their respective customers.
And there are many other examples. Dynamic AI-powered platforms can support and even predict service quality anomalies or failures, autonomously generating predictive, pre-emptive solutions to problems on the fly, as they happen, or even before they occur. This powerful predictive AI capability helps logistics companies maintain the high service levels to which they are committed, improving their reputation, and raising customer satisfaction thus increasing their competitiveness.
AI for critical time-and-temperature logistics
Specialty logistics such as time-sensitive and/or condition-sensitive (e.g., temperature-controlled) logistics are on the rise, especially in specific categories such as fresh food (think: Strawberries) and Healthcare (e.g., COVID and other critical vaccines). When a given window-of-delivery time or specific temperature is required, being able to provide real-time responsiveness and pre-emptive problem avoidance becomes even more crucial, as a pre-emptive response to unpredicted condition variations throughout the supply chain becomes essential for the success of the logistics mission.
Real-time monitoring, combined with predictive analytics algorithms can ensure safe and timely transport that upholds both customer expectations and stringent regulatory requirements, every step of the way.
Looking forward, in the next 5 years
Recent global events and supply challenges have triggered the beginning of acceptance and are driving accelerated adoption of AI in the logistics segment. In other segments such as healthcare, banking, and retail, AI is already becoming a central technology for operational improvement. I believe AI will eventually become mainstream in the logistics segment too. The benefits of this powerful game-changing technology are becoming apparent to those who understand the huge global logistics challenges and how AI can be applied to solve them. To remain competitive and profitable, logistics players, large and small, will broadly adopt AI tools both for their planning and for daily logistics management.
Written by Raviv Yatom, Co-founder & CTO of Amphorica