At Canadian Alliance, we see first hand the cost- and time-saving benefits of implementing expansive Artificial Intelligence (AI) programs in our customers’ businesses. Especially throughout the pandemic, we’ve seen where our customers have thrived—or struggled—because of how they use machine learning and real time data (or, conversely, how they do not.). From automotive parts and their corollaries to cold storage in food production, our supply chain stakeholders have begun to forge new paths using AI that have and surely will continue to yield unparalleled efficiencies and, therefore, savings.
Thus, it was only a matter of time until we turned this lens on ourselves and asked, how can AI facilitate a more effective and efficient logistics industry? In our research, we, too, have found the possibilities created by the implementation of AI to be paradigm-shifting. How paradigm-shifting? The Economist reports that McKinsey and Co. “estimates that firms will derive between $1.3trn and $2trn a year in economic value from using AI in supply chains and manufacturing.”1
Dream Team: Using AI To Extend Human Capability
When the first discussions of robotic arms in automotive factories or self-checkout machines at grocery stores arose years ago, they yielded concerns that “robots are going to take our jobs.” Years ago, this seemed a just fear—these robots didn’t need breaks, salaries, or even very much supervision; of course they would replace workers! However, this robotic workforce has not come to fruition like we thought it might and this is for one reason: you can’t automate the judgement and empathy that humans bring to the workplace.
Since awakening to this reality, industries implementing AI have affirmed less of a HAL 9000 scenario and something far more rooted in reality: AI is best put to work when it can alleviate and minimize tedious work while amplifying what humans do best. More simply, where there is more room for human error (in things like data analysis, for example) AI can step in; while humans bring dynamic and creative visioning to things like trend forecasting, AI can support operations and programming with human guidance. In logistics, this is where we can find the greatest paradigm shift: by teaming up with AI, logistics companies can create operations-altering efficiencies.1
How Does AI Complement The Logistics Industry?
AI contributes manifold revolutionary possibilities to the logistics industry, many of which have the capacity to meaningfully overhaul the way we do things. In the opinion of Canadian Alliance, the most pre-eminent of these are the real-time data tracking and sharing that is Big Data and bin packing. Each of these processes enables a series of remarkable abilities but, for our purposes, we can distill these down to a few potent shifts.
Big Data involves the manipulation and analysis of massive quantities of data—unlike anything that has ever been possible before. By feeding Big Data into AI in logistics and supply chain management, AI is able to better understand, and therefore predict, patterns. This capability means logistics companies are more able than ever to carefully and accurately forecast upcoming seasons. From trending demand to the impact of weather on international shipments in the spring months, AI learning from Big Data has made forecasting much more reliable. Companies like Amazon use Big Data to plan for supply and demand waves as well as to better organize their warehouses for optimal processing and shipping times.2
Real time data tracking and sharing relies on AI to gather data points, interpret them, and centralize them where multiple parties can have access. Making use of real time data enables producers along the supply chain to make informed and up-to-the-minute decisions, and to develop quick contingency plans if something goes sideways along the way. Having such oversight from top to bottom along the supply chain has never before been possible and creates opportunities for unprecedented efficiencies. For example, the American company CircleBack uses AI to continually check multiple data points to ensure all contact information is up to date and accurate. Practices like these are immediately transferable to logistics.3
Bin packing algorithms have long been crucial to logistics. The algorithm, when given a series of values (such as product volumes and use factors), is designed to “pack” those values in the most efficient manner possible. AI maximises the abilities of these algorithms to create space for efficiencies between companies, between industries, and up and down the supply chain. Rather than primarily designing the organization of an ocean container, AI (using both Big Data and real time tracking to solve problems as they arise) can locate the single most efficient combination of practices and organization in ways that humans simply can’t. Ocado, a supermarket based in Great Britain, uses a nearly fully automated warehouse relying on these advanced bin packing algorithms to first organize and then pull from tall and strategically packed warehouse stacks.4
Without these systems in place, none of these companies—or any supply chain stakeholder—would truly be able to reach their maximum efficiency.
Finding the Right AI Use Case
Implementing AI into one’s own logistics supply chain requires a thoughtful, measured approach. As in the cases of giants like Amazon, real time fact checkers like CircleBack, and companies like Ocado, for whom bin packing efficiency is crucial, every AI solution is different. But with the potential derivation of economic value holding steady in the trillions, it’s a mistake to ignore this new tech.