Predicting the Future: How AI is Changing Logistics Forecasting

Effective logistics operations can make or break a company's success. However, delays, bottlenecks, and unforeseen cancellations can quickly disrupt even the most efficient logistics chains.

Transportation companies lose $3.5 million annually to severe weather events alone. In addition to weather, human error, warehouse malfunctions, and traffic conditions can also significantly cost logistics companies.

While companies can leverage data analysis to forecast potential disruptions, no one can see into the future. But what if they could? While AI isn’t necessarily a crystal ball, it has proven capable of making more accurate predictions based on historical data—predictions that companies can use to save valuable time and money. 

Challenges of Traditional Logistics Predictions

Companies have used demand forecasting methods long before AI came into the picture. Accurate predictions are integral for logistics, ensuring that businesses can meet demand, don't under- or overstock, and keep their customers satisfied. Though effective in the past, these methods struggle in today’s rapidly changing and unpredictable market.

Traditional models like autoregressive integrated moving averages (ARIMA) and exponential smoothing rely heavily on historical data and often lack real-time insights into ongoing trends or changes. They don’t factor in the rapidly shifting real-time data of the modern age and, as such, lack the market’s full picture.

What Can AI Predict?

AI can rapidly analyze vast amounts of data far beyond human capabilities. Hala Point by Intel, the world's largest neuromorphic system, can support up to 20 quadrillion operations per second. Shockingly, this isn’t significantly higher than the computing power of the human brain—but the difference lies in how quickly AI can accurately take in and process complex data from varied sources. 

To that end, a recent study showed that interacting with advanced large language models (LLMs) increased human forecasting accuracy by 24-28% compared to a control group. In some cases, this improvement was as high as 41%. In another instance, McKinsey reported that AI in manufacturing reduces forecasting errors by 30% to 50%. 

By detecting patterns that would go unnoticed by human analysts, AI predicts extreme weather events and human behavior. In the future, it may even be able to predict financial crises. These forecasts, among others, are invaluable for companies planning for the long term. 

Supply Disruptions

Supply chain disruptions are on the rise worldwide, with 1,642 total disruptions globally in 2021. With natural disasters, material shortages, and other factors plaguing the logistics industry, supply procurement requires careful planning. 

AI makes sourcing easier by analyzing the factors that may affect a supply chain and providing real-time detection of anomalous activity. As AI advances and improves its information access, these solutions will become increasingly accurate.

Delivery Times

In the age of same-day shipping and other conveniences, consistent delivery times are vital for maintaining customer satisfaction. Unfortunately, this isn’t always possible. Shipments may be delayed by traffic and storms, especially when transported across long distances. The US Department of Transportation estimates that weather-related delays alone cost the US trucking industry, the country's main method of freight transportation, 8 billion to $9 billion annually.

While technology has yet to control the weather, it has reached the point of predicting it. Google DeepMind's GraphCast model outperforms 90% of the targets used by the world's best weather prediction systems. It’s also 1,000 times more energy efficient than traditional methods and can make accurate predictions in seconds. 

By analyzing weather, traffic patterns, and other factors, AI can predict when a delivery may arrive, and even help companies plan more efficient routes to avoid delays. 

Demand Changes

Knowing what’s popular among consumers is vital for keeping ahead of the curve. While various traditional forecasting methods help business owners learn what is and isn’t in demand, research takes time. AI allows companies to plan and procure in an increasingly dynamic market, improving their supply chain's resilience.

AI analyzes current market trends, historical data, demographics, reviews, and even factors like the weather and holidays to make its predictions. With this data, AI-based tools like Google's Video AI can pick up on demand changes and early signs of panic buying, alert users, and identify what caused these shifts.

By incorporating AI into the demand forecasting process, companies can maintain optimal inventory levels, reducing customer wait times and leading to higher inventory turnover rates. 

Maintenance

Unplanned downtime is a common but costly problem for many businesses. 82% of companies across all sectors have experienced at least one unplanned downtime outage, with most suffering two or more. 

These maintenance costs add up, and the time it takes to repair broken equipment eats into profit. For instance, the average automotive manufacturer loses roughly $22,000 per minute when production stops. This downtime, in total, costs industrial manufacturers an estimated $50 billion each year. 

Predictive maintenance counters equipment failures before they happen, mitigating costs. AI software uses sensors to monitor temperature, vibration, and sound, allowing companies to detect misalignments and wear and tear. 

Staffing

The US is currently undergoing a labor shortage, with the US Chamber of Commerce reporting that while there are currently 8.2 million job openings in the US, only 7.2 million unemployed workers are available. Though some industries have been hit harder than others, finding and keeping workers is still a priority across all industries and verticals. 

AI is a valuable asset for countering these problems. Using data from past hiring decisions, AI can rapidly go through resumes and make strong predictions about a candidate’s success in a certain role. Companies like Knockri already use AI in their hiring process, improving efficiency while reducing potential hiring bias.

Once a company has employees in place, AI helps maintain them. It can study employees’ behavior, engagement, and work patterns to determine which workers are most likely to leave, and which are going above and beyond. Xerox, for instance, used big data analysis to reduce its workforce turnover by a fifth in only half a year. This data lets companies reach out to and reward workers as needed, and create customized work experiences to reduce burnout. 

Risk Management

When creating a resilient supply chain, companies require a level of foresight that isn't always possible, or realistic, to maintain. Fortunately, AI can do much more than crunch and predict concrete outcomes. AI solutions can take different data sets and factors and project them against one another, creating a comprehensive forecast that informs companies of potential risks. 

With enough data, these AI reports can warn companies about crises before they happen, giving them a head start on tackling or avoiding their impact. The Georgetown Journal of International Affairs notes that AI can even help businesses plan the best response strategies by conducting simulations based on demand, supply, and recovery time from different disruptions.

The Challenges of AI Adoption

AI may be versatile, but it is only as effective as the data it can access. The more data an AI sifts through, the more patterns it'll have to analyze, making for more effective predictions.   Companies that use legacy systems with dated software struggle with integrating AI into their logistics process. 

However, while having data-light environments can make integration difficult, it’s not impossible to achieve. Companies can hire data specialists to sort through their data and convert it into an AI-friendly format. Additionally, externally sourced data from social media, weather forecasts, and other content is an excellent way to fill in the blanks, and, according to McKinsey, can significantly improve forecast accuracy. 

Should You Use AI for Your Logistics?

As AI advances and grows more refined, more companies will likely incorporate it into their own logistics process. The latest McKinsey Global Survey on AI found that 65% of respondents already regularly use generative AI for their organizations, nearly double the percentage from their survey 10 months ago. By ignoring AI's benefits to their logistics, companies aren’t just missing out: they’re falling behind. 

A custom AI solution can streamline your company’s operations by addressing its unique needs. If you’re interested in incorporating AI into your supply chain, contact FullStack Labs today. Our team specializes in building software solutions that help you meet your tech goals.