How To Leverage AI for Smarter Decision-Making

The decisions that fleet managers make surrounding their logistics operations are often critical to the success of the company as a whole. Any given decision can have a massive effect on supply chain effectiveness, cost, and even customer satisfaction. A wise decision will improve all these mentioned, but a wrong decision has the potential to be detrimental to the company. Even though a small wrong decision once in a while may not have an immediate impact, a combination of small wrong decisions will eventually hurt the company. To prevent wrong decisions, you need to base your decisions on tangible information about your operations. Technology like AI is being innovated to be used to strategically ensure the best possible decisions are made based on the best possible data and forecasting. Let’s explore how this technology works and how it translates into a company’s daily operations.

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• Loss Prevention. Reduce the amount of loss that occurs during the supply chain process
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How AI Works in Decision Making

Using AI to make strategic decisions isn’t as complex as you might imagine. AI, while a complicated technology in its invention and inner workings, is actually fairly simple to implement. That being said, there are a few misconceptions about it that might get in your way. The main misconception is that all you need to take advantage of AI is to simply purchase AI and start using it. This is incorrect. AI requires both a historic and ongoing datastream in order to operate. In other words, in order for your AI system to make accurate predictions or decisions that reflect your company’s operations, it needs data to base those decisions off of. If there isn’t any data, the AI solution will either guess based on hallucinated or nonexistent data, rendering it unable to provide any meaningful insights. This should not prevent you from investing in AI, but it should inform your investment process. If you want AI, you need a way to collect and store data first. So how do you obtain it?

For fleet and logistics management, an asset tracking system is the easiest way to gain the operational data you need to properly feed and inform AI decision-making. These solutions  track and monitor all your important assets in real time, collecting and storing information about the location, movement, and condition of your assets. As you collect this data, it can be fed to the AI, which can then analyze the content and patterns in order to make predictions and strategic decisions that can help you improve your operations.

What Decisions Can AI Make?

Once you have data collected from an asset tracking system for your logistics AI, your AI can start making decisions for you. With all the decisions you have to make as a fleet manager, is it possible for AI to make all of them? Admittedly, the answer is no. But while it can’t necessarily make informed decisions about hiring or other areas where your base system can’t collect data, it is capable of making quite a few types of important decisions that can serve to streamline and improve your operations. Let’s look at some of them.

Demand Forecasting

Within logistics, AI can be used to identify complex patterns and correlations that traditional forecasting methods might miss. Since they are continuously learning from new data, AI systems enhance their predictive accuracy over time to generate dynamic forecasts and provide actionable insights for inventory management, production planning, and procurement. For instance, an AI-driven forecast might indicate a surge in demand for a particular product, prompting preemptive actions such as adjusting stock levels or ramping up production. 

Asset Utilization

When an asset tracking solution is being used to continually monitor asset location, AI can determine any underutilized assets. For example, if you have a lot full of trailers that are often dispatched for deliveries, the asset tracking system can be used to track each trailer’s movement both inside the yard and outside of it. Based on historical location and idle times, the AI can then determine if some trailers are being underutilized and alert fleet managers to make scheduling adjustments or to relocate those trailers to a location that needs them more.

Route Scheduling

AI in logistics can analyze various factors that influence routing, including traffic patterns, weather conditions, delivery windows, and vehicle capacities. By processing this data, AI systems can be used to dynamically generate optimal routes, ensuring timely deliveries while minimizing fuel consumption and operational costs. Integrating AI with an asset tracking system also provides real-time monitoring and allows for rapid adjustment of routes in response to changing road conditions due to factors such as congestion or closures. By continuously learning and adapting from both historical data and real-time feedback, AI systems improve their routing accuracy and efficiency over time. The result is a more reliable and cost-effective supply chain operation, enhancing overall service levels and customer satisfaction.

Risk Management

Implementing AI for risk management involves integration with supply chain management systems to ensure seamless data flow and analysis. Via this integration, AI can assess the probability and impact of various risks, enabling decision-makers to prioritize actions based on projected severity. For instance, if AI predicts a high likelihood of a problem at a critical port, supply chain managers can expedite shipments or preemptively divert them to alternative routes. Moreover, AI enhances transparency and visibility across the supply chain, facilitating better communication and coordination among stakeholders. By leveraging AI in fleet management, companies can create robust contingency plans, improve resilience, and maintain continuity even in the face of unexpected disruptions. This proactive approach to risk management leads to a more stable and reliable supply chain operation, ultimately safeguarding business performance and customer satisfaction.

Bottleneck Prevention

Bottlenecks can be caused by a variety of circumstances, often going unknown and unrecognized for extended periods of time. To this end, an asset tracking system can be used to track an asset’s movement throughout the supply chain and collect the data about where it is and when it’s idle. The AI solution can then use this data to monitor for any patterns that look like a bottleneck. Before the bottleneck delays operations significantly, the AI fleet management solution can alert the fleet manager of the problem while also suggesting various solutions to address it directly. Also, even on its own without AI, tracking the location of assets will significantly reduce the chance of a bottleneck forming in the first place. Bottlenecks are often caused by someone not having the asset or assets they need to complete a task. When assets are continually tracked and monitored, the need to search for them is greatly reduced or eliminated altogether, allowing processes to be completed without delays.

Inventory Management

AI revolutionizes inventory management in supply chain operations by providing sophisticated tools for demand forecasting and optimization. Machine learning algorithms analyze vast amounts of data about historical sales, market trends, and seasonal variations to predict future demand accurately. This allows businesses to maintain optimal inventory levels, avoiding both overstocking and stockouts. AI at the edge continuously learns from new data, improving predictive accuracy over time. The AI can further automate reorder processes by setting thresholds for inventory levels and triggering automatic restocking orders when levels fall below that point. AI also aids in identifying slow-moving or obsolete inventory, allowing companies to take corrective actions, such as running promotions or discounting prices, to clear excess stock. By providing actionable insights and automating routine tasks, AI enables more efficient inventory management, reducing holding costs, and enhancing customer satisfaction by ensuring product availability. 

Cost Reduction

Costs are automatically reduced as asset tracking and AI are used to prevent loss and enhance supply chain operations. When you eliminate lost assets, you spend less on replacement costs. You also increase uptime and reduce lost labor hours by preventing the need for workers to waste time searching. These labor hours can then be used to process and send out more deliveries, resulting in increased company earnings. AI can also be used to optimize transportation routes to minimize fuel consumption and delivery times, leading to lower logistics costs. 

Predictive Maintenance

By continuously monitoring the performance and condition of machinery through IoT devices, AI systems can analyze patterns and identify anomalies indicative of potential issues. This proactive approach allows for timely maintenance interventions, minimizing unplanned downtime and extending the lifespan of critical equipment. Implementing AI for predictive maintenance involves integrating AI tools with existing maintenance management systems and IoT infrastructure. These AI tools process data such as vibration analysis, temperature readings, and historical maintenance records to predict when a machine is likely to fail. AI can optimize maintenance schedules by prioritizing tasks based on the criticality and condition of equipment, ensuring that resources are allocated efficiently. This not only reduces maintenance costs but also enhances operational efficiency by preventing disruptions in the supply chain. Monitoring asset usage can also determine when a trailer requires mileage-based maintenance. When you schedule maintenance based on usage, you are optimizing time, money, and uptime. 

Will You Use AI To Make Smart Decisions?

AI has become an indispensable tool for fleet managers looking to enhance their logistics operations. By leveraging AI’s advanced data analytics and machine learning capabilities, managers can make informed decisions that significantly impact overall supply chain performance, cost efficiency, and customer satisfaction. AI aids in demand forecasting, asset utilization, and much more. The integration of AI with an asset tracking system like our AirFinder Everywhere ensures that decisions are based on real-time, accurate data, thus optimizing operations and preventing costly mistakes. As AI continues to evolve, its ability to provide actionable insights and automate routine tasks will further enhance supply chain resilience and efficiency. Investing in AI not only streamlines logistics processes but also provides a competitive edge by reducing operational costs and improving service levels. Ultimately, the strategic implementation of AI in supply chain operations enables businesses to stay agile, responsive, and customer-focused in an increasingly complex and demanding market environment. To learn more about the benefits of AI thanks to asset tracking data, book a demo with our team of experts.