Artificial intelligence is increasingly making its way into industrial use cases, and the supply chain is no exception. AI produces powerful insights, but it relies on high volumes of high-quality data to do so. Without reliable data, an AI system will fall prey to biases or not recognize enough situations.
Supply chains are a means of achieving a competitive edge. Research shows that 79% of companies with high-performing supply chains achieve higher growth than their industry’s average rate. AI is, therefore, becoming an indispensable part of the supply chain.
The primary sources of data in supply chains are data loggers. These humble devices are attached to shipments and track a wide range of conditions. The data that these devices feed eventually find their way into analytics packages powered by AI.
Let’s take a deeper look at exactly how data loggers enable AI to produce insights that are changing the way supply chains operate.
Demand Forecasting
Manufacturers have always had to deal with shifting demand. Seasonality is built into every product, but market events and consumer trends have routinely disrupted carefully planned manufacturing schedules. Demand forecasting also involves manufacturers planning sales campaigns ahead of time and ramping up production to match potential increases in consumer demand.
In short, it’s an intricate process that requires AI input. Thanks to AI’s ability to quickly process large volumes of data and generate insights, prioritizing high-quality data feeds to analytics packages is a no-brainer.
Data loggers are involved in every step of the supply chain. Whether it’s raw material delivery tracking, warehouse storage monitoring, in-transit shipping, or storage monitoring at retail locations, data loggers provide manufacturers input into how their goods are faring and what they can expect from their supply chain partners.
For instance, before increasing production capacity, manufacturers have to take raw material supplier performance into account. Data logging reveals which suppliers can perform under pressure, as measured by the integrity of the goods they delivered in the past.
When fed to an AI platform, manufacturers can account for external factors such as weather conditions or fleet conditions affecting performance. Irrespective of their past performance, a lack of critical infrastructure can doom manufacturing plans. AI helps companies identify these situations and mitigate them, thereby avoiding losses.
Data loggers thus provide AI with raw data of the entire supply chain, which helps companies create more efficient processes.
Product Integrity
Manufacturers can produce the highest quality goods, but if these products arrive broken or in poor condition at a consumer’s doorstep, all their work is of no use. Product integrity matters the most in food, health, and precision electronics, where goods have to be transported and stored in a tight range of conditions.
Data loggers play a massive role in ensuring products arrive in safe conditions. Attached to shipments, today’s better data loggers track all kinds of conditions such as humidity, shock, light, and temperature and feed these data to powerful analytics platforms on the cloud.
These platforms have in-built thresholds that can detect and alert relevant users when they’re violated. The degree of connectivity is deep enough to allow a supply chain manager to contact the driver or transporter and alert them of threshold violation. Risk mitigation is therefore simplified.
AI uses all of these data to project third-party logistics carrier performance and also helps prescribe critical infrastructure that will be required in transit and at delivery sites. Pharmacy deliveries are a good example. Medicines that have been transported in carefully monitored conditions have to be stored in equivalent conditions for them to be effective. Many medicines have expiry dates, which makes storage and product integrity at delivery critical.
AI helps manufacturers and pharmacies account for critical infrastructure throughout the supply chain, thanks to the wealth of data that loggers provide them with.
Route Optimization
The shortest route between two places is always the best, right? When dealing with international logistics, this is not the case. Geopolitical conflicts, changing weather patterns, complex customs duty rules, and customs infrastructure at transit and delivery ports ensure that route planning is a challenging job.
Figuring out optimal route conditions is a job that a human being would find challenging, especially at scale and in a dynamic work environment where conditions keep changing. Logistics companies deal with thousands of shipments every day, and executing route planning manually is an impossible task. AI algorithms are proving to be valuable solutions to this problem. These algorithms figure out the best routes by analyzing data provided by loggers at all locations. For instance, a shipment in a customs shed that veered dangerously close to its condition threshold indicates infrastructure that can potentially compromise product integrity.
The quality of last-mile delivery options also plays a major role in figuring out the most optimal supply routes. For instance, a shipment might reach a destination quickly but incur damage due to improper last-mile handling. Choosing a slower route that involves more reliable last-mile options is the better option.
Condition-related data fed into AI systems are critical to the process. The millions of data points they transmit require powerful computing ability to make sense of. Best of all, AI can dive deep into these data to unearth trends. For instance, a particular supplier might be showing signs of greater inefficiency, which can be nipped in the bud.
Simple, Yet Effective
Data loggers are simple pieces of technology compared to the AI systems they feed. However, without accurate data logging, a powerful AI system is rendered ineffective. As with everything else, it’s the smallest of components that ensures the entire system functions smoothly.
Comments are closed.