Title: Navigating AI in Logistics for Industrial Markets: A Comprehensive Guide
Fabio Belloni, the Chief Growth Officer and co-founder of Quuppa, is a leading figure in advanced location technologies. When deploying real-time location systems (RTLS) in logistics, the abundance of data can initially feel overwhelming. With RTLS generating gigabytes of data daily, operators might struggle to derive value without leveraging artificial intelligence (AI).
Before RTLS, only scant information was available on asset movements in real-time at a granular level. Now, you have precise data on asset movements but might not know how to utilize it effectively. Enter AI in logistics – not for automation, but for gleaning insights from your process flow by delving into the data. Without AI, logistics may reap little value from RTLS-generated mountains of data.
To bypass this challenge, logistics operators should devise a strategy for collecting, training, and analyzing the new data. Here's how:
Establishing Models
The primary hurdle in implementing AI in industrial settings is determining how to utilize the data. After collecting and cleansing data from various sources, divide it into training, validation, and testing sets. The training set serves to teach the model to find patterns and relationships within the data. In logistics, for instance, a model could learn to forecast delays based on historical asset movements or suggest optimal temporary storage layouts based on real-time item flow patterns and resources.
The validation set fine-tunes the model, ensuring it generalizes well and doesn't excessively rely on the training data. Finally, the testing set evaluates the model's performance on unseen data, measuring the accuracy and providing insights that direct improvements.
Roadblocks to AI Implementation
Even with proper implementation steps, obstacles can arise:
- Unreliable or Untrustworthy Data Sources: Poor-quality data results in subpar model outputs. Clean, consistent, and reliable data are essential for delivering consistent and accurate insights.
- Lack of Expertise within the Workforce: If logistics facilities lack skilled personnel to manage and interpret AI models effectively, they may not fully utilize the tool's potential.
- Blind Faith in AI-Generated Analysis: Relying on AI indications without fully comprehending the reasoning behind them can result in flawed decisions.
To overcome these challenges, prioritize data quality, nurture internal AI expertise, and leverage explainable AI (XAI) to boost transparency and trust in AI-generated insights.
With these measures in place, logistics operators can transform raw data from RTLS into valuable insights, optimizing supply chains for improved efficiency and resilience in a competitive market. Join the Website Technology Council, an exclusive community for renowned CIOs, CTOs, and technology executives. Do you meet the criteria?
Fabio Belloni, with his expertise in advanced location technologies as the Chief Growth Officer and co-founder of Quuppa, could provide valuable insights on how to effectively utilize AI in logistics to derive value from RTLS-generated data. Despite having access to precise data on asset movements, logistics operators may still struggle without leveraging AI to glean insights from their process flow.
