In the last article, we have listed a series of behaviors that we expect AI solutions to demonstrate. Now, let us look at an AI solution and see if we can uncover these aspects in the solution.
In today’s fast-paced world, the last mile of delivery often becomes the most challenging and critical stage in the supply chain. It is the final stretch that determines whether a package arrives on time, meeting customer expectations.
To address this challenge, Last Mile Delivery Prediction/Optimization has emerged as a game-changing solution. Last Mile Delivery Prediction/Optimization utilizes the power of Artificial Intelligence (AI) to revolutionize the logistics industry. By leveraging advanced algorithms and data analytics, this AI-driven solution aims to accurately predict delivery times, optimize routes, and allocate resources efficiently. The goal is to enhance customer satisfaction, improve operational efficiency, and reduce costs by minimizing delivery delays and maximizing the utilization of delivery fleets.
To determine if an AI solution like Last Mile Delivery Prediction/Optimization qualifies as a proper AI solution, we can evaluate whether it exhibits the behavioral traits mentioned in the previous article. Here’s how we can assess it: (NOTE: These questions are for the specific use case of Last Mile Delivery.)
- Adaptability: Does the solution learn from historical data and adapt its delivery predictions and optimization strategies based on changing conditions, such as traffic congestion or weather patterns?
- Communication: Can it understand natural language instructions or queries related to last mile delivery, and can it effectively communicate with relevant stakeholders?
- Credibility: Will it provide confidence levels or reliability indicators for its delivery predictions and optimization recommendations?
- Decision-making: Can it evaluate various factors (e.g., package size, delivery distance, traffic conditions) to make informed decisions on optimal routing, resource allocation, and delivery schedules?
- Interpretation: Does the solution understand and interpret different forms of data relevant to last mile delivery, such as addresses, maps, or delivery time windows?
- Learnability: Is it possible to train the solution using expert knowledge and domain-specific data, allowing it to improve its accuracy and performance over time?
- Memorization: Does it capture and store knowledge about past delivery patterns, customer preferences, and optimization strategies to improve future predictions and decision-making?
- Pattern recognition: Will it analyze data to identify patterns and relationships related to last mile delivery, such as customer locations, traffic patterns, or delivery timeframes?
- Problem-solving: Will it plan and execute a sequence of actions to optimize last mile delivery routes, schedules, and resource allocations to meet desired goals, such as minimizing delivery time or cost?
- Rationality: Does the solution employ logical reasoning or statistical analysis to infer insights, optimize delivery routes, and improve the efficiency of last mile operations?
- Scalability: Is the solution capable of handling large volumes of data from various sources and formats, accommodating the diverse information associated with last mile delivery?
- Sensing: Will it utilize sensor data or real-time inputs (e.g., GPS data, traffic updates) to interpret and respond to changing conditions during last mile delivery?
By assessing whether the Last Mile Delivery Prediction/Optimization solution demonstrates these behavioral traits, we can determine if it aligns with the characteristics expected from an AI solution. If the solution exhibits a substantial number of these behaviors, it can be considered a proper AI solution for last mile delivery prediction and optimization.
We can apply a similar set of questions for any solution that is presented as an “AI Solution” and verify for ourselves whether it is really an AI solution. Obviously the quesitons would vary by the type of use case we are attempting to solve and the domain for which the use case is intended.
In addition to these behavioral traits, there are other elements that we need to consider for a solution to be called an AI solution. In the next article we will make a checklist of items that we can use to determine if a solution qualifies to be called “AI”.
About the author
Gopalakrishna Kuppuswamy is dedicated to driving innovative solutions around AI and Decision Intelligence at Cognida.ai.
