What is Capillary Distribution Software? Intelligent Distribution and Sales Management

Before addressing AI solutions, it is essential to look at the fundamental challenges of traditional routing methods:

• Computational Complexity: Imagine a company dealing with dozens of visitors and hundreds of customers in different areas of a large city. Manual planning or even using simple tools to find the optimal route (shortest, fastest, or cheapest) that covers all customers and considers all constraints is computationally almost impossible. This challenge grows exponentially with an increasing number of visitors and customers.
• Time and Human Resource Consumption: Planning routes manually is a very time-consuming process that takes up a significant portion of logistics managers' time. This time can be spent on more strategic tasks.
• Human Error: Humans are prone to error. Mistakes in calculating distances, estimating times, or overlooking small constraints can lead to significant deviations in planning.
• Lack of Flexibility: The real world is full of unforeseen events: sudden traffic, accidents, order cancellations, or urgent order requests from a new customer. Traditional systems cannot quickly adapt to these changes and require manual revisions, which are themselves time-consuming and inefficient.
• Ignoring Multiple Variables: An optimal route does not depend solely on distance. Other factors such as vehicle load capacity, visitor working hours, time constraints for delivery or customer meetings, customer prioritization, and even specific visitor skills (e.g., for selling specific products) must be considered simultaneously. Managing these variables simultaneously is difficult for humans.

Capillary distribution software, enhanced with AI capabilities, uses complex algorithms, Machine Learning, and optimization to solve the above challenges, instead of relying on guesswork or simple calculations. Below we will discuss the main mechanisms of this transformation:

1. Collecting and Analyzing Big and Multidimensional Data:

AI requires data. An intelligent routing system collects and analyzes vast amounts of data from various sources:

• Geographical and Traffic Data: Accurate maps, real-time traffic information from services like Google Maps or Waze, information on legal speeds on roads, restrictions on entry and exit to specific areas.
• Customer Data: Precise geographical location of each customer, their working hours and time preferences, volume and type of previous and current orders, sales history, strategic importance of the customer, special needs (e.g., need for a refrigerated vehicle).
• Fleet and Visitor Data: Number and type of available vehicles (vans, trucks, motorcycles), loading capacity of each vehicle, fuel consumption, working hours of visitors/drivers, skills and specializations of each visitor.
• Environmental Data: Weather conditions, special events (such as festivals or demonstrations that can block routes).

AI Example: Suppose AI is processing urban traffic data. Instead of just looking at average speed, the system can use Deep Learning models and analyze traffic patterns on similar days to predict what traffic will be like at a specific hour on a specific street and incorporate this prediction into travel time calculations, even if real-time traffic has not yet peaked.

2. Advanced Optimization Algorithms:

The core of AI in routing is complex optimization algorithms such as Genetic Algorithms, Particle Swarm Optimization, or mathematical programming methods. These algorithms can evaluate millions of possible route combinations in a fraction of a second and choose the best one.

• Solving the Traveling Salesman Problem (TSP): This is one of the classic problems in computer science, aiming to find the shortest possible route where a "salesman" visits each "city" (customer) exactly once and returns to the starting city. AI solves more complex versions of this problem by considering more variables.
• Vehicle Routing Problem (VRP): This is an extended TSP problem where multiple vehicles with different capacities and from one or more depots are used to serve a group of customers with varying demands. AI, by considering all these factors, performs the optimal allocation of vehicles to routes and the sequence of visits.

AI Example: The AI system can use genetic algorithms to create a set of "generations" of potential routes. Then, each generation is "scored" based on criteria such as shortest time, shortest distance, or lowest cost. The best routes are "selected," "mutated," and "combined" to create new generations of better routes. This process is repeated millions of times to finally reach the closest possible optimal state.

3. Machine Learning for Prediction and Continuous Improvement:

AI systems do not only make decisions based on existing data; they learn from the past and improve themselves.

• More Accurate Traffic Prediction: By analyzing past traffic data at different times and days, AI can identify traffic patterns and provide more accurate predictions of which route will be busy at what time.
• Improved Visit Time Estimation: By monitoring the actual time spent on each visit in the past, the system can more accurately estimate the time required for future visits and incorporate this data into subsequent planning.
• Identifying Inefficiency Patterns: AI can identify patterns in visitor or route performance that lead to inefficiencies and provide suggestions for improvement.

AI Example: Suppose AI notices that visitor X in area A always spends 15 minutes more than the average for each visit. The system can in subsequent planning allocate more time for his visits in that area or even provide suggestions for training or changing visitor X's approach. Also, if an area consistently experiences unexpected traffic, the system can learn this pattern and suggest alternative routes in the future.

4. Real-time Dynamic Optimization:

One of the biggest advantages of AI, is its ability to react quickly to changes.

• Reacting to Traffic Changes: If unexpected traffic occurs on a route, the system automatically suggests an alternative route.
• Managing New or Canceled Orders: With the addition of an urgent order or the cancellation of a visit, AI quickly reviews the remaining routes and provides the optimal plan to cause minimal disruption.
• Fleet Problems: In case of a vehicle breakdown or driver delay, the system can adjust the routes of other visitors to ensure all customers are still covered.

AI Example: A visitor is driving to their third customer when the system suddenly signals that the main road is blocked. At that moment, AI analyzes the traffic situation and other visitors, suggests an alternative route to the visitor, and if necessary, adjusts the sequence of his subsequent visits and even those of other visitors in that area, so that the entire system continues to operate with minimal delay and cost.

Artificial Intelligence in capillary distribution software is no longer a futuristic concept; it is a practical and powerful tool that can revolutionize your business today. By leveraging the unparalleled capabilities of AI, you can not only save time, fuel, and costs but also increase customer satisfaction by providing better and more accurate services, and ultimately achieve greater profitability. This technology paves the way for a smart, efficient, and flexible capillary distribution and sales system.

Are you ready to transform your capillary distribution business with the power of AI and reap its competitive benefits? To learn more and experience the intelligent power of Ase Cloudify's capillary distribution, developed with the latest AI technologies, visit our landing page today and request a free demo of our capillary distribution software! Experience the future of capillary distribution today with Ase Cloudify!