Distribution Management Customer Service App

AI Quick Summary

DMCS is an AI-driven assistant designed to enhance passenger experience during travel disruptions. Integrated into SBB’s mobile app, it provides real-time notifications about delays and offers personalized alternative public transport routes to minimize inconvenience. A built-in AI chatbot assists with common disruption-related questions, reducing the burden on customer service, especially during external events like weather disruptions or accidents. DMCS ensures proactive communication, smarter travel decisions, and improved service efficiency, keeping passengers informed and stress-free while optimizing SBB’s support operations. Execution Plan:To make public transportation smoother and less stressful during disruptions, we’re building an AI-powered travel assistant that will send real-time notifications to passengers when delays happen, along with personalized alternative route suggestions. The goal is to create a simple, easy-to-use tool that helps people stay informed without adding extra work for SBB’s team. Over the next three months, we’ll design, test, and refine a minimum viable product (MVP) that fits seamlessly into SBB’s existing mobile app.Month 1: Research & SetupThe first step is to figure out exactly what’s needed and how this assistant can best work within SBB’s current system. We’ll start by analyzing publicly available train schedules and past disruption data to understand patterns and train the AI to recognize potential delays. Since external factors like weather and big events also impact travel, we’ll include that data too.At this stage, we’ll also have initial discussions with SBB’s digital team to make sure the assistant is compatible with their app and notification system. The key here is to keep things simple, no deep access to internal data, just using existing public sources in a smart way.Month 2: Building & Testing the AI ModelNow comes the fun part, actually building the AI system. The assistant will be trained to predict disruptions based on live transit patterns and notify passengers before they get stuck waiting. When a delay happens, it will suggest an alternative public or shared transport solution, helping people get to their destination with minimal stress.A chat-based AI assistant will also be included, handling common disruption-related questions. This means that when there’s a major delay due to bad weather or an accident, passengers won’t have to overload customer service with calls, they can just ask the AI for help.By the end of this phase, we’ll have a basic working version running in a test environment, ready for real-world trials.Month 3: Testing, Feedback & RefinementsThe final month is all about fine-tuning the assistant to make sure it’s actually useful. A small group of test users, such as SBB employees or selected passengers, will try out the system in real travel situations. We’ll gather feedback to see if the notifications are clear, the alternative routes make sense, and the chat assistant actually helps.From there, we’ll make improvements, adjusting the AI so it gives better recommendations and making sure notifications are timely but not annoying. By the end of the three months, SBB will have a fully functional MVP that’s been tested in real-world scenarios, along with a roadmap for future improvements and scaling.What SBB GetsAt the end of the project, SBB will have a working AI travel assistant that helps passengers deal with delays efficiently without requiring major internal data access. The solution is designed to be low-maintenance for SBB, while giving customers a stress-free, more predictable travel experience. It’s a win-win—passengers stay informed, and SBB’s customer service team can focus on more complex issues instead of handling the same disruption-related questions over and over again.By keeping things practical and straightforward, this project delivers something useful, scalable, and easy to integrate, a smart upgrade for modern mobility.

Project Idea Metadata

Project Idea Name: Distribution Management Customer Service App
Date: 3/16/2025 9:21:09 PM
Administrators:
- Anna Chrobak

Project Idea Description

Execution Plan:

To make public transportation smoother and less stressful during disruptions, we’re building an AI-powered travel assistant that will send real-time notifications to passengers when delays happen, along with personalized alternative route suggestions. The goal is to create a simple, easy-to-use tool that helps people stay informed without adding extra work for SBB’s team. Over the next three months, we’ll design, test, and refine a minimum viable product (MVP) that fits seamlessly into SBB’s existing mobile app.

Month 1: Research & Setup

The first step is to figure out exactly what’s needed and how this assistant can best work within SBB’s current system. We’ll start by analyzing publicly available train schedules and past disruption data to understand patterns and train the AI to recognize potential delays. Since external factors like weather and big events also impact travel, we’ll include that data too.

At this stage, we’ll also have initial discussions with SBB’s digital team to make sure the assistant is compatible with their app and notification system. The key here is to keep things simple, no deep access to internal data, just using existing public sources in a smart way.

Month 2: Building & Testing the AI Model

Now comes the fun part, actually building the AI system. The assistant will be trained to predict disruptions based on live transit patterns and notify passengers before they get stuck waiting. When a delay happens, it will suggest an alternative public or shared transport solution, helping people get to their destination with minimal stress.

A chat-based AI assistant will also be included, handling common disruption-related questions. This means that when there’s a major delay due to bad weather or an accident, passengers won’t have to overload customer service with calls, they can just ask the AI for help.

By the end of this phase, we’ll have a basic working version running in a test environment, ready for real-world trials.

Month 3: Testing, Feedback & Refinements

The final month is all about fine-tuning the assistant to make sure it’s actually useful. A small group of test users, such as SBB employees or selected passengers, will try out the system in real travel situations. We’ll gather feedback to see if the notifications are clear, the alternative routes make sense, and the chat assistant actually helps.

From there, we’ll make improvements, adjusting the AI so it gives better recommendations and making sure notifications are timely but not annoying. By the end of the three months, SBB will have a fully functional MVP that’s been tested in real-world scenarios, along with a roadmap for future improvements and scaling.

What SBB Gets

At the end of the project, SBB will have a working AI travel assistant that helps passengers deal with delays efficiently without requiring major internal data access. The solution is designed to be low-maintenance for SBB, while giving customers a stress-free, more predictable travel experience. It’s a win-win—passengers stay informed, and SBB’s customer service team can focus on more complex issues instead of handling the same disruption-related questions over and over again.

By keeping things practical and straightforward, this project delivers something useful, scalable, and easy to integrate, a smart upgrade for modern mobility.

DMCS is an AI-driven assistant designed to enhance passenger experience during travel disruptions. Integrated into SBB’s mobile app, it provides real-time notifications about delays and offers personalized alternative public transport routes to minimize inconvenience. A built-in AI chatbot assists with common disruption-related questions, reducing the burden on customer service, especially during external events like weather disruptions or accidents. DMCS ensures proactive communication, smarter travel decisions, and improved service efficiency, keeping passengers informed and stress-free while optimizing SBB’s support operations.