docker build configuration: docker build -t my-html-app .

docker run configuration: docker run -d -p 8080:80 my-html-app

Entrypoint: http://127.0.0.1:8080/mac-book-air-5.html

A large multinational company has a huge amount of data, which is not always structured in a simple and organized way. This is often due to the inherent challenges of managing such a diverse and dynamic set of information, which is typical for companies with multiple plants and countries.

Scenario:

Data:

The master data is distributed across four different ERP systems: • Customer data • Supplier data • List of raw materials • List of finished products • Order list • List of shipments • List of accounts • Stocks • Customer Experience Index (PPM, used to measure the level of failure per million units produced for a particular customer. Typically provided by the customer, but sometimes intercepted by the company) • OTIF (on time and in full) • Number of new codes provided to each client (development/innovation rate)

Due to the constant expansion and development of the company, this data is stored on various IT platforms created during the growth and evolution of the company. The available data is not updated in real time and can sometimes be inconsistent between the different systems on which it is distributed, resulting in decisions that are not always timely or accurate.

The main activity of the company is related to the purchase of raw materials and their transformation into finished products, adapted to customer requirements. Manufacturing is done to order, while raw materials are standard and managed in inventory.

Problem to be solved:

While purchases made by a company's large customers can be monitored and analyzed with greater accuracy, there is no such detailed analysis for smaller customers. Although small customers generate less turnover, they are significantly more numerous than large customers and are therefore critical to the overall financial health of a company. Currently, monitoring is carried out subjectively. In addition, there is no system that detects delays in receiving delivery schedules.

Using historical company information, rumors and opinions, and relevant information from the web, how can you use generative intelligence to identify early signs of customer churn or significant decline in value? You are tasked with developing a specific and actionable concept through a solution sandbox to help the company anticipate customer needs and prevent them

going to competitors.

Task:

• The solution must be implemented as a sandbox. • It should be able to collect data from various datamarts and ERP systems. • It should provide analytical output. • It should offer multiple solutions tailored to the specific type of customer being analyzed.

Requirements that the solution must cope with:

• Why does the client choose a competitor? • What can be done to improve customer loyalty? • How can you anticipate customer dissatisfaction? • How much time should be devoted to each client?