No ratings yet.

Today I will share my personal experience about implementing Generative AI for a company I worked for. During my time at the company, I was part of the IT department Solution Team. The marketing department was short on staff and struggled with writing product descriptions for our eCommerce web shop. As a result, the information on our web shop was outdated and new products did not even have product description. Since this could have a negative impact on sales, the marketing manager asked us to find a solution for the problem.

I immediately thought about using a ChatGPT API to automatically generate product descriptions. First, I made a prompt for ChatGPT to test if the AI could generate a ‘good’ product description. The output was evaluated by the Marketing and after some changes the prompt was usable. The second step was to set up a flow in the Enterprise Service Bus (ESB) to make a GET-call with the prompt to the ChatGPT API. The next step was to build a product description generator tool. The prompt needed to be variable and therefore I made a Google Form page, where marketing employees could fill in some variable values in the prompt. For instance, could say like: highlight the sustainable advantages of the product. I also integrated all the Masterdata information about the product in the prompt,  matching each product with its relevant information using unique article numbers. Employees have access to the tool page in the Customer Relationship Management (CRM) portal. The tool page allowed them to simply generate product descriptions in a short time period. The marketing employees only had to fill out the Google Form and hit the action button to receive the desired product description in four languages.

The end result? The process of writing product description was less time consuming and the marketing employees were very pleased with the solution. The production descriptions were not perfect, but after some minor changes they were ready to be put on our eCommerce web shop. In conclusion, the solution made the process less labour intensive, especially since we were short on staff. Every product now has a product description and the outdated product descriptions were renewed. 

5/5 (1)

Today I will share my personal experience about implementing Generative AI for a company I have worked for. The maintenance industry can be split up in different categories. In this blog I will focus on: Preventive, Reactive, and Predictive Maintenance. Preventive maintenance is the regular maintenance a building or house needs in order to prevent bigger issues. Preventive maintenance follows a yearly planning. For instance, restoring roofs or replacing central heating boilers. Most of the time this type of maintenance is done for multiple houses at once. Reactive maintenance happens when unexpected failures occur. These events are random and therefore the contractors are unable to plan this type of maintenance. Examples are a smashed window, leaking taps or a door lock that is not functioning anymore. A newer concept of maintenance is predictive maintenance and data-driven decision making. Predictive maintenance is data-driven decision making based on analyzing repairings in certain environmental conditions. The goal is to anticipate on certain issues before they become a problem.

Why is reactive maintenance inefficient?

Reactive maintenance is a significant challenge for social housing corporations, because of the randomness. Reactive maintenance cannot be scheduled or prepared for in advance, making it inefficient for contractors. In addition, the corporations struggled with taking in reactive maintenance repair requests. Tenants can request reactive maintenance by contacting the housing corporation. However, it is quite hard to get a complete technical problem description from the tenants, which resulted in unclear repair requests for the contractors. Moreover, the process of scheduling appointments with contractors was inefficient, since the corporations had no direct access to the contractors’ agenda. As a result, the tenants were called multiple times to resolve the problem, which had a negative impact on customer satisfaction.

Intake Module for Reactive Maintenance

In order to make the process more efficient, the company I worked for developed a software module for the intake of reactive maintenance requests. The Service & Repair Intake module uses a decision tree to help the employees with the technical description for the contractor. The employee is guided  through a series of questions, ensuring a complete problem description, even without having a technical background. In addition, the service employees can directly schedule the appointment, because the agenda of the contractors are integrated in the intake module. The software solution led to an improvement of the One-Call-Does-It-All KPI: Renters only have to make one call to report the problem and schedule the repair appointment. The First-Time-Fix KPI also improved significantly, because the contractors could now resolve the issue during the first appointment due to the more complete repair description. In addition the customer satisfaction also improved significantly, because the planning of repairs was more organized than before.

Data-Driven Maintenance Predictions

Over time the company collected an extensive amount of reactive maintenance data. Reactive maintenance is unpredictable and therefore relatively more expensive compared to other types of maintenance. In an ideal world, reactive maintenance would be eliminated entirely and replaced by preventive or predictive maintenance. However, it is almost impossible to predict when a tenant breaks a window by accident, but there were some reactive maintenance repairs that were easier to predict. For instance sewer-related issues. Most of the time a street uses the same main sewer pipe, so problems can relate to multiple houses. By using machine learning algorithms and clustering techniques, the company I worked for analyzed historical and real-time data to identify patterns and peaks in specific areas. The prediction model uses the cluster analysis to identify maintenance patterns based on the historical data from the intake module. As a result, the algorithm can make a prediction about the development of the issue over time. The valuable insights are shared with the social corporations and they have to decide to stick with the reactive approach or opt for preventive maintenance for the entire area, because addressing an entire street’s issues at once is more efficient and cost-effective than dealing with individual complaints over several weeks.

The adoption of Generative AI 

The process of sharing the signals and predictions to corporations was quite labor intensive and costly. The module is used to manage more than 150.000 social houses throughout the Netherlands. My idea to automate the process was to implement ChatGPT. I set up a prompt with a reporting template that is filled with the data-output from our model, including the signal, corporation details, maintenance prediction, and the costs and benefits. Whenever our model gave a signal, ChatGPT would generate the report instantly. The reports are automatically sent to the appropriate corporation through our Enterprise Service Bus. The integration of ChatGPT saved me 2 full working days a week and this was a significant cost-reduction for the company.

If I look back at my journey, I really enjoyed working with Generative AI especially when I saw the results. I got excited about implementing generative AI in other business areas of the company. I started straight with brainstorming about the adoption of Generative AI in other processes. As the technology will continue to advance, I expect the technology to have more possibilities for several inefficient business processes of social housing corporations.