Jenova MOM

It all started about 5 years ago, where a business opportunity for a potential MAIDOT client, the company Natstone, wanted a platform to manage its industrial process.

After an exhaustive analysis of the entire business, the problems that were intended to be solved and what would have to be done, a conclusion was reached: quite complete and specific platforms already existed for the stone industry, so that we are developing something of root, did not compensate.

However, during all the analysis, something was left in the air as a possible opportunity: the budgeting calculation and consequent transition to production, was something done in Excel sheets and that this specific platform for the industry did not cover.

Conversations were started with several other factories, from different sectors such as blinds, doors, windows, glass, leather, wood, iron, among others. Among other complaints that existed on the part of workers on the shop floor, directors and administrative staff, some in common were: difficulty/complexity in using existing platforms, too much time invested in budgeting and production planning and lack of historical data for business analysis.

After these conclusions, before considering moving forward with a new project, dozens of platforms already on the market were tested. The conclusions in view of the difficulties encountered in the factories were as follows:

• In simple and cheap platforms, these only serve as a database but few or no automatisms offer
• In more complex and expensive platforms, they have a large learning curve and investments that are not bearable by most SMEs

And it was in this way, after a year and a half of study, that Pedro Lima, founder of MAIDOT, decided to move forward with a new project of his own that responded to the needs detected in the market, leading to a process of 3 years of development until now.

But how does the Jenova platform work in practice?

This digital tool can work as a platform for virtually all processes within a factory, such as budgeting, stock management, production, human resources, orders, sales, etc. but also, for those who prefer to maintain the use of existing tools, it can work together with those existing platforms and software, just “drinking” the information contained in them.

This is because the learning and automation processes using different artificial intelligence techniques will then take all the data and prepare various interpretations and recommendations.

By way of example, here are some practical cases for a real customer, the company Cxestores which, as the name implies, produces blinds:

• Assuming a scenario in which a blind for a customer with a 10Nm electric motor is being produced and none of these motors are in stock, but a 12Nm motor has been in stock for 5 months.
  • The solution in this case is for the AI ​​system to suggest that, instead of ordering a specific engine for this order, simply use what is in stock because, although it is a little more powerful than necessary, that is how you move forward. with production more quickly and dead stock is avoided.
• In another scenario where there is a planned production order that the customer has not paid anything yet, and a new production order that has just entered but the customer has already paid in advance.
  • The solution for this case may simply be to have the AI ​​system recommend that in the production orders (visually displayed in a Gantt chart) the new production order be placed in front of the previous one, since the previously planned order is not yet complete. paid (of course, in this scenario, other parameters are taken into account, such as the customer’s seniority, payment agreements, etc.)
• Finally, in one more example, in the case where there is a waste above normal in a certain section
  • The solution can be something together between obtaining information from the machines or IoT devices, and the system will also be able to compare that section with others, analyze the human resources that are in those sections and eventually even suggest some training in case any of the resources are being used. cause delays and/or greater waste due to lack of knowledge, which could simply be a lack of maintenance on the machine (e.g. a saw that is no longer cutting well, a gauge that is out of calibration, etc.)

A SaaS version (Software as a Service) is also being developed, in order to allow for a much faster growth and a much greater scalability of the project.

For more information about this platform, visit jenovamom.com

Category: Inteligência Artificial & Notícias & Novidades

Tags:

Looking for something like this? Talk with us!