In the Pipes and Fittings segment, Grupo Tigre has approximately 4,500 products, most of them injection- or extrusion-molded. With such a high number of SKUs, production scheduling was a task that consumed a great deal of time and energy. The injection/extrusion scenario involves many variables that are complex to manage without the proper software, such as alternative molds, labor constraints, changeover team (setup) constraints, and analysis of raw materials and components.

With so many variables in such a dynamic environment for PPCP, Tigre and NEO implemented Preactor AS (Advanced Scheduling), targeting the main objectives: reducing PPCP effort, reducing work-in-process inventory, optimizing setups, and dynamic materials analysis.

The implementation was carried out for the manufacturing units that work specifically on Pipes and Fittings production. The scheduling process is run weekly, and as soon as the data is loaded into Preactor, the planned production orders are generated — exploding all levels of the bill of materials and already accounting for semi-finished product inventory.

Based on these orders, a raw materials analysis is performed, highlighting which inputs are partially and/or fully out of stock. The scheduling takes into account all the constraints present in the plants, such as the amount of labor, the number of changeovers (setups), and molds. Due to the complexity of the machine and mold combinations, Tigre has a custom scheduling rule that, in addition to optimizing mold changeovers, balances resources for a uniform loading of the machines.

Tigre increased its production scheduling adherence by 48 p.p.

Before the project, production orders were created manually; today they are created automatically in the SAP ERP, which represents a significant gain in reducing scheduling effort. Supply management also benefited, as it now receives weekly the shortage status of each input along with its priorities for follow-up with suppliers.

Through the custom scheduling rule, Opcenter Preactor groups orders with the same characteristics, minimizing changeovers (setups) and increasing machine productivity, always respecting shop-floor constraints so that the proposed scenario is feasible. These aspects brought a considerable increase in production adherence (planned vs. actual), from 47% to 95%.

The custom reports provide many views for analysis and decision-making support — such as the loading of each machine per shift, the labor load, and setup times. With a better overview of the machines, together with more optimized order generation and other internal actions, the company managed to reduce its inventory levels by volumes close to 50%.