Gülbahçe Mah. Gülbahçe Cad. İYTE Teknopark B2 Binası no:1/48 iç kapı no:15 Urla/izmir
Gülbahçe Mah. Gülbahçe Cad. İYTE Teknopark B2 Binası no:1/48 iç kapı no:15 Urla/izmir
MAGNETO AI Decision Support Systems are developed to quickly determine the most optimal production plan. It automatically responds to real-time changes in production, supply chain, stock, and warehouse areas, keeping production at an optimal level.
MECHANICA MAGNETO
MAGNETO’s advanced artificial intelligence model quickly responds to dynamic changes in the factory using deep learning algorithms designed to maximize production efficiency and minimize inefficiencies.
Günümüzde fabrikalar, hızlı üretim süreçlerinde yaşanan değişime ayak uydurabilmek ve olası darboğazları önceden tahmin etmek için hiç olmadığı kadar esnek ve çevik olmalıdır. MAGNETO tam bu noktada üretim alanlarının dijital ikizini oluşturarak fabrikalara bu yetkinlikleri kazandırmayı vaat eder. Fabrikaların stok masraflarını düşürmesine, kesin ve hızlı bir şekilde planlama yapmasına imkan tanır.
MAGNETO, ERP, MES ve IoT sistemlerinizden gelen üretim verilerine, iş merkezlerinin kapasitesine, zaman kaydına, malzemelere, personele, belirlenen öncelikli kısıtları referans alarak bir fabrikadaki görevlerin hangi sırayla tamamlanması gerektiğini söyler ve yönetir.
MAGNETO, talebi tahminler, talebe göre kapasiteyi planlar, kapasiteyi planlarken fabrikanın mevcut kısıtlarını referans alır, kaynakları planlar ve bunu senaryo bazlı optimize eder, özellikle kesikli üretim gerçekleştiren tesislerde alt yüklenici ve tedarikçilerden gelen verileri optimize ederek planlamayı gerçekleştirir. Oluşturduğu en optimum planı yayınlar ve mevcut otomasyon sistemini yönetir.
Determining the sequence of production orders, along with which machine and time slot they will be processed in, is a complex optimization problem. The purpose of this module is to organize the production flow in the most efficient sequence, taking all constraints into account. Job sequences are optimized by considering setup times, maintenance needs, operator availability, and process dependencies. As a result, unnecessary waiting in production lines is prevented, delivery dates are secured, and customer demands are responded to more quickly.
The primary objective of this module is to ensure the optimal utilization of available resources in the factory, including machinery, production lines, labor, and energy. In production processes, each machine or work center operates at different speeds and efficiency levels; some machines may create bottlenecks while others remain idle. This module balances capacity across the entire factory, enabling the early identification and resolution of bottleneck points. As a result, it contributes to both the accurate planning of investments and achieving higher production volumes with existing resources.
The most critical component of production continuity is the timely availability of raw materials and semi-finished goods in the correct quantities. The purpose of this module is to integrate material flow with production plans, preventing downtime caused by material shortages and reducing the cost of excess inventory. Dynamic material plans are created by considering supplier lead times, minimum stock levels, and production recipes (BOMs). As a result, production lines do not wait for materials, and capital tied up in inventory is minimized.
Customer orders often reach the production line with varying delivery dates, urgent priorities, or fluctuating quantities. The purpose of this module is to accurately analyze customer demand and plan orders according to both commercial priorities and production constraints. By forecasting demand fluctuations in advance, sudden capacity bottlenecks and overstock problems are prevented. Furthermore, dynamic planning is performed for different order types (standard, custom production, export, etc.), providing flexible solutions that enhance customer satisfaction.
In factories, production planning is often executed based on limited information, leading to significant discrepancies between planned and actual outcomes. Through the integration of IoT, SCADA, and MES, the objective is to enable real-time data from the shop floor to flow directly into the planning system, ensuring that plans remain dynamic, flexible, and constantly up to date. Thus, production automation not only manages machinery but also becomes an ecosystem directly fueled by planning decisions.
In the world of manufacturing, unexpected changes always occur: new customer orders, urgent jobs, machine breakdowns, or supply delays. The purpose of this module is to objectively demonstrate which plan offers the optimal solution by generating alternative scenarios, and to provide decision support to management. Different scenarios are compared in terms of cost, delivery time, and resource utilization. As a result, the enterprise becomes prepared for potential crises and selects the plan that ensures the highest customer satisfaction at the lowest cost.
Our AI-based solutions for optimizing industrial production processes make your business more efficient at a lower cost by developing more systematic and planned approaches.
