mIQ Project
Develop automated project ideas by analyzing production, maintenance, and energy data. mIQ Project creates a 6–12 month roadmap for your factory with AI-powered recommendations and ROI calculations.
Discover AI-Powered Project Opportunities with MES Data
The user can either use integrated MES data or load an external data set (e.g., a CSV report from another system) to provide data to the module. The system analyzes the factory's performance from different angles by running artificial intelligence algorithms on this data and lists potential improvement or investment opportunities. For example: “Station 5 frequently malfunctions, presenting an opportunity for a Predictive Maintenance project” or “Energy consumption has increased over the past year, making an energy optimization project appropriate.”
Prioritization with ROI Calculation
An ROI (Return on Investment) or expected profit is calculated for each proposal. For example: “Invest in automation for Machine X, and you will save 100,000 TL annually; the payback period for the investment is 1.5 years.” These calculations include the costs and benefits of the proposal. The system takes into account parameters such as downtime cost, labor cost, and energy price in the ROI analysis and applies a practical financial model. This allows the user to compare projects based on concrete numerical returns rather than intuition.
Benchmarking Between Different Locations
If the company has multiple factories or production lines, mIQ Project identifies areas for improvement by comparing them. For example, if OEE is 85% at Factory A and 75% at Factory B, it can suggest projects such as training programs or machine upgrades to help B reach A's level. This feature facilitates the spread of best practices by benchmarking units performing the same tasks.
AI-Supported Roadmap (6–12 Month Plan)
The module not only suggests individual projects but also presents them from a calendar perspective. In other words, it transforms them into a roadmap that considers resource constraints to determine which projects should be done first and which later. For example, maintenance improvements could be proposed for the first 3 months, followed by a quality control project for the next 6 months. This planning prioritizes projects based on their impact and urgency. This allows the user to see holistically which projects will be implemented in the coming year.
Predictive Maintenance and Quality Control Projects with Ready-Made Templates
Certain project types are quite common in production (e.g., Predictive Maintenance, automated visual quality control). mIQ Project offers ready-made project templates in these areas. For example, it summarizes the data types required for Predictive Maintenance, the algorithms that can be used, and success stories from around the world. If data analysis indicates machine failures as a critical issue, the system suggests: “Predictive Maintenance Project: Develop a model to predict failures by analyzing machine failure records.” Similarly, if there is a high error rate, the “Visual Quality Control Project: Detect defective products using cameras and artificial intelligence” template is recommended.
Project Proposals Focused on Energy and Production Costs
The mIQ Project identifies efficiency opportunities by analyzing energy consumption and production costs. In the energy sector, suggestions such as “Boiler efficiency: Reducing fuel consumption” or “Feasibility of solar energy investment” may arise. For production costs, options such as “Waste reduction project” or “Material optimization” may be offered. A concrete example: When energy data was examined, it was found that some machines consumed high amounts of energy while idle. In this case, the system suggests “Machine standby modes project: Integrating automatic shutdown systems to reduce idle energy consumption.”
Quick Analysis (From Data to Insight in Minutes)
The key difference of the mIQ Project is its ability to extract meaningful project ideas from raw data in a very short time. Insights that traditional data analysts would take days to uncover, the mIQ Project delivers in minutes. This is achieved through optimized algorithms and ready-made model libraries. For example, when a user uploads a dataset, the system automatically performs outlier analysis, trend analysis, and correlation analysis; lists the findings in a report, and generates project ideas from them.
Identifying Bottlenecks with MES Reports
The mIQ Project examines critical KPIs in existing MES reports to identify bottlenecks in production. For example, if a line's OEE is consistently low, this is a bottleneck; the system highlights this and suggests a “Line B capacity is insufficient, OEE is low – capacity increase project.” If a product has high quality fluctuations, a “Process stabilization project” is recommended. Problems in the factory may already be known; however, mIQ Project clarifies them based on data and provides a strong basis for prioritization.
Calculating Investment Priorities with Artificial Intelligence
The system collects all project opportunities and generates a priority score based on multiple criteria such as ROI, strategic alignment, and feasibility. This allows the user to be guided, for example, “Focus on Project A first; the highest return is here.” Artificial intelligence may assign a score of 85 to Project A and 60 to Project B. This score is based on an ML model or a set of rules. The goal is to simplify the decision-making process for management and clarify which projects should be prioritized.
Identifying Opportunities from MES + PMS Data
This module analyzes data by integrating production (MES) and energy (PMS) data. For example, if the unit energy cost of a product is high, the system suggests a process improvement project for that product. When maintenance (MMS) data is also included, projects can be developed that provide both maintenance and energy savings for machines with high downtime and energy consumption. Thus, data is analyzed in an integrated manner rather than in silos, revealing opportunities at the intersection points.