Operational optimization is not an option, but a constant necessity for any industrial company seeking to be more competitive and profitable. According to a Deloitte report, 35% of operational costs in the industry stem from process inefficiencies. Factors such as rising energy costs, the pressure to meet delivery deadlines, and the need to maximize productivity have led organizations to rethink how to improve their production processes.
The implementation of technologies such as AI in predictive maintenance or digital twins to simulate and optimize infrastructures allows companies to identify areas for improvement and make faster and more accurate decisions. Facing these challenges, optimizing production processes is key to transforming operational efficiency into tangible results.
What is production process optimization?
Industrial production processes are the set of structured operations and tasks that transform raw materials into final products. Optimizing these processes involves identifying and eliminating inefficiencies to maximize productivity, reduce operational costs, and improve operational efficiency in the industry.
There are different types of optimizations depending on the focus: from production flow optimization, which focuses on eliminating bottlenecks, to energy efficiency in production processes or the implementation of digital twins in the industry. These processes are often developed in key stages, such as initial diagnostics, solution implementation, and continuous improvement.
However, before optimizing, it is essential to identify the productive inefficiencies that limit companies’ performance. Below, we’ll walk you through practical steps to detect and resolve them effectively.
How to identify productive inefficiencies
Detecting and resolving productive inefficiencies is the first step toward optimizing industrial processes. If you aim to improve your company’s operational efficiency, follow a structured process to identify weak points.
Step 1: Conduct a diagnostic of current processes
The first step is to conduct a thorough analysis of current processes. It is essential to identify how tasks are being performed, what resources are being used, and what results are being achieved.
- Map out processes to visualize each production stage.
- Collect key data on production times, resources consumed, and potential delays.
- Extract key metrics such as OEE (Overall Equipment Effectiveness).
TOKII can diagnose and resolve inefficiencies by combining real-time monitoring, historical data analysis, and the ability to create personalized metrics. It also facilitates comparisons between the current situation and desired objectives, enabling informed decisions to enhance performance and optimize operational efficiency.
Step 2: Identify improvement opportunities
Once processes have been diagnosed, it is crucial to identify bottlenecks: stages that slow production or generate waste. These may include:
- Equipment operating below production capacity.
- Excessive waiting times between processes.
- Lack of synchronization between machines or equipment.
At this stage, TOKII employs visual analytics techniques to detect patterns, identify bottlenecks and inefficiencies, and suggest improvement opportunities. This is achieved using AI, machine learning, and big data to structure and analyze massive amounts of information quickly and accurately.
Step 3: Technological implementation
After identifying the weak points in your production chain, it is necessary to use the right tools to correct and optimize these processes.
One common industry issue is equipment not reaching maximum performance due to unexpected failures, inefficient maintenance, or underutilization. To address this, AI and ML-based solutions can be incorporated for predictive maintenance.
For instance, TOKII performs real-time simulations using digital twins integrated with industrial machinery’s IoT sensors. This anticipates failures and improves both the individual productivity of each machine and the overall production capacity of a factory.
Additionally, TOKII includes an alert system to notify staff at the right time if a machine exhibits irregular performance patterns, recommending preventive maintenance actions and avoiding production losses.
Step 4: Measurement and continuous improvement
Once bottlenecks are identified and solutions implemented, it’s crucial to measure the results and establish a process of continuous improvement to ensure inefficiencies do not reappear.
With TOKII, the system constantly self-updates with feedback it gathers. As the digital twin learns from the physical copy’s processes, its predictions and analyses become increasingly precise and realistic. This continuous learning ensures operational efficiency and precision.
You can set performance indicators such as OEE, production cycle time, energy efficiency, and production rate. Monitor these metrics, adjust, and consistently optimize each process to maximize your factory’s performance.
With TOKII you can make use of 3D interactive dashboards that allow analyzing KPIs in real-time, or access your traditional 2D dashboard.
Methodologies to optimize industrial production processes
A smart factory to identify and resolve operational inefficiencies
The Smart Factory represents the evolution of the industry toward digitized and connected production plants. It can detect, analyze, and correct inefficiencies in real time by collecting and processing large volumes of data from IoT sensors installed on machines and equipment.
TOKII acts as a comprehensive solution, transforming traditional factories into smart factories. Combining real-time data analysis with digital twin technology, TOKII provides total visibility, control, and optimization of production processes, helping resolve inefficiencies quickly and anticipate potential problems before they affect production.
Lean Manufacturing: Eliminate to optimize
While the Smart Factory offers real-time visibility and control through technologies like IoT and digital twins, Lean Manufacturing provides a methodological foundation for identifying inefficiencies structurally and optimizing workflows.
Using TOKII, you can map the value stream, detect the root cause of the problem, and simulate a new, more efficient workflow. This eliminates waste and achieves a smooth, synchronized production.
By combining Lean Manufacturing principles with TOKII, not only can you detect and eliminate waste, but you can also ensure the continuous improvement of production processes, enabling industrial companies to reach their maximum operational efficiency potential.
Common industrial processes with significant improvement opportunities
Unscheduled downtime due to lack of maintenance
Unscheduled downtime is one of the biggest challenges for Spanish industrial companies. The average cost of an unscheduled downtime ranges between €1,000-€50,000 per minute. This issue becomes even more alarming considering that 68% of companies report experiencing significant or moderate downtimes due to bottlenecks, while around 29% had to halt production for at least 20 days due to a lack of essential components.
TOKII features a module powered by AI and ML to identify anomalous patterns in machinery performance, anticipating issues like component wear, mechanical failures, or operational misalignments. Its digital twin technology simulates equipment behavior in a virtual environment, enabling remote supervision and alerting operators to plan well-executed maintenance operations.
Energy efficiency and resource waste
Energy efficiency is critical in the Spanish industry, both economically and due to the pressure to comply with decarbonization and sustainability regulations.
In sectors like manufacturing and metallurgy, energy and natural gas can account for up to 40% of operational costs, meaning any inefficiency results in significant economic losses. Issues like machinery wear, misaligned systems, and lack of real-time monitoring cause unnecessary consumption and waste that affect plant profitability and productivity.
TOKII enhances energy efficiency by integrating IoT sensors to collect and analyze energy consumption data in real time, identifying areas of excessive consumption and proposing precise adjustments to reduce waste.
For example, if a production line shows abnormal energy consumption due to a misaligned piece of equipment, TOKII alerts the technical team and suggests corrective actions to optimize consumption. This not only reduces energy costs but also improves operational efficiency and contributes to more sustainable and competitive production.
How to improve industrial production processes using digital twins: Real examples
Manufacturing sector
SIDENOR has optimized its production processes through smart alerts powered by AI. These alerts can detect anomalous behaviors in data without requiring users to configure manual alerts.
TOKII continuously monitors processes in real time, analyzing data patterns that might go unnoticed by the human eye. This constant supervision allows users peace of mind, as the alerts automatically notify them when something is amiss so they can act before it becomes a problem.
Thanks to this technology, SIDENOR has reduced response times to incidents, optimizing resources.
Steel sector
VICINAY faced the challenge of integrating and organizing over 20 years of data stored across various applications, accessed through tools that were neither intuitive nor interactive.
Previously, tasks such as sending invoices, reports, and other documents required a person to manually gather data and send it. Now, with TOKII, VICINAY’s customers can directly access this information, generate reports, and view all relevant data in a centralized manner.
This change has significantly reduced the time spent on administrative tasks and increased customer transparency and autonomy by providing immediate access to the necessary data. In doing so, VICINAY has improved its internal production processes while offering a more agile and efficient service.
Machine tool sector
TOKII plays a crucial role in optimizing production processes with its integrated calculator for simulation and analysis, enabling DIMECO to support its customers in configuring their production lines. Previously, this calculation was a manual, lengthy, and repetitive process. Now, it is fully automated with TOKII.
DIMECO’s customers can easily access the intelligent calculator integrated into the digital twins to simulate specific scenarios, configure, and adjust their production lines according to their objectives using machine learning and artificial intelligence.
The ability to simulate scenarios and calculate configurations saves material resources and minimizes errors. This way, DIMECO has optimized its internal production processes and improved its customers’ experiences.
Tools to enhance industrial production capacity
When optimizing industrial production processes, companies can implement various tools and methodologies according to their needs and resources. From operational approaches like Lean Manufacturing to advanced technologies like digital twins, each solution provides specific advantages and addresses different production challenges.
| Solution | Description | Limitations | Cost | Reliability |
|---|---|---|---|---|
Value Stream Mapping (VSM) | A manual or digital visual tool used to map and analyze the flow of production processes and identify waste. | Provides only a static view of processes; lacks real-time monitoring, data, and predictive solutions. | Low | Low |
Six Sigma | A methodology based on data and statistical software to reduce variability and defects in processes. | Not designed for real-time processes or predictive solutions; reactive approach. | High | High |
ERP | Software that integrates all areas of a company, facilitating process planning and control. | Does not offer predictive analysis or advanced simulations; limited to administrative and operational data. | High | Medium |
Simulators | Creation of virtual models to analyze and optimize industrial processes before implementing changes. | Lacks real-time analysis; simulated solutions do not always reflect current performance. | High | High |
Benefits of Applying Digital Twins to Improve Operational Capacity in Industrial Companies
Unlike individual solutions like simulators, ERP systems, or software that address problems in isolation, digital twins, such as TOKII, represent a comprehensive and advanced solution for optimizing industrial production processes. TOKII combines technologies such as IoT, artificial intelligence (AI), and Machine Learning, overcoming the limitations of other tools. While many traditional solutions require long implementation periods and highly skilled personnel, TOKII is designed to be used intuitively, accelerating its integration into daily operations without interruptions.
Trends for 2025 in the Industrial Sector
Emerging trends for 2025, supported by consultancies such as McKinsey and Deloitte, highlight the importance of digitalization, artificial intelligence, advanced automation, and the circular economy as fundamental pillars for the industrial future.
Digital transformation remains a priority. Deloitte states that manufacturing companies are investing in digital infrastructure, data analysis, and IoT to address critical issues such as supply chain disruptions and the lack of specialized talent.
It also points out that by 2025, 25% of companies will implement generative AI agents capable of automating complex tasks with minimal human intervention. This will result in a significant leap in operational efficiency, reducing costs while improving decision-making accuracy.
