The manufacturing industry faces numerous breakdowns and disruptions every day, such as small incidents that, when accumulated over the year, end up having a significant impact on operational results. According to BBVA Foundation's Productivity Tracker report, industrial productivity in Spain is either stagnant or growing very slowly, mainly due to operational inefficiencies and plant interruptions. These issues generate persistent annual capacity losses of between 5% and 10%.

Main challenges in production lines

• Supply delays that lead to shortages of essential materials or complete line stoppages.

• Lack of real-time visibility and control across the production chain: machines, quality, production, etc.

• Unplanned downtime due to equipment or automation failures that reduce production time.

• Defective batches caused by flaws in quality control systems or unmanaged variability.

• Bottlenecks in critical stages that slow the entire production flow and extend cycle times.

• Poor planning that results in overproduction or shortages, poorly sized inventory, or idle time.

• Reactive maintenance that increases the frequency of breakdowns and lengthens the MTTR (mean time to repair).

All of these are warning signs that a manufacturing system needs optimization. Detecting them early is the first step toward transforming a traditional plant into a smart factory.

How to detect inefficiencies: step-by-step diagnostic

To effectively optimize manufacturing processes, the first step is a thorough diagnosis. This can be broken down into three main stages:

1. Collect and analyze real-time and historical data

Start by gathering all available data: historical production logs, most frequent failures, maintenance performed, downtime, planned versus actual production, etc. The more data you have, the easier it will be to detect patterns and trends.

2. Define and monitor key metrics

Once you’ve analyzed the data, identify which metrics have the greatest impact on your operations or highlight the biggest deficiencies. These might include MTBF (Mean Time Between Failures), MTTR (Mean Time to Repair), unplanned downtime rate, or OEE (Overall Equipment Effectiveness).

These indicators allow you to quantify performance and assess the severity of each issue so you can prioritize actions accordingly.

3. Identify root causes and set priorities

Involve maintenance, quality, and production teams to answer questions like: Why is this failure occurring? Which phase of the line is slowing down? Which resource is saturated?

With clear causes, prioritize actions based on their impact on costs, times, and quality, and define an action plan. Always assess the expected return of each measure: what level of effort it requires and what quantifiable benefit it will have.

Proven methodologies and strategies to optimize manufacturing

To overcome the current challenges of the manufacturing sector, it is essential to invest in proven methodologies and strategies that help maximize efficiency, reduce waste, and minimize unplanned downtime.

• Lean Manufacturing: is a management methodology that seeks to maximize value for the customer by systematically eliminating waste and activities that do not add value in the production process. Its main objective is to optimize resources, reduce times and costs, and promote continuous improvement across the organization.

  
  

  On a production line, applying Lean Manufacturing can lead to:

  • Significantly reducing changeover times using the SMED technique.

  • Optimizing workflow with value stream mapping (VSM).

  • Maintaining minimal inventory levels without losing responsiveness, thereby improving operational efficiency and flexibility.

  
  

• Six Sigma is a process improvement methodology that focuses on reducing variability and defects in the process, using statistical tools and a structured approach (for example, DMAIC: Define, Measure, Analyze, Improve, Control).

  
  

  In manufacturing, it allows for reducing defect rates, improving the final quality of the product, decreasing rework, and therefore, costs associated with failures or returns.

  
  

• IoT-based predictive maintenance: this strategy focuses on anticipating equipment failures by continuously monitoring assets such as motors, pumps, compressors, or conveyors. IoT sensors collect data like vibration, temperature, and pressure, which are then analyzed to detect patterns and early signs of wear. This allows factories to schedule interventions proactively and cut downtime dramatically.

  
  

• Data analysis and Big Data: by collecting massive real-time data from sensors, ERP systems, and IoT devices, manufacturers can process vast amounts of information to detect trends, anomalies, and performance issues. Predictive models and machine learning enable plants to adjust production on the fly and maximize operational profitability.

Implementing the right tools

Optimizing manufacturing does not only depend on methodologies or good practices. Nowadays, technology is the decisive factor that differentiates a reactive plant from a Smart Factory.

Most process-improvement tasks, such as data collection, performance analysis, and maintenance forecasting, require significant time, expertise, and data interpretation skills. Modern digital tools automate and simplify much of this work, offering full visibility, process traceability, and instant response to any deviations

Digital Twins in manufacturing

The use of digital twins in manufacturing has grown exponentially. According to Forbes,manufacturers that have implemented digital twins in their plants achieve, on average, a 15% higher operational efficiency and a notable reduction in both emissions and energy costs.

Industrial digital twins consolidate and unify data from multiple sources, platforms, sensors, and systems to virtually replicate the real behavior of a plant or asset.

Our digital twin software TOKII goes beyond simple 3D monitoring. It’s a comprehensive industrial optimization platform that combines real-time monitoring, visual analytics, and AI-powered predictive maintenance to give a complete view of each asset’s status and performance.

Numerous companies in Spain are optimizing their manufacturing processes with digital twins. Among them are Vicinay Marine, Sidenor, or Teicon, all of which have trusted TOKII to optimize their processes.

• Vicinay Marine succeeded in centralizing and managing its previously scattered historical manufacturing data, achieving real-time supervision and a remarkable improvement in cost and time efficiency. You can read it all in Vicinay Success Case.

  
  

• DIMECO was able to simulate material loops and production speeds in its metal parts manufacturing lines, allowing it to fine-tune optimal configurations using machine learning and artificial intelligence. You can read it all in DIMECO Success Case.

If you want to discover how TOKII can help you transform your processes towards smart manufacturing, request a personalized demo and try an internal pilot program adapted to your specific goals.