Industrial Companies: How to Evaluate Plant Productivity

In industrial environments, every second counts. The efficiency with which a plant transforms inputs into final products directly impacts costs, competitiveness, and the ability to respond to market demands. However, many companies still operate without precise and continuous measurement of their actual productivity.

Evaluating productivity in industrial plants is not only a matter of overall performance but also of visibility: detecting hidden losses, anticipating bottlenecks, and making data-driven decisions. This article takes a concrete look at how to achieve that, without overlooking the complexity of the operational context.

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Industrial productivity: more than output per hour

At its most basic, industrial productivity is the relationship between the amount of product generated and the resources used (time, materials, labor, energy). However, in practice, that relationship is influenced by dozens of variables: downtime, micro-stoppages, unplanned maintenance, input quality, and even workplace ergonomics.

That’s why measuring only “production per shift” or “monthly output” is no longer enough. Companies that seek true efficiency must go one step further: breaking down productivity by lines, cells, or processes, and using plant data to make smarter operational decisions.

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What to measure to understand real productivity?

One of the most widely used models in the industrial world is OEE (Overall Equipment Effectiveness). This indicator combines three key dimensions:

• Availability: measures how much actual time the equipment was available to produce. It is affected by planned downtime, changeovers, and unexpected failures.
• Performance: compares the actual production speed with the ideal speed. Micro-stoppages or slower pace impact this dimension, even if production doesn’t fully stop.
• Quality: considers how many defect-free products were manufactured compared to the total produced.

Multiplying these three factors provides a percentage that reflects the actual effectiveness of a machine, line, or plant. A plant with an OEE of 85% is already considered world-class. Most operate between 60% and 75%, though many don’t even know it due to a lack of precise measurement.

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How to identify hidden losses

One of the greatest advantages of measuring productivity with tools like OEE is the ability to detect losses that often go unnoticed.

For example, in a metalworking plant, Line A may have higher production volume than Line B. However, when measuring OEE, it turns out that Line A loses more hours in tool changeovers, while Line B operates more steadily. That difference may reveal issues in scheduling, training, or maintenance.

These hidden losses can be divided into three main types:

• Time losses: technical stoppages, delays due to lack of inputs, operator waiting times, or frequent adjustments.
• Speed losses: processes running below capacity without apparent reason.
• Quality losses: nonconforming products, rework, or scrap that reduce total productivity.

Measuring them requires integrating sensors, MES (Manufacturing Execution Systems), or even well-designed manual dashboards. But above all, it requires the willingness to look at data objectively.

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Evaluating by cell or line: micro-analysis with big impact

Plants that achieve sustained productivity improvements don’t do so only through large investments, but through well-targeted micro-adjustments. To achieve this, breaking down measurements by line, cell, or even by shift can provide critical insights.

Example: in a food plant, breaking down productivity data by line revealed that the night shift had lower performance—not due to lack of capacity, but due to the absence of technical supervision for minor failures. Simply adding a senior technician to that shift raised OEE by 7%.

These analyses allow operational decisions such as:

• Redesigning preventive maintenance sequences.
• Rescheduling shifts to avoid inefficient overlaps.
• Changing the logic of internal input supply.
• Redefining team-level KPIs and training employees to interpret them.

The key lies in establishing reliable measurement systems with daily indicators, and fostering a culture where data is seen as a tool, not punishment.

Anticipating capacity problems before they explode

When productivity isn’t properly measured, plants often operate at the limit without realizing it. Demand grows, orders pile up, and the typical response is to add overtime or request investment in new equipment. But often, the plant already has unused capacity.

Proper evaluation of productivity in industrial plants allows these problems to be anticipated before they affect delivery or costs. Measuring efficiency by line helps estimate how much more could be produced with current equipment, if certain bottlenecks were resolved.

These analyses are also key to making more strategic decisions, such as:

• Is it time to automate a process, or is there still room for human improvement?
• Should a new shift be added, or is it more efficient to redistribute production?
• Does the purchase of a new machine make sense, or can the current one yield more with adjustments?

Towards a culture of continuous improvement based on data

In recent years, digital tools have made it easier to collect and analyze operational data. IoT sensors, SCADA software, Power BI dashboards, or mobile production apps are already present even in small and medium-sized industrial companies. However, the true transformation doesn’t come only from technology, but from mindset.

A productive plant is not just a factory of goods, but also a source of real-time data. Operations leaders must become “plant readers,” understanding what data reveals about people, processes, and machines.

Industrial productivity doesn’t improve with more effort, but with smarter decisions. And those decisions require visibility. Evaluating productivity is not an accounting exercise—it is a way of managing the present and designing the future.