How to Work with KPIs in an MES System? A Practical Guide for Production
Managing, automating, and controlling production in real time are tasks that cannot be effectively performed without specialized software and clearly defined procedures.
Transparent data on operational metrics, workforce efficiency, and production results are also essential. Only then can you evaluate how well production goals are being met and make decisions that genuinely impact operational cost reduction. How can a production manager leverage KPIs and MES systems in practice? Read the guide to discover practical insights.
KPI in an MES System – What Matters Most?
An MES (Manufacturing Execution System) is software used to manage and monitor production in real time. It integrates data from machines, operators, and production orders to control processes on the shop floor and enables full traceability — from raw material to finished product.
An MES represents what is commonly referred to as the third layer of an enterprise IT architecture, in accordance with the ISA-95 standard. It acts as the operational “brain” of a manufacturing organization, collecting comprehensive data from machines and workstations while continuously monitoring production progress.
KPIs (Key Performance Indicators) are performance metrics designed to measure how effectively production processes are executed — for example, whether production goals are being met as planned. In practice, they focus on line efficiency, scrap levels, downtime duration, and delivery timeliness.
The primary objective of implementing a production quality monitoring system is to achieve full visibility into manufacturing processes, allowing rapid identification of factors that contribute to downtime.
Table 1: KPI Categories, Names, and Data Sources in an MES Environment
KPI Category | KPI Name | MES Data Source | Example Interpretation | Managerial Decision |
Machine Efficiency | OEE – Overall Equipment Effectiveness | Machine runtime, actual vs planned production, downtime | OEE < 70% → equipment is operating inefficiently | Analyze downtime causes, invest in maintenance or operator training |
MTBF – Mean Time Between Failures | Machine failure history | Decreasing MTBF → failures are becoming more frequent | Plan preventive maintenance, evaluate modernization needs | |
MTTR – Mean Time To Repair | Repair and intervention time records | High MTTR → downtime lasts too long | Implement faster repair procedures, team training, or outsource maintenance | |
Production Progress | Order fulfillment timeliness index | Production schedule vs actual execution | Delays > 5% → risk of customer dissatisfaction | Prioritize orders, reschedule production, add extra shifts |
Production plan adherence index | Number of tasks completed as planned | Large variance → loss of production control | Identify bottlenecks, optimize workflows | |
Quality & Waste | Scrap / reject rate (%) | Quality inspection and rejection data | High scrap rate → quality issue | Root cause analysis, process parameter adjustment, operator training |
Number of customer complaints | Quality reports | Increase in complaints → customer retention risk | Investigate root causes, improve production processes or materials | |
Resources & Materials | Material consumption / loss ratio | Inventory levels and raw material usage data | Excessive consumption → waste | Process optimization, supplier renegotiation, lean implementation |
Warehouse utilization index | Combined MES + ERP data | Low turnover → inventory bottlenecks | Reduce surplus stock, reorganize inventory management | |
Human Resources | Operator efficiency index | Work time and completed operations data | Efficiency drop → underutilized workforce | Training, shift reorganization, automation of repetitive tasks |
Operator error rate | Quality and process control records | High error rate → competency gap | Coaching, control procedures, improved work instructions | |
Financial / Strategic | Unit production cost | Combined MES and ERP data | Rising cost → inefficient process | Process optimization, material analysis, technology adjustments |
MES implementation ROI | Implementation cost vs savings and performance gains | Low ROI → expected benefits not realized | Process review, implementation correction, improvement roadmap |
The above MES data show that drops in OEE and MTBF, along with an increase in MTTR, usually indicate simultaneous issues with machine availability, maintenance, and repair organization. Therefore, it is crucial to quickly identify the root causes of downtime and strengthen preventive actions. Parallel deviations in order fulfillment timeliness, product quality, and material usage point to process bottlenecks or incorrect production parameters, which, if not corrected, directly increase unit costs and the risk of customer complaints.
What Do KPIs Show, How Do They Affect Production, and How to Interpret MES Data?
KPIs in production are measurable indicators that reflect actual performance, quality, and resource utilization. These indicators can influence production, and in practice:
- KPIs reveal bottlenecks and downtime, allowing you to quickly address the sources of loss;
- They help simultaneously control quality, efficiency, and costs;
- They enable data-driven decisions instead of relying on intuition;
- They allow monitoring of trends and proactive responses before issues halt the line;
- They support maintenance planning and minimize machine failures.
A dedicated MES system offers many advantages, such as more efficient production management, the ability to digitize data, and integration with multiple devices within a single system.
Did you know? A modern MES system with clearly defined KPIs can be combined with artificial intelligence solutions, such as AI agents. This allows for more efficient MES data analysis, for example, by predicting machine failures and proactively responding to order changes.
For instance, in consumer electronics manufacturing, an AI agent can analyze MES data related to product quality. If it detects any anomalies, it can automatically adjust production machine parameters, reduce scrap, and improve overall product quality.
How to Manage Production with an Active MES System – Practical Managerial Decisions
Production managers responsible for achieving targets and measuring production results must understand how to plan actions based on MES data. To manage and automate production processes efficiently:
- Focus on trends, not single data points – a decline in OEE over several shifts indicates a real problem, while a single drop may be an isolated event.
- Break down KPIs into components – analyze machine efficiency separately: availability, performance, quality.
- Look for correlations between indicators – an increase in output with a drop in quality may indicate process overload.
- Set alert thresholds – react before KPIs fall below operational levels.
At the production management level, KPIs in an MES are not just reporting metrics or goals to achieve; they are also tools for controlling processes in real time.
Interdependencies Between MES KPIs and Their Mutual Influence
MES, as a managerial tool in production, is tightly integrated with KPIs, and none of these indicators operates in isolation. While each metric represents a different aspect of the production process, they are interconnected.
For example, OEE consists of availability, performance, and quality, where a decline in one component automatically reduces overall results.
Table 2: Example of KPI Interdependencies in MES for Production Monitoring
MES KPI | What It Measures | What It Impacts | Practical Example of Interdependency |
Machine Availability | Actual working time vs planned production time | OEE, order fulfillment timeliness, resource utilization | A machine breakdown reduces operating time → availability drops → OEE decreases, delays increase |
Production Performance (Speed / Tempo) | Actual speed vs ideal speed | OEE, unit cost, workforce utilization | Tool wear slows down the machine → performance drops → unit cost rises |
Production Quality (Quality / FPY – First Pass Yield) | Share of good units produced | OEE, scrap/waste, material cost | Higher scrap rate → quality decreases → OEE decreases, raw material usage increases |
OEE – Overall Equipment Effectiveness | Overall process efficiency | Production planning, investment decisions | Improving changeover procedures increases availability → OEE automatically increases |
Cycle Time | Time to produce one unit | Productivity, throughput, delivery timeliness | Longer cycles due to manual corrections → performance drops → lead time increases |
In practice, improving a single KPI without monitoring the others can even worsen overall production performance, which is why analysis should always consider a comprehensive set of indicators simultaneously.
Warning Signals to Watch for When Using KPIs in Production Monitoring
Managerial decisions based on MES data and KPIs make it easier to identify warning signals that may indicate problems within production processes. In practice, a production manager should respond to the following signals:
- Sudden drops in machine or operator performance, visible in system data;
- Production errors or an increasing number of error causes reported;
- Unplanned downtime or more frequent work stoppages;
- Data suggesting a potential machine failure (e.g., trends indicating an upcoming breakdown);
- Increasing material losses or suboptimal raw material usage;
- Deviations in production schedules or delays in order fulfillment;
- Lack of progress or delays in production stages, visible through online monitoring.
Ignoring these signals allows problems to grow unnoticed, only becoming apparent when significant production losses or quality issues occur. Real-time KPI monitoring is essential to act before scrap or delays occur.
Common Mistakes When Working with KPIs
A production manager can make several critical mistakes that disrupt not only production but also management procedures and the automation of production processes. Sometimes, simply misinterpreting collected data or failing to respond to warning signals can lead to excessive production costs or delivery delays.
Common mistakes in managing and digitizing production through an MES system include:
- Monitoring too many KPIs simultaneously;
- Reacting too late to problems;
- Ignoring isolated drops in performance;
- Failing to link KPIs to actual processes, machines, or teams;
- Not implementing corrective actions based on KPI insights.
Lack of clearly defined procedures, such as who responds to KPI alerts in MES, is also a critical issue. Managers should ensure that every KPI ultimately triggers operational decisions. Indicators that only generate reports without action fail to deliver value.
Production Control and Process Automation with MES KPIs
An MES integrates production planning, monitoring, and control into a single environment, providing real-time data on machine operation, product quality, and order progress.
Automatic KPI collection shows actual performance, error counts, downtime, and material usage, highlighting where processes are losing pace or generating costs. This analysis naturally enables decision automation: the system can send alerts, generate reports, or help optimize schedules and process parameters.
As a result, production control becomes continuous and data-driven, rather than a retrospective activity.
For organized production data and constant KPI visibility, consider HDF MES, which supports digital control and process automation in manufacturing enterprises.
Sources:
1. https://en.wikipedia.org/wiki/Manufacturing_execution_system