Process-oriented key figures in production: time-related & cost-related key figures, quality and performance key figures

The efficient control of modern production processes requires a well-founded analysis and continuous monitoring of relevant key figures. In sectors such as the chemical, petrochemical and food industries, time-related, cost-related, quality and performance indicators form the backbone of target-oriented process improvement.

Scientific approaches and practical concepts make it clear that systematic data collection and evaluation form the basis for identifying weak points in the production process at an early stage and initiating targeted measures.

A key result of these considerations is that the isolated optimization of individual processes often leads to negative interactions in upstream and downstream areas. Instead, the overall effect must always be considered.

Key figures such as Overall Equipment Effectiveness (OEE) play a central role here. With their help, not only can downtimes and quality losses be reduced, but the overall efficiency and flexibility of production can also be sustainably improved.

The most important key production figures are explained in the rest of the article.

Production key figures are quantitative metrics that represent the state and performance of manufacturing processes. They provide an objective basis for analyzing individual process steps and the overall process, enabling the identification of improvement potentials and the derivation of targeted optimization measures.

Production key figures can generally be categorized as follows:

• Time-related key figures:
  These measure lead times, cycle times, or takt times and provide insights into how quickly and efficiently production processes operate. Precisely recording actual times is essential to detect deviations from target times and adjust processes accordingly.

• Cost-related key figures:
  These focus on the economic aspect of production by tracking unit costs, operating costs, and resource utilization. Comparing costs with achieved performance allows for concrete identification of efficiency gains and cost-saving potentials.

• Quality key figures:
  A key indicator is the quality level, which represents the proportion of defect-free products relative to total production. Quality key figures help identify sources of errors and quality losses, enabling targeted implementation of measures for defect prevention and early detection.

• Performance key figures:
  A crucial performance indicator is the Overall Equipment Effectiveness (OEE), which consists of availability, performance, and quality rates. The OEE provides a comprehensive assessment of machine and equipment productivity.

Production KPIs (Key Performance Indicators) are essential key metrics that quantify the state and performance of manufacturing processes. They provide an objective and continuous evaluation of relevant areas, enabling managers to systematically identify bottlenecks, inefficiencies, and improvement potentials.

Integrating production KPIs into operational controlling creates a transparent and measurable foundation for systematically implementing process improvements. This not only enhances manufacturing flexibility but also reduces downtime and quality losses—key factors in increasing competitiveness.

Key Characteristics of Production KPIs:

• Measurability: KPIs are based on precisely recorded data, such as takt times, lead times, setup times, or quality rates. For example, the combination of various key figures into a single KPI—such as OEE—demonstrates how different production aspects can be consolidated into a meaningful metric.
• Relevance: The selected indicators cover essential production aspects, ranging from time parameters and cost factors to quality and performance metrics. They are designed to align directly with strategic goals and operational processes.
• Transparency: The systematic use of KPIs structures the production process clearly. Continuous data collection and analysis allow for the early detection of deviations and the implementation of targeted corrective actions.
• Goal Orientation: KPIs serve as a basis for defining concrete objectives and measuring the success of optimization efforts. They support data-driven decision-making and contribute to increasing overall process efficiency.

Manufacturing companies collect a wide range of key figures that quantitatively represent all aspects of the production process—from takt and lead times to quality and cost parameters. However, not all of these figures directly contribute to business management. The key difference lies in the following:

Key figures serve as baseline data, providing a comprehensive view of process performance. They capture detailed information that documents the current state but often lead to an overwhelming flood of data.

KPIs (Key Performance Indicators) are carefully selected key metrics directly linked to the company's strategic and operational goals. They act as focused management tools, enabling precise measurement and control of the success of optimization efforts.

The key production metrics can be divided into four main categories that together provide a comprehensive picture of process performance. These serve as a foundation for developing and implementing measures to achieve the perfect production.

Production metrics can be categorized into the following four types:

1. Time-based Metrics:

• Takt Time: Measures the rhythm at which a product is completed, determining the production speed.
• Cycle Time: Captures the processing time of a single work step.
• Lead Time: Encompasses the total time from production start to completion, including all waiting and idle times.

2. Cost-based Metrics:

• Unit Costs: Indicate the cost per produced unit and help assess economic efficiency.
• Operating Costs: Capture the total resource usage and help identify cost-saving potentials.

3. Quality Metrics:

• Quality Rate: Represents the ratio of defect-free products to total production, serving as a key indicator of product quality.
• Scrap and Rework Rates: Measure the number of defective products to systematically identify and address causes of quality losses.

4. Performance Metrics:

• Overall Equipment Effectiveness (OEE): This index combines availability, performance, and quality rates into a single, meaningful metric. The OEE index enables quick identification of bottlenecks and inefficiencies in production.
• Process Efficiency (PE): Measures the proportion of value-added time in the total lead time, providing insights into the effectiveness of the process chain.
• Lean Performance Index (LPI): Combines various process-oriented metrics to enable a holistic assessment of production performance.

The takt time is a key metric in production control that defines the ideal rhythm at which a product must be completed to optimally meet customer demand. It is calculated by dividing the available production time by customer demand. This target value serves as a benchmark for all production steps.

Adhering to the takt time is essential for ensuring a smooth production flow. If the actual takt time deviates from the target takt time, issues such as inventory buildup, extended lead times, or inefficient resource utilization may arise. An excessively long takt time often leads to delays in the overall process, while an overly short takt time may indicate overloading of individual process steps.

Takt time charts are frequently used to monitor takt time. These charts visualize the actual times of individual production steps, allowing deviations to be detected early. By comparing actual takt time with the target value, bottlenecks or delays can be identified. Based on this analysis, targeted measures can be derived to optimize processes—such as eliminating non-value-adding activities or restructuring workflows.

The system-supported, continuous tracking of takt time is a crucial success factor. Only through high data quality and timely evaluation can short-term adjustments be made, ultimately contributing to increased production efficiency and flexibility.

The cycle time describes the actual time span required for a single production step to be completed. It captures the entire sequence of a work cycle—from the initiation of a process to the finished work result—and includes all processing steps, including potential setup or changeover times.

Unlike takt time, which represents an idealized target value for controlling the production flow, the actual cycle time reflects the real time required for a process. Deviations between the target takt time and the measured cycle time can indicate inefficiencies or bottlenecks in a specific work operation. An excessively long cycle time signals that a work step is taking longer than planned, which can negatively impact the overall process and delivery times.

Cycle-time-based diagrams are used for process analysis and optimization. These diagrams visualize the individual time segments of a production step, allowing early detection of deviations. Based on this data, targeted measures can be developed to reduce non-value-adding activities and streamline workflows.

The continuous, system-supported tracking of cycle times is essential to ensure high data quality. Only through timely evaluation can bottlenecks be identified and operational adjustments quickly implemented—a crucial factor in increasing production efficiency and flexibility.

Lead time describes the total time required to bring a product from the start of production to completion. It includes not only the actual processing time but also all intermediate times—such as waiting, idle, and changeover times—that occur at various process steps.

Unlike takt time or cycle time, which focus on individual operations, lead time provides a comprehensive overview of the entire production process. A shorter lead time positively impacts a company's flexibility and competitiveness, as it leads to shorter delivery times and lower capital commitment.

Lead time is often tracked using system-based methods, such as modern MES systems or value stream analyses. By examining all process steps in detail, bottlenecks can be identified and optimization potential uncovered. The goal is to minimize non-value-adding times and achieve seamless synchronization of production steps.

In practice, continuous analysis of lead time enables a quick response to deviations from the target state. This allows targeted measures to be implemented to reduce waiting times and improve process stability, ultimately enhancing overall manufacturing efficiency.

Unit costs are a key indicator of economic efficiency in production. They indicate the costs incurred in manufacturing a single unit by distributing all expenses – such as material, labor, energy, and overhead costs – over the produced quantity. This provides a clear picture of whether production processes are cost-efficiently designed.

Continuous analysis of unit costs allows early identification of deviations between planned and actual costs. Increased unit costs may indicate inefficient workflows or disproportionately high resource usage in certain process steps. Targeted optimization measures can be derived from this, such as reducing setup times, improving material utilization, or optimizing personnel and energy usage.

Furthermore, unit costs serve as a basis for strategic decisions. By comparing data over different time periods, companies can identify trends, assess competitiveness, and make informed adjustments to pricing and investment decisions. Overall, regularly recording and evaluating unit costs contributes to efficiently managing production and achieving long-term sustainable improvements.

Operating costs encompass all ongoing expenses necessary for the maintenance and management of production processes. They consist of both direct and indirect costs that extend beyond mere material consumption. Direct costs include energy costs and specific operating supplies, while indirect costs often take the form of personnel, maintenance, and administrative expenses.

Continuous monitoring and analysis of operating costs allow businesses to critically assess resource utilization and identify inefficient structures. Deviations between planned and actual costs provide valuable insights into areas where optimization potential exists. Targeted measures can be implemented to reduce unnecessary expenditures and increase cost efficiency.

Moreover, operating costs serve as a crucial basis for strategic decisions. Companies use this metric to justify investment decisions and assess the economic viability of individual production areas. Accurate cost control thus plays a key role in ensuring long-term competitiveness.

In summary, systematic analysis of operating costs supports targeted process improvement by creating transparency regarding cost drivers and enabling sustainable cost reductions.

The quality rate indicates the percentage of defect-free products in total production. It is a key metric in the manufacturing process, directly reflecting the stability and efficiency of production workflows and serving as a crucial component of the Overall Equipment Effectiveness (OEE) index.

A high quality rate signifies that most produced units meet the specified quality standards, while a low quality rate indicates frequent deviations, such as scrap or necessary rework. Such quality losses not only lead to higher defect-related costs but also unnecessarily increase resource consumption, particularly in reworking and labor.

To optimize the quality rate, systematic and continuous data collection is essential. Automated quality inspections, operator self-checks, and Statistical Process Control (SPC) help detect deviations early and implement targeted corrective actions. Techniques such as Poka Yoke help prevent defective processes from occurring in the first place, thereby improving the quality rate sustainably.

By regularly comparing actual and target values, companies can identify weaknesses in their processes and make targeted improvements – from the startup phase to continuous production. Thus, the quality rate not only forms the foundation for high product quality but also ensures efficient and cost-effective production management.

Scrap and rework rates capture the proportion of produced units that fail to meet specifications due to quality defects. Two key areas are distinguished: Scrap consists of defective products that cannot be reused and are directly considered a loss, while rework refers to parts that, despite being produced with defects, can be corrected and reintroduced into the production flow through targeted corrective measures.

These rates are typically determined by comparing defective units (whether as scrap or reworked products) with the total production volume. These metrics provide clear insights into process stability and the effectiveness of quality assurance measures. A high scrap rate often indicates systematic issues that not only lead to increased defect costs but also significantly burden resource utilization – for example, through additional material consumption, extended production times, and increased labor requirements for rework.

Continuous analysis of scrap and rework rates is therefore essential for identifying and addressing the causes of quality losses. Measures to reduce these metrics include early defect detection, process optimization, and employee training.

Overall Equipment Effectiveness (OEE) is a key metric that quantitatively represents the level of value creation in production processes. It combines three essential components:

• Availability Rate: Measures the proportion of planned production time during which the machine or system was actually operational. Factors such as unplanned downtimes and setup losses are included.
• Performance Rate: Compares actual production speed with the ideal cycle time. It indicates the extent to which machines and systems utilize their full potential and highlights short downtimes or inefficient processes.
• Quality Rate: Determines the ratio of defect-free products to total production. Quality losses, such as startup losses or rework processes, reduce this value and point to weaknesses in production quality.

The combination of these three factors results in the OEE formula:

OEE = Availability Rate × Performance Rate × Quality Rate

An OEE value of 100% would indicate an ideal, disruption-free production process – a goal that is rarely achieved in practice. Instead, continuously recorded OEE helps identify specific losses and implement targeted measures.

This enables companies to uncover technical and organizational weaknesses, minimize downtimes, optimize production speed, and improve product quality.

The Lean Performance Index (LPI) is an integrative metric that combines two key process indicators – Overall Equipment Effectiveness (OEE) and Process Efficiency Rate (PWG) – to comprehensively evaluate the overall performance of a production process. This combination enables the consideration of both the technical and organizational aspects of manufacturing.