Lead time: Definition, calculation & optimization

Lead time is a key performance indicator in production and describes the period from the start of production to the completion of a product.

Lead time influences capital commitment, operational flexibility and adherence to delivery dates. A long lead time is often the result of high stock levels, inefficient processes or a lack of synchronization between production steps.

Through measures such as value stream mapping, inventory reduction and process synchronization, lead times can be shortened and production output sustainably increased.

In this article, you will learn how companies can optimize their lead time and thus operate more efficiently.

Lead time describes the period of time from the start of production of a product to its completion. It includes all time components along the value chain, including processing times, waiting and idle times as well as administrative delays. In industrial production, lead time is a key performance indicator as it reflects process stability and efficiency.

The concept of lead time can be viewed from two different perspectives: the theoretical value stream analysis and the order-related perspective. Both approaches provide important insights into the optimization potential in production processes.

The value stream analysis considers the lead time under the assumption of constant and synchronized customer demand without fluctuations. Lead time includes all process steps, including external processing. A key feature of this approach is the direct correlation between inventories and lead time: the higher the inventories of semi-finished and finished goods, the longer the average lead time.

An optimal scenario according to this model would be a perfectly synchronized production in which there are no stocks at all. In reality, however, the complete elimination of inventories is hardly feasible, as external factors such as variable customer requirements or technical restrictions require a certain amount of stock to be held. Nevertheless, value stream mapping shows that reducing inventories and improving process coordination can lead to significant increases in efficiency.

The order-based approach extends the value stream mapping model to include administrative processes such as triggering requirements, order creation and detailed planning. It also takes into account internal production disruptions such as unplanned machine downtimes or rework costs.

Typical weak points that lead to an increase in lead time are

• Long queues in front of bottleneck resources
• Inaccurate master data in ERP systems, which leads to incorrect planning
• Lack of synchronization between the individual process steps, resulting in waiting times

Lead time is a decisive factor for the competitiveness of manufacturing companies. It has a significant influence on capital commitment, operational flexibility and adherence to delivery dates. Lead time is a time-related, process-oriented key figure for optimizing production.

Long lead times lead to increased inventory levels, which tie up capital and limit a company's financial flexibility. According to the available data, high stock levels can significantly increase operating costs, as not only the storage space but also the tied-up capital causes additional costs.

Companies with short lead times can react more quickly to fluctuations in demand or unexpected disruptions. A flexible production process with minimal lead time enables more efficient adaptation to market conditions and reduces the need for high safety stocks.

Unplanned downtime or inefficient processes can lead to an extended lead time, which in turn causes delivery delays. High process stability and precise detailed planning are therefore essential in order to reliably meet promised delivery dates.

Long throughput times are often due to structural and organizational weaknesses in the production process. A key factor is the batch-oriented planning of many ERP systems that work with weekly production intervals. This leads to sluggish control loops and delayed production adjustments.

Another problem is process decoupling: Buffer stocks between individual operations significantly increase waiting times and extend the overall throughput time.

Furthermore, a lack of synchronization within the production chain leads to unbalanced cycle rates. This can cause idle times as well as selective overloads and thus contribute to inefficient processes.

Throughput time covers all time blocks in the production process and distinguishes between value-adding and non-value-adding times.

Value-adding times include the processing and set-up times required for the actual manufacture of a product.

Non-value-adding times are made up of waiting times due to material shortages or machine downtimes, idle times due to storage buffers between process steps and administrative delays.

Process-related waiting times, such as chemical reaction times, are particularly critical and are often underestimated, although they can last for days.

The calculation of the lead time is based on the sum of three time components:

The cycle time describes the active processing time of a product at a station, such as the filling of a bag or the packaging of a granulate. It is significantly influenced by machine speed, employee qualifications and the degree of process standardization.

The transition time includes times for set-up processes or the transportation of material between different production stations.

The interim time includes all inefficient time blocks such as queues in front of machines or storage times. For example, a safety stock with a 4-week range doubles the interim time compared to a 2-week strategy.

The terms lead time and cycle time are often confused, but have different meanings. While the lead time represents the entire process chain including all delays, the cycle time only measures the processing time of an individual work step. A production process with several stages can have a very short cycle time per work step, while the total lead time still remains high.

An example from practice illustrates this difference: a seven-step production process with an average quality rate of 98% per step leads to an overall reject rate of 13.2%. This high reject rate extends the lead time, as additional reworking and material replenishment are required. While the individual cycle times remain stable, the lead time is considerably longer due to inefficient reworking steps.

Differentiating between the two key figures is crucial for targeted process optimization. While a reduction in cycle time can be achieved through more efficient machines or optimized workflows, a reduction in lead time requires an overarching improvement of the entire production chain, including inventory management, synchronization of process steps and minimization of waiting times.

Reducing throughput time is a key lever for optimizing production processes. Various measures can increase efficiency and minimize delays, as demonstrated in two GREIF-VELOX case studies. The classic levers for shortening throughput times are

• Carry out value stream mapping
• Minimize inventories
• Synchronize processes
• Increase transparency
• Involve employees

Losses are particularly common in the form of excessive inventories, long waiting times between production steps or inefficient material flows. The value stream analysis uncovers these weak points and forms the basis for targeted optimization measures.

Another important aspect is the reduction of throughput times by eliminating unnecessary transportation and idle times. In many companies, materials are stored for an unnecessarily long time between different production stations or transported over long distances. By optimizing the layout of production halls, these inefficient processes can be minimized and process synchronization improved.

Warehousing is one of the main causes of long lead times and high capital commitment. Safety stocks in particular are often generously dimensioned in order to compensate for uncertainties in the supply chain or production downtimes. However, excessive stock levels not only lead to high storage costs, but also to longer idle times, which slow down the material flow and make the entire production process inefficient.

Targeted inventory management will reduce this effect. By implementing precise replenishment times in ERP systems, material availability can be optimized and excess stock avoided. Companies that control their inventory management based on data can reduce stock levels without this leading to production bottlenecks. Dynamic scheduling processes, such as demand-driven material planning or Kanban systems, are particularly effective here.

In addition to the reduction of safety stocks, the optimization of batch sizes also plays a central role. Production orders are often run in excessively large batches in order to minimize set-up times. However, this leads to long interim storage times and increases the amount of capital tied up. Switching to smaller, needs-based batches can help to speed up the material flow and significantly reduce the overall throughput time.

Uneven timing of work steps often leads to inefficient processes, as individual production steps either have to wait for material or are slowed down by subsequent processes. Continuous synchronization of all process steps is therefore crucial in order to avoid idle times and reduce bottlenecks.

A proven approach to improving process synchronization is the introduction of synchronized production lines. U-shaped production lines are particularly effective here, as they optimize material flows and enable closer cooperation between the individual workstations.

Another element of process synchronization is the reduction of overloads at individual stations. Bottleneck processes must be specifically identified and relieved with additional capacity or alternative production routes. A flexible shift system or the use of modern automation solutions ensure a balanced utilization of production capacities.

One of the biggest challenges in modern production is the lack of a real-time overview of production progress and resource utilization. Delays often occur because relevant information is either provided too late or in incorrect formats. This is where an end-to-end transparency concept comes in, which records and evaluates all relevant production data in real time and makes it available for decision-making.

A powerful Manufacturing Execution System (MES) plays a key role here. It replaces manual shift reports, which are often incomplete or prone to errors, with automated data collection systems. This allows production managers to see in real time where bottlenecks are occurring, whether machines are being used efficiently and where there is potential for optimization - all of which are cornerstones of GREIF-VELOX customer service for our systems.

Another important factor is the introduction of Kanban control loops. This system ensures self-controlled post-production by adapting material flows to actual consumption. Through the consistent use of Kanban, the number of unnecessary stocks can be reduced and the production process can be made considerably more flexible.

Employees play a crucial role in identifying and implementing process improvements. Specialists in production in particular have in-depth knowledge of everyday challenges and inefficiencies. Companies that focus on the knowledge of their workforce can identify inefficient processes more quickly and develop practical solutions.

Long search times for tools, materials or components are a key problem for many production companies. This leads to interruptions in the workflow and delays production. Systematic workplace organization, for example according to the principles of the 5S method (sorting, systematization, cleaning, standardization, self-discipline), can reduce unnecessary travel times and speed up processes.

In addition, a continuous target/actual comparison of planning data helps to identify deviations at an early stage and initiate targeted optimization measures. Employees should be actively involved in this process by providing regular feedback on processes and participating in improvement initiatives.

Another important aspect is the training and empowerment of employees. Targeted training on lean methods or process optimization not only increases the understanding of efficient processes, but also the willingness to actively contribute to improvement. Companies that systematically involve their employees benefit in the long term from higher productivity, better process stability and greater innovative strength.