Manufacturing Lead Time
Manufacturing lead time is the total span a production order or workpiece takes from release to completion on the shop floor. It combines setup, processing, transport and queue times and is a core metric of production planning.
Manufacturing lead time is the total span an order, a lot or a single workpiece needs from release to completion on the shop floor. It describes how long a product is actually in production – across all operations, machines and waiting points. Unlike pure processing time, lead time also covers all ancillary and waiting times: setting up the machines, transport between workstations and above all the queue times, during which a part waits for the next operation.
Lead time is usually measured in hours or working days, and depending on the depth of manufacturing also in minutes. It is one of the most important metrics in production planning and control (PPC), because it directly affects delivery time, tied-up capital and responsiveness. In practice, lead time consists for the most part of queue times – the actual value creation is often only a small fraction. That is exactly where the biggest lever lies: whoever reduces queue times noticeably shortens lead time without having to work faster.
At a glance
- Span from order release to completion on the shop floor
- Sum of setup, processing, transport and queue times
- Queue times usually account for 80 % or more – that is where the biggest lever sits
- Core metric of production planning (PPC/MRP) and the basis of scheduling
- Measurable and reliably plannable in the ERP system from the routing and shop-floor data feedback
What lead time is made up of
Lead time is not a single figure but the sum of all time components an order passes through on its way through manufacturing. Classically – for example following the REFA methodology – it can be broken down per operation into four blocks: setup time (preparing the machine), processing time (the actual value creation), transport time (moving to the next workstation) and queue time (waiting before and after processing). Setup and processing time are often combined as execution time, transport and queue time as transition time. The total lead time of an order is then the sum of these times across all operations of the routing.
What matters is the ratio of the components: while setup and processing are plannable and relatively stable, queue times arise from waiting for free capacity, for raw material or for the lot to run through. In many plants a part simply sits around for most of its lead time. That is why lot size, sequencing and utilization are the strongest levers – not the speed of the machines.
Setup, processing and transition time
Setup time occurs once per lot, regardless of the quantity – large lots spread it over more parts but lengthen the lead time of the individual lot. Processing time, by contrast, scales with the quantity and is the value-adding component. Transition time – transport plus queuing – is the part that creates no value and yet often dominates. Whoever lowers the lot size and smooths the sequence primarily reduces transition time and thus overall lead time.
Queue time as the biggest lever
Queue times arise when an order waits for an occupied resource, or when only part of a lot is being processed while the rest sits idle. According to the funnel model and the relationship of queueing theory (Little’s Law), lead time rises with the backlog of open orders in front of the workstations: the fuller the shop floor, the longer the individual parts wait. A low work-in-process inventory – for instance through Kanban or a deliberate limit on order release – keeps queue times and thus lead time short.
Calculating lead time
For a single order, lead time is the difference between the moment of completion and the moment of release. For planning, however, it is calculated in advance per operation from the routing: setup time plus processing time per unit multiplied by lot size, supplemented by flat-rate or statistical transition times between workstations. The sum across all operations yields the planned order lead time, from which the ERP or PPC system derives backwards the required start date.
The metric becomes meaningful only when plan and actual are compared. From the shop-floor data feedback of the actual start and end times, the real lead time can be determined and set against the planned one. As with any process metric, it is worth looking beyond the mean at the median and the spread: a few orders with extremely long queue times distort the average, and a stable lead time is often more valuable for on-time delivery than a short but fluctuating one.
Why lead time matters
Lead time largely determines how quickly a company can respond to orders. It feeds directly into the delivery time that can be promised: whoever manufactures short and reliably can offer tighter deadlines and meet them. Especially in make-to-order production, a short lead time is a competitive advantage, because it creates flexibility and reduces the need for held finished-goods stock.
In business terms, lead time affects tied-up capital and costs. Every workpiece sitting in production ties up material, floor space and half-finished value creation without being sold – high work-in-process is tied-up capital. Short lead times reduce this stock of unfinished goods, improve cash flow and make quality problems visible sooner, because less product is manufactured between a defect arising and being detected. Short lead times are therefore a core goal of lean production.
Lead time in the ERP and PPC system
In the ERP or PPC system, lead time is both a planning and an evaluation figure. When scheduling a production order, the system works with the times stored in the routing and the transition times between workstations to determine start and end dates. Material requirements planning (MRP) uses the planned lead time as a lead-time offset to calculate the latest point at which an order must start so that the delivery date holds.
For control in ongoing operations, shop-floor data collection (BDE) supplies the actual times per operation. This reveals where orders actually get stuck, and the planned times can be realistically refined. Combined with capacity planning and sequencing, lead time is not just measured but actively influenced – for instance by giving bottleneck resources priority or by splitting lots as the situation demands.
Planned versus actual lead time
The planned lead time stems from master data and flat-rate transition times and is often too optimistic or too coarse. The actual lead time results from the shop-floor data feedback and regularly deviates, because queues build up in front of bottlenecks. A regular plan-actual comparison in the ERP keeps the planned times current and improves scheduling; if the planned times are never maintained, however, the system plans permanently past reality.
Distinction: lead time, order lead time and takt time
Lead time in the narrower sense denotes the time an order or workpiece takes through production. Order lead time in sales, by contrast, measures the time from the customer order to shipment and includes commercial steps such as entry, release, picking and dispatch – the manufacturing lead time is only one building block there. When production is order-driven, the manufacturing lead time is often the longest part of the overall order lead time.
Lead time must also be kept separate from takt time: takt time indicates the interval at which finished parts leave a line, but says nothing about how long an individual part was in production overall. Likewise, the processing or cycle time is only the value-adding core of lead time. Because of these overlaps, it is important to define the start and end point of the measurement clearly – only then are lead times comparable across orders, periods and sites.
Example
Example: metalworking firm halves its lead time
A mid-sized metalworking firm manufactured assemblies in lots of 500 units and planned a lead time of ten working days for them. An analysis of the shop-floor data in the ERP showed that the pure processing accounted for only about one and a half days – the rest was queue and waiting time, because each lot had to be fully finished before moving to the next operation, and because a high work-in-process inventory sat in front of the bottleneck machine.
The company lowered the lot size to 100 units, split lots at the bottleneck resource and capped order release against the work-in-process. As a result, fewer parts waited at the same time, and individual orders ran through much faster. The average lead time fell to a good four working days, the stock of unfinished goods dropped noticeably, and because the spread decreased, on-time delivery rose – without additional machines or staff.
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