Throughput Time vs. Cycle Time in Bulk Material Filling: Differences and Leverage Points

In bottling, it is not just the speed of the machine that determines delivery capability—but the total time from the start of the order to the pallet ready for shipment. A bagging system that fills 300 bags per hour sounds productive. But if 45 minutes elapse between two orders for product changeovers and cleaning, if palletizing isn’t synchronized, and the operator has to shuttle back and forth between loading the hopper and preparing pallets, then the order loses hours—not at the machine, but around it.

This total time is called throughput time: the span from the moment the order starts at the bagging system until the last pallet of the order is ready for shipment. It includes setup times, filling times, waiting times, and internal transport times. Experience shows that those who optimize only the cycle time at the packer—i.e., the seconds per bag—ignore 40 to 60 percent of the total lead time. Kletti and Schumacher describe this effect as typical: in many production facilities, the actual processing time accounts for less than half of the lead time—the rest is waiting, setup, and transport.

This article explains the composition of throughput time in bulk material filling, shows the typical time allocations, and identifies the levers that go beyond mere machine speed. The systematic optimization of production begins where one stops looking only at the packer.

Lead time measures the period from the start of production for an order at the bagging line until the end of production: the last pallet of the order is ready for shipment at the loading zone. It is not a machine metric, but an order metric—it encompasses everything that happens between the first action and the last container, including the times when no one is doing anything.

Distinguishing it from two related terms is crucial because confusion leads to incorrect optimization approaches:

Cycle time, as part of lead time, measures only the pure machine time for a single bag: bag mounting, dosing, filling, sealing, and discharge. At 300 bags per hour, the cycle time is 12 seconds. The cycle time determines the theoretical maximum throughput of the system—but it says nothing about how long an order of 500 bags actually takes, because it does not include setup times, waiting times, and transport times. The related article on cycle time delves deeper into the sub-cycles at the machine level.

Lead time, on the other hand, is broader than cycle time: it begins when the order is received by sales and ends with delivery to the customer. It encompasses order processing, material procurement, production planning, the cycle time itself, and transport to the customer. This article focuses exclusively on the production segment—the lead time within the bottling plant—because that is where the levers lie that the production manager can directly influence.

Bertagnolli places lead time within the lean framework: it is the barometer for flow in the value-added system. Short lead times mean that materials and orders flow rather than stagnate—fewer intermediate inventories, less capital tied up, and shorter response times to customer requests. Dennis describes the ideal as one-piece flow: each container passes through the entire process without interruption. On a bagging line, this ideal state is never fully achieved—but it defines the direction in which every reduction in lead time works.

The lead time for a bagging job can be broken down into four time segments that differ fundamentally in terms of how they can be influenced and their contribution to value creation. Only one of these—the filling time—directly produces what the customer pays for: a filled, sealed, and ready-to-ship bag. The other three are necessary but do not add value in the Lean sense. Bertagnolli classifies them as waste—not because they are superfluous, but because every minute spent on setup, waiting, or transport is a minute during which no bag leaves the line.

Kletti and Schumacher document that this percentage is significantly higher in many manufacturing facilities—reaching 60 percent or more—when setup times are not systematically optimized and waiting times are not tracked. Availability directly influences lead time: Every unplanned disruption that appears as a loss of availability in the OEE calculation example simultaneously extends the lead time of the affected order—the same downtime viewed from two perspectives.

The implication for optimization: If you want to shorten lead time, you don’t necessarily have to buy a faster machine. In many companies, the greater leverage lies in the 50 to 70 percent of non-value-added time—in setup, waiting, and transport. This is precisely where the levers described in the next section come into play.

The table shows where the time is spent—this section shows how it can be reduced. The levers follow an order consistent with lean logic: first eliminate the greatest waste, then the next. For most bagging systems, this means: first setup time, then waiting times, then flow.

Reduce setup time: Separate external setup from internal setup. In many facilities, 30 to 45 minutes are spent on a product change—and the line stands idle the entire time. The SMED principle from the Lean Production article applies directly here: anything that can be prepared while the system is still running is brought forward into the ongoing production. The next bag type is ready at the magazine, the new dosing parameters are stored in the system, and the cleaning tool is within reach. When the system stops, only the internal part begins: cleaning, bag type change, calibration. Hänggi, Fimpel, and Siegenthaler document that through this organizational separation alone—without any technical changes—setup time reductions of over 50 percent can be achieved. In a plant with four product changes per shift and 40 minutes of setup time per change, halving this time saves 80 minutes per shift—time during which the machine produces bags instead of standing idle. SMED for shorter product changeovers is explored in depth in a separate article as soon as it is published.

Eliminating waiting times: Synchronize instead of sequential. Waiting times are the least well-recorded source of loss in lead time—because they do not appear in any error reports. The machine isn’t idle; it’s waiting: for material from the silo, for a pallet from the warehouse, for the operator who is currently fixing a malfunction on the auxiliary line. Each individual wait lasts only a few minutes—but three to four interruptions per hour add up to a significant block of time over the course of a shift. The key lies in synchronization: material feeding, pallet provision, and operator tasks must be timed to the machine cycle, not to the availability of individual resources. Automation eliminates manual wait times structurally: Inline palletizing, which is directly connected to the packer, eliminates transport to a separate palletizing station and the wait time for pallet changes. Automatic bag mounting eliminates the wait time for the operator between two bags.

Flow instead of batch thinking: Small batches, short distances. Dennis describes the lean ideal as one-piece flow—each part passes through the entire process without interruption. On a bagging line, single-bag flow is the design principle of every modern line: the bag moves continuously from station to station without intermediate storage. But at the order level, many companies still think in terms of large batches: 2,000 bags at a time, then 45 minutes of setup time, then the next batch. Kletti and Schumacher demonstrate the connection: Halving setup time enables smaller batch sizes with the same or higher total throughput—because while the system changes over more frequently, each changeover takes less time and the next order starts faster. The result: Continuous flow instead of batch thinking shortens lead time, reduces work-in-process inventory, and increases delivery flexibility—three effects that Bertagnolli describes as the core benefits of a well-functioning value-added system.

If you only know the cycle time at the packer, you’re only optimizing half the process. The total lead time shows the full picture—including setup, waiting, and transport times, which often offer more potential for improvement than machine speed alone. In the calculation example in this article, filling time accounted for only 59 percent of the lead time—41 percent was spent on setup, waiting, and transport. In many companies, this ratio is even less favorable.

The levers are well known: SMED for shorter product changeovers, synchronization for reduced waiting times, inline integration for shorter distances, and smaller batches for better flow. What determines success is not knowledge of these levers, but their application to your own line—with an honest assessment of actual time allocations, not estimated ones.

Sources

Kletti, Jürgen / Schumacher, Jochen: Die perfekte Produktion. 2. Auflage, Springer Vieweg, Berlin Heidelberg 2014.

Hänggi, Roman / Fimpel, André / Siegenthaler, Roland: LEAN Production – einfach und umfassend. Springer Vieweg, Berlin 2021.

Bertagnolli, Frank: Lean Management. Springer Gabler, Wiesbaden 2018.

Dennis, Pascal: Lean Production Simplified. 3rd Edition, CRC Press, Boca Raton 2015.