Artikel

Reliable shop floor bottleneck detection for flow lines through process and inventory observations: the bottleneck walk

Bottleneck detection in manufacturing is the key to improving production efficiency and stability in order to improve capacity. Yet, common bottleneck detection methods in industry and academia lack either accuracy or practicability, or both, for dynamic systems. The new methodology is conducted by the observation of processes and inventories. Blocked processes and full inventories indicate a downstream bottleneck. Starved processes and empty inventories indicate an upstream bottleneck. Through subsequent observations of multiple process states and inventory levels within a system, it is possible to determine the direction of the bottleneck at the given time and hence to find the momentary bottleneck in the system. The shifting of bottlenecks can be observed directly. Work sampling techniques can be used to obtain a long-term picture of the dynamically shifting bottleneck. The new methodology does not require any calculations, statistics, or time measurements. Hence, the method is suited for practical use by shop floor supervisors and clerks. The direct observation of the bottleneck also gives additional information about the underlying causes of the bottlenecks, simplifying the improvement of the system capacity. Extensive field testing of the method received positive feedback not only from management but also from shop floor operators. The method is already in use at the Robert Bosch GmbH, where it is known as the bottleneck walk.

Language
Englisch

Bibliographic citation
Journal: Logistics Research ; ISSN: 1865-0368 ; Volume: 8 ; Year: 2015 ; Issue: 1 ; Pages: 1-9 ; Heidelberg: Springer

Classification
Management
Subject
Bottleneck detection
Shifting bottleneck
Theory of constraints
Variability

Event
Geistige Schöpfung
(who)
Roser, Christoph
Lorentzen, Kai
Deuse, Jochen
Event
Veröffentlichung
(who)
Springer
(where)
Heidelberg
(when)
2015

DOI
doi:10.1007/s12159-015-0127-2
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Roser, Christoph
  • Lorentzen, Kai
  • Deuse, Jochen
  • Springer

Time of origin

  • 2015

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