Balancing Workload and Workforce Capacity in Lean Management: Application to Multi-Model Assembly Lines

While multi-model assembly lines are used by advanced lean companies because of their flexibility (different models of a product are produced in small lots and reach the customers in a short lead time), most of the extant literature on how to staff assembly lines focuses either on single-model lines...

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Bibliographic Details
Published in:Applied sciences 2020-12, Vol.10 (24), p.8829
Main Authors: Fortuny-Santos, Jordi, Ruiz-de-Arbulo-López, Patxi, Cuatrecasas-Arbós, Lluís, Fortuny-Profitós, Jordi
Format: Article
Language:English
Subjects:
assembly line
Assembly lines
Automobiles
Cycle time
Lead time
lean manufacturing
Manufacturing
OR-Tools
Python
Work in process
Work stations
workforce
Workload
Workloads
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Summary:While multi-model assembly lines are used by advanced lean companies because of their flexibility (different models of a product are produced in small lots and reach the customers in a short lead time), most of the extant literature on how to staff assembly lines focuses either on single-model lines or on mixed-model lines. The literature on multi-model lines is scarce and results given by current methods may be of limited applicability. In consequence, we develop a procedure to staff multi-model assembly lines while taking into account the principles of lean manufacturing. As a first approach, we replace the concepts of operation time and desired cycle time by their reciprocal magnitudes workload and capacity, and we define the dimensionless term of unit workload (load/capacity ratio) in order to avoid magnitudes related to time such as cycle time because, in practice, they might not be known. Next, we develop the necessary equations to apply this framework to a multi-model line. Finally, a piece of software in Python is developed, taking advantage of Google’s OR-Tools solver, to achieve an optimal multi-model line with a constant workforce and with each workstation performing the same tasks across all models. Several instances are tested to ensure the performance of this method.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10248829