Productivity increase in a large size slaughterhouse: a simulation approach applying lean manufacturing

Purpose The main purpose of this paper is to develop a study of the determination of the most appropriate execution steps, necessary for the construction of modelling, simulation and optimization for the specific area of slaughter line balancing. And through the developed model to demonstrate the application of simulation to increase productivity in a large-size swine slaughterhouse, focusing on operator stations balancing. The built model may be applied to support the management of the plant, allowing to evaluate and decide the optimized scenario that meets current needs considering operational cost, production demand and productivity. Design/methodology/approach For the research development, the selected software supports the characteristics of the evaluated process, in this case, a discreet simulation with stochastics variables. The studied plant was modelled door by door, from the swine reception until the packaging area. The research methodology was based on lean manufacturing (LM) principles, particularly in workstations balancing, by optimizing the idle time of the operators, comparing with the cycle time of each task, in the evaluated workstations. Findings The achieved result with the modelling and simulation was the increase of 11.89% in plant productivity through manpower optimization. The study indicates that the simulation applied with LM concepts as operative stations balancing and value stream map can be a very useful tool to support decision-making for productivity improvement. Originality/value This study approaches how modelling and simulation can support decision-making to implement improvements associated to workforce balancing optimization, especially in the studied area (agribusiness, animal slaughter). The studied process presents great variability associated with the processing time of each phase, making the analysis and modelling more complex. The number of workstations involved, with more than 800 employees, is an important point in the research, considering that cases with higher values than the case presented were not identified in literature.