Using a scalable simulation model to evaluate the performance of production system segmentation in a combined MRP and kanban system

Felberbauer, Thomas; Altendorfer, Klaus; Hübl, Alexander

doi:10.1109/wsc.2012.6465053

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…The hierarchical production planning approach as described above is modelled with a simulation generator (Hübl et al 2011;Felberbauer, Altendorfer, and Hübl 2012;Felberbauer and Altendorfer 2014), whereby the calculation of the APP is a function call within the simulation model. During simulation there is a cross data exchange between the simulation model and the MILP-Solver as described in previous section.…”

Section: Production System Structures and Simulation Modelmentioning

confidence: 99%

“…To evaluate the complex relationships within hierarchical production planning structures, a set of conference papers (Felberbauer, Altendorfer, and Hübl 2012;Felberbauer et al 2013;Felberbauer and Altendorfer 2014) apply simulation to discuss different specific planning decisions. Felberbauer, Altendorfer, and Hübl (2012) investigate different machine allocation strategies when MRP and KANBAN controlled materials are produced on the same resources. Inspired by a simulation study for an automotive supplier, Felberbauer et al (2013) compare the effect of different employee skill profiles, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

Altendorfer

Felberbauer

Jodlbauer

2016

International Journal of Production Research

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The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long term planning but the real production system faces a set of stochastic influences. Therefore, a planned utilization factor has to be included into this deterministic aggregate planning problem. In practise this decision is often based on past data and not consciously taken. In this paper the effect of long term forecast error on the optimal planned utilization factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision's potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilization factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments.

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Section: Production System Structures and Simulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

Altendorfer

Felberbauer

Jodlbauer

2016

International Journal of Production Research

Self Cite

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“…To address the sblock assembly scheduling in shipyard production and by considering spatial optimization, Zhuo et al [30] employ discrete event simulation to evaluate and improve system performance. Through a scalable production planning simulation model, Felberbauer et al [31] study the performance of two different machine allocation (i.e., segmentation) policies in a combined MRP and Kanban production system. Through a case study, Mahfouz et al [32] integrate simulation with optimization techniques to evaluate the implementation of lean principles in small and medium enterprises (SMEs) with regards to three performance measures, namely cycle time, WIP, and workforce utilization.…”

Section: Background Of the Researchmentioning

confidence: 99%

Performance and Improvement of Production Line Function Using Computer Simulation (Case Study: An Iron Foundry)

Saidabad¹,

Taghizadeh²

2015

AJCM

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Using the accurate methods and tools for evaluation of function in different areas is of great importance. In recent years, much more studies have been done on the nature and methodology of function evaluation in organizations and its improvement. This study aimed to evaluate and improve the function of production line through computer simulation in one of the iron foundries in Iran. For this purpose, at first data has been gathered on the basis of timing with chronometer for each one of the production line machineries and using this data, distribution functions governing the activity cycle of each one of these machineries have been determined; and then, using the required gathered data for computer simulation (taking the advantage of "ED" simulation software), a model was simulated to introduce the current situation of production line and the validity of simulated model was measured on the basis of production rate. And then, considering the criteria such as the production rate, average waiting time, machine utilization coefficient, production yield, production cost, production income and the production time production line function was evaluated. Finally, applying amendment changes on the simulated model improved model was offered.

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“…[26]. The method is appropriate if the company has a sufficient overview on lead times and inventories and operates with stable and predictable demand [27]. This fits the characteristics of serial parts.…”

Section: Mrpmentioning

confidence: 99%

“…As the case company already deploys MRPII, the investment is not of relevance. Another disadvantage is the missing flexibility and responsiveness when confronted with changes in demand [27]. This is the case for spare parts.…”

Section: Mrpmentioning

confidence: 99%

The fit of planning methods for the part manufacturing industry, a case study

et al. 2014

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This paper investigates the specific planning environment of an automotive part manufacturer and characterizes this environment based on variables regarding the product, its demand and the manufacturing process. The fit of different planning methods, namely material requirements planning, reorder point, cyclic planning, kanban, and heijunka to this specific planning environment is analyzed and evaluated by exploratory research. The results show that different product characteristics make it hard to apply a single planning method. None of the investigated methods show the ability to cover the complex requirements of a combined planning environment of two partly divergent product types with a deep value chain.

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Using a scalable simulation model to evaluate the performance of production system segmentation in a combined MRP and kanban system

Cited by 11 publications

References 16 publications

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

Performance and Improvement of Production Line Function Using Computer Simulation (Case Study: An Iron Foundry)

The fit of planning methods for the part manufacturing industry, a case study

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