Comparing the performance of two different customer order behaviors within the hierarchical production planning

Felberbauer, Thomas; Altendorfer, Klaus

doi:10.1109/wsc.2014.7020066

Cited by 4 publications

(6 citation statements)

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“…In this paper, a classical APP such as presented in Gansterer (2015), is extended by an additional decision on planned utilization (see also Felberbauer and Altendorfer 2014). Table 1 shows all variables used in the MILP problem.…”

Section: Details On App Problem Studiedmentioning

confidence: 99%

“…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%

“…Ho and Ireland (2012) mitigate forecast errors by applying different lot sizing rules in enterprise resource planning controlled manufacturing systems. 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.…”

Section: Introductionmentioning

confidence: 99%

“…broader employee qualification, on inventory and service level. Based on the nervousness of MRP production orders, a customer information quality measure for hierarchical production planning is developed in Felberbauer and Altendorfer (2014). Using a similar planning setup, especially with respect to the APP, simulation is applied there to evaluate different customer order behaviours, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…"customer required lead time" and the "martingale model of forecast evolution", for one specific production system. Note that the forecast error and demand uncertainty effects, which are focussed in the current paper, are neglected in Felberbauer and Altendorfer (2014). In addition, the current study includes a comprehensive set of cost rates, different production system structures and demand patterns, as well as a set of practical implications.…”

Section: Introductionmentioning

confidence: 99%

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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: Details On App Problem Studiedmentioning

confidence: 99%

Section: Production System Structures and Simulation Modelmentioning

confidence: 99%