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Coupled modelling of sugarcane supply planning and logistics as a management tool

Le Gal P.Y., Le Masson J., Bezuidenhout C.N., Lagrange L.F.. 2009. Computers and Electronics in Agriculture, 68 (2) : p. 168-177.

DOI: 10.1016/j.compag.2009.05.006

Agro-industry supply chains involving several stakeholders need coordination. Such systems, however, can be highly complex and may require a wide range of decision support. Models may be valuable when analysing complex supply chains, but are commonly specialised with little scope to evaluate a system holistically. Ideally, models with different specialised focus areas, such as a short-termlogistics or seasonal planning, need to be integrated into a single descriptive framework. In this paper, such a framework is demonstrated within a sugarcane supply management regime. The aim is to couple a tactical supply planning model, named MAGI, with a daily logistics model to more holistically explore the relationships between these supply components. This will help to further identify limitations in the approach and to recommend areas for further research. The research was carried out in conjunction with an investigation into different harvest mechanisation strategies at a South African sugar mill. The mill's weekly crushing capacity, the length of the milling season and logistical harvest and transport capacities were balanced to meet supply demands over different planning horizons. The most important link between the two models was the use of production units. These units provided sufficient diversity in terms of geography and agricultural systems to accommodate both models' data requirements. The model pair was used successfully to explore the mill area's response, in terms of the number of harvesters, vehicles, length of the milling season and sensitivity to risk, after the current harvest mechanisation regime of 16% of the annual crop was escalated to 75%. This paper contains a detailed description of the mill area and the current management practices and logistical configurations used. It then describes the construction of the model pair, validation of the models, and produces plausible future scenarios to support stakeholder negotiations. Recommendations for further research are highlighted.

Mots-clés : canne à sucre; saccharum officinarum; récolte; modèle de simulation; méthode mécanique; organisation du travail; industrie du sucre; afrique du sud

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