Supply Planning for Processors of Agricultural Raw Materials
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Renewable resources are rapidly gaining importance as alternative raw materials for industrial production. Supply planning with agricultural raw materials poses several challenges to processors. These challenges include seasonal availability, uncertain harvest quality and quantity as well as uncertain commodity market prices. In this work, decision support based on stochastic programming is developed to optimize supply planning of processors of agricultural raw materials given industrial requirements for material use. The approach depicts uncertain parameter values with probability distributio...
Renewable resources are rapidly gaining importance as alternative raw materials for industrial production. Supply planning with agricultural raw materials poses several challenges to processors. These challenges include seasonal availability, uncertain harvest quality and quantity as well as uncertain commodity market prices. In this work, decision support based on stochastic programming is developed to optimize supply planning of processors of agricultural raw materials given industrial requirements for material use. The approach depicts uncertain parameter values with probability distributions and maximizes the expected profit. Two examples are used to illustrate possible applications: supply planning for a processor of linseed and for a seed company. As stochastic programs can be adapted quickly, this approach can be applied to other supply planning decisions in bio-based supply chains.
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