Modeling Production Costs with Bayesian Belief Networks
نویسنده
چکیده
The problem of setting production lead times in manufacturing is well known and many firms set the value as an average of past operations, frequently resorting to inventory, overtime and other unplanned activities to match supply with demand. In this paper, we use Bayesian belief networks to model a common production process with probabilistic manufacturing lead time based on more realistic assessment of factors which contribute to variance in the estimation of true lead time, such as delivery performance, unplanned downtime, production run, and setups. The objective is to provide better estimates of production costs due to variances in lead times and improve the accuracy of customer order commitments.
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