  Uncertainties in core quality condition, return quantity and timing can
propagate and accumulate in process cost and complicate cost assessments.
However, regardless of cost assessment complexities, accurate cost models are
required for successful remanufacturing operation management. In this paper,
joint effects of core quality condition, return quantity, and timing on
remanufacturing cost under normal and extreme return conditions is analyzed. To
conduct this analysis, a novel multivariate stochastic model called Stochastic
Cost of Remanufacturing Model (SCoRM) is developed. In building SCoRM, a Hybrid
Pareto Distribution (HPD), Bernoulli process, and a polynomial cost function
are employed. It is discussed that core return process can be characterized as
a Discrete Time Markov Chain (DTMC). In a case study, SCoRM is applied to
assess remanufacturing costs of steam traps of a chemical complex. Its accuracy
analyzed and variations of SCoRM in predictive tasks assessed by bootstrapping
technique. Through this variation analysis the best and worst cost scenarios
determined. Finally, to generate comparative insights regarding predictive
performance of SCoRM, the model is compared to artificial neural network,
support vector machine, generalized additive model, and random forest
algorithms. Results indicate that SCoRM can be efficiently utilized to analyze
remanufacturing cost. Keywords: Remanufacturing, extreme value theory, hybrid
Pareto distribution, stochastic model.

Applications