  This study addresses the difficulties associated with inventory management of
products with stochastic demand. The objective is to find the optimal
combination of order quantity and reorder point that maximizes profit while
considering ethical considerations in inventory management. The ethical
considerations are risk assessment, social responsibility, environmental
sustainability, and customer satisfaction. Monte Carlo simulation (MCS) is used
in this study to generate a distribution of demand and lead times for the
inventory items, which is then used to estimate the potential profit and risk
associated with different inventory policies. This work proposes a hybrid
optimization approach combining Gaussian process regression and conditioning
function to efficiently search the high-dimensional space of potential
continuous review (r, Q) and periodic review (p, Q) values to find the optimal
combination that maximizes profit while considering ethical considerations. The
findings show that both the (r, Q) and (p, Q) approaches can effectively manage
inventory with stochastic demand, but the (r, Q) approach performs better
(profits up by 12.73%) when demand is more volatile. The study adds
quantifiable risk assessment and sensitivity analysis to these considerations,
considering the variation in demand and expected output in profit percentage.
The results provide useful information for making ethical and responsible
choices in supply chain analytics, boosting efficiency and profits.

Computational Engineering, Finance, and Science;Applications