Production managers constantly seek ways to minimise waste and costs, so as to maximise profit. However, constraints such as budget, machine, manpower and storage capacities tend to make these optimisation tasks complex. Therefore, this study proposes a profit maximisation and optimal order quantity estimation model, considering various constraints. The study compares two optimisation methods, namely machine learning (Bayesian Optimisation) and nonlinear programming (PuLP Optimisation). The optimisation methods were applied to a small bottled water supply chain that produces bottled water of various sizes. The optimal order quantities from the Bayesian Optimisation for the 33cl, 50cl, 75cl and 150cl bottled water products are 300 packs, 483 packs, 150 packs and 33 packs, respectively, giving a maximum profit of ₦1,487,884. On the other hand, the optimal order quantities from the PuLP Optimisation for the 33cl, 50cl, 75cl and 150cl bottled water products are 295 packs, 519 packs, 177 packs and 91 packs, respectively, giving a maximum profit of ₦1,967,499. Though the PuLP Optimisation provided a higher profit, some of its order quantity estimates were above the average demand for the products. This could lead to product overstocking, and subsequent waste in the form of overproduction or excess inventory. On the other hand, though the Bayesian Optimisation was more computationally expensive, its order quantity estimates were less than or equal to the average demand for the products. Therefore, in the context of this study, the Bayesian Optimisation can be deemed to be better than its PuLP Optimisation counterpart. The study is significant to production management in general, and bottled water supply chains in particular, because it proposes an order quantity estimation and profit maximisation model, as well as a comparison of various methods for solving the model.

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APA Referencing Format

Wofuru-Nyenke, O. K., & Aikhuele, D. O. (2025). Optimal Order Quantity Estimation in a Manufacturing Supply Chain. International Journal of Production and Maintenance Engineering (IJPME), 1(1), 1-12.

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