In response to the ongoing global energy crisis, particularly prevalent in Europe, there is a critical need to identify alternative energy sources and reduce energy consumption. This study presents an integrated inventory model that incorporates energy consumption from a supplier’s production and storage operations. The model focuses on a manufacturing system consisting of a single machine for production and a warehouse responsible for storing a single product. Energy consumption in the production process is attributed to the machine’s operation, which can be either in a production or non-production phase. Additionally, energy is utilized to maintain goods at the required temperature, accounting for ambient temperature fluctuations and varying levels of warehouse occupancy. The objective is to determine optimal decision variables, including production rate, cycle time, and machine status in the non-production phase, to minimize the overall system cost, encompassing energy costs. The proposed optimisation procedure simplifies solution identification for the nonlinear programming (NLP) problem. A realistic numerical example illustrates the model’s applicability and reveals that optimal machine status during non-production phases significantly reduces both energy costs and total costs compared to the remaining status. Sensitivity analysis explores the effects of different parameters on the model, emphasizing the considerable impact of factors like energy consumption per unit, energy cost, and demand rate on the total cost.

As companies strive to reduce energy consumption and minimize their carbon footprint, there is a growing trend among customers to choose eco-friendly products. In order to address these concerns, the present study puts forth a new economic production quantity (EPQ) model that takes into account various factors such as energy usage, carbon emissions, and market demand in relation to product price and environmental impact. This is achieved through an analysis of the working phases of the manufacturing machine. By maximizing the overall profitability of the system, optimal decisions can be made regarding cycle time, production rate, demand rate, and machine states during non-production phases (standby or powered off). A Mixed integer nonlinear programming (MINLP) problem is proposed and analyzed. A case study demonstrates that meeting customer expectations for sustainable products can lead to lower profits for businesses, but it also results in reduced energy consumption and environmental emissions. The sensitivity analysis demonstrates that market demand, price sensitivity coefficient, and unit production cost have a more substantial impact on profit compared to the other parameters.