This research centres on developing a Home Electricity Management (HEM) system, a pivotal component within the modern supply chain for home electrical power. The system optimizes the scheduling of intelligent home gadgets through advanced meta-heuristics, specifically the Social Spider Algorithm (SSA) and Strawberry Algorithm (SWA), to efficiently manage home energy consumption. Within the supply chain context, HEM acts as a crucial link in the distribution and utilization of electricity within households, akin to optimizing resource allocation and demand balancing within a supply chain for efficient operation and cost-effectiveness. Simulations and comparisons demonstrate that SWA excels in cost savings, while SSA is more effective in reducing peak-to-average power ratios. The proposed solution reduces costs for residences by up to 3.5 percent, highlighting the potential for significant cost savings and efficiency improvements within the home electricity supply chain. It also surpasses existing cost and Peak Average (PAR) ratio meta-heuristics, indicating superior performance within the overall energy supply and consumption framework. Moreover, implementing the HEM system contributes to reducing carbon emissions, aligning with sustainability goals in the energy supply chain. It promotes energy efficiency, integrates renewable sources, and facilitates demand response, mirroring the emphasis on sustainability in supply chain practices. Overall, this research offers a practical and sustainable approach to home energy management, bringing substantial cost savings and environmental benefits to the modern supply chain for residential electricity.