In recent decades, manufacturing system, particularly small and medium-sized enterprises (SMEs) have faced rising challenges due to increased demand for customization and shorter delivery times. To adapt, many firms rely on temporary workforce teams, yet determining optimal staffing remains complex due to budget limits, contractual constraints, and forecast uncertainties. Although Advanced Planning and Scheduling (APS) systems have long been proposed to address such issues, they often focus on complex mathematical models with limited practical implementation guidance. This work introduces a practical APS framework built around three modules. The first handles data structuring, analysis, and demand forecasting. The second focuses on production planning, combining a multi-objective lot-sizing optimization sub-module for aggregated planning and a discrete-event simulation sub-module for operational scheduling. A regulation heuristic corrects forecast deviations in real time. The third module offers decision support, assessing the impact of strategies on KPIs such as service level, inventory, and workforce use. The results from the case study demonstrate that the proposed system consistently achieves a 100% service level, while the customer satisfaction rate remains close to 100% as well.