## 6 Discussion  
Consumer desire for variety, shorter product life cycles, changing markets due to global competition, and fast development of new goods, services, and processes have all boosted interest in comprehensive production scheduling among academics and industry. The aforementioned economic and social market constraints highlight the need for a production planning system that requires little inventory, reduces waste output, and maintains customer satisfaction by delivering the right product to the right consumer at the right time. As a result, manufacturing processes such as make-to-order, cellular manufacturing, group technology, demand management, and engineering-to-order have emerged during the previous three decades. Except for process reengineering, different plant layouts, and a reinvented business culture, all of these processes need the facilitation and control of efficient, effective, and precise scheduling. Due to the complexity of scheduling in all but the smallest production facilities, it remained jolted until the mid-1990s, when the first APS systems were launched. These systems integrate operational research algorithms and decision support tools to optimize the whole supply chain, not just the shop floor. Although once hailed as the preferable method for resolving scheduling challenges, their high purchase prices, many installation hurdles, and limited connection with ERP systems rendered them too expensive for the majority of SMEs. Following that, customized, comprehensive production scheduling DSS systems emerged as a trend. These systems are capable of effectively depicting the unique characteristics of a manufacturing process and are not encumbered by the functionality of typical APS systems. That was also the objective of the research effort described in the paper: To create and implement a customized scheduling DSS in a repetitive make-to-stock SME. While the case study does not use customer-oriented manufacturing processes such as make-to-order, the significant use of subcontractors, the existence of dynamic events, and the piling of late orders highlight the need for shop floor visibility and control. The requirements were satisfied by the G-DSS release/dispatcher, which incorporates all of the essential features and techniques for swiftly producing schedules and makes them accessible through a user-friendly, aesthetically attractive Gantt chart-based GUI. The decision-support part of the system was emphasized through manual selection in order releasing, transferring, preemption, report creation, what-if analysis, and alternative scenario management. The system's interoperability with the surrounding IT infrastructure, specifically the PPC system and the ERP package, is fully supported, allowing for unhindered information flow through a three-tier planning framework that begins with the calculation of end item requirements over the long term, progresses to disaggregation of those requirements over the medium term, and finally collapses to component and material requirements for a single day or shift via the G-DSS. The algorithmic framework embedded in the G-DSS enhances decision support functionality by containing all the necessary heuristics, metaheuristics, and methods necessary to facilitate efficient scheduling and full workload control in a complex make-to-stock manufacturing environment with a high order-to-machine ratio, parallel work centers, integrated BOMs and routings, and extensive outsourcing. Dispatching is performed on a single machine level via fast and effective PDRs, and workload control uses manual input supported by proposed preemptions and the utilization of parallel machines. Finally, BOM tree constraints are handled by the hybrid TS/VNS-ATM metaheuristics. Due to this assortment of tools and algorithms, the scheduling process is initiated in a single functional group by selecting the appropriate orders from the backlog and releasing them to the shop floor.