article · R E M (Rekayasa Energi Manufaktur) Jurnal
Production planning and control in job-shop manufacturing faces serious hurdles such as high product variety, small batch sizes, changing routings, and frequent disruptions. In developing economies, these challenges are heightened by unreliable energy supplies, infrastructure deficits, financial constraints, and shortages of specialist expertise. A systematic review examines how expert systems can integrate interconnected functions including forecasting, manpower planning, machine scheduling, energy utilisation, inventory, costing, and due-date setting. Current research predominantly optimises single functions in isolation, with very few frameworks uniting rule-based reasoning, optimisation models, shop-floor data, and operational feedback. To address this, an explainable, feedback-based architecture is proposed for small and medium-sized enterprises. Future priorities for making these systems practical include enabling real-time adaptation, achieving low-cost deployment, delivering transparent recommendations, and validating architectures using real shop-floor data.
Manufacturing enterprises in developing regions must manage complex custom orders despite severe infrastructure and budget constraints. Traditional planning software is often too costly or complex to maintain. Outlining an expert system framework tailored to these environments helps small and medium-sized factories improve operational efficiency, schedule reliability, and energy management without requiring unaffordable infrastructure or deep specialist expertise.
The proposed architecture provides a conceptual blueprint for production planning and control software tailored to small and medium-sized job-shop manufacturers in developing economies. Key commercial features include low-cost deployment, explainable decision-making, and energy-aware scheduling. Because the work is a review and conceptual framework proposing future validation with shop-floor data, the technology remains at an early, pre-implementation stage rather than near market.
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Production planning and control (PPC) in job-shop manufacturing is complicated by factors such as high product variety, small batch sizes, changing routings, and frequent disruptions. These difficulties are more severe in developing economies, where limited infrastructure, shortage specialist expertise, unreliable energy supply, and financial constraints restrict the adoption of advanced planning systems. This review critically examines the application of expert systems (ESs) to PPC in job shops. It evaluates their capacity to integrate forecasting, manpower planning, energy utilization, machine scheduling, inventory control, cost estimation, and due-date determination. The review follows a sequential process of literature identification, screening, thematic classification, quality appraisal, and synthesis. Its novelty lies in treating these PPC functions as interdependent rather than isolated decisions and in translating evidence into an explainable, feedback-based ES–PPC architecture designed for the operational realities of small and medium-sized enterprises in developing economies. The synthesis shows that existing studies generally optimize individual functions, while only a limited number integrate rule-based reasoning, optimization models, shop-floor data, and performance feedback within a unified framework. The review therefore proposes an integrated architecture and identifies priorities for real-time adaptation, low-cost deployment, explanation of recommendations, and validation with shop-floor data. These contributions provide a structured foundation for the development of practical ES-enabled PPC systems for job-shop manufacturing.
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DOI: 10.21070/r.e.m.v11i2.1869
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