This study explores how KPI-driven frameworks can optimise packing and logistics costs in the cement industry. It highlights cost structures and demonstrates how mathematical optimisation can reduce supply chain costs significantly.
The cement industry operates in a highly competitive and cost-sensitive environment where packing and logistics expenses form a substantial portion of total operational costs. This case study examines how Key Performance Indicators (KPIs) can be used to systematically optimise logistics and packing operations. The research emphasises that logistics activities – ranging from raw material handling to final distribution—are complex and require efficient coordination across transportation, warehousing, and inventory systems to maintain profitability and competitiveness.
A key finding of the study is the significant share of logistics costs in overall investment. Based on empirical data from eight cement projects in Indonesia, total logistics costs account for 14.60 per cent of total investment on average, with project-level variations ranging from 13.53 per cent to 22.56 per cent. Among the cost components, foreign logistics costs (6.62 per cent) and customs clearance costs (6.52 per cent) emerge as the largest contributors, together accounting for nearly 90 per cent of total logistics expenses. In contrast, domestic logistics (0.89 per cent), domestic manufacturing delivery (0.47 per cent), and insurance (0.11 per cent) contribute relatively smaller shares.
The study further highlights how geographical and infrastructural factors influence logistics costs. For instance, projects located in Java benefit from better port infrastructure and transportation networks, resulting in lower logistics costs (as low as 13.53 per cent), whereas regions like Kalimantan experience significantly higher costs (up to 22.56 per cent) due to limited infrastructure and reliance on transshipment. This regional disparity underscores the importance of location-based decision-making in logistics planning.
To address these inefficiencies, the research applies mathematical optimisation techniques, particularly Mixed Integer Linear Programming (MILP). The findings reveal that such models can achieve overall supply chain cost reductions of around 4 per cent, with production cost improvements of 3 per cent and distribution cost reductions of 7 per cent. Notably, the highest optimisation potential lies in the plant-to-packing distribution stage, with cost reductions reaching up to 44 per cent, making it a critical focus area for cost-saving initiatives.
The study also introduces a comprehensive KPI framework covering five major dimensions: cost efficiency, operational efficiency, service quality, inventory management, and sustainability. Key metrics include total logistics cost ratio (benchmark 14.60 per cent), on-time delivery performance (target >95 per cent), order fill rate (>98 per cent), vehicle capacity utilisation (>85 per cent), and inventory turnover ratio (>12 times/year). This framework enables organisations to monitor performance holistically and identify areas for continuous improvement.
In conclusion, the research demonstrates that KPI-based monitoring combined with advanced optimisation techniques can significantly improve cost efficiency and operational performance in the cement industry. By leveraging data-driven decision-making, companies can reduce inefficiencies, enhance delivery reliability, and optimise resource utilisation. The study ultimately provides a structured roadmap for implementing logistics optimisation strategies in a complex industrial environment.
This case study by Riddhish Pandey, was published in the Journal of Informatics Education and Research (Vol 5, Issue 3, 2025).
