<p>In the contemporary context marked by wars, conflicts, and economic sanctions, manufacturers encounter significant difficulties in controlling the rising prices of finished goods. Sudden increases in the cost of raw materials lead to substantial growth in overall production expenses, which in turn elevates the inflation rate within a single production cycle. This escalation in production cost reduces the manufacturer’s profit margin, which is often offset by increasing the selling price. Consequently, developing a production–inventory model that simultaneously mitigates price escalation and minimize overall production cost under abrupt inflationary changes has become essential. This study proposes an economic production quantity (EPQ) model designed to address this challenge. A single-item production–inventory system is formulated in which demand is price-dependent and the raw material cost varies with time under the inflation rate. The primary objective of the model is to determine the optimal production rate and selling price that enable the manufacturer to sustain economic production cost despite sudden fluctuations in inflation rate within a finite planning horizon. The result suggests that production rate remains unaffected by sudden hike in inflation. However, the overall production cost increases due to increase in the rate of inflation while the manufacturer maintains the minimum average total cost. The novelty of the proposed model is the investigation made in production cost under a sudden jump in the rate of inflation while minimizing the costing of both production and inventory. The optimization problem is solved using an algorithmic approach incorporating two variants of the particle swarm optimization algorithm, and the optimality of the results is demonstrated through convergence behavior. The model is further validated using numerical experiments. In place of experiment write example, accompanied by sensitivity analysis and managerial insights from a practical business perspective.</p>

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An EPQ-based investigation of single-item production dynamics affected by sudden inflation rise and linear raw material cost increase to minimize the cost with price reliant demand

  • Nabajyoti Bhattacharjee

摘要

In the contemporary context marked by wars, conflicts, and economic sanctions, manufacturers encounter significant difficulties in controlling the rising prices of finished goods. Sudden increases in the cost of raw materials lead to substantial growth in overall production expenses, which in turn elevates the inflation rate within a single production cycle. This escalation in production cost reduces the manufacturer’s profit margin, which is often offset by increasing the selling price. Consequently, developing a production–inventory model that simultaneously mitigates price escalation and minimize overall production cost under abrupt inflationary changes has become essential. This study proposes an economic production quantity (EPQ) model designed to address this challenge. A single-item production–inventory system is formulated in which demand is price-dependent and the raw material cost varies with time under the inflation rate. The primary objective of the model is to determine the optimal production rate and selling price that enable the manufacturer to sustain economic production cost despite sudden fluctuations in inflation rate within a finite planning horizon. The result suggests that production rate remains unaffected by sudden hike in inflation. However, the overall production cost increases due to increase in the rate of inflation while the manufacturer maintains the minimum average total cost. The novelty of the proposed model is the investigation made in production cost under a sudden jump in the rate of inflation while minimizing the costing of both production and inventory. The optimization problem is solved using an algorithmic approach incorporating two variants of the particle swarm optimization algorithm, and the optimality of the results is demonstrated through convergence behavior. The model is further validated using numerical experiments. In place of experiment write example, accompanied by sensitivity analysis and managerial insights from a practical business perspective.