A Discrete-Event Simulation for Optimising Cutting Process Productivity in Furniture Manufacturing Production.

Abstract

In the competitive landscape of furniture manufacturing, the cutting process remains a critical determinant of production lead time and overall operational efficiency. Traditional lot sizing decisions often fail to account for dynamic shop-floor variables, leading to inefficiencies and unpredictable delivery timelines and lead to customer dissatisfaction. This study aims to enhance productivity in furniture manufacturing SMEs by reducing lead time inconsistency and improving on-time delivery performance by optimising cutting operations using discrete-event simulation. The specific objectives include conducting time and motion studies on current cutting processes in SME furniture manufacturing to identify operational inefficiencies and their impact on lead time. To develop a discrete-event simulation model of the existing cutting process, incorporating identified bottlenecks to analyse their contribution to extended lead times. To compare the performance of the optimised simulation model with the baseline process using productivity metrics such as cycle time and throughput. A mixed-methods approach was adopted, combining time studies conducted to pinpoint specific areas of inefficiency, value stream mapping VSM is utilized to map out the production process, and discrete-event simulation using Anylogic software to validate the proposed improvements. Empirical data were collected from a  South African SME furniture manufacturing facility. Regression analysis was applied to evaluate the influence of variables such as number of panels, cut length, layout wastage, and operator adjustments on cutting duration. Findings revealed that operator adjustments had the most significant impact on cutting time, with a 32% reduction in adjustments achieved through the proposed optimization model. Simulation results demonstrated a 25% reduction in average cycle time, from 957 seconds to 714 seconds per cut list, validating the model's effectiveness. The linear regression model also enabled accurate forecasting of future demand trends, supporting proactive resource planning.
The integration of lean principles with statistical and simulation tools offers a robust framework for optimizing cutting operations in furniture manufacturing. The study recommends the development of an intelligent software system that automates lot sizing and cutting process decisions, thereby reducing lead time and enhancing productivity. This approach holds significant potential for broader application across manufacturing sectors seeking agile and sustainable production systems.