Task Matching System to Optimize High-Mix Low-Volume Manufacturing using Design Thinking Methodology

Abstract

High mix-low volume (HM-LV) manufacturing environments face significant inefficiencies due to low repetition, high product variation, and complex labor allocation challenges. This study develops a Task Matching System to Optimize High-Mix Low-Volume Manufacturing using Design Thinking Methodology to address these issues at Startiara (pseudonym), an Indonesian commercial display manufacturer. By creating a human-centered, iterative solution, the system optimizes task assignments, enhances production speed, maintains product quality, and improves worker satisfaction. Drawing on theories like Adam Smith's division of labor, Wright's learning curve, and the theory of identical elements, the system sequences similar tasks to maximize efficiency and reduce cognitive switching. A mixed-methods approach incorporated historical production data, Likert-scale surveys, and two rounds of iterative testing. The system was implemented on seven product types with four experienced workers over six days. Results show an average productivity increase of 7-21%: Product AT by 7.35% (13.08 to 14.04 pcs/hr), Product AL by 4.26% (19.03 to 19.84 pcs/hr), and Product TB by 21.7% (8.07 to 9.82 pcs/hr). Reject rates remained stable (e.g., Product AL at 2.56/100 produced, Product TB at 0/100 produced). Worker satisfaction improved markedly : Interested Feelings from 4 to 4.5, Inspired Feelings from 3.25 to 4.25 and Tired Feelings from 3.75 to 3.25 (5-point Likert scale). Key sustainability factors include dynamic priority logic, task repetition thresholds, time buffers, and data-driven refinement. This framework offers SMEs in HM-LV contexts a practical tool to overcome operational inefficiencies.