Dynamics of human-machine task allocation in intelligent production processes: A case study

Abstract

Efficiency and effectiveness in advanced production systems depend on judicious task allocation between humans and machine entities. Despite growing academic interest in human–machine task allocation, theoretical research that can effectively guide practice remains lacking. This paper develops a dynamic human–machine task allocation framework aimed at addressing this gap through the identification and evaluation of task complexity and economic benefits influencing task allocation. We conduct a case study on human–machine task allocation during the transition from manual to intelligent production, utilizing actual task data from both manual and intelligent production lines for the same products. This case study provides practical support for our theoretical framework and yields additional insights into human–machine task allocation. Key findings include: (1) Technological progress has demonstrated significant replacement and reinstatement effects, promoting machines to replace humans in performing more complex tasks while generating new manual tasks related to smart devices. (2) The core considerations for human–machine task allocation are task complexity and task intensity. Although technological progress has facilitated the automation of highly complex tasks, the final allocation is ultimately determined by the economic benefits associated with task intensity. (3) The production pattern in the electronics manufacturing industry has evolved from a traditional manual-led approach to a human–machine collaboration pattern in intelligent production lines. Machines focus on production, while human workers handle setup tasks. This transformation allows intelligent production lines to combine the high productivity of machines with the flexibility of humans.