Mass personalization strategy under Industrial Internet of Things: A case study on furniture production

The Industrial Internet of Things (IIoT) is characterized by digitalization, networking, and smartness, which opens a world of the interconnection of all things and makes mass personalization possible. As a result, traditional industrial firms are forced to change their operation mode from the manufacture-oriented one to the manufacture-and-service-oriented type. In particular, furniture production is a typical domain featured by mass personalization from networking, where order selection (service aspect) and board cutting (manufacture aspect) are mainly concerned. We formulate customized furniture production as a multi-objective optimization problem and propose two algorithms to solve it, i.e., an integrated algorithm and a two-stage decoupling algorithm. As a secondary, the robust mixed-integer linear optimization algorithm is proposed to deal with the uncertainty such as fluctuations in production capacity and raw materials cost. The numerical experiments using real industrial data demonstrate that the proposed algorithms effectively improve firms’ operational efficiency by achieving a balance between service and manufacture. Moreover, they present remarkable performance under various circumstances. The developed methods could apply to a wide range of mass personalization for related manufacturing scenarios of IIoT with digital servitization, including computer, communication, and consumer electronics products (3C products) machining, automobile accessory production, and chip manufacturing.