Enabling industrial internet of things-based digital servitization in smart production logistics

被引:24
作者
Flores-Garcia, Erik [1 ]
Jeong, Yongkuk [1 ]
Liu, Sichao [2 ]
Wiktorsson, Magnus [1 ]
Wang, Lihui [2 ]
机构
[1] KTH Royal Inst Technol, Dept Sustainable Prod Dev, Sodertalje, Sweden
[2] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden
关键词
IIoT-enabled digital servitization; smart production logistics; systems modelling language; case study; material handling; CYBER-PHYSICAL SYSTEMS; DYNAMIC OPTIMIZATION; SERVICE SYSTEMS; SUPPLY CHAIN; TRANSFORMATION; DIGITIZATION; METHODOLOGY; VEHICLES; FIRMS;
D O I
10.1080/00207543.2022.2081099
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Digital servitization (DS) enabled by the Industrial Internet of Things (IIoT) is essential for securing long-term competitiveness in manufacturing. The literature identifies the need for developing data models for multichannel communications across IIoT devices that fulfil the DS vision. This is crucial for avoiding isolated systems based on proprietary solutions and for promoting data sharing and interoperability across existing and future DS applications. Accordingly, this study proposes a data model for multichannel communication that facilitates IIoT-enabled DS for smart production logistics (SPL). We present three findings from a case study focussing on material handling in a manufacturing company. First, this study provides a model with four modelling profiles, including IIoT devices, databases, and services for multichannel communication. Second, it shows how the proposed modelling profiles transfer information across monitoring, control, optimisation, and autonomous decision services. Third, it presents the operational benefits of applying the proposed data models, including improvements in the delivery, makespan, and energy of material handling. These findings are essential for capturing, processing, and transferring information across products, services, and software databases in IIoT-enabled DS for SPL. They are relevant for manufacturing managers and academics and improve our understanding of IIoT-enabled DS's deployment for SPL in manufacturing.
引用
收藏
页码:3884 / 3909
页数:26
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