ReviewProduction logistics digital twins: Research profiling, application, challenges and opportunities
Introduction
Manufacturing enterprises are experiencing subtle changes under the impact of Industry 4.0, particularly in the field of Production Logistics (PL). PL is committed to the direction of automation, digitalization and wisdom with thinking, perception, learning, reasoning and autonomous decision-making capabilities [1]. As an essential link between shop-floor supply and production processes, PL accounts for approximately 95% of the entire production life cycle [2]. This shows that effective and reasonable PL management plays a momentous role in augmenting the competitiveness and economic efficacy of enterprises, especially for the intelligent development and digital transformation of today's enterprises.
The initial focus of the PL management approach was on scientifically planning, allocating, and controlling material flow within production processes. However, with the emergence of Industry 4.0, the emphasis of PL management has shifted towards information flow management, resulting in an increased demand for flexibility, agility, and consistency of real and virtual interactions in the Production Logistics System (PLS) [3]. This poses various challenges for researchers and practitioners alike. For instance, (i) how to effectively connect the virtual and physical worlds while realizing seamless integration and real-time interaction in the complex technical landscape [1]. (ii) How to systematically integrate heterogeneous systems and aggregated data platforms under big data environments characterized by massive volume and heterogeneity [4]. And (iii) how to optimally utilize economical computational resources to accomplish the most precise synchronous control of PL in complex environments featuring varying levels of dynamic disturbances [5].
Aligned with Industry 4.0, the concept of Digital Twins (DTs) has been extensively employed in manufacturing research since its inception. DTs is aimed at representing and optimizing physical objects through virtual models driven by a combination of data and models. Recently, the terms Logistics Digital Twin (LDT) and PLDTs have appeared in many works, with the goal of improving the performance of logistics. For example, Piancastelli et al. [6] derived the architecture of LDT and pointed out that LDT helps management to make decisions, evaluate and reduce the risk of Production Logistics Activities (PLAs) scenarios, etc. Thürer et al. [7] proposed a new architecture for PLDTs. Kaiblinger et al. [1] summarized the common definitions of DTs in the field of PL. More studies have discussed that DTs brings new concepts, models and ideas to the intelligent operation of logistics with its characteristics of virtual simulation, evaluation, prediction and autonomous decision-making [8]. It is also shown that DTs is an effective way to realize the integration, interaction and intelligent interconnection of production and logistics processes in the virtual world and the physical world [4].
In recent years, there has been a proliferation of reviews exploring PLDTs, with each offering unique insights. (i) In terms of research interests, Pawlewski et al. [8] described the research implications of using DTs to optimize intralogistics processes and named it Digital Twin Lean Intralogistics. The research results have indicated a growing interest in the terms “digital twins” and “intralogistics”. (ii) As to the virtualization integration of PLS, Fottner et al. [9] discussed recent research advances in autonomous systems in intralogistics. And the importance of DTs, modeling and simulation techniques for the virtualization of the entire intralogistics system was emphasized. Kosacka-Olejnik et al. [10] addressed the question of how the DTs concept can support internal transport systems. Explained that DTs supports internal transport systems by establishing dynamic links and correspondences with real objects and internal transport processes. (iii) As for the application scope of DTs, Zafarzadeh et al. [11] classified PLAs into 3 categories. The share of 10 groups of enabling technologies in PLs, such as DTs, was systematically reviewed and evaluated. It was also shown that there are applications of DTs in PLAs such as tracking and location, material distribution and warehousing. Most recently, Kaiblinger et al. [1] discussed the common definitions, features and functions of DTs in the field of PL. The current state of development and implications of the latest implementations were outlined, and 20 application cases were evaluated to identify current research gaps.
As previously mentioned, researchers have emphasized the role of DTs as a crucial factor for virtualizing PL processes in their analysis of PLDTs. This is consistent with the urgent need for information-physical fusion technology during the digital transformation of today's manufacturing industry. To further describe the role of DTs in specific PLAs, this study focuses on the latest applications of DTs in PL and supplements the discussion of common application scenarios, methods or theories, and related intelligent technologies of existing PLDTs from the perspective of functional characteristics. Firstly, the common application scenarios of DTs in PL are discussed based on keyword analysis, including transportation, packaging, warehousing, material distribution and information processing. And the distribution of decision support, simulation, planning, monitoring, evaluation, tracking and positioning, predication and design in each logistics scenario are analyzed. Then, more detailed analysis of the methodologies, functions, and application validation methods of DTs in specific PL activities are analyzed. The roles played by intelligent technologies such as Internet of Things (IoT), big data, and Cloud Computing (CC) in PLDTs system are summarized and discussed. Secondly, the application advantages and challenges of DTs in PL are discussed. Finally, the possible future directions of PLDTs are described from the perspective of industrial applications. This study is a complement to the above research work, providing an appropriate classification method for existing theoretical studies and industrial applications of PLDTs.
The remainder of the paper is structured as follows: Section 2 describes the research profiling of PLDTs. Section 3 provides a detailed analysis of the role of DTs in PLAs. Section 4 describes the role that intelligent technologies play in PLDTs system. The advantages and challenges of existing research methods are summarized in Section 5. Furthermore, Section 6 describes the possible future directions of PLDTs in industrial applications. Finally, to conclude this paper.
Section snippets
Definition
To date, research in the field of LDT has covered several branches of logistics, including PL [10], general logistics [12], e-logistics [13,14], cold chain logistics [15,16] and military logistics [17], etc. The application of DTs within each logistics branch reflects a slightly different focus, as outlined in Fig. 1, which provides an overview of LDT research within each branch of logistics.
PL, also referred to as shop-floor logistics or plant logistics. Based on logistics scope analysis, the
Detailed analysis of the functions of DTs in PLAs
Based on the results of the analysis of the PLDTs research profile, 132 publications are described in detail in this section from the perspective of PL application scenarios, including specific types of PLAs, research methods, main functions, intelligent technologies and validation approaches. The common application scenarios and basic functions of PLDTs, as well as related intelligent technologies, are summarized in Fig. 8, the purpose of this section is to provide a detailed overview of the
The role of intelligent technologies in PLDTs
Based on the classification statistics in Table 2–6, it can be seen that the construction and application of PLDTs require the support of intelligent technologies such as IoT, CC, and big data. Combining with the specific application cases of these intelligent technologies in the PL context, this article focuses on their possible roles in PLDTs. In this section, six intelligent technologies are discussed, including IoT, CC, big data, AI, simulation, and CPS.
- 1.
Internet of Things. IoT is the basis
Advantages
Based on the analysis in chapters 3 and 4, it can be cleared that the integration of DTs with technologies such as IoT, CPS, and CC, enables PLDTs to have capabilities for monitoring, simulation, and prediction. This provides a comprehensive platform that can monitor and visualize, expand functions and integrate technologies for PLAs such as transportation, warehouse management and packaging [155]. To highlight the application advantages of DTs in the PL field, this section summarizes four
Future directions
Based on the challenges faced by PLDTs in industrial applications, research directions for PLDTs are discussed, taking into account the existing application scenarios, functions, and related intelligent technologies of PLDTs.
- 1.
Focusing on the construction methods of the PLDTs. Existing modeling approaches mostly rely on statistical algorithms to transform data into representations of the physical process or system. However, these models are not interpretable enough to provide a deep understanding
Conclusion
This paper presents a comprehensive overview of DTs’ application in PL. The discussion begins with a detailed analysis of the latest research on PLDTs, followed by an exploration of the diverse functions played by DTs within various PLAs. Furthermore, the application of DTs in each PLAs is discussed in detail, from the perspective of transportation, packaging, warehousing, material distribution and information processing. And the roles played by intelligent technologies such as IoT in PLDTs
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The research presented in this work was supported by the National Natural Science Foundation of China (NSFC) under Grant 52005026, the National Natural Science Foundation of China (NSFC) under Grant 52005025 and the Fundamental Research Funds for the Central Universities under Grant YWF-22-L-1278. We sincerely appreciate the editors and the anonymous reviewers for their valuable work.
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