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This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

More and more enterprises have realized the importance of business model innovation. However, the model tools for it are still scarce. There is a clear research gap in this academic field. Therefore, the aim of this study is to put forward a visual business model innovation model.

The scientific literature clustering paradigm of grounded theory is used to design business model innovation theory model (BMITM). BMITM and the business model innovation options traced back from 870 labels in the grounded process are integrated into a unified framework to build the business model innovation canvas (BMIC).

BMIC composed of three levels and seven modules is successfully developed. 145 business model innovation options are designed in BMIC. How to use BMIC is explained in detail. Through the analysis of innovation hotspots, the potential business model innovation directions can be found. A new business model of clothing enterprises using 3D printing is innovated with BMIC as an example.

Compared with the previous tools, BMIC owns a clearer business model innovation framework and provides a problem-oriented business model innovation process and mechanism.

BMIC provides a systematic business model innovation solution set and roadmap for business model innovation practitioners.

BMIC, a new tool for business model innovation is put forward for the first time. “Mass Selection Customization-Centralized Manufacturing” designed with BMIC for the clothing enterprises using 3D printing is put forward for the first time.

Additive manufacturing has achieved rapid development in recent years. The purpose of this paper is to visualize the intellectual landscapes of additive manufacturing and identify the hotspots and emerging trends of additive manufacturing, which can provide references for scholars, enterprises and governments to promote the development of theory and practice in the additive manufacturing field.

Science mapping is a fast-growing interdisciplinary field originated in information science and technology. Based on this methodology, guided by a computational approach, the paper visualizes the co-occurring keywords network and co-citation references network by CiteSpaceIII software to explore the hotspots and emerging trends of additive manufacturing by the following five indicators: highly cited keywords, burst keywords, clusters, landmark references and burst references.

“Additive manufacturing,” “3D printing,” “3D powder printing,” “consolidation phenomena,” “microstructure,” “rapid prototyping,” etc., are the main hotspots of additive manufacturing. The trends of additive manufacturing generally consist of three stages: the fundamental concepts stage from 1995 to 2000 (“rapid prototyping,” “additive manufacturing,” etc.), the approaches and techniques applications stage from 2001 to 2010 (“stereolithography,” “scaffold,” etc.), and the emerging trends stage from 2011 to the present (“stem cell”, “selective laser,” “ti-6al-4v,” etc.). The research is most abundant in 2010 and 2012. The medical field is an important hotspot of additive manufacturing. Additive manufacturing has been researched in interdiscipline.

The paper maps the perspective of additive manufacturing and explore the hotspots and emerging trends of additive manufacturing.

The Physical Internet (PI) application in a supply chain is explored by automakers to achieve a digital supply chain to challenge timely delivery while maintaining high customised production at the lowest operating cost.

A bi-objective mixed integer model is formulated, where production is performed in multistage manufacturing systems (MMS) and then delivered in a two-level distribution system. Next, a hybrid iterative method algorithm is developed to solve the practical-scale problem within an admissible time. Finally, PI's benefits on production and supply chain operation are discussed through extensive computational experiments in different supply chain configurations.

Three significant findings are obtained. First, PI can achieve a comparable or better service level, while the cost is always lower. Second, PI can improve the utilisation of production and transportation resources. Third, with a more complex supply chain and a higher production cost or truck fixed cost, PI's advantages over traditional supply chain become more vigorous, but the increase in orders will weaken it.

The auto enterprise should adopt a PI-enabled supply chain (PI-SC), especially with the increase of network complexity and specific cost factors.

Importance should be attached to the PI-SC to make customers better involved in the supply chain.

First, the application of PI in the existing plant is described. Second, MMS production with multi-mode transportation is jointly scheduled. Third, the decision support of the PI-SC is provided for auto enterprises.

3D printing is believed to be driving the third industrial revolution. However, a scientometric visualizing of 3D printing research and an exploration its hotspots and emerging trends are lacking. This study aims to promote the theory development of 3D printing, help researchers to determine the research direction and provide a reference for enterprises and government to plan the development of 3D printing industry by a comprehensive understanding of the hotspots and trends of 3D printing.

Based on the theory of scientometrics, 2,769 literatures on the 3D printing theme were found in the Web of Science Core Collection’ Science Citation Index Expanded (SCI-EXPANDED) index between 1995-2016. These were analyzed to explore the research hotspots and emerging trends of 3D printing with the software CiteSpaceIII.

Hotspots had appeared first in 1993, grew rapidly from 2005 and peaked in 2013; hotspots in the “medical field” appeared earliest and have remained extremely active; hotspots have evolved from “drug”, “printer”, “rapid prototyping” and “3D printing” in the 1990s, through “laser-induced consolidation”, “scaffolds”, “sintering” and “metal matrix composites” in the 2000s, to the current hotspots of “stereolithography”, “laser additive manufacturing”, “medical images”; “3D bioprinting”, “titanium”, “Cstem cell” and “chemical reaction” were the emerging hotspots in recent years; “Commercial operation” and “fusion with emerging technology such as big data” may create future hotspots.

It is hard to avoid the possibility of missing important research results on 3D printing. The relevant records could be missing if the query phrases for topic search do not appear in records. Besides, to improve the quality of data, this study selected articles and reviews as the research objects, which may also omit some records.

First, this is the first paper visualizing the hotspots and emerging trends of 3D printing using scientometric tools. Second, not only “burst reference” and “burst keywords” but also “cluster” and “landmark article” are selected as the evaluation factors to judge the hotspots and trends of a domain comprehensively. Third, overall perspective of hotspots and trends of 3D printing is put forward for the first time.

The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.

Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.

The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.

This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.

The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.