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For decades, it was emphasized that manufacturing and trading companies should aim to be lean with very small inventories. However, in the recent decade, time-significant change has taken place as nearly all of the “old west” countries have now low interest rates. Holding inventories have been beneficial for the sake of customer service and for achieving savings in transportation and fixed ordering costs.

In this study, inventory management change is examined in publicly traded manufacturing and trade companies of Finland and three Baltic states (Estonia, Latvia and Lithuania) during the years 2010–2018.

Inventory efficiency has been leveled off or falling in these countries and mostly declining development has concerned small- and medium-sized enterprises (SMEs). It is also found that inventory efficiency is in general lower in SMEs than in larger companies. Two companies sustaining in inventory efficiency are used as an example that lean has still significance, and higher inventories as well as lower inventory efficiency should not be the objective. Two companies show exemplary financial performance as well as shareholder value creation.

Work concerns only four smaller countries, and this limits its generalization power. Research is one illustration what happens to private sector companies under low interest rate policies.

Continuous improvement of inventory efficiency becomes questionable in the light of current research and the low interest rate environment.

This is one of the seminal studies from inventory efficiency as the global financial crisis taken place in 2008–2009 and there is the implementation of low interest rates.

The Chinese government hopes to achieve the goal of benefiting citizens by building a National Integrated Online Government Service Platform (NIOGSP). However, citizens' low adoption of the platform makes it difficult for the government to achieve its goal. Research on the influencing factors of citizen adoption of NIOGSP can help the government fully understand the concerns and needs of its citizens and take targeted measures to increase citizen adoption.

First, this research builds a model of the citizen adoption process, including attention, retention and motivation, based on an observational learning model. Next, research variables are determined based on social cognitive theory, literature review and real-world needs. Finally, based on the questionnaire survey and structural equation model, the influencing factors of each stage of the citizen adoption process model are studied and the relationship between the three stages of the model is verified.

Results show that perceived usefulness (PU) and self-efficacy (SE) positively affect attention. SE positively affects retention, while perceived privacy (PP) negatively affects retention. PU, social influence, PP and anxiety positively affect motivation.

The conclusion of this study can provide reference for governments in various countries to establish and improve online one-stop government. In addition, this study verifies the citizen adoption process model and finds that there is no obvious causal relationship between attention and retention, but both have positive effects on motivation.

The digital economy has become a new engine for economic development, promoting the upgrading and transformation of traditional industries as well as fostering emerging industries and forms of business. Nonetheless, how does the digital economy affect innovation? The research objective is to explore the specific impact of the digital economy on innovation output.

This paper innovatively adopts the dynamic panel data model (DPDM) to carry out an empirical study on the impact of the digital economy on innovation output, through the observation of 30 provincial-level administrative regions in China. Furthermore, the paper innovatively analyzes the impact of different dimensions of the digital economy on innovation output and the impact of the digital economy on different dimensions of innovation output.

It is found that the digital economy is conducive to boosting innovation output considering innovation continuity. Specifically, the driving impact of core industries and enterprise application of digital economy on innovation output is more prominent, but the driving impact of infrastructure and personal application on innovation output is not fully played. Meanwhile, the driving impact of the digital economy on the innovation output quality is more significant than that digital economy on the innovation output quantity.

This study employs a DPDM for the first time to investigate the specific impact of the digital economy on innovation output, and contributes to the existing literature on the digital economy and digital economy-driven innovation. The findings offer a comprehensive explanation for the impact of the digital economy on innovation output, which has reference value for the formulation of innovation policies driven by digital economy, thereby providing impetus for the sustained and stable development of China's economy.

This research examines factors that determine the improvement of the Russian supply chain sustainability. The strategic business model incorporates ecological, social and economic aspects.

Considering Trans-Siberian Railway as a typical case, the study selected several cases or the parts of the primary rail connection with the affiliation of important neighbouring countries. The study uses quantitative analyses of a variety of size parameters (e.g. volumes of traffic, logistics costs, delivery distance and air pollutants).

The empirical findings stress that supply chains should favour in the future railway (or intermodal) connections for piggyback and containerised cargo. Herein lays the reason for the examination of all traditional factors affecting the modal choice and their complementation by the parameters to measure sustainability throughout the supply chains. The inclusion of the green practices positively affected the environmental, social and economic performance of the new approach.

This research focuses on the delivery of cargo in containers and semi-trailers within wagons through the overland corridors. Despite the restrictive empirical findings within the national transport system, some elements can be representative of the international supply chains, provided that intermodal services are the most appropriate for the transportation over long distances.

Clients, media and regulatory bodies stress the consideration of environmental aspects at all stages of a global supply chain. Therefore, their adoption into strategic imperatives of local supply chains becomes inevitable.

An assessment of supply chains for longer distance transportation in Russia has not taken sustainability into account within cost analyses.

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

Ant colony algorithm is widely used in recent years as a heuristic algorithm. It provides a new way to solve complicated combinatorial optimization problems. Having been enlightened by the behavior of ant colony's searching for food, positive feedback construction and distributed computing combined with certain heuristics are adopted in the algorithm, which makes it easier to find better solution. This paper introduces a series of ant colony algorithm and its improved algorithm of the basic principle, and discusses the ant colony algorithm application situation. Finally, several problems existing in the research and the development prospect of ACO are reviewed.

The purpose of this paper is to propose an improved method which can shorten the calculation time and improve the calculation efficiency under the premise of ensuring the calculation accuracy for calculating the response of dynamic systems with periodic time-varying characteristics.

An improved method is proposed based on Runge–Kutta method according to the composition characteristics of the state space matrix and the external load vector formed by the reduction of the dynamic equation of the periodic time-varying system. The recursive scheme of the holistic matrix of the system using the Runge–Kutta method is improved to be the sub-block matrix that is divided into the upper and lower parts to reduce the calculation steps and the occupied computer memory.

The calculation time consumption is reduced to a certain extent about 10–35% by changing the synthesis method of the time-varying matrix of the dynamics system, and the method proposed of paper consumes 43–75% less calculation time in total than the original Runge–Kutta method without affecting the calculation accuracy. When the ode45 command that implements the Runge–Kutta method in the MATLAB software used to solve the system dynamics equation include the time variable which cannot provide its specific analytic function form, so the time variable value corresponding to the solution time needs to be determined by the interpolation method, which causes the calculation efficiency of the ode45 command to be substantially reduced.

The proposed method can be applied to solve dynamic systems with periodic time-varying characteristics, and can consume less calculation time than the original Runge–Kutta method without affecting the calculation accuracy, especially the superiority of the improved method of this paper can be better demonstrated when the degree of freedom of the periodic time-varying dynamics system is greater.

Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling.

First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation.

The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased.

The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

This paper aims to study the transient flow characteristics in a mixed-flow pump during the start-up period.

In this study, numerical calculation of the internal flow field in a mixed-flow pump using the sliding mesh method was carried out. The regulation of the pressure, streamline and the relative speed during the start-up period was analyzed.

The trend of the simulated head is consistent with the experimental results, and the calculated head is around 0.3 m higher than the experimental head when the rotation speed reached the stable stage, indicating that the numerical method for the start-up process simulation of the mixed-flow pump has a high accuracy. At the beginning, the velocity inside the impeller changes little along the radius direction and the flow rate increases slowly during the start-up process. As the rotation speed reached the stable stage, the flow inside the impeller became steady, the vortex reduced and transient effects disappeared gradually.

The study results have significant value for revealing the internal unsteady flow characteristics of the mixed-flow pump and providing the reference for the design optimization of the mixed-flow pump.