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Drawing on information processing theory, the linkage between buffering and bridging and the ability on the part of procurement to resolve demand–supply imbalances is investigated, as well as contexts in which these strategies may be particularly useful or detrimental. Buffering may be achieved through demand change or redundancy, while bridging may be achieved by the means of collaboration or monitoring.

This study employs a hierarchical regression analysis of a survey of 150 Finnish and Swedish procurement and sales and operations planning professionals, each responding from the perspective of their own area of supply responsibility.

Both the demand change and redundancy varieties of buffering are associated with procurement's ability to resolve demand–supply imbalances without delivery disruptions, but not with cost-efficient resolution. Bridging is associated with the cost-efficient resolution of imbalances: while collaboration offers benefits, monitoring seems to make things worse. Dynamism diminishes, while the co-management of procurement in S&OP improves procurement's ability to resolve demand–supply imbalances. The most potent strategy for tackling problematic contexts appears to be buffering via demand change.

The results highlight the importance of procurement in the S&OP process and suggest tactical measures that can be taken to resolve and reduce the effects of supply and demand imbalances.

The results contribute to the procurement and S&OP literature by increasing knowledge regarding the role and integration of procurement to the crucial process of balancing demand and supply operations.

This paper examines global imbalance and rebalancing issues from the viewpoint of Korea. As IMF (2009) notes, the unwinding of global imbalance seems inevitable and, in fact, it is in progress. We illustrate that Korea, with a flexible exchange rate system and relatively balanced current accounts, has little direct linkage to global imbalance. However, we also find that Korea is not immune to the costly adjustment process of imbalance due to the triangular trade between Korea, China and the U.S. The fact that Korea is ‘indirectly’ linked to global imbalance limits Korea’s ability to cope with the situation. Boosting domestic demand, often mentioned recommendation for East Asia, is not an appropriate solution for Korea with low personal savings rate. A lot depends on China’s policy. If China reduces its dependence on U.S. market and increases domestic consumption despite unemployment risk in export manufacturing sector, it will provide Korea with an opportunity for more stable growth based on China’s final demand. Korea can also make efforts to increase economic integration and expand monetary cooperation in Asia that would help to increase consumption demands and final goods trade in the region.

The purpose of this study is to reveal the magnitude of empty container movements (ECM) arising from cargo seasonality by means of long-term datasets of Turkish terminals. Trade imbalance is one of the well-known major reasons of ECM. Cargo seasonality apart from some other operational drivers and market effect, i.e. commercial decisions of the ship operators, is the major operational driver in Turkish terminals effecting ECM. Furthermore, this study highlights the significance of market effect, leading to take measures for more effective empty container operations in terms of decision makers leading the ship operators.

Time series analysis of full container datasets was performed through X-13ARIMA-SEATS methodology, implementing seasonal adjustment.

The results indicate that 17 of 112 time series in hand, based on a terminal/hinterland, container type and “in and out” foreign trade, exhibit cargo seasonality. Roughly, the amount of ECM originating from cargo seasonality in Turkish terminals represents 10 per cent of total ECM except trade imbalance in those terminals where seasonality is present. This reveals that ECM arising from market effect should not be underestimated.

Reefer container traffic could not be sorted from the datasets.

This paper focuses on one of the major reasons of ECM, cargo seasonality. It brings a novel point of view and interpretations which were not suggested previously about ECM, motivating to overcome inefficiency in container operations.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

Trade relations between China and the USA have been marked by conflict, especially since China’s membership in the World Trade Organization (WTO). These conflicts have been analyzed from a variety of perspectives, including the loss of jobs in the USA due to Chinese imports, competition in high technology sectors and the balance of trade. Conceptual frameworks have employed models of domestic differences as well as models of international power distribution. Among domestic differences examined are the existence of state-owned enterprises in China compared to the domination of the USA economy by private firms, the large role of the Communist Party in China and the influence of labor and environmental and labor groups in the USA. Power distribution theories focus on the systemic effects of the distribution of power on trade openness and on the pattern of intra-bloc versus between-bloc trade. This paper aims to examine the role of macroeconomic policy factors in China and the USA, in particular, the role of national patterns of savings, investment and consumption (both private and government). The paper concludes that insofar as the balance of trade is an important component of the trade conflict, domestic macroeconomic factors continue to be important. The resolution of the conflict will have to take into account the respective macroeconomic policies of China and the USA.

The design is an analytic case study of US–China trade relations with a particular focus on the balance of trade. The conceptual framework employed involves an analysis of macroeconomic policy categories, especially the overall pattern of savings (household, firm and government), investment and consumption. Process tracing over time since China's membership in the WTO is carried out with an eye toward the relationship between the balance of trade and macroeconomic policy.

The main findings are that there is a strong relation between the respective macroeconomic policies of the USA and China and their trade relations. The domestic political economy of the USA encourages consumption and a low rate of savings. The opposite is true of China where household income is low by design and national savings are high. China depends on the USA to consume what is not consumed domestically. The USA depends on Chinese imports for additional consumption encouraged by its low rate of savings. The two economies are locked in a mutual dependence.

Key research implications are that there should be more focus on domestic macroeconomic policies since these are the root causes of the trade imbalance. This is not to say that trade frictions centering on jobs, subsidies and competition in high technology are unimportant. However, without the resolution of differences in the management of macroeconomic policies, trade conflicts between the USA and China will continue.

Practical implications are huge, in some ways much more important than the academic implications. Macroeconomic policy differences in savings, investment, government spending, taxation and infrastructure are important. Furthermore, there are available tools in both China and the USA to manage the macroeconomy, particularly, monetary and fiscal policy.

One implication of this paper is that satisfaction or dissatisfaction of workers is dependent on income distribution which in turn affects trade. Treatment of people in different socioeconomic categories, such as the elderly, the young, and those at working age are a function of macroeconomic policies.

Many people have written about macroeconomics. It is a conventional subfield of economics. The originality of this paper lies in its advocacy of a shift of focus and attention and in the argument that traditional macroeconomics is related to trade. Despite its importance, macroeconomics has not been the center of attention for most political scientists, though economists have made it more central.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

Additive manufacturing, that is, layer-based manufacturing technologies, is thought to change supply chain operations from global to local, while also affecting design processes and product structures. As this transformation happens, a power struggle among various actors relating themselves to additive manufacturing has emerged. The purpose of this paper is to discuss and explain the development of additive manufacturing from a power dependence point of view.

The paper is based on data collected from a number of seminars hosting a total of 620 industry experts representing 102 companies in the area, and reflecting every step of the supply chain.

The paper points out how measures to deal and create power imbalances occur also related to indirect parties, and how the disruptive character of the supply chain leads to exercised power.

The power struggle provides new insights into how an emerging technology is realised and the effect of protectionism on such attempts. Specifically related to additive manufacturing, the paper illustrates the business side from various actors’ point of view, which adds to technological perspectives on additive manufacturing, as well as studies viewing the supply chain from a bird’s-eye perspective.

The purpose of this paper is to advance the understanding of paradoxes, underlying tensions and potential management strategies when integrating digital technologies into existing lean-based production systems (LPSs), with the aim of achieving synergies and fostering the development of production systems.

This study adopts a collaborative management research (CMR) approach to identify patterns of organisational tensions and paradoxes and explore management strategies to overcome them. The data were collected through interviews and focus group interviews with experts on lean and/or digital technologies from the companies, from documents and from workshops with the in-case researchers.

The findings of this paper provide insights into the salient organisational paradoxes embraced in the integration of digital technologies in LPS by identifying different aspects of the performing, organising, learning and belonging paradoxes. Furthermore, the findings demonstrate the intricacies and relatedness between different paradoxes and their resolutions, and more specifically, how a resolution strategy adopted to manage one paradox might unintentionally generate new tensions. This, in turn, calls for either re-contextualising actions to counteract the drift or the adoption of new resolution strategies.

This paper adds perspective to operations management (OM) research through the use of paradox theory, and we (1) provide a fine-grained perspective on why integration sometimes “fails” and label the forces of internal drift as mechanisms of imbalances and (2) provide detailed insights into how different management and resolution strategies are adopted, especially by identifying re-contextualising actions as a key to rebalancing organisational paradoxes in favour of the integration of digital technologies in LPSs.