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This study aims to investigate how relatively weaker manufacturers respond to the dominance of stronger suppliers and/or customers. The study also analyzes how the competitive intensity perceived by manufacturers moderates their responses to powerful chain partners.

Using hierarchical regression, data from 1,417 manufacturing companies sampled from the fifth and sixth versions of the International Manufacturing Strategy Survey were analyzed.

This study found that relatively weaker manufacturers often adopt exploration strategies to countervail the dominance of suppliers and adopt exploitation strategies to deal with more powerful customers. In dealing with both dominant suppliers and customers, relatively weaker manufacturers are prone to adopt exploration and exploitation strategies simultaneously and hence become ambidextrous. Furthermore, the link between dominance in supply chains and the exploration (exploitation) strategy is strengthened (weakened) as market competition perceived by manufacturers intensifies.

The contribution of this paper is multi-folds. First, this paper develops and test a novel theoretical model on how relatively weaker manufacturers create tailored strategies to defend their positions in the supply chain. Second, it integrates resource dependence theory and organizational learning theory to propose that relatively weaker manufacturers could use a unique configuration of exploration and exploitation strategies to counteract the dominance of their suppliers and customers. Third, it investigates supply chain power by considering the manufacturers’ upstream and downstream powerful partners together, rather than individually and fourth, it reveals that relationships linking supply chain power to manufacturers’ tailored strategies are contingent on competitive intensity.

Most of the literature on just‐in‐time (JIT) production lines has focused on modelling the system into Markov‐chain. However, in practice, demands for products are known in a short planning horizon. Further, there is scarcity in the literature describing the dynamics and timing of material movements in JIT systems. Models a serial line into a discrete event dynamic system (DEDS) by a Max algebra approach incorporating the timing of material movements. Uses perturbation analysis to study the impact of parameter variation on the system performance. Compares the performances of push and pull systems using simulation analysis. The simulation results show that the pull system dominates the push system in lead time, in‐process inventories and stability of the production process.

Supplier selection is a complex decision that involves not only the consideration of unit purchasing cost but also product life-cycle cost (LCC), which affects the company’s after-sale costs over the life cycles of their products. Product structure and its impact on the supplier selection evaluation process are rarely investigated in the literature. Therefore, product structure for a multi-criteria multi-product supplier selection problem with uncertainty is considered. In the model, we address product structure, the competitive supply environment, diverse criteria, and standard requirements. The objective is to choose suppliers that minimize LCC and maximize the reliability of the finished products.

Our model provides straightforward representation of interrelationships among multi-objectives and analysis of tradeoffs among conflicting objectives affected by product structure. We illustrate our model by using real life data from lubrication systems in the offshore reliability data (OREDA) handbook. Sensitivity analysis is provided for the case study in which various scenarios that describe product structure, the uncertainties in purchasing prices, reliabilities of purchased components, machine down-time due to poor quality components, suppliers’ capacity and delivery times. Different priority ranking among objectives is also tested to examine the impact of each objective on the overall objective.

Our computational results are based on real data and would provide useful guidelines for the management in OEM to choose right suppliers.

Product structure and its impact on the supplier selection evaluation process are rarely investigated in the literature. Therefore, product structure for a multi-criteria multi-product supplier selection problem with uncertainty is considered.

Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.

In this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.

The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.

The fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

The authors aim to develop and test a theory of dual responsibility to explain the relationship between corporate social responsibility (CSR) and firm performance. The authors empirically examine whether firms that meet their economic and social responsibilities simultaneously perform better than firms that fail to do so. In doing so, the authors theoretically extend and empirically test Barney's (2018) call to incorporate the stakeholder perspective with resource-based view (RBV). The authors also examine the moderating effects of firm status on this relationship.

The authors use a longitudinal panel sample of 137 S&P 500 firms and data for the years between 2004 and 2013 collected from multiple data sources. The authors use stochastic frontiers analysis to measure firm capabilities in the areas of R&D, operations and marketing. These capability measures are then used along with CSR measures and a measure of firm status to test the hypotheses of this study. The authors also conducted several robustness checks and various supplementary analyses using different econometrics techniques and different operationalizations of the key variables of interests.

The results show that firm CSR is positively related to firm performance and that the effect of CSR on performance is stronger for firms with higher levels of R&D capability and operational capability. The authors also find support for the three-way interaction between CSR, economic responsibility and firm status, suggesting that firms high in both social and economic responsibilities and status will enjoy the highest levels of performance.

The findings of this study are based on large, publicly listed firms in North America. Therefore, their generalizability to other contexts and other types of firms require additional research. The reliance on KLD measures is also a limitation, especially because they have not reported CSR ratings after 2013.

For practicing managers, the main implication of this study is that an optimal balance between market and nonmarket strategies is key for superior performance.

The continued debate regarding the firm's purpose can be understood by focusing equally on the two main responsibilities of firms: nonsocial responsibility and social responsibility toward all stakeholders.

The study answers the call to incorporate stakeholder theory into the RBV of the firm by highlighting the critical role of firm capabilities in the relationship between CSR and performance. The study also highlights the role that firm status plays in the relationship between market and nonmarket strategies and firm performance.