Purpose Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity but without providing a thorough discussion about when and why they should be deployed. This research aims to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity. Design/methodology/approach This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. The authors illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer. Findings Regardless of its type or origin, supply chain complexity can arise because of the strategic business requirements of the BU (strategic) or because of suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed. Research limitations/implications The case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results. Practical implications The conceptual model presented here provides a more granular view of supply chain complexity and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. Originality/value There are no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual BU should respond to supply chain complexity. As such, this paper improves the understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.

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Purpose Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage. Methods We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model’s performance. From the external test dataset, we calculated the model’s accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. Conclusions Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

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