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AbstractWalmart built end to end optimization capabilities in its supply chain to make strategic and operational decisions consisting of network planning and transformation, routing and loading systems and a simulation platform. This optimization-empowered decision framework is evolving and transforming Walmart’s supply chain while keeping its Every-Day-Low-Price (EDLP) promise to its customers.

Fractal and optical characteristics of self-affine surfaces of silver(Ag) thin films deposited through direct current (dc) magnetron sputtering as a function of thickness are investigated and explored here. The surface morphology of Ag thin films is characterized by field emission electron microscopy, and atomic force microscopy technique. The cube counting algorithm is used to extract the fractal dimension of Ag thin film. The surface roughness (interface width) shows monotonic increases with film thickness, while the other parameters, such as lateral correlation length, roughness exponent, and fractal dimension exhibit linear variation with thickness. Our findings reveal distinctive scaling behaviors, with scaling exponents α, β, and 1/z indicating unique growth characteristics. The interface width w increases as a power law of thickness t, w(t)∝tβ, with β=0.39± 0.007, and the lateral correlation length ξ grows as ξt∝t1/zwith 1/z=0.14± 0.002. The roughness exponent extracted from height-height correlation analysis is α=0.61–0.41. The self-affine nature of the Ag thin films is further confirmed by the autocorrelation function. X-ray photoelectron spectroscopy (XPS) is used to the confirm the growth of Ag thin film. Additionally, we have studied the role of fractal dimensions and lateral correlation length (ξ) on the surface plasmon resonance (SPR) of Ag thin film. Our results indicate a red-shifting behavior of SPR with increasing interface width (w), lateral correlation length (ξ), and fractal dimensions. This study suggests the significance of not only the roughness exponent and fractal dimension but also the local surface slope in SPR activity in Ag thin films.

The primary objective of this paper is to offer a structured and comprehensive list of the barriers associated with implementation of Artificial Intelligence (AI) solutions in Supply Chain Management (SCM). While the broader field of AI has made rapid advances in a relatively short period of time, there are significant barriers that still need to be addressed to harness the true potential of AI. SCM’s dependency on multi-actor collaboration, disparate data sources, unwillingness of actors to embrace AI, change management issues, and lack of AI governance framework poses significant barriers for successful implementation of AI. Drawn from extensive literature review as well as real-world experience, this paper systematically explores and compiles a robust list of barriers of AI implementation in supply chain functions by categorizing them and elaborating their impact at inter- and intra-organizational SCM. Lastly, the paper offers recommendations for practitioners, policymakers, researchers, and governments on how they can work together for AI to be successful.

We developed a novel multiobjective markdown system and deployed it across many merchandising units at Walmart. The objectives of this system are to (1) clear the stores’ excess inventory by a specified date, (2) improve revenue by minimizing the discounts needed to clear shelves, and (3) reduce the substantial cost to relabel merchandise in the stores. The underlying mathematical approach uses techniques such as deep reinforcement learning, simulation, and optimization to determine the optimal (marked-down) price. Starting in 2019, after six months of extensive testing, we implemented the new approach across all Walmart stores in the United States. The result was a high-performance model with a price-adjustment policy tailored to each store. Walmart increased its sell-through rate (i.e., the number of units sold during the markdown period divided by its inventory at the beginning of the markdown) by 21% and reduced its costs by 7%. Benefits that Walmart accrues include demographics-based store personalization, reductions in operating costs with limited numbers of price adjustments, and a dynamic time window for markdowns.

Design and development of battery-type electrode materials with high capacitance, wide potential window, and desirable cycle stability are essential to enhance the performance of hybrid supercapacitors (HSC). In this study, a simple sol-gel synthesis strategy has been adopted to fabricate spinel NiCo2O4nanoflakes. The morphological and structural analysis shows that the NiCo2O4is formed with nanoflakes morphology having high phase purity and good stoichiometry. The electrochemical study of the NiCo2O4electrode in 1 M Na2SO4aqueous electrolyte reveals that the electrode has a maximum specific capacitance of 488 F g−1at 2 A g−1. Detailed electrochemical examinations of cyclic voltammogram (CV) and electrochemical impedance spectroscopy (EIS) profiles reveal the pseudocapacitive charge storage kinetics of NiCo2O4electrode. Furthermore, a hybrid supercapacitor device is constructed by employing molybdenum-disulfide (MoS2) & reduced graphene oxide (rGO) nanocomposite and NiCo2O4as the negative and positive electrodes, respectively. PVA-Na2SO4is utilized as the polymer gel electrolyte. The HSC device delivered the highest specific capacitance of 106 F g−1at a current density of 0.8 A g−1with superior cyclic stability. Thus, exploring strategies for superior performance through material modification and selection of suitable electrolyte has been useful and makes this study significant among the reported related works.