This study conducts a comprehensive systematic literature review of 107 Machine Learning (ML) studies in Supply Chain (SC) Management published from 2019 until 2023. Descriptive analysis (chronological, geographical, publication, ML algorithms) and thematic analysis via iterative theme identification reviewed key ML themes and barriers in the SC context. ML has emerged as a disruptive technology, significantly benefiting supply chain planning, execution, and control. Yet, no review has examined its applicability and barriers in the supply chain context, especially with the advent of Generalised Artificial Intelligence (AI) and Large Language Models (LLMs). This review revealed specific literature gaps and discusses 4 major ML themes and 14 sub-themes in SC: (i) Demand forecasting, (ii) procurement, (iii) supply chain risk and resilience, and (iv) supply chain network optimisation. Further, the analysis uncovered technical (retraining, scalability security), social (resistance to change, ethical), and contextual (dependency, regulations) barriers. This study provides five research propositions. It sets a research agenda based on the 4Vs of ML (Volume, Variety, Variation, Visibility) to provide insights for future research, which can be especially relevant with the emergence of Generalised AI and LLMs. It also discusses the technical, social, and business implications of ML for supply chain practitioners.

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Data Center Storage Market Size 2025-2029

The data center storage market size is forecast to increase by USD 157 billion, at a CAGR of 20.7% between 2024 and 2029.

The market is experiencing significant growth driven by the increasing volume, velocity, veracity, and variety (4Vs) of data. The proliferation of IoT-enabled devices is leading to an exponential increase in data generation, necessitating robust and scalable data center storage solutions. Furthermore, the trend towards data center consolidation is intensifying, as organizations seek to optimize their IT infrastructure and reduce costs. Additionally, advancements in technology, such as edge computing and the Internet of Things (IoT), are creating new opportunities for data center providers. However, this market landscape is not without challenges. Power consumption and cooling requirements for data centers continue to pose significant operational challenges, necessitating energy-efficient storage solutions. Additionally, data security and privacy concerns are becoming increasingly critical, with the risk of data breaches and cyber attacks growing in frequency and sophistication.
Companies seeking to capitalize on the opportunities presented by the market must prioritize energy efficiency, data security, and scalability to meet the evolving demands of the digital economy. Navigating these challenges effectively will require strategic investments in innovative technologies and operational best practices. Data center storage solutions are increasingly being integrated with lawful interception to ensure secure and compliant data handling in response to regulatory requirements.

What will be the Size of the Data Center Storage Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant evolution, driven by the adoption of cloud native architectures and the integration of machine learning technologies. Performance monitoring and data lifecycle management have become essential for optimizing storage resources in this dynamic environment. Edge computing and edge storage are gaining traction, enabling real-time data processing and reducing latency. Data governance and security are paramount, with capacity monitoring, storage availability, and data privacy becoming increasingly important. AI and serverless computing are revolutionizing data analytics, while hybrid cloud solutions offer flexibility and cost savings.
Data center optimization, storage consolidation, and migration are key strategies for managing the complexities of big data. Data sovereignty, data center virtualization, and storage maintenance are also critical aspects of the market, ensuring regulatory compliance, efficient resource utilization, and system reliability. Data loss prevention and storage automation are essential for mitigating risks and streamlining operations.

How is this Data Center Storage Industry segmented?

The data center storage industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  SAN system
  NAS system
  DAS system


Component

  Hardware
  Software


End-user

  IT and telecommunications
  BFSI
  Healthcare
  Retail
  Others


Geography

  North America

    US
    Canada


  Europe

    Germany
    Italy
    The Netherlands
    UK


  APAC

    Australia
    China
    India
    Thailand


  Rest of World (ROW)

By Deployment Insights

The SAN system segment is estimated to witness significant growth during the forecast period. In today's data-driven business landscape, data center storage solutions have become a critical investment for organizations. The need for data retention, security, and efficient management of large volumes of data has led to the adoption of advanced storage technologies. One such technology is Storage Area Networks (SAN), which offers centralized control and flexibility to share capacity between multiple hosts. SAN systems have gained popularity due to their cost-effective upgrades and independence from additional hardware storage. This trend has spurred technological advancements in SAN systems, resulting in the development of new storage solutions tailored to support the SAN protocol. Moreover, energy efficiency is a significant concern for data center operations, leading to the integration of cooling systems and power consumption optimization.

Data security remains a top priority, driving the adoption of data encryption and deduplication techniques. File storage, data archiving, and disaster recovery are essential components of a robust data center infrastructure. Tiered storage, ob

The dataset is comprised of text responses from GPT-4 after reading passages of text. It is scored and organized in folders.

Psoriasis has been related to metabolic dysfunction-associated fatty liver disease and, liver fibrosis. This study aimed to evaluate the prevalence of liver fibrosis in psoriasis and identify predictors for fibrosis. This is a cross-sectional study conducted from December 2012 to June 2016 assessing psoriasis and psoriatic arthritis patients attended at four centers in Mexico City. Data regarding history of the skin disease, previous and current medication, and previously diagnosed liver disease was collected. Liver fibrosis was assessed with four different non-invasive methods (FIB4, APRI, NAFLD score and elastography). We compared data based on the presence of fibrosis. Adjusted-logistic regression models were performed to estimate OR and 95% CI. A total of 160 patients were included. The prevalence of significant fibrosis using elastography was 25% (n = 40), and 7.5% (n = 12) for advanced fibrosis. Patients with fibrosis had higher prevalence of obesity (60% vs 30.8%, P = 0.04), type 2 diabetes (40% vs 27.5%, P = 0.003), gamma-glutamyl transpeptidase levels (70.8±84.4 vs. 40.1±39.2, P = 0.002), and lower platelets (210.7±58.9 vs. 242.8±49.7, P = 0.0009). Multivariate analysis showed that body mass index (OR1.11, 95%CI 1.02–1.21), type 2 diabetes (OR 3.44, 95%CI 1.2–9.88), and gamma-glutamyl transpeptidase (OR 1.01, 95%CI1-1.02) were associated with the presence of fibrosis. The use of methotrexate was not associated. Patients with psoriasis are at higher risk of fibrosis. Metabolic dysfunction, rather than solely the use of hepatotoxic drugs, likely plays a major role; it may be beneficial to consider elastography regardless of the treatment used. Metabolic factors should be assessed, and lifestyle modification should be encouraged.

Data obtained from computational DFT calculations on Hexagonal Nb4VS8 is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files. This structure was obtained from ICSD (Collection code = 645339)

Comparison between patients with and without significant fibrosis.

Multivariate analysis for significant of fibrosis (elastography).

Age and sex adjusted and fully adjusted hazard ratios for overall and cause specific mortality, for categories of total potato consumption (n = 410,701).

ObjectivesLu’s approach for video-assisted thoracoscopic surgery (LVATS), which derives from UVATS, is a novel surgical approach for VATS and carries out micro-innovation for lung cancer resection. The objective of this study is to elucidate the safety, feasibility, and efficacy of this novel surgical approach.MethodsThe clinical data of patients with non-small cell lung cancer (NSCLC) who underwent a curative thoracoscopic lobectomy between Mar. 2021 and Mar. 2022, were retrospectively collected, and analyzed. According to whether applied Lu’s approach during the VATS operation, patients were divided into the LVATS group and the UVATS group. The propensity score (PS) matching method was used to reduce selection bias by creating two groups. After generating the PSs, 1:1 ratio and nearest-neighbor score matching was completed. Perioperative variables, including the operation time, intraoperative blood loss, lymph node stations dissected, total drainage volume, drainage duration, postoperative hospital stay, pain score (VAS, Visual Analogue Scale) on the postoperative first day (POD1) and third day (POD3), and incidence of postoperative complications, were compared between the two groups. The data were analyzed statistically with P

IntroductionNon-technical skills are essential for surgical patient safety and are implemented in clinical practice. However, training for non-technical skills has not been thoroughly investigated. This study aimed to evaluate the learning curve for non-technical skill-based education in herniorrhaphy.MethodsQuality improvement initiatives, including non-technical skill-based intervention, were performed in the department of surgery. The intervention included declaring the patient safety policy, briefing and debriefing, and criterion for the switching of places of the trainee and instructor as defined by the department. Patients who underwent herniorrhaphy from April 2014 to September 2017 were included.ResultsA total of 14 trainees and nine instructors in the pre-intervention period and 14 trainees and seven instructors in the intervention period were included in this study. The median experience of each trainee was 28 and 15 cases in the pre-intervention and intervention groups, respectively. A total of 749 patients were included: 473 in the pre-intervention period and 328 in the intervention period. Demographics and hernia types were mostly similar between groups, and morbidity was not statistically different between the two groups (3.4 vs. 1.2%, p = 0.054). The nonlinear regression model showed an early decline and deep plateau phase of the learning curve in the intervention group. A significant difference was observed in the plateau operation time (61 min in the pre-intervention group and 52 min in the intervention group).ConclusionThis study demonstrated the effectiveness of non-technical skill-based intervention for surgical training. An early decline and deep plateau of the learning curve can be achieved with well-implemented quality improvement initiatives. Nonetheless, further studies are needed to establish a training program for non-technical skill-based learning.