A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Type Data : Structured Data : DataCoSupplyChainDataset.csv Unstructured Data : tokenized_access_logs.csv (Clickstream)

Types of Products : Clothing , Sports , and Electronic Supplies

Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.

The size of the Supply Chain Management Market market was valued at USD 23.26 billion in 2023 and is projected to reach USD 48.90 billion by 2032, with an expected CAGR of 11.2 % during the forecast period. The supply chain management (SCM) market is made up of technologies and services that help in the planning, management and execution of supply chain with its key subsystems of materials, services, information and cash. Some of the systems include those for, logistics, supply chain inventory, procurement, and demand planning. SCM is vital in improving on operational performance, cost reduction and in meeting the customer needs. Uses are in manufacturing, retailing, and in health care domain among others. Today’s trends include the use of innovative technologies – Artificial Intelligence, Machine Learning and Blockchain for improved transparency, real-time tracking and analytics-based predicting. Because of e commerce ad globalization the supply chain is continuously on the look out for more advanced and developed SCM that can effectively and efficiently work with more complicated and constantly changing supply networks. Recent developments include: In May 2023, Accenture and Blue Yonder, Inc. announced the expansion of their strategic partnership to enhance organizations' supply chains by leveraging Accenture's technology and industry expertise. Accenture's cloud-native platform engineers and industry experts will collaborate with Blue Yonder to develop new solutions on the Blue Yonder Luminate Platform, offering end-to-end supply chain synchronization. The partnership aimed to help clients achieve a more modular, digitized, and agile supply chain of the future through co-innovation and the Vertical of emerging technologies such as generative artificial intelligence and robotics process automation. , In April 2023, Oracle introduced advanced artificial intelligence (AI) and automation capabilities designed to assist customers in optimizing their supply chain management processes. These new features leveraged AI and automation technologies to enhance efficiency, streamline operations, and enable better decision-making within supply chain management for its customers. The updates included improved quote-to-cash procedures in Oracle Fusion Vertical s and new planning, usage-based pricing, and rebate management features in Oracle Fusion Cloud Supply Chain & Manufacturing (SCM). .

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Supply Chain Analytics Market 2024-2028

The supply chain analytics market size is estimated to grow at a CAGR of 19.28% between 2023 and 2028. The market size is forecast to increase by USD 10.38 billion. The growth of the market depends on several factors, including an increased need to improve business processes, rising e-commerce and omnichannel retailing and an increasing need to improve supply chain visibility. Supply chain analytics refers to the use of data analysis tools and techniques to gain insights, optimize processes, and make informed decisions within the supply chain. It involves collecting, processing, and analyzing data related to the various components of the supply chain, from procurement and production to distribution and logistics, including fleet management. The goal of supply chain analytics is to improve efficiency, reduce costs, enhance visibility, and ultimately contribute to better decision-making across the entire supply chain.

The report includes a comprehensive outlook on the supply chain analytics market offering forecasts for the industry segmented by Deployment, which comprises cloud-based and on-premises. Additionally, it categorizes End-user into retail, manufacturing, transportation, healthcare, and others and covers Region, including North America, APAC, Europe, Middle East and Africa, and South America. The report provides market size, historical data spanning from 2018 to 2022, and future projections, all presented in terms of value in USD billion for each of the mentioned segments.

What will be the size of the Supply Chain Analytics Market During the Forecast Period?

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Supply Chain Analytics Market Overview

Driver

The increasing need to improve supply chain visibility is notably driving market growth. Organizations desire better transparency for their orders, inventory, and shipment information. They also require information regarding all inbound and outbound processes. The adoption of supply chain software, along with analytics, helps organizations get better visibility with cross-functional access. Companies can use supply chain analytics to monitor warehouses, partner responses, and customer needs for better-informed decisions. Vendors in the market have developed different types of software to optimize supply chain performance.

For instance, IBM has developed many software products to increase the effectiveness of supply chain analytics by using AI. Supply chain software can anticipate production flow and changes with the help of AI technologies. Thus, the benefits offered by supply chain analytics to improve supply chain visibility in different business sectors are expected to drive the demand for supply chain analytics, which will strengthen the growth of the global supply chain analytics market during the forecast period.

Trends

The emergence of procurement analytics is an emerging trend shaping market growth. Analytics is one of the emerging trends in the peer-to-peer (P2P) outsourcing market. Analytical solutions use statistical modelling tools and a methodology for an in-depth analysis of the procurement supply chain process. Advanced analytical tools, such as predictive analytics, provide insights connected to the procurement process by clustering numerous factors expected in the supply chain management systems. Further, procurement analytics help users streamline the procurement process and effectively manage their business.

For instance, procurement spend analysis from Anaplan enables procurement and finance users to automate the process of analyzing spend data from transactional systems (ERP + P2P) to generate future predictions that better estimate supplier expenditure and savings opportunities. Thus, the emergence of procurement analytics will accelerate the growth of the supply chain analytics market during the forecast period.

Restrain

Concerns associated with the implementation of supply chain analytics is a significant challenge hindering market growth. The application of analytics is difficult in supply chain networks that are becoming increasingly interconnected at the global level. This is because the coordination of demand and capacities between suppliers and customers is a complex task. Additionally, optimization is infeasible as the supply chain process involves so many key variables. The different B2B standards present are also evolving, causing SCM software vendors to support different business processes within a customer supply chain.

Therefore, market competition is no longer fought merely by individual enterprises but by the entire supply chains. This has increased the complexity of SCM software solutions, which negatively affects the growth of the global supply chain analytics market over the forecast period.

Supply Chain Analytics Market Segmentation By Deployment

The market share growth by

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

A 2018 survey found that the biggest challenge for global supply chain executives was visibility, with 21.8 percent of respondents selecting this response. Fluctuating customer demand was second, with 19.7 percent, while data management was lowest with 1.3 percent. Visibility The nature of the challenges generated by visibility differs depending on whether a company is a producer or a supplier of goods. Producers were most concerned with having oversight on how materials were provisioned to their production facilities, while suppliers were concerned with visibility over the quality and availability of the products they intend to sell. Both producers and suppliers though were concerned with being able to trace the flow of materials and/or goods through their supply chain process. Supply chain management Given the concerns producers and suppliers have over visibility, supply chain management (SCM) software has been a growing industry over the last decade. One sub-segment of this industry expected to see very strong growth is supply chain analytics, whereby the data captured in a SCM system is used in more sophisticated ways (for example, to identifying the main causes and predict the risk of supply chain disruptions). A 2016 survey found that advanced analytics was the technology manufacturing executives expected to impact their supply chain the most, while some analysts expect the size of the supply chain analytic market to almost double between 2018 and 2023.

The dataset contains all the raw data and elaboration in support of the latest editions of the List of Critical Raw Materials for the EU

Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

The report covers Global Supply Chain Big Data Analytics Market Size and it is segmented by Type (Solution, Service), End User (Retail, Manufacturing, Transportation and Logistics, Healthcare, Other End Users), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East and Africa). The market size and forecasts are provided in terms of value (USD) for all the above segments.

In 2020, the global supply chain management market was valued at 15.85 billion U.S. dollars and is expected to reach almost 31 billion U.S. dollars by 2026. In 2020, Germany's SAP was the leading supply chain management software supplier with revenue of around 4.4 billion U.S. dollars.

The U.S. Supply Chain Management Market size was valued at USD 7263.82 million in 2023 and is projected to reach USD 12611.19 million by 2032, exhibiting a CAGR of 8.2 % during the forecasts period. The U. S. SCM market covers systems and practices which enable a firm to manage its way of receiving, processing, storing, and distributing materials to the customer. Enterprise applications relate to SCM solutions which are as follows; inventory, logistics, procurement, and demand planning. They can be widely used in manufacturing industries, sale, service, medical care industries and others; industries which the supply chain management is significant influential to the operating cost and service level. The trends or objectives prevailing within the market are the incorporation of AI and ML to augment the predictive and self-reliant applications, blockchain technology to improve the transparency and traceability of the execution and the escalating concern for sustainability and risk mitigation due to unprecedented events or regulations. Recent developments include: In March 2024, Oracle announced new generative AI capabilities within the Oracle Fusion Cloud Applications Suite that will help customers improve decision-making and enhance the employee and customer experience. The latest AI additions include new generative AI capabilities embedded in existing business workflows across finance, supply chain, HR, sales, marketing, and service, as well as an expansion of the Oracle Guided Journeys’ extensibility framework to enable customers and partners to incorporate more generative AI capabilities to support their unique industry and competitive needs. , In February 2024, Blue Yonder, a leading supply chain solutions provider, announced its acquisition of Flexis AG, a flexible, innovative software technology provider specializing in production optimization and transportation planning and execution. With a robust customer base in the automotive and industrial original equipment manufacturer (OEM) sectors, flexis strengthens Blue Yonder’s capabilities to help companies with highly configurable products and expansive suppliers to plan and optimize their complex production facilities and network structures. , In November 2023, Epicor, a global leader of industry-specific enterprise software designed to promote business growth, announced it has acquired Elite EXTRA, a leading provider of cloud-based last-mile delivery solutions. The acquisition expands Epicor's ability to help its customers across the make, move, and sell industries simplify last-mile logistics and compete in a hyper-competitive market more effectively. Financial terms were not disclosed. .

Supply Chain Pressure Index in World increased to -0.22 points in December from -0.32 points in November of 2024. This dataset includes a chart with historical data for Global Supply Chain Pressure Index.

The Office of Manufacturing and Energy Supply Chains is responsible for strengthening and securing manufacturing and energy supply chains needed to modernize the nation’s energy infrastructure and support a clean and equitable energy transition. The office is catalyzing the development of an energy sector industrial base through targeted investments that establish and secure domestic clean energy supply chains and manufacturing, and by engaging with private-sector companies, other Federal agencies, and key stakeholders to collect, analyze, respond to, and share data about energy supply chains to inform future decision making and investment. The office manages programs that develop clean domestic manufacturing and workforce capabilities, with an emphasis on opportunities for small and medium enterprises and communities in energy transition. The Office of Manufacturing and Energy Supply Chains coordinates closely with the Office of Clean Energy Demonstrations for the management of major demonstration projects, and across all of DOE’s programs on manufacturing and supply chain issues, including with the Advanced Manufacturing Office in the Office of Energy Efficiency and Renewable Energy.

The global supply chain pressure index reached -0.85 points in April 2024, down from -0.3 points in the previous month. After the challenging conditions caused by the COVID-19 pandemic, the supply chain pressure index has returned to the pre-pandemic levels.

This dataset provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

Abstract: Because of their vital role and the need to protect the patient information, interest in information security in Healthcare Supply Chains (HSCs) is growing. This study analyzes how decisions related to information security practices in HSCs contribute to protecting patient information. Eleven semi-structured interviews were performed. The interviewees were managers from Brazilian HSC organizations. Four dimensions and 14 variables identified in a literature review were used to perform categorical content analysis. The findings suggest organizations, while aware of their critical information and internal processes, lack the necessary metrics to measure the impacts of possible failures. It seems organizations tend to invest in standard security measures, while apparently ignoring the specificity and complexity of information in HSCs.

The adoption rate of artificial intelligence (AI) is expected to grow in companies operating in supply chains and manufacturing industries from 2022 to 2025. In 2022 over a third of executives expected their companies to have a widescale adoption of AI in their companies.

In recent years, the topic of digitalisation and sustainability of supply chains has become increasingly important. In addition, as the environmental dynamism becomes more complex, it is essential to explore how technologies impacts on sustainability under the supply chain dynamism. Hence, there is a study to explore the relationship between technologies and sustainability under the supply chain dynamism in the energy supply chain. In this study, the author collects quantitative data from two Chinese companies, including China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd. This is a questionnaire survey and it has 24 questions, including 3 general questions, 5 technologies dimension questions, 12 sustainability dimension questions and 4 supply chain dynamism questions. The author collected data from 30 May 2024 to 6 June May 2024, and there are totally 316 answers.

The global supply chain analytics market size was valued at USD 7.36 billion in 2023. It is expected to reach USD 32.89 billion in 2032, growing at a CAGR of 18.1% over the forecast period (2024-32). The adoption of technologies like artificial intel Report Scope:

Abstract Paper aims This study reviews the available literature regarding big data analytics applications in supply chain management and provides insight on topics that received a good deal of attention and topics that still require investigation. This review considers the expansion of big data analytics in supply chain management from 2010 to 2019. Originality Beyond displaying the increasing frequency of using big data analytics in supply chain management, the authors also aim to develop a useful categorization of applying business analytics in supply chain management and define opportunities for future research in the field. Research method This paper briefly discusses big data applications in supply chain management. Four common steps in review papers are performed: collecting articles (Thomson Reuters Web of Science), descriptive analysis, defining categories, and evaluating the material. Main findings According to both information technology development trends and the availability of data, more companies are using big data analytics in their supply chains. About 60% of the research on big data applications in supply chain management were published after 2017. These publications have increasingly focused on big data applications in predictive analysis, rather than in the other three types of data analysis: descriptive analysis, diagnostic analysis, and prescriptive analysis. Implications for theory and practice This review shows that the collected data by many companies can be analyzed using big data analytics methods to develop the business growth plan, market direction forecast, manufacturing process simulation, delivery optimization, inventory management, and marketing and sales processes, among many other activities in a supply chain. The number of articles using case studies in the literature is greater than the number of theoretical publications. This shows that big data analytics has now been properly developed for practical applications, rather than just being a theoretical concept.