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.

Problem set : This is a multi-label delivery delay prediction problem, which is a challenge often encountered by supply chain practitioners from various industries in their daily operations.

****Testing :**** A tabular dataset that consists of a set of variables related to delivery will be provided to the participants to develop their delay prediction models.

Evaluation Metric: An ideal delay prediction algorithm shall be able to accurately predict delivery arrival status.

Dataset is divided into 7 tables, one table for all orders that needs to be assigned a route – OrderList table, and 6 additional files specifying the problem and restrictions. For instance, the FreightRates table describes all available couriers, the weight gaps for each individual lane and rates associated. The PlantPorts table describes the allowed links between the warehouses and shipping ports in real world. Furthermore, the ProductsPerPlant table lists all supported warehouse-product combinations. The VmiCustomers lists all special cases, where warehouse is only allowed to support specific customer, while any other non-listed warehouse can supply any customer. Moreover, the WhCapacities lists warehouse capacities measured in number of orders per day and the WhCosts specifies the cost associated in storing the products in given warehouse measured in dollars per unit.Order ID is ID of the order made by the customer, product ID is the specific product ID customer ordered."tpt_day_cnt" in the FrieghtRates table means transportation day count, i.e. estimated shipping time. WhCapacities correspond to the number of orders. For example, let's say Customer 1 requests 10 units of X, Customer 2 requests 20 units of Y. The total number of orders is 2, thus total capacity in "whCapacity" is 2.WhCapacities table is the maximum number of orders that can be processed per each plant, it is not dependant on specific products.The OrderList contains historical records of how the orders were routed and demand satisfied. The whCapacities and rest of the tables are the current state constraints of the network. Thus, we can calculate the costs of historical network and also optimize for the new constraints. In order to build Linear Programming (LP) model, you would take the following from the OrderList: the product ID that needs to be shipped, the destination port, unit quantity (for cost) and unit weight (for weight constraints). And then use the limits of those constraints from other tables.Questions: There is a Carrier V44_3 in OrderList table, but it is missing in the FreightRates table? V44_3 is a carrier that was historically used for supplying given demand, but since it has been discontinued and therefore do not appear in the Freight Rates List. Also, all of the V44_3 instances are CRF - i.e. customer arranges their own shipping and hence cost is not calculated either way.

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 Size 2024-2028

The supply chain analytics market size is forecast to increase by USD 10.38 billion at a CAGR of 19.28% between 2023 and 2028.

The market is experiencing significant growth due to the increasing need to optimize business processes and enhance operational efficiency. A key driver is the increased adoption of predictive analytics in the supply chain, enabling organizations to anticipate demand and proactively manage inventory. However, challenges persist in implementing supply chain analytics, including data integration and security concerns. In the context of e-commerce and fleet management, real-time data analysis is crucial for effective order fulfillment and fleet optimization. Market trends include the integration of machine learning algorithms and the use of cloud-based solutions for data processing and storage. Overall, the market presents opportunities for businesses to gain a competitive edge through data-driven insights and improved decision-making.

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

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The global supply chain management software market is experiencing significant growth due to the increasing digitization of business operations and the demand for customized solutions. Logistics optimization, cost optimization, and network design are key focus areas, with businesses seeking cost-efficient solutions to enhance their supply chain performance. Automation plays a crucial role in improving efficiency and enabling data-driven decision-making, while customer loyalty and strategy are also driving market growth.
Traceability, finance, and performance metrics are essential elements of supply chain analytics, providing valuable insights into inventory transactions, transportation management, and warehouse management. Cost reduction is a primary objective, with companies addressing security breaches and disruptions through consulting services and innovation. Sustainability, demand planning, and resilience are emerging trends, with a focus on cost savings, management software, and optimization. Procurement optimization, training, logistics management, and CRM systems are also integral to the market's development.

How is this Supply Chain Analytics Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud-based
  On-premises


End-user

  Retail
  Manufacturing
  Transportation
  Healthcare
  Others


Geography

  North America

    US


  Europe

    Germany
    UK
    France


  APAC

    China


  Middle East and Africa



  South America

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

Cloud-based supply chain analytics enables organizations to adapt to their expanding analytics requirements and data volumes through on-demand resources. Eliminating the need for significant upfront investments in hardware and infrastructure, cloud solutions offer a pay-as-you-go model, ensuring cost savings and predictable budgeting. Remote access to analytics tools and data fosters collaboration among stakeholders, enhancing communication and informed decision-making. Real-time data analysis facilitates timely responses to supply chain challenges, such as elevated warehousing costs, data loss, demand-supply gaps, and customer requirements. Cloud-based analytics optimizes resource utilization, business growth, and performance, addressing inventory risks, procurement transactions, and retail trends.

Additionally, it integrates with ERP systems, warehouse management systems, SCM strategies, procurement analytics, artificial intelligence, and big data technologies to improve business productivity, efficiency, and manufacturing processes. Cloud solutions address waste minimization, economic viability, and unethical activities, ensuring accountability for large enterprises.

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The Cloud-based segment was valued at USD 2.47 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market is projected to expand due to the region's technological advancements and complex business environments. Advanced analytics technologies, such as arti

Introduction

Supply Chain Statistics: A supply chain is a network of people and companies that work together to create and deliver a product to the customer. It begins with those who provide raw materials and ends when the finished product reaches the consumer. Managing the supply chain is very important because a well-run supply chain can reduce costs and make production more efficient. Companies aim to improve their supply chains to lower expenses and stay competitive in the market.

By keeping track of inventory levels, analyzing shipping costs, and spotting any delays, businesses can use these statistics to make their supply chain operations better. In this article, “Supply Chain Statistics†, we shall explore the main metrics used in SCM and show how businesses can use this data to stay ahead of the competition.

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.

ABOUT THE DATA The dataset includes over 164 million cargo records from global importers and exporters, featuring more than 20 raw and processed data elements to provide comprehensive and high-quality business information. This covers details about shipment weight and value, shippers, destinations, destination countries, ports of unloading, shipment dates, HS codes, and more.

USERS AND USE CASES Suppliers, buyers, trade finance companies, logistics firms, and investment groups utilize this data to monitor investments, analyze market trends, discover new business opportunities, enhance or diversify supply chains and operations, mitigate risks, and keep track of competitors.

DETAILS ON THE COMPLETE DATA SET Available from January 2014 to the present and updated daily, this data is refined from bill of lading records, shipping lines, customs declarations, and commercial invoices to create standardized firm names.

INFORMATION ABOUT THE SAMPLE DATA SET A sample dataset of shipment statistics for a publicly traded US company and its international subsidiaries, covering the period from January 1, 2023, to January 31, 2023, is provided. For a customized dataset tailored to specific businesses, time frames, products, HS codes, ports, or other details, contact [dm@trademo.com].

ABOUT TRADEMO Trademo acts as a single source of truth for global supply chains. It compiles billions of data points and utilizes big data, machine learning, NLP, entity resolution, and graph databases to clean, enrich, and analyze unstructured data, providing in-depth insights into over 50% of global trade by dollar value.

Pharmaceutical Supply Chain Optimization - Objective: Optimize drug inventory management and supply chain logistics using AI. - Datasets: Real-time inventory data, demand forecasting datasets. - Models: Time Series Analysis, Reinforcement Learning, Optimization Algorithms. - Outcome: Efficient supply chain management with reduced wastage and stock-outs.

The supply chain management market size is projected to grow from USD 31.27 billion in 2024 to USD 94.71 billion by 2035, representing a CAGR of 10.60%, during the forecast period till 2035.

Report Overview

The Global AI in Supply Chain Market size is expected to be worth around USD 157.6 Billion by 2033, from USD 4.5 Billion in 2023, growing at a CAGR of 42.7% during the forecast period from 2024 to 2033.

Artificial intelligence (AI) is transforming the supply chain industry by enhancing efficiency, accuracy, and cost-effectiveness across various processes from procurement to customer delivery. AI technologies, such as machine learning, predictive analytics, and automation, are being integrated into supply chain management to optimize inventory levels, improve demand forecasting, and streamline logistics. These technologies help companies anticipate market demands, manage risks, and make informed decisions based on real-time data analysis.

The market for AI in supply chains is growing rapidly, driven by the increasing need for automation and more sophisticated data analysis capabilities in industries ranging from manufacturing to retail. Businesses are investing in AI solutions to gain a competitive edge through improved supply chain visibility and operational efficiency. This market expansion is supported by advancements in AI technology and the increasing availability of data, which together facilitate more effective supply chain solutions.

https://market.us/wp-content/uploads/2024/04/AI-in-Supply-Chain-Market-1-1024x595.jpg" alt="AI in Supply Chain Market" width="1024" height="595">

Key players in the AI in supply chain market are developing sophisticated AI solutions tailored to specific supply chain challenges. These solutions integrate with existing systems and leverage data from various sources, including sensors, IoT devices, and enterprise systems. The market is characterized by a diverse ecosystem of technology providers, system integrators, and consulting firms, all working together to harness the power of AI in transforming supply chain operations.

According to a study by Capgemini, 68% of supply chain organizations have adopted AI-enabled traceability and visibility solutions. This technology has significantly boosted transparency across the entire supply chain, leading to a notable 22% increase in efficiency. In 2023, around 70% of manufacturers employed AI-driven predictive maintenance techniques, which have proven to be highly effective.

Additionally, 75% of supply chain professionals utilized AI-powered data analytics in 2023. These tools have been instrumental in uncovering hidden insights, allowing for more informed and data-driven decisions. This strategic adoption of technology has been pivotal in enhancing supply chain management, helping organizations to handle complexities and boost efficiency more effectively.

Moreover, 82% of supply chain organizations have implemented AI-powered quality control and inspection systems, achieving a significant 18% reduction in product defects. The impact of AI-driven supply chain planning and optimization solutions has been substantial.

Early adopters have reported a 15% decrease in logistics costs and a remarkable 35% reduction in inventory levels. Furthermore, these companies have improved their service levels by an impressive 65%, markedly outperforming their slower-moving competitors. These advancements highlight the transformative effect of AI in streamlining supply chain operations and enhancing overall business performance

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 “agri-food supply chain” has led to great attention for researchers worldwide due to utilize it in wide range of applications in various fields. In order to understand the emerging integrated effort, it is necessary to examine the state of publications, understand the collaboration networks and evaluate the topics of interest. This study aims to assess and visualize current trend in agri-food supply chain (AFSC) domain based on publication year, collaborators, collaborations, source title, and co-occurrences as keywords. This paper adopts a bibliometric analysis methodology based on 303 papers obtained from the Scopus database between 1997 and 2021. Microsoft Excel was used to analyze the frequency and VOSviewer for data visualization. The research results shows that publications on AFSC has significantly increased since 2006. Results of this study also indicated that AFSC studies in the last two decades has explored seven major areas of “agri-food supply chain system”, “agri-food supply chain management”, “agri-food supply chain industry”, “agri-food supply chain risk factor”, “agri-food supply chain information”, “agri-food supply chain advancement”, and “agri-food supply chain risk”. Finally, among the research title, the three dominant clusters include “agri-food supply chain”, “food supply”, and “supply chains”, creating an opportunity for researchers to conduct future cross-country and cross-institutional studies through which prolific authors have constructed a core foundation for the AFSC topic.

The market size of the Supply Chain Big Data Analytics Market is categorized based on Application (Retail, Healthcare, Transportation & logistics, Manufacturing, Others) and Product (On-Premise Supply Chain Big Data Analytics, On-Cloud Supply Chain Big Data Analytics) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This report provides insights into the market size and forecasts the value of the market, expressed in USD million, across these defined segments.

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.

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.

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 dataset contains the data used in our study "Optimisation model for multi-item multi-echelon supply chains with nested multi-level products". We provide one dataset that describes an automotive production network that comprises of one OEM, four first-tier suppliers and two second-tier suppliers (nodes), their relations (arcs) and their production and logistics processes to fulfil customers demand. The dataset includes information about: - product structure of 28.049 products (e.g. cars, engines, gears ..) - 12 nodes (e.g. car plant, engine supplier, ..), their assigned products and initial inventory as well as maximum inventory - 11 arcs (e.g. lead-time from engine supplier to car plant), their available transport or production capacity for each period and the initial flows - bill of materials - customers demand for each car over a horizon of 14 days

The global supply chain management market size was valued at USD 28.05 billion in 2022 and is projected to surpass USD 67.23 billion by 2031, expanding at a CAGR of 10.2% during the forecast period, 2023–2031. The growth of the market is attributed to the increasing adoption of cloud supply chain management among SMEs and rising business continuity by reducing the risk of failure.



Supply chain management includes the management of services and products. It refers to the entire production flow, from raw materials to distribution of the final product to the client. A business includes a network of distributers to deliver the product from raw material suppliers to organizations and finally to customers. Logistics services determine efficient supply chain management, which control merchandise costs and allows transporters to offer supply chain visibility.

Cloud-based deployments are estimated to be cost effective, easy to implement, and positively affect businesses around the globe. Cloud-based deployments in supply chain management are expected to grow in the coming years, due to the capabilities of SaaS solutions and the convenience of cloud security.



Lockdowns imposed due to the COVID-19 outbreak are affecting the global supply chain. Therefore, several industries shifted their typical manufacturing processes to produce PPE kits, necessary medical supplies, and other objects. COVID-19 compelled several businesses and industries to reconsider their global supply chain models.

• For instance, according to the Economist Intelligence Unit (EIU), over-reliance on suppliers in Asia, particularly China, already posed a major concern for businesses in MEA prior to the COVID-19 disruption in 2020, and following the outbreak, businesses in MEA plan to diversify supply chains and refocus on local production.

Supply Chain Management Market Trends, Drivers, Restraints, and Opportunities

• Rising cloud-based deployments are anticipated to drive the market during the forecast period.


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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.