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

In a 2020 survey, respondents indicated major impact of the coronavirus (COVID-19) pandemic on the supply chains. For instance, ** percent of respondents stated that delays in cross border transportation had a limited impact on their business.

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 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.

IBM reports a significant increase, or ** percent, in business efficiency in supply chains for companies adapting artificial intelligence (AI). Companies that adopted AI reported improvements in efficiency, coupled with reduction in structural costs.

In 2023, the Digital Supply Chain Market reached a value of USD 5.4 billion, and it is projected to surge to USD 12.8 billion by 2030.

Introduction

Semiconductor Supply Chain Statistics: Semiconductors are materials with an electrical conductivity between the conductor and the insulator. Their behavior has made them a pivotal component of electronic devices, and this field has a considerable market size. To realize their significance on a global scale, it is essential to have a holistic understanding of Semiconductor Supply Chain Statistics. In this digital age, we will discuss the importance of semiconductor development.

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Supply Chain Analytics Market Size 2025-2029

The supply chain analytics market size is forecast to increase by USD 15.51 billion, at a CAGR of 22.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing need to optimize business processes and enhance operational efficiency. Companies are recognizing the value of leveraging data-driven insights to anticipate market trends, minimize risks, and streamline their supply chain operations. A key trend in this market is the increasing use of predictive analytics, which enables organizations to go beyond historical data analysis and gain a more proactive and strategic perspective on their supply chain operations. However, the implementation of supply chain analytics is not without challenges. Concerns around data security and privacy, as well as the complexity of integrating analytics tools with existing systems, can hinder adoption. Additionally, the need for specialized skills and resources to effectively analyze and interpret data can create a barrier to entry for some organizations. To capitalize on the opportunities presented by this market, companies must navigate these challenges by investing in robust data security measures, partnering with experts in analytics and implementation, and fostering a data-driven culture within their organizations. By doing so, they can unlock the full potential of supply chain analytics to gain a competitive edge and drive sustainable growth.

What will be the Size of the Supply Chain Analytics 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.
Request Free SampleThe market continues to evolve, driven by the integration of advanced technologies and shifting market dynamics. Businesses are leveraging data-driven insights to optimize their operations, enhance sustainability, and ensure resilience. Data integration plays a crucial role in this process, enabling seamless flow of information between various systems and stakeholders. Ethical sourcing and social responsibility are increasingly prioritized, with supply chain visibility providing transparency into operations and enabling continuous improvement. Transportation routing algorithms and demand forecasting models help streamline logistics networks, reducing lead times and improving customer satisfaction. Risk management strategies, including disruption management and outsourcing, ensure business continuity and cost savings. Cloud-based solutions and machine learning algorithms facilitate real-time tracking and predictive analytics, providing valuable insights for agile supply chains. Performance metrics and warehouse automation further enhance efficiency and enable continuous improvement. Environmental sustainability is a growing concern, with supply chain optimization and lean manufacturing strategies being adopted to reduce carbon footprint and minimize waste. Blockchain technology offers increased security and transparency, while contract logistics and reverse logistics help manage complex supply chain networks. The ongoing unfolding of market activities and evolving patterns highlight the importance of data governance and data analytics platforms in the supply chain ecosystem. Supply chain collaboration and six sigma methodologies further contribute to the continuous improvement and optimization of operations.

How is this Supply Chain Analytics Industry segmented?

The supply chain analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userRetailManufacturingHealthcareTransportationOthersDeploymentOn-premisesCloud-basedServiceProfessional servicesManaged servicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.In today's business landscape, retailers leverage advanced analytics solutions to optimize their supply chain operations. By integrating artificial intelligence (AI) and machine learning algorithms into inventory management systems, retailers can analyze historical sales data, market trends, and external factors to forecast demand accurately. This data-driven approach enables retailers to optimize inventory levels, reducing stockouts or excess inventory, and improving overall supply chain efficiency. AI-powered predictive analytics help retailers identify slow-moving or obsolete products and adjust inventory levels accordingly. Real-time tracking and demand forecasting models ensure the right products are available at the right locations, minimizing carrying costs and stockouts. Six S

Supply chain disruptions are an economic hardship, costing organizations around the world an average of *** million U.S. dollars per year according to a 2021 survey. On a regional distribution, the financial burden is highest in the United States, where the estimated average annual cost of respondents' organizations amounted to *** million U.S. dollars.

The company which provided the dataset is the world leader in manufacturing of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. The current revenue of the company is estimated to be on the order of tens of billions and they sell products and parts via a worldwide dealer network. The company sells more than 3 million products and 700,000 parts in more than 20 countries around the world every year. They operate with more than 3,000 suppliers and 3,000 dealerships and their logistics operations alone are worth more than 60 million dollars per year. The dataset provided is one example of supply chain problem for one product of the company - a medium size excavator. In the current dataset, the number of dealers, production facilities and shipping ports is the same as in the original problem; it is only the demand figures, the production capacities, the transportation times and costs and the sale prices that have been randomly generated. The figures have been randomly generated in an interval between 0 and an upper limit which is a random increase over the maximum value in the original data, according to a negative exponential distribution.

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.

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.

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.

The asset contains commodity shipment data describing all anti-retroviral and rapid test kit shipments into PEPFAR countries. Included are SCMS’ transactions, not all purchases made under PEPFAR. The data are not intended to determine lead times or total landed cost because a single order may have different freight costs, points of origin, or delivery dates (e.g., emergency orders). Not all orders include cost of freight and/or insurance. None of the data include costs of customs clearance, security, or in-country distribution costs. See more information in the “Data Dictionary”.

The dataset consists of features such as transaction hash, block number, timestamp, sender address, receiver address, transaction fee, gas limit, product name, product ID, product description, quantity, price, and a binary target feature indicating fraudulent (1) or non-fraudulent (0) transactions.

Supply Chain Big Data Analytics Market size was valued at USD 6.21 Billion in 2024 and is projected to reach USD 22.5 Billion by 2031, growing at a CAGR of 17.47% during the forecast period 2024-2031.

The rising complexity of global supply chains and the volume of data being generated throughout the supply chain ecosystem are driving factors in the supply chain big data analytics industry. Advanced analytics solutions with the ability to process and analyse massive datasets from multiple supply chain sources, such as manufacturers, distributors, retailers, and suppliers, are in greater demand as companies look to improve decision-making, cut costs, and optimise operational efficiency. Furthermore, the integration of RFID technology, IoT devices, and sensors into supply chain operations produces enormous volumes of real-time data that are invaluable for risk reduction, demand forecasting, inventory management, and logistics optimisation. In addition, companies may now leverage data analytics to drive continuous improvement programmes throughout their supply chains and obtain actionable insights thanks to the development of cloud computing and big data technology. Big data analytics solutions for supply chain management are becoming more and more popular due to regulatory compliance requirements, rising customer expectations for sustainability and transparency, and the need to minimise supply chain interruptions.

The number of supply chain disruptions worldwide is on the rise. In 2021, this number amounted to ****** supply chain chain disruptions worldwide. North America was the region with the highest share of disruptive events.

Business or organization expectations of how long supply chain obstacles will last, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2024.

Dataset containing detailed information about all health commodity orders delivered through the USAID Global Health Supply Chain Program - Procurement and Supply Management (GHSC-PSM) project. This includes orders funded through PEPFAR, PMI, family planning / reproductive health, maternal and child health, COVID-19, and other USAID and USG programs.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

This dataset provides values for SUPPLY CHAIN PRESSURE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.