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.

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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. 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 the cloud-based segment wi

The supply chain analytics market size reached USD 3.41 Billion in 2020 and is expected to reach USD 13.52 Billion in 2028 and register a CAGR of 18.9%. Supply chain analytics industry report classifies global market by share, trend, and on the basis of deployment, component, solution, end-use, and region

The perfect combination of supply chain data, company data and trucking data: With this novel geolocation approach, it is possible to "draw" a knowledge graph of a private or public company´s relations with other facilities and companies within the country.

"Company-to-Company Relations & Supply Chain Mapping" is a dataset containing relevant information that allows to infer economic, logistics, legal and business relationships between companies. Using supply chain data and company data, you will be able to detect relationships between corporations and between facilities from different companies.

It also allows to visualize and map an entire supply chain nationwide and statewide within the United States.

The information that can be inferred from the data allows to discover undisclosed or unknown economic, financial, logistic, and business relationships between companies (both private and public) in different areas of the country.

The insights obtained through this supply chain data are an ideal complement to the information that public corporations usually disclose in their reports (Company Data).

Another relevant aspect is that it allows to obtain insights from private companies.

The dataset is produced on a per-company basis. Given a specific U.S.-based company (defined as the “seed” company), a dataset including all detected trips and relationships between that company and others will be created.

With our PREDIK Data-Driven supply chain mapping solution you can: -Infer economic, logistics, legal, and business relationships between companies and facilities within the United States.

-Visualize all the tiers and map the entire supply chain of any company. -Improve the planning, procurement, and compliance management by increasing the visibility of any multi-tier supply chain.

-Identify not just the first tier of suppliers in the value chain, but also the second, third, and fourth tiers, as well as other providers.

-Detect and measure relationships between firms and facilities from various companies in the United States.

• Type of companies covered: Both public & private companies • Stocks tickers Covered: Around 1500. • Examples of sectors that can be covered: Transportation, Consumer Durables &Apparel, Household & Personal Products, Energy, 3PL, Food & Beverage, Manufacture.

Use Cases and Applications -Identify, quantify, and comprehend supplier and customer relationships.

-Identify and define interconnections and connections within the United States between individual firms or facilities, as well as between industries.

-Identify and comprehend spatial relationships.

-Map the logistics and domestic distribution supply chain in the United States, either nationally or regionally.

-Trace items across the supply chain and investigate criminal networks. -Improve risk transfer assessment.

-Improve risk management in the supply chain.

Our supply chain data mapping solution can help you answer the following questions: -Are there any other logistic providers with whom my client collaborates? -How do I find out who a company's clients are? -Can you tell me who a company's suppliers are? -Who are the distributors that my competitors use? -How does my competitor's supply chain look? -How does my competitor's distribution chain look? -How can you find out about a company's capabilities?

Other Valuable Insights extracted from the underlying data include: -How frequently do employees visit a facility? (daily, monthly, absolute, and averages). -Differentiation of the visits (worker, resident, visitors). -Building shape and size, as well as a complete footprint analysis -Where the workers visit at night and throughout the day. -The frequency of workers' visits by weekday and hour of the day. -Company locations based on relative income. -Buildings with the potential to be used as a warehouse. -Behavior resembling that of a truck (calculated based on average distance). -The names and addresses of the facilities. -Service Providers and Traded Goods.

Why should you trust PREDIK Data-Driven? In 2023, we were listed as Datarade's top providers. Why? Our solutions for supply chain data, company data, and trucking data adapt according to the specific needs of companies. Also, PREDIK methodology focuses on the client and the necessary elements for the success of their projects.

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.

Report Metric	Details
Forecast Period	2022 to 2029
Base Year	2021
Historic Years	2020 (Customizable to 2014 - 2019)
Quantitative Units	Revenue in USD Billion, Volumes in Units, Pricing in USD
Segments Covered	Solutions (Logistics Analytics, Manufacturing Analytics, Planning and Procurement, Sales and Operations Analytics, Visualization & Reporting), Service (Professional, Support and Maintenance), Deployment (Cloud and On-premise), Enterprise size (Large Enterprise, Small and Medium Enterprise), End-Use (Retail and Consumer Goods, Healthcare, Manufacturing, Transportation, Aerospace and Defense, High Technology Products and Others)
Countries Covered	U.S., Canada, Mexico, Brazil, Argentina, Rest of South America, Germany, France, Italy, U.K., Belgium, Spain, Russia, Turkey, Netherlands, Switzerland, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, U.A.E, Saudi Arabia, Egypt, South Africa, Israel, Rest of Middle East and Africa
Market Players Covered	Oracle (U.S.), IBM (U.S.), SAS Institute Inc.(U.S.), Software AG (Germany), Micro Strategy Incorporated(U.S.), TABLEU SOFTWARE,LLC(U.S.), Qlik (U.S.), TIBCO Software Inc.(U.S.), Cloudera, Inc.(U.S.), American Software,Inc., (U.S.), Accenture(Ireland), Aera Technology (U.S.), Birst, Inc., (U.S.), Capgemini (France), Genpact (U.S.), JDA Software Inc., (U.S.), Kinaxis (Canada), Lockheed Martin Corporation(U.S.), A.P. Moller – Maersk (Denmark)
Market Opportunities	Rise in The Demand of Cloud Based Supply Chain Analytics By The Enterprises Cloud Advantages and Other Technical Innovations

The main aim of supply chain management is resilience, transparency, and optimization.​ Streamlining and innovating supply chains through digitalization and automating processes can help overcome supply chain disruptions without significantly impacting margins and pr Read More

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.

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.

Cloud Supply Chain Management Market size was valued at USD 25.38 Billion in 2023 and is projected to reach USD 280.89 Billion by 2030, growing at a CAGR of 35.09% during the forecast period 2024-2030.

Global Cloud Supply Chain Management Market Drivers

The market drivers for the Cloud Supply Chain Management Market can be influenced by various factors. These may include:

Initiatives for Digital Transformation: To update their operations and maintain their competitiveness, several firms are engaged in digital transformation projects. Scalability, adaptability, and accessibility are features of cloud SCM solutions that complement these transformation objectives.
Globalization and Complex Supply networks: As supply networks become more globalized, businesses must deal with an ever-increasing level of operational complexity. The visibility, communication, and analytical tools required for managing intricate supply chains across borders are provided by cloud SCM solutions.
Demand for Real-Time Visibility: The need for supply chain processes to be visible in real-time is increasing. Real-time tracking of inventory, shipments, and manufacturing processes is made possible by cloud SCM solutions, which improves decision-making and increases responsiveness to variations in supply and demand.
Cost Reduction and Efficiency Gains: By optimizing inventory levels, cutting waste, and streamlining procedures, cloud SCM solutions can help businesses cut expenses. Utilizing cloud-based technologies allows businesses to operate their supply chains more productively and efficiently.
Adoption of IoT and sophisticated Analytics: Supply chain management is increasingly utilizing Internet of Things (IoT) devices and sophisticated analytics. Demand forecasting, predictive maintenance, and operational efficiency can all be enhanced by integrating cloud SCM systems with IoT sensors and devices to gather and analyze data.
Subscription-Based Business structures: Cloud SCM is now more affordable for businesses of all sizes because to the move toward subscription-based software pricing structures. Comparing subscription-based models to traditional on-premises software deployments, the former offers flexibility, scalability, and reduced upfront expenses.
Regulatory Compliance and Sustainability: As regulations pertaining to ethical sourcing, traceability, and sustainability become more stringent, cloud SCM solutions are becoming more and more popular. Throughout the supply chain, these platforms offer the ability to track and report on social and environmental data.

Datafeed Introduction Trademo's “public company supply chain data” is a comprehensive data feeds that empower organizations with an all-encompassing view the supply chain activities of all public companies globally, covering 190+ countries. These feeds provide 25+ raw and enriched data points, including: 1. Shipment Date & Type 2. Consignee & Shipper Details: Name, Parent Name, Address, State, Country, 3. Consignee Shipper Stock Details: Stock Ticker, Stock Exchange, Parent Stock Ticker, Parent Stock Exchange 5. HS Code & Product Description 6. Shipment Value, Quantity & Quantity Unit 7. Weight (kg) & Mode of Transport

Datafeed Overview: 1. Geographic Coverage: 190+ countries 2. Industry Coverage: All 3. Data Available from: Jan 2011 4. Data Source: Government and authoritative sources 5. Update Frequency: Dynamic, As low as 1 day

The global supply chain analytics market size was estimated at USD 6.12 billion in 2022 and is anticipated to grow at CAGR of 17.8% from 2023 to 2030

Caterpillar Inc. 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 Caterpillar is on the order of tens of billions and they sell products and parts via a worldwide dealer network. Caterpillar 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 Caterpillar - a medium size excavator.

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.

The supply chain management market revenue totaled ~US$ 25.7 Billion for 2023, according to Future Market Insights (FMI’s) latest study. The overall market is expected to reach ~US$ 78.5 Billion by 2033, growing at a CAGR of 11.8% from 2023 to 2033.

Attributes	Details
Supply Chain Management Market Value (2023)	US$ 25.7 Billion
Supply Chain Management Market Forecast Value (2033)	US$ 78.5 Billion
Supply Chain Management Market CAGR (2023 to 2033)	11.8%

2018 to 2022 Supply Chain Management Demand Outlook Compared to 2023 to 2033 Forecast

Historical CAGR (2017 to 2022)	8.9%
Forecast CAGR (2023 to 2033)	11.8%

Supply Chain Management Market: Country-wise Insights

Country	USA
CAGR (2023 to 2033)	9.6%
Market Size (2033)	US$ 16.6 Billion

Country	United Kingdom
CAGR (2023 to 2033)	11.5%
Market Size (2033)	US$ 3.5 Billion

Country	China
CAGR (2023 to 2033)	11.8%
Market Size (2033)	US$ 8 Billion

Country	Japan
CAGR (2023 to 2033)	14.9%
Market Size (2033)	US$ 6.7 Billion

Country	South Korea
CAGR (2023 to 2033)	10.1%
Market Size (2033)	US$ 2.2 Billion

Attributes	Details
Market Size (2033)	US$ 16.6 Billion
Market Absolute Dollar Growth (US$ Billion/Billion)	US$ 10 Billio

Historical CAGR (2017 to 2022)	7.1%
Forecast CAGR (2023 to 2033)	9.6%

Attributes	Details
Market Size (2033)	US$ 6.7 Billion
Market Absolute Dollar Growth (US$ Billion/Billion)	US$ 5 Billion

Historical CAGR (2017 to 2022)	12.3%
Forecast CAGR (2023 to 2033)	14.9%

Supply Chain Management Market: Category-wise Insights

Taxonomy	Solution
Top Segment	Software
Forecast CAGR	12.8%

Taxonomy	Enterprise Size
Top Segment	Large Enterprises
Forecast CAGR	11.6%

Historical CAGR (2017 to 2022)	8.4%
Forecast CAGR (2023 to 2033)	11.6%

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.

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.

Report Metric	Details
Forecast Period	2023 to 2030
Base Year	2022
Historic Years	2021 (Customizable to 2015 - 2020)
Quantitative Units	Revenue in USD Billion, Volumes in Units, Pricing in USD
Segments Covered	Solution (Transportation Management, Procurement and Sourcing, Order Management, Sales and Operation Planning, Inventory and Warehouse Management, Demand Planning and Forecasting), Service (Training and Consulting, Support and Maintenance, Managed Services), Deployment Model (Public, Private, Hybrid), Organization Size (Small and Medium Enterprises, Large Enterprises), Vertical (Food and Beverage, Healthcare and Life Sciences, Manufacturing, Retail and Wholesale, Transportation and Logistics)
Countries Covered	U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, Israel, Egypt, South Africa, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America
Market Players Covered	Orcale (U.S.), SAP (Germany), IBM (U.S.), CloudLogix (U.S.), Cloudera, Inc. (U.S.), JDA Software Inc., (U.S.), Kinaxis (Canada), THE DESCARTES SYSTEMS GROUP INC (Canada), Infor (U.S.), Manhattan Associates (India), American Software, Inc. (U.S.), JAGGAER (U.S.), Proactis Holdings Limited (U.K.), Blue Yonder Group, Inc. (U.S.), BluJay Solutions Ltd (U.S.), Coupa Software Inc. (U.S.), HighJump (Canada)
Market Opportunities	Growth and expansion of e-commerce Increasing adoption of cloud supply chain management in retail industry

Supply Chain Management (SCM) Market size was valued at USD 1002.2 Million in 2023 and is projected to reach USD 2263.42 Million by 2030, growing at a CAGR of 10.72% during the forecast period 2024-2031.

Global Supply Chain Management (SCM) Market Drivers

The market drivers for the Supply Chain Management (SCM) Market can be influenced by various factors. These may include:

Globalization and International Trade: The demand for reliable supply chain management (SCM) solutions is driven by the growth of international trade and the rising globalization of supply chains. Efficient supply chain management systems are necessary for businesses that operate in numerous countries in order to manage inventories, optimize logistics, and coordinate activities across intricate worldwide networks.

E-commerce and Omnichannel Retailing: In order to satisfy customer needs for quick, dependable, and seamless order fulfillment, agile and flexible SCM solutions are required given the explosive expansion of e-commerce platforms and omnichannel retailing models. Retailers, manufacturers, and logistics companies can improve customer happiness, control inventory levels, and streamline supply chain procedures with the use of SCM software.

Demand for Real-time Transparency and Visibility: Those involved in supply chains want to be able to see real-time information on order fulfillment procedures, shipment status, and inventory levels. SCM technologies provide for end-to-end visibility, traceability, and data-driven decision-making throughout the whole supply chain ecosystem. Examples of these technologies include blockchain, Internet of Things (IoT), and RFID tracking.

Emphasis on Cost Optimization and Efficiency: Through efficient SCM procedures, companies aim to minimize operational inefficiencies, maximize supply chain expenses, and boost profitability. In order to achieve cost savings and operational efficiency, SCM solutions assist businesses in minimizing the costs associated with maintaining inventory, cutting down on transportation costs, and optimizing production scheduling.

Risk Mitigation and Resilience Planning: Demand for Supply Chain Management (SCM) solutions that improve risk mitigation and resilience planning is driven by heightened awareness of supply chain risks, interruptions, and vulnerabilities. In order to lessen the effects of interruptions like natural disasters, geopolitical crises, and supply chain disruptions, supply chain management software (SCM) offers proactive risk identification, scenario analysis, and contingency planning.

Stressing Corporate Social Responsibility (CSR) and Sustainability: Increasing focus on CSR, ethical sourcing, and sustainability affects supply chain management techniques and tactics. Supply chain visibility, compliance monitoring, and sustainability reporting are made possible by SCM systems, which also support ethical procurement, environmental stewardship, and sustainable sourcing.

Technological Development and Digital Transformation: Digital transformation efforts in supply chain management are propelled by the quick developments in artificial intelligence (AI), machine learning (ML), and big data analytics. In order to optimize supply chain operations, increase forecast accuracy, and strengthen decision-making capabilities, advanced SCM platforms make use of AI-driven insights, predictive analytics, and prescriptive optimization algorithms.
Trade rules and regulatory compliance: Supply chain management faces difficulties in adhering to industry standards, trade regulations, and regulatory obligations. SCM solutions assist businesses in navigating complicated regulatory environments, guaranteeing adherence to trade agreements, tariffs, and customs laws, and reducing the risk of supply chain disruptions and noncompliance.

Customer Experience and Service Level Expectations: Agile and responsive supply chains are required to meet the growing demands of customers for prompt delivery, customized experiences, and smooth order fulfillment. Through effective supply chain management, SCM solutions help businesses achieve customer service level agreements (SLAs), accurately and promptly fulfill orders, and improve the entire customer experience.

Partnerships and Collaboration in Supply Chain Networks: To maximize the performance and agility of the supply chain, partnerships and collaboration are crucial between suppliers, manufacturers, distributors, and logistics companies. Supply chain visibility, responsiveness, and resilience can be increased by trading partners working together, exchanging information, and coordinating activities through SCM systems.