There are 7 tables in total, the task is, to assign routes to the Orders in the "Order List" Table given the restrictions (e.g. weight restriction). - The order list already contains Historical data of how the orders were assigned in the past.

Please refer to https://brunel.figshare.com/articles/dataset/Supply_Chain_Logistics_Problem_Dataset/7558679 for further clarification.

The other 6 tables describe the restrictions imposed on the system. - some customers can only be serviced by a specific plant - plants and ports have to be physically connected. - plants can only handle specific items

Notes:

• The terms "Warehouse" and "Plant" are used interchangeably, essentially a warehouse is a plant.

• This is a (deterministic) optimization problem, there is only one order date since we are only looking at orders from one specific day and trying to assign them to routes/factories.

• We have to ship all the orders to PORT09

• The goal is to schedule routes while minimizing freight and warehousing costs.

• I am also just working on understanding the Dataset, maybe we can have a discussion in the comment section for clarifications.

Acknowledgements:

This dataset was taken from the Brunel University of London Website

Snapshot img

Supply Chain Analytics Market Size 2025-2029

The supply chain analytics market size is valued to increase USD 15.51 billion, at a CAGR of 22.2% from 2024 to 2029. Increased need to improve business processes will drive the supply chain analytics market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By End-user - Retail segment was valued at USD 1.45 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 389.88 million
Market Future Opportunities: USD 15,514.40 million
CAGR : 22.2%
North America: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving domain, driven by the increasing need to optimize business processes and enhance operational efficiency. A key trend fueling market growth is the rising adoption of predictive analytics in supply chain management. However, the implementation of supply chain analytics comes with challenges, including data security concerns and the need for significant IT investment.
Core technologies, such as machine learning and artificial intelligence, are at the forefront of innovation, enabling real-time insights and forecasting capabilities. In the application space, logistics and inventory management are leading sectors, while regulatory compliance and regional variations add complexity to the market landscape.

What will be the Size of the Supply Chain Analytics Market during the forecast period?

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How is the Supply Chain Analytics Market Segmented and what are the key trends of market segmentation?

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-user

  Retail
  Manufacturing
  Healthcare
  Transportation
  Others


Deployment

  On-premises
  Cloud-based


Service

  Professional services
  Managed services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.

In today's dynamic business landscape, retailers are leveraging advanced analytics solutions to gain valuable insights from their supply chain operations. These insights enable retailers to make data-driven decisions, optimize inventory levels, and enhance supplier performance. According to recent studies, the adoption of supply chain analytics in retail has seen a significant increase, with approximately 30% of retailers reporting an improvement in inventory management and a 25% reduction in stockouts. Looking forward, industry experts anticipate that this trend will continue, with up to 40% of retailers planning to invest in advanced analytics tools in the next three years. Predictive modeling and data visualization are essential components of these solutions.

They help retailers analyze historical sales data, market trends, and external factors to accurately forecast demand. Furthermore, AI-powered insights, risk management, and digital twin technology facilitate supply chain design and optimization, while optimization algorithms, network optimization, and performance dashboards ensure efficient operations. Demand planning and forecasting are critical aspects of supply chain analytics. By analyzing demand patterns and lead times, retailers can optimize inventory levels, minimize carrying costs, and maintain stockouts. Moreover, retailers can evaluate their suppliers' performance using statistical modeling and cloud computing solutions. This evaluation includes analyzing delivery times, product quality, and overall reliability, enabling retailers to collaborate more effectively and negotiate better terms.

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The Retail segment was valued at USD 1.45 billion in 2019 and showed a gradual increase during the forecast period.

In the realm of logistics optimization, real-time tracking using IoT sensors and blockchain technology ensures transparency and security. Additionally, linear programming, inventory optimization, production scheduling, warehouse management, order fulfillment, and machine learning algorithms facilitate efficient operations and minimize costs. In conclusion, the application of supply chain analytics in retail is a continuous and evolving process. By utilizing advanced tools and techniques, retailers can optimize inventory levels, enhance supplier performance, and improve overall supply chain efficiency. With the increasing adoption of these s

Dataset

This dataset was created by Divyesh Ardeshana

Contents

Supply Chain Management Market Outlook

According to our latest research, the global supply chain management market size reached $28.7 billion in 2024, demonstrating robust momentum driven by digital transformation and increasing complexity in global trade. The market is projected to grow at a CAGR of 11.6% from 2025 to 2033, reaching a forecasted value of $77.2 billion by 2033. This growth is primarily fueled by the rapid adoption of advanced technologies, such as artificial intelligence, blockchain, and IoT, which are revolutionizing supply chain operations and enhancing transparency, efficiency, and resilience across multiple industries.

A primary growth factor for the supply chain management market is the accelerating pace of globalization, which has significantly increased the complexity of supply chains. Businesses are now required to manage vast networks of suppliers, manufacturers, distributors, and retailers across multiple geographies. This complexity necessitates robust supply chain management solutions that can provide real-time visibility, optimize logistics, and ensure seamless coordination among all stakeholders. The ongoing shift towards e-commerce and omnichannel retailing has further intensified the need for agile and responsive supply chain systems, driving organizations to invest heavily in advanced software and automation tools to maintain competitive advantage and meet evolving customer expectations.

Another significant driver is the increasing emphasis on risk management and supply chain resilience in the wake of global disruptions, such as the COVID-19 pandemic, geopolitical tensions, and natural disasters. Organizations have recognized the critical importance of having resilient and flexible supply chains that can quickly adapt to unforeseen events. This has led to a surge in demand for supply chain management solutions equipped with predictive analytics, scenario planning, and end-to-end visibility features. The ability to proactively identify risks, assess their impact, and implement mitigation strategies has become a top priority for companies across all sectors, fueling the growth of the supply chain management market.

Furthermore, the integration of emerging technologies, such as artificial intelligence, machine learning, and blockchain, is transforming traditional supply chain processes. These technologies enable automation of routine tasks, enhance decision-making through data-driven insights, and improve traceability and transparency across the supply chain. For instance, AI-powered demand forecasting and inventory optimization tools are helping businesses minimize stockouts and reduce excess inventory, while blockchain technology is facilitating secure and transparent transactions. The continuous innovation in supply chain management software and hardware is expected to drive market expansion over the forecast period.

From a regional perspective, North America currently dominates the supply chain management market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology providers, early adoption of digital solutions, and high concentration of large enterprises in these regions contribute to their market leadership. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by rapid industrialization, expanding manufacturing sectors, and increasing investments in digital infrastructure. The region's growing focus on supply chain optimization, particularly in China and India, is expected to create significant opportunities for market players in the coming years.

Data Integration for Supply Chain Execution plays a pivotal role in the seamless operation of modern supply chains. As businesses strive to enhance efficiency and responsiveness, integrating data from various sources becomes crucial. This integration allows for real-time visibility and coordination across different supply chain functions, from procurement and manufacturing to logistics and distribution. By leveraging advanced data integration techniques, organizations can break down silos, streamline processes, and ensure that all stakeholders have access to accurate and timely information. This not only improves decision-making but also enhances the overall agility and resilience of the s

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.

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.

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 generated according to a normal distribution with the same mean and standard deviation as in the original dataset (e.g. the demand figures have the same mean and standard deviation as those found in the original problem).The dataset has been extended with 9 more years of demand distributions. The additional 9 years have been created with the same random problem generator. The purpose of the dataset is to provide more instances of the problem. The dataset may be interpreted as containing 10 years of demand for one product or the demand figures of 10 similar products. For instance, we adopted this dataset in a machine learning context to have a larger and more comprehensive training set.

Supply Chain Analytics Market is Segmented by Component (Software [Demand Planning and Forecasting, and More], by Deployment (On-Premise, Cloud, and More), by Analytics Type (Descriptive, and More), by Application (Inventory Management, and More), by End-Use Industry (Retail and E-Commerce, and More), by Enterprise Size (Large Enterprises, and More), and Geography.

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.

In a 2021 survey, over **** of surveyed supply chain professionals stated that they found supply chain disruptions and shortages extremely or very challenging. During the survey, demand-side challenges, such as faster response time were cited among the most difficult hurdles supply chain companies face.

In 2019, the global next-gen supply chain market generated roughly ** billion U.S. dollars. By 2030, the size of this market is forecasted to more than ******. Next-gen supply chain market refers to the high utilization of the digital revolution by logistics companies or start-ups to enhance the economic and environmental sustainability of supply chain services.

The historical sales dataset for this research is obtained from a Bangladeshi retailer. The dataset covers a period of 1826 days and includes daily sales data for a particular product from 01 January 2013 to 31 December 2017. The raw sales data has 2 columns: the first column contains timestamps, while the remaining column reflects the quantity sold.

The global supply chain management market size reached around USD 27.93 Billion in 2024. The market is projected to grow at a CAGR of 11.40% between 2025 and 2034 to reach nearly USD 82.21 Billion by 2034.

The global SCM (supply chain management) market is facing massive growth as companies are moving toward implementation of latest technologies to streamline operations. This shift stems from factors such as supply chain optimization, procurement & sourcing, waste minimization, transportation management systems, and more. SCM solutions are using data and analytics to make better decisions, manage inventories, and optimize processes. A growing number of businesses investing in innovative SCM solutions recognized during the COVID-19 pandemic that the supply chains needed to be more resilient.

In addition, transportation management systems can help save costs and increase efficiency. As businesses grapple with countless challenges, SCM management is expected to be the backbone together with powerful technology and analytics for carving operational success and sustainability.

In 2023, the Supply Chain Management Market reached a value of USD 29.3 billion, and it is projected to surge to USD 58.7 billion by 2030.

This statistic depicts the most important information/analytics technologies for supply chains over the next one to three years according to supply chain professionals in 2018. During the survey, 73 percent of respondents believed big data analytics will be either very or extremely important to their supply chain.

The global cloud supply chain management market is projected to register a CAGR of 11.1 % by 2034, to reach USD 16.02 Billion in 2034 from USD 6.93 Billion in 2020

The Eora global supply chain database consists of a multi-region input-output table (MRIO) model that provides a time series of high-resolution IO tables with matching environmental and social satellite accounts for 190 countries.

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.

Automated analysis of supply chain data and logistics metrics with AI agents

The Global IoT for Supply Chain Management Market Size Was Worth USD 21.38 Billion in 2023 and Is Expected To Reach USD 57.6 Billion by 2032, CAGR of 13%.