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

This dataset provides real-time data for smart logistics operations, capturing various aspects of supply chain management over the past year (2024). It includes information on asset tracking, inventory levels, shipment statuses, environmental conditions, traffic, and user behaviors. The dataset features multiple stakeholders within the logistics network, including asset IDs, timestamps, traffic conditions, waiting times, and reasons for delays. Additionally, the data is enriched with real-time information from IoT sensors, such as temperature, humidity, and asset utilization, to facilitate advanced logistics optimization and decision-making. The target variable, Logistics_Delay, helps in identifying delays in logistics processes, which is essential for enhancing supply chain efficiency through proactive management and optimization techniques. This dataset is designed to be used for research and machine learning applications focused on smart logistics and supply chain performance improvement.

Key Features: Timestamp: Date and time when the data was recorded, representing logistics activity. Asset_ID: Unique identifier for the logistical assets (e.g., trucks). Latitude & Longitude: Geographical coordinates of the asset for tracking and monitoring. Inventory_Level: Current level of inventory associated with the asset or shipment. Shipment_Status: Status of the shipment (e.g., In Transit, Delivered, Delayed). Temperature: Temperature recorded at the time of the shipment or transportation. Humidity: Humidity level at the time of recording. Traffic_Status: Current traffic condition (e.g., Clear, Heavy, Detour). Waiting_Time: Time spent waiting during the logistics process (in minutes). User_Transaction_Amount: Monetary amount associated with user transactions. User_Purchase_Frequency: Frequency of purchases made by the user. Logistics_Delay_Reason: Reason for any delays in the logistics process (e.g., Weather, Mechanical Failure). Asset_Utilization: Percentage of asset utilization, indicating how effectively assets are being used. Demand_Forecast: Predicted demand for the logistics services in the coming period. Logistics_Delay (Target): Binary variable indicating whether a logistics delay occurred (1 for delay, 0 for no delay).

This supply chain analysis provides a comprehensive view of the company's order and distribution processes, allowing for in-depth analysis and optimization of various aspects of the supply chain, from procurement and inventory management to sales and customer satisfaction. It empowers the company to make data-driven decisions to improve efficiency, reduce costs, and enhance customer experiences. The provided supply chain analysis dataset contains various columns that capture important information related to the company's order and distribution processes:

• OrderNumber • Sales Channel • WarehouseCode • ProcuredDate • CurrencyCode • OrderDate • ShipDate • DeliveryDate • SalesTeamID • CustomerID • StoreID • ProductID • Order Quantity • Discount Applied • Unit Cost • Unit Price

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

The Supply Chain Big Data Analytics Market Report is Segmented by Component (Solution, Service), End User Industry (Retail, Transportation and Logistics, Manufacturing, Healthcare, Other End-User Industries), Deployment Model (On-Premise, Cloud), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Data Set Bibliometric Green Supply Chain

This project focuses on optimizing the allocation of resources in a medium-sized hospital by developing a dataset that captures the hospital's inventory management, patient demand, staffing availability, vendor interactions, and financial expenditures. The objective is to analyze resource usage, forecast demand, and ensure efficient supply chain operations that prevent shortages while minimizing costs. The datasets generated can be used to build predictive models for resource optimization, improving hospital efficiency, and supporting better decision-making.

Supply Chain Analytics Market size is estimated to be valued at USD 8.2 billion in 2025 and is expected to expand at a CAGR of 10.5%, reaching USD 16.5 billion by 2032.

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 Supply Chain Analytics Market size is expected to reach a valuation of USD 43.0 billion in 2033 growing at a CAGR of 18.00%. The Supply Chain Analytics Market research report classifies Market by share, trend, demand, forecast and based on segmentation.

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.

This dataset is designed to simulate supply chain operations in large-scale engineering projects. It integrates realistic data from IoT sensors, digital twins, and blockchain-enabled monitoring systems over the years 2023 to 2024.

It aims to support research in predictive maintenance, resource optimization, secure data exchange, and supply chain transparency through advanced analytics and machine learning.

⭐ Key Features Time-bound IoT Sensor Data: Includes real-time-like sensor outputs such as temperature and vibration across multiple locations and assets.

Digital Twin Sync Fields: Tracks Condition_Score and Last_Maintenance to simulate digital twin feedback loops.

Operational KPIs: Features supply chain metrics like Resource_Utilization, Delivery_Efficiency, and Downtime_Hours.

Blockchain Contextual Fit: Designed to be compatible with blockchain audit trails and smart contract triggers (e.g., anomaly response, automated logistics payments).

Labeled Targets: SupplyChain_Efficiency_Label classifies overall efficiency into 3 tiers (0: Low, 1: Medium, 2: High) based on predefined KPI thresholds.

Location-aware Simulation: Assets and operations are tagged by realistic geographic locations.

Supply Chain Economics: Captures Inventory_Level and Logistics_Cost for resource allocation analysis.

Year-specific Scope: Covers the period from 2023 to 2024, aligning with recent and ongoing digital transformation trends.

The global logistics & supply chain industry market size is expected to reach USD 2,800 Billion in 2034, and register a CAGR of 8.5%. Logistics & supply chain industry report classifies global market by share, trend, and on the basis of transportation mode, application, industry vertical, and region

Healthcare supply chain management operates under complex uncertainty, high risk, and the imperative of sustainable resource optimization. This study investigates the underlying mechanisms through which digital intelligence drives strategic decision optimization in healthcare supply chains. Drawing on the Resource Based View (RBV) and Dynamic Capabilities Theory (DCT), we develop a chain mediated model in which innovation capability and supply chain resilience (absorptive, response, and restorative capabilities), serve as sequential mediators. Using structural equation modeling (SEM) on data collected from healthcare supply chain organizations in China, we find that digital intelligence indirectly enhances decision optimization by fostering innovation and resilience in tandem. Specifically, digital intelligence strengthens innovation capability, which in turn activates all three dimensions of resilience, and together these capabilities produce a synergistic effect that sustains decision improvement. Our findings offer practical theoretical guidance for healthcare institutions seeking to deploy digital intelligence technologies, reinforce dynamic process management, and achieve continuous optimization of supply chain decision making.

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.

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.

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

tutkuozdeniz/supply-chain-management dataset hosted on Hugging Face and contributed by the HF Datasets community

Snapshot img

Food Supply Chain Market Size 2024-2028

The food supply chain market size is forecast to increase by USD 59.51 billion at a CAGR of 7.86% between 2023 and 2028.

The market is experiencing significant growth, driven by several key trends and challenges. One of the major trends transforming the industry is the integration of blockchain technology into food supply chain management. This innovation enhances transparency, traceability, and security, enabling consumers to access detailed information about the origin and journey of their food. Another significant trend is the increasing number of mergers and acquisitions among market participants, which is intensifying competition and leading to the formation of larger, more efficient supply chains. However, data security and cyber threats remain critical challenges for market players, necessitating strong security measures to safeguard sensitive information and protect against potential breaches. These factors, among others, are shaping the future of the market.

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

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The market encompasses the production, transportation, warehousing, and distribution of a diverse range of food products, including fresh fruits, vegetables, meats, dairy, and processed foods. This market is characterized by its intricate nature, involving temperature-controlled logistics, cold chain management, and adherence to stringent safety regulations. E-commerce platforms have significantly disrupted traditional food trade channels, necessitating advanced technologies such as artificial intelligence, the Internet of Things, and blockchain technologies to ensure efficient and secure food supply.



Agriculture remains a critical upstream component, while downstream activities include transportation, warehousing, and warehouse management systems utilizing positioning systems and radio frequency identification for real-time tracking and inventory management. Consumer preferences for healthier, safer food options continue to shape market dynamics, driving innovation and investment In the sector.

How is this Food Supply Chain 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.

Product Type

  Packaged food
  Fresh food


End-user

  Households
  Commercial
  Industrial


Geography

  APAC

    China
    India
    Japan
    South Korea


  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  South America



  Middle East and Africa

By Product Type Insights

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

The food supply chain encompasses various sectors, including fresh and perishable foods, food trade, temperature-controlled logistics, e-commerce platforms, and sustainability. Perishable foods, such as meats, dairy, fruits, and vegetables, require specialized handling and cold chain management to ensure safety and quality. E-commerce platforms and consumer preferences for convenience have led to increased demand for customized logistics solutions and multi-modal transportation. Temperature-controlled logistics and cold chain capabilities are crucial for maintaining food safety regulations and product integrity. Advancements in technology, such as artificial intelligence, the Internet of Things, blockchain technologies, and precision farming, are revolutionizing the food supply chain. These technologies enable better inventory management, traceability, and transparency, enhancing consumer trust and product provenance.

Sustainability and economic growth are essential considerations, with a focus on reducing food waste and loss throughout the supply chain. The food supply chain is complex, involving agriculture, food processing, transportation, warehousing, and food retailers. Standards and regulations, including health and safety, positioning systems, radio frequency identification, and warehouse management systems, play a critical role in ensuring food safety and quality. Funding and investment in food supply chain innovation are essential to addressing the challenges of meeting consumer demands while maintaining efficiency, safety, and sustainability.

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The packaged food segment was valued at USD 54.22 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 47% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that sha

Global supply chain analytics market size, share valued at USD 7540.00 million and is predicted to reach at a CAGR of 18.4% over the forecast period 2032.