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

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.

Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.

The dataset contains information related to supply chain operations, including orders, products, inventory, suppliers, logistics, and demand. It aims to optimize supply chain efficiency and improve performance through predictive analytics, inventory management, and logistics optimization.

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.

This graph presents the result of a worldwide survey of senior executives, conducted by Accenture, into the impact of big data analytics on company supply chains in 2014. In 2014, ** percent of respondents stated that their company had achieved an improvement in customer service and demand fulfillment of ** percent or greater using big data analytics.

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.

Supply chain management companies benefited from robust growth amid a boom in the number of businesses, corporate profit and manufacturing investment. When businesses expand, they use supply chain management companies to ensure that they can maintain profit and quality shipping standards for their customers. Improvements in supply chain technology spurred growth as services have become more seamless and efficient, attracting customers. Advancements in software have been somewhat of a double-edged sword for supply chain management companies as supply chain management software has advanced and become more user-friendly, enabling clients to manage supply chains internally. Geopolitical instability amid conflicts in Europe and the Red Sea in 2022 and 2023 provided extra demand, as clients sought to navigate higher shipping uncertainty across global shipping routes. Although technological adoption has been a big boost for supply chain managers, the continued effects of elevated interest rates curtailed smaller clients’ abilities to hire professional managers. Revenue grew at a CAGR of 6.7% to an estimated $16.5 billion over the past five years, including an anticipated 1.4% boost in 2025 alone, with profit remaining stable. Despite inflationary headwinds playing a key role in undermining consistent global supply and demand, supply chain management companies continued to expand. Surging e-commerce sales, which grew at a CAGR of 5.5% in the past five years, boosted growth from consumer product companies that sought supply chain management services to optimize their operations. Commodity price volatility encouraged customers to increasingly invest in materials and transportation management services, as companies sought more cost-efficient procurement to avoid destabilizing their profit margin. Overarching threats from package and cargo theft continues to force incumbent supply chain managers to adopt digital solutions, such as blockchain-based systems that maintain inventory and shipping data in decentralized digital storage. Moving forward, supply chain management companies face positive headwinds, although interest rates and an evolving landscape will generate a variety of client needs. While core sectors of the economy, such as manufacturing and consumer products, remain resilient, rising tariffs may cause changes in national aggregate demand, forcing companies to adapt their inventory orders and prioritize more cost-efficient shipping routes. The continued proliferation of automated equipment and breakthrough of artificial intelligence (AI) will exacerbate direct competition from in-house competitors, especially among smaller companies seeking cost-savings in supply chain oversight. Revenue is expected to grow at a CAGR of 2.1% to an estimated $18.3 billion over the next five years.

Number of Businesses statistics on the Supply Chain Management Services industry in the US

This statistic shows the revenue earned by supply chain management and procurement software vendors from 2008 to 2017. In 2017, SAP's revenue from SCM and procurement software totaled around *** billion U.S. dollars.

Supply Chain Management (SCM) Market Outlook

The global Supply Chain Management (SCM) market size was valued at approximately USD 24.5 billion in 2023 and is expected to reach around USD 45.8 billion by 2032, growing at a CAGR of 7.2% during the forecast period. This robust growth can be attributed to the increasing complexity of global supply chains, the integration of advanced technologies, and the growing need for enhanced transparency and efficiency across industries.

One of the primary growth factors driving the SCM market is the rapid digital transformation occurring within various industries. Companies are increasingly adopting advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and the Internet of Things (IoT) to optimize their supply chain processes. These technologies aid in real-time monitoring, predictive analytics, and automation, significantly improving the efficiency and reliability of supply chain operations. Furthermore, the rising demand for e-commerce and the need for faster and more reliable delivery services have necessitated the adoption of sophisticated SCM solutions.

Another crucial growth factor is the increasing globalization of businesses. With companies expanding their operations across borders, there is a heightened need for efficient supply chain management to handle the complexities of international logistics, compliance with regulatory standards, and coordination among various stakeholders. This has led to a surge in demand for comprehensive SCM solutions that can provide end-to-end visibility and control over the supply chain, from procurement to delivery. Additionally, the emphasis on sustainability and ethical sourcing has prompted companies to invest in SCM solutions that ensure compliance with environmental and social governance (ESG) criteria.

Furthermore, the COVID-19 pandemic has underscored the importance of resilient supply chains. The disruptions caused by the pandemic exposed vulnerabilities in traditional supply chain models, prompting companies to reassess their strategies and invest in more robust and flexible SCM systems. This shift towards building more resilient and adaptable supply chains is expected to drive the demand for advanced SCM solutions in the coming years. The need for real-time data, accurate demand forecasting, and efficient inventory management has become more critical than ever, further bolstering the market growth.

The emergence of Supply Chain as a Service Software is revolutionizing the way organizations manage their supply chains. This innovative approach allows companies to outsource their supply chain management functions to specialized service providers, who leverage advanced software solutions to optimize operations. By utilizing Supply Chain as a Service Software, businesses can achieve greater flexibility and scalability, adapting quickly to changing market demands without the need for significant capital investment in infrastructure. This model is particularly beneficial for small and medium-sized enterprises (SMEs) that may lack the resources to develop and maintain comprehensive supply chain systems in-house. Furthermore, it enables organizations to focus on their core competencies while ensuring efficient supply chain management through expert service providers.

From a regional perspective, North America has been a dominant player in the SCM market, driven by the presence of leading technology providers and early adopters of advanced supply chain solutions. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, fueled by the rapid economic development, expansion of manufacturing activities, and increasing adoption of digital technologies in countries like China, India, and Japan. Europe also holds a significant share of the market, with a strong focus on innovation and sustainability in supply chain operations.

Component Analysis

The SCM market can be segmented by component into software, hardware, and services. The software segment is anticipated to hold the largest market share, driven by the increasing demand for advanced SCM software solutions that offer features such as inventory management, order processing, transportation management, and demand forecasting. Companies are investing heavily in SCM software to streamline their operations, reduce costs, and enhance customer satisfaction. The integration of AI and ML in SCM software is further propell

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.

In a 2022 survey, ** percent of organizations from different industries worldwide revealed that they expect robotic process automation to have a major or moderate impact on supply chains by 2025. If integrated diligently into the supply chain process, robotic process automation can boost productivity and efficiency.

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).

The size of the supply chain management (SCM) software market worldwide is forecast to grow at a compound annual growth rate of *** percent from 2023 to 2028. The market is projected to have a reach a valuation of over ** billion U.S. dollars in 2028.

This dataset is designed to predict supply chain risks by leveraging various data sources from real-world operations. It includes key operational metrics like temperature, humidity, vibration levels, stock quantities, and supplier ratings. These data points are combined with timestamps, shipment statuses, and social media feeds to capture the dynamic nature of the supply chain environment. The goal is to predict disruptions such as delays, equipment failure, and supply shortages. The dataset offers insights into both the underlying conditions and external factors affecting the supply chain. It is useful for forecasting risks and optimizing decision-making processes.

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.

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).

This dataset contains raw, unprocessed data files pertaining to the management tool group 'Supply Chain Management' (SCM), including related concepts like Supply Chain Integration. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "supply chain management" + "supply chain logistics" + "supply chain" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Supply Chain Management + Supply Chain Integration + Supply Chain Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("supply chain management" OR "supply chain integration" OR "supply chain") AND ("management" OR "strategy" OR "planning" OR "logistics" OR "implementation" OR "optimization" OR "approach" OR "system" OR "practice") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Supply Chain Integration (1999, 2000, 2002); Supply Chain Management (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Supply Chain Integration (1999, 2000, 2002); Supply Chain Management (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.