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Report Description

Report Description

Forecast Period

2027-2031

Market Size (2025)

USD 11.79 Billion

CAGR (2026-2031)

26.51%

Fastest Growing Segment

Solutions

Largest Market

North America

Market Size (2031)

USD 48.34 Billion

Market Overview

The Global Predictive Analytics And Maintenance In Supply Chain Market will grow from USD 11.79 Billion in 2025 to USD 48.34 Billion by 2031 at a 26.51% CAGR. Predictive analytics and maintenance in the supply chain utilize historical data, machine learning algorithms, and statistical modeling to anticipate equipment failures and optimize maintenance schedules before disruptions occur. The primary drivers propelling this market include the critical need to minimize unplanned downtime, which significantly erodes profit margins, and the imperative to extend the operational lifecycle of high-value assets. Organizations are actively prioritizing capital allocation to secure these efficiencies. According to the '2025 MHI Annual Industry Report', in 2025, 55% of supply chain leaders reported increasing their supply chain technology and innovation investments to bolster operational resilience and performance.

However, a significant challenge impeding broader market expansion is the difficulty of integrating modern analytical tools with outdated legacy infrastructure. Many supply chain networks rely on disparate data silos that prevent the seamless aggregation of information required for accurate modeling. This technical barrier complicates the implementation process and delays the realization of return on investment, causing some enterprises to hesitate in adopting comprehensive predictive maintenance solutions despite the clear advantages.

Key Market Drivers

The Rapid Proliferation of Industrial IoT and Connected Devices serves as the primary technical enabler for the Global Predictive Analytics And Maintenance In Supply Chain Market. By embedding networked sensors across logistics infrastructure and production assets, organizations are generating the continuous, granular data streams required to detect early warning signs of equipment failure. This widespread connectivity transforms static supply chains into responsive digital ecosystems, allowing operators to monitor asset health in real-time rather than relying on scheduled manual inspections. According to Rockwell Automation, March 2024, in the '9th Annual State of Smart Manufacturing Report', 95% of manufacturers are now using or evaluating smart manufacturing technology, creating the necessary digital foundation for implementing robust predictive maintenance strategies.

Simultaneously, the Growing Integration of Artificial Intelligence and Machine Learning Technologies acts as the intelligence engine that processes this influx of data to optimize maintenance schedules. These algorithms analyze historical performance and real-time telemetry to predict breakdowns before they disrupt operations, significantly reducing the financial impact of idle machinery. According to Zebra Technologies, June 2024, in the '2024 Manufacturing Vision Study', 61% of manufacturing leaders globally expect AI to drive growth by 2029, underscoring the shift toward algorithmic decision-making. This adoption is further accelerated by resource constraints; according to Descartes Systems Group, in 2024, 76% of supply chain and logistics leaders reported experiencing notable workforce shortages, forcing enterprises to rely on automated predictive tools to maintain operational continuity with fewer personnel.

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Key Market Challenges

The difficulty of integrating modern analytical tools with outdated legacy infrastructure acts as a primary restraint on the Global Predictive Analytics And Maintenance In Supply Chain Market. Advanced predictive models demand high-quality, centralized data to accurately forecast equipment failures and optimize schedules. However, a substantial portion of the industry continues to operate on fragmented, manual systems that create deep data silos, making the necessary seamless information flow nearly impossible. This disconnection forces organizations to expend excessive resources on data retrieval and cleaning rather than analysis, thereby neutralizing the efficiency gains that predictive maintenance promises.

According to the '2024 Data and Analytics Survey' by the Institute for Supply Management (ISM), in 2024, 92% of supply management organizations reported utilizing Excel "always or very often" as their primary data tool, while 32% of respondents indicated they spend at least 21% of their operational time simply locating data. Such entrenched reliance on non-integrated, manual tools complicates the deployment of automated predictive solutions, causing enterprises to delay adoption due to the sheer complexity of modernizing their foundational data architecture.

Key Market Trends

The Integration of Generative AI and Advanced Machine Learning is fundamentally altering how maintenance teams interact with data and execute repairs. While traditional predictive models merely flag anomalies, generative AI functions as an intelligent co-pilot, capable of synthesizing vast amounts of technical documentation to generate instant, step-by-step repair guides and troubleshoot complex issues via natural language prompts. This shift democratizes technical expertise, allowing less experienced technicians to perform high-level maintenance tasks and significantly accelerating the time-to-resolution for equipment faults. According to Rockwell Automation, June 2025, in the '10th Annual State of Smart Manufacturing Report', the number of organizations investing in generative and causal AI increased by 12% year-over-year, marking a decisive shift from experimental pilots to scalable, value-driven deployments.

Simultaneously, the Focus on Sustainability and Green Supply Chain Analytics is reshaping market priorities by leveraging predictive insights to meet rigorous environmental, social, and governance (ESG) standards. Organizations are increasingly deploying analytics not just to prevent downtime, but to optimize the energy consumption of aging assets and extend their operational life, thereby reducing the carbon footprint associated with manufacturing new spare parts and machinery. This "green maintenance" approach transforms asset management into a critical component of corporate decarbonization strategies. According to MHI, March 2025, in the '2025 MHI Annual Industry Report', 44% of supply chain professionals identified environmental concerns and sustainability initiatives as the most significant trend impacting their operational strategies.

Segmental Insights

The Solutions segment is currently positioning itself as the fastest-growing category within the Global Predictive Analytics and Maintenance in Supply Chain Market. This rapid expansion is primarily driven by the surging adoption of integrated software platforms that leverage artificial intelligence and machine learning to interpret complex datasets in real time. Organizations are increasingly prioritizing these automated tools to achieve end-to-end visibility, which allows them to predict equipment failures and optimize inventory levels proactively. Furthermore, the industry-wide shift toward scalable, cloud-based deployment models is making these advanced analytical capabilities more accessible to enterprises, thereby accelerating market penetration and revenue growth.

Regional Insights

North America leads the global predictive analytics and maintenance in supply chain market due to the widespread implementation of automation and artificial intelligence in industrial operations. The region benefits from a high concentration of key technology providers, including IBM and Microsoft, which facilitates the integration of maintenance solutions. Additionally, the focus on modernizing manufacturing infrastructure in the United States drives the demand for tools that reduce operational interruptions. This established industrial base allows North American organizations to utilize data effectively for optimizing logistics and asset management strategies.

Recent Developments

  • In September 2024, Oracle unveiled new artificial intelligence and predictive analytics features for its Fusion Cloud Supply Chain & Manufacturing suite. The update included the Supply Chain Command Center, an intelligent application designed to detect risks and recommend actions by analyzing real-time data from across the network. Additionally, the company introduced a maintenance supervisor workbench that leverages machine learning to predict asset failures and optimize repair schedules. These enhancements were developed to help organizations proactively manage constraints and maintain operational efficiency by predicting potential issues before they impacted the broader supply chain.
  • In June 2024, Kinaxis introduced Maestro, an AI-infused supply chain orchestration platform intended to provide complete transparency from strategic planning to last-mile delivery. The new solution featured an intelligent engine that continuously synchronized data and people to deliver real-time insights and predictions. By incorporating generative artificial intelligence, the platform empowered users to query complex data using natural language and receive immediate guidance on managing disruptions. This launch marked a significant technological evolution for the company, focusing on helping supply chain leaders navigate volatility through advanced scenario modeling and automated decision-making support.
  • In May 2024, Manhattan Associates launched Manhattan Active Supply Chain Planning, a unified business planning platform designed to bridge the gap between planning and execution. The solution introduced bi-directional collaboration, allowing changes in execution to instantly influence supply chain plans and vice versa. This innovation utilized a single cloud-native architecture to optimize inventory, labor, and transportation simultaneously in real-time. By eliminating data silos, the platform enabled organizations to assess operational factors comprehensively and generate more accurate, feasible predictions, thereby reducing the disconnect often found between high-level strategy and daily operational realities.
  • In March 2024, Blue Yonder announced a binding agreement to acquire One Network Enterprises for approximately $839 million to enhance its position in the supply chain management sector. The acquisition was aimed at integrating a digital supply chain network with existing planning and execution capabilities to create a unified ecosystem. This collaboration enabled real-time data sharing across multi-tier trading partners, allowing companies to improve visibility and predictive capabilities. By leveraging the acquired technology, the company sought to help businesses better anticipate disruptions and optimize their supply chain resilience through a more connected and responsive network architecture.

Key Market Players

  • international Business Machines Corporation
  • Microsoft Corporation
  • SAP SE
  • General Electric Company
  • Schneider Electric SE
  • Google LLC
  • Oracle Corporation
  • Hewlett Packard Enterprise Co.
  • SAS Institute Inc.
  • TIBCO Software Inc.
  • Siemens AG
  • Robert Bosch GmbH
  • Cisco Systems, Inc.
  • Dell, Inc.
  • Intel Corporation

By Component

By Deployment

By Application

By Organization Size

By End-Use Industry

By Region

  • Solutions
  • Services (Managed Services, Professional Services)
  • On-Premises
  • Cloud
  • Inventory Management
  • Predictive Maintenance
  • Predictive Route Planning
  • Demand Forecasting
  • Others
  • Large Enterprises
  • SMEs
  • Retail
  • Manufacturing
  • Aviation
  • Healthcare
  • Energy and Power
  • Others
  • North America
  • Europe
  • Asia Pacific
  • South America
  • Middle East & Africa

Report Scope:

In this report, the Global Predictive Analytics And Maintenance In Supply Chain Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

  • Predictive Analytics And Maintenance In Supply Chain Market, By Component:
  • Solutions
  • Services (Managed Services, Professional Services)
  • Predictive Analytics And Maintenance In Supply Chain Market, By Deployment:
  • On-Premises
  • Cloud
  • Predictive Analytics And Maintenance In Supply Chain Market, By Application:
  • Inventory Management
  • Predictive Maintenance
  • Predictive Route Planning
  • Demand Forecasting
  • Others
  • Predictive Analytics And Maintenance In Supply Chain Market, By Organization Size:
  • Large Enterprises
  • SMEs
  • Predictive Analytics And Maintenance In Supply Chain Market, By End-Use Industry:
  • Retail
  • Manufacturing
  • Aviation
  • Healthcare
  • Energy and Power
  • Others
  • Predictive Analytics And Maintenance In Supply Chain Market, By Region:
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Predictive Analytics And Maintenance In Supply Chain Market.

Available Customizations:

Global Predictive Analytics And Maintenance In Supply Chain Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Global Predictive Analytics And Maintenance In Supply Chain Market is an upcoming report to be released soon. If you wish an early delivery of this report or want to confirm the date of release, please contact us at [email protected]

Table of content

Table of content

1.    Product Overview

1.1.  Market Definition

1.2.  Scope of the Market

1.2.1.  Markets Covered

1.2.2.  Years Considered for Study

1.2.3.  Key Market Segmentations

2.    Research Methodology

2.1.  Objective of the Study

2.2.  Baseline Methodology

2.3.  Key Industry Partners

2.4.  Major Association and Secondary Sources

2.5.  Forecasting Methodology

2.6.  Data Triangulation & Validation

2.7.  Assumptions and Limitations

3.    Executive Summary

3.1.  Overview of the Market

3.2.  Overview of Key Market Segmentations

3.3.  Overview of Key Market Players

3.4.  Overview of Key Regions/Countries

3.5.  Overview of Market Drivers, Challenges, Trends

4.    Voice of Customer

5.    Global Predictive Analytics And Maintenance In Supply Chain Market Outlook

5.1.  Market Size & Forecast

5.1.1.  By Value

5.2.  Market Share & Forecast

5.2.1.  By Component (Solutions, Services (Managed Services, Professional Services))

5.2.2.  By Deployment (On-Premises, Cloud)

5.2.3.  By Application (Inventory Management, Predictive Maintenance, Predictive Route Planning, Demand Forecasting, Others)

5.2.4.  By Organization Size (Large Enterprises, SMEs)

5.2.5.  By End-Use Industry (Retail, Manufacturing, Aviation, Healthcare, Energy and Power, Others)

5.2.6.  By Region

5.2.7.  By Company (2025)

5.3.  Market Map

6.    North America Predictive Analytics And Maintenance In Supply Chain Market Outlook

6.1.  Market Size & Forecast

6.1.1.  By Value

6.2.  Market Share & Forecast

6.2.1.  By Component

6.2.2.  By Deployment

6.2.3.  By Application

6.2.4.  By Organization Size

6.2.5.  By End-Use Industry

6.2.6.  By Country

6.3.    North America: Country Analysis

6.3.1.    United States Predictive Analytics And Maintenance In Supply Chain Market Outlook

6.3.1.1.  Market Size & Forecast

6.3.1.1.1.  By Value

6.3.1.2.  Market Share & Forecast

6.3.1.2.1.  By Component

6.3.1.2.2.  By Deployment

6.3.1.2.3.  By Application

6.3.1.2.4.  By Organization Size

6.3.1.2.5.  By End-Use Industry

6.3.2.    Canada Predictive Analytics And Maintenance In Supply Chain Market Outlook

6.3.2.1.  Market Size & Forecast

6.3.2.1.1.  By Value

6.3.2.2.  Market Share & Forecast

6.3.2.2.1.  By Component

6.3.2.2.2.  By Deployment

6.3.2.2.3.  By Application

6.3.2.2.4.  By Organization Size

6.3.2.2.5.  By End-Use Industry

6.3.3.    Mexico Predictive Analytics And Maintenance In Supply Chain Market Outlook

6.3.3.1.  Market Size & Forecast

6.3.3.1.1.  By Value

6.3.3.2.  Market Share & Forecast

6.3.3.2.1.  By Component

6.3.3.2.2.  By Deployment

6.3.3.2.3.  By Application

6.3.3.2.4.  By Organization Size

6.3.3.2.5.  By End-Use Industry

7.    Europe Predictive Analytics And Maintenance In Supply Chain Market Outlook

7.1.  Market Size & Forecast

7.1.1.  By Value

7.2.  Market Share & Forecast

7.2.1.  By Component

7.2.2.  By Deployment

7.2.3.  By Application

7.2.4.  By Organization Size

7.2.5.  By End-Use Industry

7.2.6.  By Country

7.3.    Europe: Country Analysis

7.3.1.    Germany Predictive Analytics And Maintenance In Supply Chain Market Outlook

7.3.1.1.  Market Size & Forecast

7.3.1.1.1.  By Value

7.3.1.2.  Market Share & Forecast

7.3.1.2.1.  By Component

7.3.1.2.2.  By Deployment

7.3.1.2.3.  By Application

7.3.1.2.4.  By Organization Size

7.3.1.2.5.  By End-Use Industry

7.3.2.    France Predictive Analytics And Maintenance In Supply Chain Market Outlook

7.3.2.1.  Market Size & Forecast

7.3.2.1.1.  By Value

7.3.2.2.  Market Share & Forecast

7.3.2.2.1.  By Component

7.3.2.2.2.  By Deployment

7.3.2.2.3.  By Application

7.3.2.2.4.  By Organization Size

7.3.2.2.5.  By End-Use Industry

7.3.3.    United Kingdom Predictive Analytics And Maintenance In Supply Chain Market Outlook

7.3.3.1.  Market Size & Forecast

7.3.3.1.1.  By Value

7.3.3.2.  Market Share & Forecast

7.3.3.2.1.  By Component

7.3.3.2.2.  By Deployment

7.3.3.2.3.  By Application

7.3.3.2.4.  By Organization Size

7.3.3.2.5.  By End-Use Industry

7.3.4.    Italy Predictive Analytics And Maintenance In Supply Chain Market Outlook

7.3.4.1.  Market Size & Forecast

7.3.4.1.1.  By Value

7.3.4.2.  Market Share & Forecast

7.3.4.2.1.  By Component

7.3.4.2.2.  By Deployment

7.3.4.2.3.  By Application

7.3.4.2.4.  By Organization Size

7.3.4.2.5.  By End-Use Industry

7.3.5.    Spain Predictive Analytics And Maintenance In Supply Chain Market Outlook

7.3.5.1.  Market Size & Forecast

7.3.5.1.1.  By Value

7.3.5.2.  Market Share & Forecast

7.3.5.2.1.  By Component

7.3.5.2.2.  By Deployment

7.3.5.2.3.  By Application

7.3.5.2.4.  By Organization Size

7.3.5.2.5.  By End-Use Industry

8.    Asia Pacific Predictive Analytics And Maintenance In Supply Chain Market Outlook

8.1.  Market Size & Forecast

8.1.1.  By Value

8.2.  Market Share & Forecast

8.2.1.  By Component

8.2.2.  By Deployment

8.2.3.  By Application

8.2.4.  By Organization Size

8.2.5.  By End-Use Industry

8.2.6.  By Country

8.3.    Asia Pacific: Country Analysis

8.3.1.    China Predictive Analytics And Maintenance In Supply Chain Market Outlook

8.3.1.1.  Market Size & Forecast

8.3.1.1.1.  By Value

8.3.1.2.  Market Share & Forecast

8.3.1.2.1.  By Component

8.3.1.2.2.  By Deployment

8.3.1.2.3.  By Application

8.3.1.2.4.  By Organization Size

8.3.1.2.5.  By End-Use Industry

8.3.2.    India Predictive Analytics And Maintenance In Supply Chain Market Outlook

8.3.2.1.  Market Size & Forecast

8.3.2.1.1.  By Value

8.3.2.2.  Market Share & Forecast

8.3.2.2.1.  By Component

8.3.2.2.2.  By Deployment

8.3.2.2.3.  By Application

8.3.2.2.4.  By Organization Size

8.3.2.2.5.  By End-Use Industry

8.3.3.    Japan Predictive Analytics And Maintenance In Supply Chain Market Outlook

8.3.3.1.  Market Size & Forecast

8.3.3.1.1.  By Value

8.3.3.2.  Market Share & Forecast

8.3.3.2.1.  By Component

8.3.3.2.2.  By Deployment

8.3.3.2.3.  By Application

8.3.3.2.4.  By Organization Size

8.3.3.2.5.  By End-Use Industry

8.3.4.    South Korea Predictive Analytics And Maintenance In Supply Chain Market Outlook

8.3.4.1.  Market Size & Forecast

8.3.4.1.1.  By Value

8.3.4.2.  Market Share & Forecast

8.3.4.2.1.  By Component

8.3.4.2.2.  By Deployment

8.3.4.2.3.  By Application

8.3.4.2.4.  By Organization Size

8.3.4.2.5.  By End-Use Industry

8.3.5.    Australia Predictive Analytics And Maintenance In Supply Chain Market Outlook

8.3.5.1.  Market Size & Forecast

8.3.5.1.1.  By Value

8.3.5.2.  Market Share & Forecast

8.3.5.2.1.  By Component

8.3.5.2.2.  By Deployment

8.3.5.2.3.  By Application

8.3.5.2.4.  By Organization Size

8.3.5.2.5.  By End-Use Industry

9.    Middle East & Africa Predictive Analytics And Maintenance In Supply Chain Market Outlook

9.1.  Market Size & Forecast

9.1.1.  By Value

9.2.  Market Share & Forecast

9.2.1.  By Component

9.2.2.  By Deployment

9.2.3.  By Application

9.2.4.  By Organization Size

9.2.5.  By End-Use Industry

9.2.6.  By Country

9.3.    Middle East & Africa: Country Analysis

9.3.1.    Saudi Arabia Predictive Analytics And Maintenance In Supply Chain Market Outlook

9.3.1.1.  Market Size & Forecast

9.3.1.1.1.  By Value

9.3.1.2.  Market Share & Forecast

9.3.1.2.1.  By Component

9.3.1.2.2.  By Deployment

9.3.1.2.3.  By Application

9.3.1.2.4.  By Organization Size

9.3.1.2.5.  By End-Use Industry

9.3.2.    UAE Predictive Analytics And Maintenance In Supply Chain Market Outlook

9.3.2.1.  Market Size & Forecast

9.3.2.1.1.  By Value

9.3.2.2.  Market Share & Forecast

9.3.2.2.1.  By Component

9.3.2.2.2.  By Deployment

9.3.2.2.3.  By Application

9.3.2.2.4.  By Organization Size

9.3.2.2.5.  By End-Use Industry

9.3.3.    South Africa Predictive Analytics And Maintenance In Supply Chain Market Outlook

9.3.3.1.  Market Size & Forecast

9.3.3.1.1.  By Value

9.3.3.2.  Market Share & Forecast

9.3.3.2.1.  By Component

9.3.3.2.2.  By Deployment

9.3.3.2.3.  By Application

9.3.3.2.4.  By Organization Size

9.3.3.2.5.  By End-Use Industry

10.    South America Predictive Analytics And Maintenance In Supply Chain Market Outlook

10.1.  Market Size & Forecast

10.1.1.  By Value

10.2.  Market Share & Forecast

10.2.1.  By Component

10.2.2.  By Deployment

10.2.3.  By Application

10.2.4.  By Organization Size

10.2.5.  By End-Use Industry

10.2.6.  By Country

10.3.    South America: Country Analysis

10.3.1.    Brazil Predictive Analytics And Maintenance In Supply Chain Market Outlook

10.3.1.1.  Market Size & Forecast

10.3.1.1.1.  By Value

10.3.1.2.  Market Share & Forecast

10.3.1.2.1.  By Component

10.3.1.2.2.  By Deployment

10.3.1.2.3.  By Application

10.3.1.2.4.  By Organization Size

10.3.1.2.5.  By End-Use Industry

10.3.2.    Colombia Predictive Analytics And Maintenance In Supply Chain Market Outlook

10.3.2.1.  Market Size & Forecast

10.3.2.1.1.  By Value

10.3.2.2.  Market Share & Forecast

10.3.2.2.1.  By Component

10.3.2.2.2.  By Deployment

10.3.2.2.3.  By Application

10.3.2.2.4.  By Organization Size

10.3.2.2.5.  By End-Use Industry

10.3.3.    Argentina Predictive Analytics And Maintenance In Supply Chain Market Outlook

10.3.3.1.  Market Size & Forecast

10.3.3.1.1.  By Value

10.3.3.2.  Market Share & Forecast

10.3.3.2.1.  By Component

10.3.3.2.2.  By Deployment

10.3.3.2.3.  By Application

10.3.3.2.4.  By Organization Size

10.3.3.2.5.  By End-Use Industry

11.    Market Dynamics

11.1.  Drivers

11.2.  Challenges

12.    Market Trends & Developments

12.1.  Merger & Acquisition (If Any)

12.2.  Product Launches (If Any)

12.3.  Recent Developments

13.    Global Predictive Analytics And Maintenance In Supply Chain Market: SWOT Analysis

14.    Porter's Five Forces Analysis

14.1.  Competition in the Industry

14.2.  Potential of New Entrants

14.3.  Power of Suppliers

14.4.  Power of Customers

14.5.  Threat of Substitute Products

15.    Competitive Landscape

15.1.  international Business Machines Corporation

15.1.1.  Business Overview

15.1.2.  Products & Services

15.1.3.  Recent Developments

15.1.4.  Key Personnel

15.1.5.  SWOT Analysis

15.2.  Microsoft Corporation

15.3.  SAP SE

15.4.  General Electric Company

15.5.  Schneider Electric SE

15.6.  Google LLC

15.7.  Oracle Corporation

15.8.  Hewlett Packard Enterprise Co.

15.9.  SAS Institute Inc.

15.10.  TIBCO Software Inc.

15.11.  Siemens AG

15.12.  Robert Bosch GmbH

15.13.  Cisco Systems, Inc.

15.14.  Dell, Inc.

15.15.  Intel Corporation

16.    Strategic Recommendations

17.    About Us & Disclaimer

Figures and Tables

Frequently asked questions

Frequently asked questions

The market size of the Global Predictive Analytics And Maintenance In Supply Chain Market was estimated to be USD 11.79 Billion in 2025.

North America is the dominating region in the Global Predictive Analytics And Maintenance In Supply Chain Market.

Solutions segment is the fastest growing segment in the Global Predictive Analytics And Maintenance In Supply Chain Market.

The Global Predictive Analytics And Maintenance In Supply Chain Market is expected to grow at 26.51% between 2026 to 2031.

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