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Artificial Intelligence in Supply Chain Market by Component (Software, Hardware, and Services), Technology (Natural Language Processing, Machine Learning, Computer Vision, and Context Aware Computing), Application (Risk Management, Freight Brokerage, Supply Chain Planning, Warehouse Management, Fleet Management, and Others), and Industry Vertical (Healthcare, Retail, Automotive, Aerospace, Manufacturing, Food & Beverages, and Others): Global Opportunity Analysis and Industry Forecast, 2020–2027

A09362
Pages: NA
Aug 2021 | 437 Views
 
Author(s) : NA
Tables: NA
Charts: NA
 

COVID-19

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Artificial Intelligence in Supply Chain Market Insight

Artificial Intelligence (AI) is a technology that enables machinery, software, and systems to replicate human intelligence and behavior in form of computer interactions. Adoption of AI system is driven by the use of complex algorithms to grasp information and perform various monotonous tasks.  AI has numerous applications within the supply chain management including extracting information, data analysis, supply & demand planning, and autonomous vehicles management. Moreover, it can analyze warehouse processes and maximize sending, receiving, storing, picking, and management of products. The optimization of warehouse operations and distribution results in the advancements of the logistics in supply chain.

Artificial Intelligence in Supply Chain Market Scope and Structure Analysis:

Report Metric

Details

  Market size available for years

  2020–2027

  Base year considered

  2019

  Forecast period

  2021–2027

  Forecast units

  Value (USD)

  Segments covered

  Component, Technology, Application, Industry Vertical, and Region

  Regions covered

North America (U.S. and Canada), Europe (Germany, UK, France, and the Rest of Europe), Asia-Pacific (China, Japan, India, and the Rest of Asia-Pacific), Latin America (Brazil, Mexico, and the Rest of LATAM) and Middle East and Africa

  Companies covered

Major players analyzed in the market include Amazon Web Services, Inc., IBM Corporation, Intel Corporation, Logility, Inc., Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, SAP SE, and Xilinx, Inc.

 

Impact of COVID-19 on Artificial Intelligence in Supply Chain Market 

  • One of the major businesses affected by COVID-19 in the global market is just-in-time supply chain business.
  • All manufacturing companies are having trouble in obtaining materials needed to form products, and are struggling to ship their products to stores and wholesalers. Unlike other disruptions, COVID-19 is impacting every single aspect of supply chains.
  • Due to government implications such as lockdowns in many countries, import and exports of goods and services were closed, which, in turn, caused the scarcity of consumer goods resources.
  • To meet supply of essential goods, supply chain companies are following all measures required for safety of their employees including running production lines at slower speeds, assigning workers to designated work areas, and sanitizing equipment between shifts.
  • Owing to COVID-19 scenario, all companies are focused on moving production into origin country of manufacturing, which reduces the loss of revenue.

Top impacting factors: Market Scenario Analysis, Trends, Drivers, and Impact Analysis

Rise in demand for greater visibility and transparency in supply chain data and processes have grown the adoption of AI technology at a significant rate among various industries from the recent past. Positive revenue impact witnessed by implementing cloud-base supply chain solutions among various manufacturing and production industries and rise in demand for intelligent business processes and automation among various organizations are major factors that are expected to drive growth of the AI in supply chain market during the forecast period. However, inadequate number of AI experts and data privacy concerns are some of the factors that might hamper the adoption of AI within the supply chain operations. Contrarily, the adoption of supply chain analytics is one of the opportunistic factors for growth of the market.

Advent of Big Data 

Rise in endorsement for big data projects is a major factor that drives the adoption of artificial intelligence in supply chain management to help end users improve consumer services experiences and satisfaction. As data analytics is considered among one of the essential technologies that translates Big Data into meaningful insights, many industries prefer analytics solutions into their supply chain. In inventory management, an important supply chain discipline, most companies use simple equations to set their inventory targets. Using advanced analytics upon big data such as Multi-Echelon Inventory Optimization (MEIO), companies can optimize their inventory by 20 to 30% of optimized processes and storage. MEIO are basically used to optimize the inventory levels to match the demand in the market. Thus, the need to improve ROI and to provide better visualization technologies such as AI is being adopted at a high rate, which in turn is driving the growth of the market.

Data security issues 

Artificial Intelligence represents a digital system. Breach of sensitive business information can be a big problem, if companies don’t invest in high quality protection systems. One security incident can endanger supply chain performance at a significant rate.  Moreover, it is not always easy to replace old technologies and focus on AI to the full extent. This whole process requires time and expert knowledge, which is a big burden for companies to raise funds for these transformations. However, the fact remains that nearly 63% of businesses still do not use any advanced technology to monitor their supply chain performance, owing to lack of investments and concerns associated with data security.

Key segments covered:

Segments

Sub-segments

  Component

  • Software
  • Hardware
  • Services

  Technology

  • Natural Language Processing
  • Machine Learning
  • Computer Vision
  • Context Aware Computing

  Application

  • Risk Management
  • Freight Brokerage
  • Supply Chain Planning
  • Ware house Management
  • Fleet Management
  • Others

  Industry Vertical

  • Healthcare
  • Retail
  • Automotive
  • Aerospace
  • Manufacturing
  • Food & Beverages
  • Others

 

Key benefits of the report:

  • This study presents the analytical depiction of the global artificial intelligence in supply chain industry along with the current trends and future estimations to determine the imminent investment pockets.
  • The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the global market share.
  • The current market is quantitatively analyzed from 2020 to 2027 to highlight the global Artificial Intelligence in supply Chain market growth scenario.
  • Porter’s five forces analysis illustrates the potency of buyers & suppliers in the market. 
  • The report provides a detailed market analysis based on the present and future competitive intensity of the market. 
 
With collective industry experience of about 200 years of its analysts and experts, Allied Market Research (AMR) encompasses most infallible research methodology for its market intelligence and industry analysis. We do not only engrave the deepest levels of markets but also sneak through its slimmest details for the purpose of our market estimates and forecasts. Our approach helps in building greater market consensus view for size, shape and industry trends within each industry segment. We carefully factor in industry trends and real developments for identifying key growth factors and future course of the market. Our research proceeds are the resultant of high quality data, expert views and analysis and high value independent opinions. Our research process is designed to deliver balanced view of the global markets and allow stakeholders to make informed decisions.

We offer our clients exhaustive research and analysis based on wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics and regional intelligence. Our in-house industry experts play instrumental role in designing analytic tools and models, tailored to the requirements of particular industry segment. These analytical tools and models sanitize the data & statistics and enhance the accuracy of our recommendations and advice. With AMR’s calibrated research process and 360` degree data-evaluation methodology, our clients are assured of receiving:

  • Consistent, valuable, robust and actionable data & analysis that can easily be referenced for strategic business planning
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With a strong methodology we are, therefore, confident that our research and analysis are most reliable and guarantees sound business planning.

Secondary research
We refer a broad array of industry sources for our secondary, which typically include; however, not limited to: Company SEC filings, annual reports, company websites, broker & financial reports and investor presentations for competitive scenario and shape of the industry

  • Patent and regulatory databases for understanding of technical & legal developments
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Our primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks and face-to-face interactions. We are also in professional corporate relations with various companies that allow us greater flexibility for reaching out industry participants and commentators for interviews and discussions, fulfilling following functions:

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  • Further develops analyst team’s market understanding and expertise
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Our primary research interview and discussion panels are typically composed of most experienced industry members. These participants include; however, not limited to:

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AMR has developed set of analyst tools and data models to supplement and expedite the analysis process. Corresponding to markets, where there is significant lack of information and estimates, AMR’s team of experts and analyst develop specific analyst tools and industry models to translate qualitative and quantitative industry indicators into exact industry estimates. These models also allow analysts to examine the prospects and opportunities prevailing in the market to accurately forecast the course of the market.
 
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