Artificial Intelligence in Supply Chain Market by Component (Software, Hardware, and Services), growing with latest trends during focusing on Global Opportunity Analysis and Industry Forecast, 2021 – 2027

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.

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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.
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.

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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 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.

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