User:Sabina Ahmed Bangash/sandbox: Difference between revisions

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With the evolution of B2B platforms, wholesale buyers now have a platform they can directly source from, interact directly with [[manufacturers]]<ref>{{Cite web |date=2024-05-30 |title=How B2B Marketplaces Are Rewriting the Rules of Trade |url=https://www.bcg.com/publications/2024/how-b2b-marketplaces-are-rewriting-rules-of-trade |access-date=2025-11-18 |website=BCG Global |language=en}}</ref>, compare prices between different vendors, and place orders with a few small steps, decreasing the time it takes to place an order from start to finish. In the early 2020’s AI became more widespread and adopted when it came to B2B sourcing.<ref>{{Cite journal |last=Keegan |first=Brendan James |last2=Dennehy |first2=Denis |last3=Naudé |first3=Peter |date=2024-06 |title=Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective |url=https://link.springer.com/10.1007/s10796-022-10294-1 |journal=Information Systems Frontiers |language=en |volume=26 |issue=3 |pages=1025–1039 |doi=10.1007/s10796-022-10294-1 |issn=1387-3326 |pmc=9134975 |pmid=35637917}}</ref>

With the evolution of B2B platforms, wholesale buyers now have a platform they can directly source from, interact directly with [[manufacturers]]<ref>{{Cite web |date=2024-05-30 |title=How B2B Marketplaces Are Rewriting the Rules of Trade |url=https://www.bcg.com/publications/2024/how-b2b-marketplaces-are-rewriting-rules-of-trade |access-date=2025-11-18 |website=BCG Global |language=en}}</ref>, compare prices between different vendors, and place orders with a few small steps, decreasing the time it takes to place an order from start to finish. In the early 2020’s AI became more widespread and adopted when it came to B2B sourcing.<ref>{{Cite journal |last=Keegan |first=Brendan James |last2=Dennehy |first2=Denis |last3=Naudé |first3=Peter |date=2024-06 |title=Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective |url=https://link.springer.com/10.1007/s10796-022-10294-1 |journal=Information Systems Frontiers |language=en |volume=26 |issue=3 |pages=1025–1039 |doi=10.1007/s10796-022-10294-1 |issn=1387-3326 |pmc=9134975 |pmid=35637917}}</ref>

Between the years of 2023 and 2025, there were great advancements when it came to how AI was being used to source.<ref>{{Cite journal |last=Guida |first=Michela |last2=Caniato |first2=Federico |last3=Moretto |first3=Antonella |last4=Ronchi |first4=Stefano |date=03-2023 |title=The role of artificial intelligence in the procurement process: State of the art and research agenda |url=https://linkinghub.elsevier.com/retrieve/pii/S1478409223000079 |journal=Journal of Purchasing and Supply Management |language=en |volume=29 |issue=2 |pages=100823 |doi=10.1016/j.pursup.2023.100823}}</ref><ref>{{Cite journal |last=Balkan |first=Dursun |last2=Akyuz |first2=Goknur Arzu |date=2025-08-20 |title=Artificial intelligence (AI) and machine learning (ML) in procurement and purchasing decision-support (DS): a taxonomic literature review and research opportunities |url=https://link.springer.com/10.1007/s10462-025-11336-1 |journal=Artificial Intelligence Review |language=en |volume=58 |issue=11 |doi=10.1007/s10462-025-11336-1 |issn=1573-7462}}</ref> AI-powered algorithms provided supplier discovery, assured that vendors met product requirements and certifications, and could even forecast demand for specific products. Buyers were able to learn about the entire procurement process from end to end, leading to a more transparent way of sourcing, besides increasing efficiency. In the last two years (2023-2025), many major B2B companies adopted AI tools and integrated them at different points in the customer’s experience of sourcing wholesale products.<ref>{{Cite journal |last=Babai |first=M Z |last2=Arampatzis |first2=M |last3=Hasni |first3=M |last4=Lolli |first4=F |last5=Tsadiras |first5=A |date=2023-12-11 |title=On the use of machine learning in supply chain management: a systematic review |url=https://academic.oup.com/imaman/article/36/1/21/7849817 |journal=IMA Journal of Management Mathematics |language=en |volume=36 |issue=1 |pages=21–49 |doi=10.1093/imaman/dpae029 |issn=1471-678X}}</ref>

Between the years of 2023 and 2025, there were great advancements when it came to how AI was being used to source.<ref>{{Cite journal |last=Guida |first=Michela |last2=Caniato |first2=Federico |last3=Moretto |first3=Antonella |last4=Ronchi |first4=Stefano |date=03-2023 |title=The role of artificial intelligence in the procurement process: State of the art and research agenda |url=https://linkinghub.elsevier.com/retrieve/pii/S1478409223000079 |journal=Journal of Purchasing and Supply Management |language=en |volume=29 |issue=2 |pages=100823 |doi=10.1016/j.pursup.2023.100823}}</ref><ref>{{Cite journal |last=Balkan |first=Dursun |last2=Akyuz |first2=Goknur Arzu |date=2025-08-20 |title=Artificial intelligence (AI) and machine learning (ML) in procurement and purchasing decision-support (DS): a taxonomic literature review and research opportunities |url=https://link.springer.com/10.1007/s10462-025-11336-1 |journal=Artificial Intelligence Review |language=en |volume=58 |issue=11 |doi=10.1007/s10462-025-11336-1 |issn=1573-7462}}</ref> AI-powered algorithms provided supplier discovery, assured that vendors met product requirements and certifications, and could even forecast demand for specific products. Buyers were able to learn about the entire procurement process from end to end, leading to a more transparent way of sourcing, besides increasing efficiency. In the last two years (2023-2025), many major B2B companies adopted AI tools and integrated them at different points in the customer’s experience of sourcing wholesale products.<ref>{{Cite journal |last=Babai |first=M Z |last2=Arampatzis |first2=M |last3=Hasni |first3=M |last4=Lolli |first4=F |last5=Tsadiras |first5=A |date=2023-12-11 |title=On the use of machine learning in supply chain management: a systematic review |url=https://academic.oup.com/imaman/article/36/1/21/7849817 |journal=IMA Journal of Management Mathematics |language=en |volume=36 |issue=1 |pages=21–49 |doi=10.1093/imaman/dpae029 |issn=1471-678X}}</ref>

==== Applications in B2B landscape ====

==== Applications in B2B landscape ====

This user, in accordance with the Wikimedia Foundation’s Terms of Use, discloses that they have been paid by Alibaba.com Singapore E-Commerce Private Limited for their contributions to Wikipedia.

The technological advancements that have resulted in the creation of AI tools for wholesale market sourcing have changed the way businesses source inventory in the last few years^[1]. With modern AI sourcing tools, information can now be searched and scanned across thousands of digital sources^[2] while synchronizing with small to medium businesses require. The time and cost benefits are immeasurable and significant, including cutting lead time and environmental and business risks. ^[3]

More and more business-to-business (B2B) platforms are providing their customers with AI tools that can help in sourcing almost any kind of product they require, in a shorter time and more efficiently^[4]. The importance of the customer journey when it comes to sourcing wholesale goods is dependent upon the number of customer touchpoints that many buyers must go through in order to place an order for their business. More and more B2B companies are trying to improve these long-term customer supplier relationships. ^[5]

In addition to providing AI tools for sourcing, many B2B websites offer real-time market data by providing AI tools that look at present statistics in specific industries to identify products that tend to have higher sales than others. The two biggest players in the market right now, IndiaMart, Trade India, Alibaba.com, and Amazon Business, all have their own AI tools, which include predictive trend analysis, allowing buyers to search through extensive datasets to predict product demand, including demand that may be impacted by seasonality and other external factors.

Although the advantages of adopting AI tools are vast, it does not come without its drawbacks and disadvantages in the form of alogrithm biases, archiac data structures, and challenges in integrating AI applications with old outdated systems.

Early Digital Sourcing

The transformation of retail supply chains by digital tools has resulted in great variances when it comes to how buyers now manage, evaluate and interact with wholesalers. But it was not always this way. Paper catalogs were replaced with digital ones in the late 80s and early 90’s. This helped companies find vendors in a more efficient manner.^[6] By the late 90s’s e-commerce platforms provided small and medium companies the advantage of being able to order online, but only with front-end transparency in the form of the automation of purchase orders and invoicing. There were many intermediaries that were involved when it came to placing a single order, like wholesalers, distributors, and procurement agents.^[7]

With the evolution of B2B platforms, wholesale buyers now have a platform they can directly source from, interact directly with manufacturers^[8], compare prices between different vendors, and place orders with a few small steps, decreasing the time it takes to place an order from start to finish. In the early 2020’s AI became more widespread and adopted when it came to B2B sourcing.^[9]

Between the years of 2023 and 2025, there were great advancements when it came to how AI was being used to source.^[10][11] AI-powered algorithms provided supplier discovery, assured that vendors met product requirements and certifications, and could even forecast demand for specific products. Buyers were able to learn about the entire procurement process from end to end, leading to a more transparent way of sourcing, besides increasing efficiency. In the last two years (2023-2025), many major B2B companies adopted AI tools and integrated them at different points in the customer’s experience of sourcing wholesale products.^[12]

Applications in B2B landscape

There are numerous applications of AI in the B2B landscape and specifically relative to sourcing, but broadly they can be divided as follows:

• Supplier Discovery and Ranking: AI systems provide supplier discovery and ranking that provides buyers with important information, including certifications, reliability metrics, shipping times, pricing trends, etc., to maximize the chances of choosing a supplier that will minimize business risk. Machine learning algorithms study large amounts of data to predict which suppliers will improve cost efficiency. ^[13]

• Intelligent Request for Quotation (iRFQ): This system automates and enhances the standard RFQ process by making requests uniform, analyzing supplier responses using AI large language models (LLMs), which help to reduce errors, accelerate sourcing cycles, and improve supplier matching accuracy.^[14]

• Price Forecasting and Cost Optimization: Predicting how prices will fall or rise is another advantage of AI tools when it comes to B2B sourcing. Based on historical price data and trends, AI sourcing tools can negotiate better deals, reduce risks, and create better sourcing strategies for the future.^[15]

• Automated Quality Assurance: Having AI tools that can support and help with quality assurance creates a space for companies to guarantee consistent monitoring and decrease defects in products. Manual errors are no longer an issue and in fact machines can detect sub-par products better than humans^[16] so there is more uniformity when it comes to product quality, reducing costs associated with defective inventory. Better procurement decisions can be made. ^[17]

• Logistics and Lead-Time Prediction: AI tools can help with lead time prediction in B2B sourcing by analyzing multifaceted data like supplier performance, order volumes, and geographical factors. AI-driven models can provide accurate delivery forecasts, creating an atmosphere for proactive risk minimization.  ^[18]

Major Platforms and Implementations

• Alibaba
• Global Sources
• IndiaMart and Trade India
• Amazon Business