Babelus, a pioneer in AI-driven supplier data solutions, has been selected to join the prestigious 1871 Supply Chain Innovation Lab 2024 Cohort in partnership with Accenture, McKinsey, and Microsoft. By joining this dynamic community of innovators, Babelus aims to accelerate its growth and deliver even greater value to the supply chain industry.
Babelus' mission is to provide the most comprehensive and insightful supplier profiles available, with a special focus on their technical capabilities. Through advanced AI algorithms and human expertise, the company collects, analyzes, and curates data from a vast array of public and private sources. This strategic alliance will accelerate the development of innovative solutions that address critical supply chain challenges. Together, we will leverage AI to unlock unprecedented levels of data-driven insights, enabling businesses to optimize operations, mitigate risks, and achieve greater resilience. Through access to 1871's extensive network and resources, Babelus can rapidly scale its solutions and bring them to market with increased impact.
Becoming part of this program is a testament to our shared commitment to driving industry transformation. Babelus will receive invaluable advice from 1871 Lab’s partners and corporate clients, laying the groundwork for a future with more efficient, sustainable, and transparent supply chains.
"Joining the 1871 Lab ecosystem is a game-changer for Babelus and our customers. It will lead to the development of innovative tools that deliver unparalleled visibility, efficiency, and sustainability across the supply chain."
Luis Juarez, Babelus' CEO
Babelus is a leading provider of supplier data, empowering organizations to make informed decisions and mitigate risks. With an unwavering commitment to transparency and accuracy, Babelus delivers actionable insights that drive operational efficiency and strategic advantage, providing reassurance about our values and dedication to our clients.
1871, Chicago's renowned innovation hub and the world's top-ranked private business incubator foster a dynamic environment for innovators across all stages. They aim to empower early-stage, growth-stage, late-stage, and corporate innovators to build exceptional businesses.
Supplier data is the foundation of any successful supply chain management strategy. Decisions affecting billions of dollars worldwide are made every day based on supplier data. But what if your supplier data is not as reliable as you think? What if most of it is inaccurate, needs to be updated, or is irrelevant? As you’ll learn today, this is true for many large corporations.
At BabelusAI, we are passionate about helping our clients improve their supplier data quality and visibility. One of our clients, a global leader in the automotive industry, asked us to audit one of their in-house databases of approximately 800 suppliers in the transport category. They wanted to know how accurate and complete their supplier data was and how they could improve it.
Having worked in procurement for large corporations for years, I expected to find some bad data to clean up, but what we discovered shocked us!
After running our AI-powered tool Insight360 on our client’s database, they discovered that only 300 suppliers were unique vendors offering transportation services.
In other words, out of 800 suppliers in our client’s database, around 500 were either inactive, out of business, belonged to different categories, or were duplicates. This implies that over 60% of the data (almost two-thirds!) was worthless.
You may think:
“That’s just bad supplier data management on their part. We don’t have that problem. We’re fine!”
To be fair, that may be true. But it is also possible that you are overlooking potential problems with your supplier data. A recent survey by HICX showed that 53% of businesses considered their supplier data to be of “very good quality” and in the “top 20% of their industry.”
But how can 53% of businesses be in the top 20%? It is obvious that, at least for some companies, supplier data is not as good as they think.
Furthermore, Experian reports that 88% of businesses see an average negative impact of inaccurate data on their bottom line above 12%, proving the problem is widespread.
Knowing how good your supplier data is is critical, as bad supplier data can pose severe financial and operational costs to your business.
Bad supplier data can:
The statistics outlined above, show a problem with supplier data quality in large organizations. The natural question is, why? Here are three reasons:
When supplier data is collected and updated manually, such as through emails, spreadsheets, or questionnaires, it is prone to human errors, inconsistencies, and delays. Many Chief Procurement Officers in large corporations struggle with mastering the digital complexity of automating these processes.
In a previous article, we discussed how most professionals in procurement have backgrounds in marketing, business, or supply chain management. Only a handful have a background in science or engineering, hindering their ability to understand a supplier’s technical datasheets and assign them to the right category.
The report by HICX I mentioned earlier also showed that a surprising 89% of senior procurement professionals admitted they don’t have total oversight of their supplier data. When supplier data is not governed by clear and consistent standards, policies, and procedures, or when they are but there is insufficient oversight, mistakes are bound to happen.
As you can see, bad supplier data is a serious problem you must address as soon as possible. But how can you do that without spending a fortune or wasting a lot of time? The answer is Insight360.
Insight360 is a revolutionary platform that uses generative AI to analyze, clean, and enrich your supplier data in minutes. With Insight360, you can:
Contrary to what some people believe, generative AI-powered tools like Insight360 are not chatbots or simple conversational AIs.
Insight360 uses a personalized large language model (LLM) that is custom-trained with human feedback from industry experts for a specific task. That task is analyzing and understanding supplier information within an industry and structuring it in valuable ways.
Contact us today to discuss how BabelusAI can help you discover and clean bad supplier data in minutes, or schedule a call now to discuss developing a pilot for your company at no cost.
Even when companies have sophisticated purchasing software, data is one of the most influential variables to ensure their permanence. This means data is the new currency that boosts business performance.
In today’s volatile and complex world, data is the most valuable asset for any business. Data can help companies make better decisions, optimize their operations, reduce costs, and improve customer satisfaction.
Data can also have a profound influence on a company’s supply chain.
A study by S&P Global highlighted the importance of data and data management to enhance supply chain resilience, generate new insights, and inform decision-making.
Data is also constantly changing and evolving. This rings especially true in some sectors, where many factors, such as pandemics, climate change, geopolitics, and technological innovation, influence supply and demand.
For example, a report by Deloitte revealed that the COVID-19 pandemic and the war in Ukraine have caused multiple supply chain disruptions in the electric energy sector. This has resulted in increased operational costs in 86% of the cases, project delays in 64%, and loss of productivity in 62%.
With new potential large-scale conflicts already brewing in the Middle East, it is likely this will worsen.
Despite the fact that data is the new currency, and the importance of having up-to-date and accurate supplier data, it’s common for large enterprises to work with only 4 or 5 suppliers in a particular category where there may be 50 or more relevant global vendors available.
Covering only 10% of the available supplier market in a given category means missing out on opportunities to diversify their sources, increase their bargaining power, and reduce their supply management risk.
In this context, supplier data is power; it’s a new form of currency for modern enterprises.
This is where BabelusAI comes in. BabelusAI is a revolutionary solution that provides updated supplier information in the most relevant categories through an innovative model that requires no implementation or training and is affordable compared to large software solutions.
With BabelusAI, companies can easily expand their supplier lists and discover new vendors that meet their specifications and conditions.
BabelusAI uses artificial intelligence to scan the market and identify the most important suppliers for each category, regardless of whether they are registered on the platform. BabelusAI invites qualified suppliers to complete or enrich their profiles and participate in customized technical analyses.
BabelusAI also updates and maintains its database through AI, augmented by the suppliers. Vendors of all sizes for industries as varied as the energy, telecom, and automotive sectors are actively submitting technical data and credentials on our platform for our customers in Europe, the Middle East, and the Americas.
Furthermore, through data harvesting, our AI crawls each vendor’s website and available catalogs to ensure that all relevant data is kept up-to-date, maintaining the value of the data throughout time.
Unlike marketplaces and e-procurement platforms that only show registered suppliers, BabelusAI ensures that buyers have a 360° view of the supplier market and do not miss any potential opportunities.
BabelusAI also helps buyers compare suppliers easily through its large language model using natural language. Say goodbye to complex and often inaccurate queries full of technical jargon. In short, BabelusAI enables buyers to make informed and strategic decisions that enhance their competitiveness and resilience.
BabelusAI is the fruit of 25 years of experience in the global market. All the data we offer is curated by experts in the categories that occupy relevant roles in utilities worldwide.
For example, if we collect data on distribution transformers in Brazil or South Korea, the information is curated by engineers working in utilities in those countries.
We have response times of minutes. We also solve the digitization of the supplier discovery process as part of our corporate strategic goals.
At BabelusAI, our database is constantly growing and contains thousands of suppliers in critical and non-critical categories for the energy sector and many other industries. Contact us today to learn more about how we can help you transform your supplier data, or schedule a call now to discuss developing a pilot for your company at no cost.
Risk management in a resilient supply chain pertains to the strategies and actions taken to identify, assess, and mitigate risks within a supply chain to ensure its effective operation, even under unforeseen events or shocks. It has become a pressing issue due to increased globalization and the complexity of supply chains, which are exposed to various risks, ranging from natural disasters to cyber threats.
Supply chain risk management involves understanding two main components: underlying vulnerabilities within the supply chain that make it fragile and the exposure or susceptibility to unforeseen events that can exploit these vulnerabilities.
There are several strategies to ensure risk management in a resilient supply chain and how Generative AI can help:
Large language models are revolutionizing the way we interact with computers. Until now, we've had to adapt to computer interaction rules, often leading to frustrating experiences with various applications like travel websites. Large language models allow us to communicate with computer programs in our natural language, eliminating much of this frustration. This capability, coupled with existing functionalities like search or business APIs and data analytics, unveils a world of potential. These AI copilots will always be available, whether it's for composing business emails, analyzing data, or strategizing sales approaches. They will communicate with us, understanding and remembering the context of our activities, thereby enhancing productivity and ease of use.
Having said that, the most effective way to acquire real-time data is by:
Our interface, "Just Ask," advocates direct communication with data and suppliers. This approach facilitates a dynamic flow of information and ensures access to real-time data, essential in an ever-changing world.
Our innovative approach offers a significant advantage in a world increasingly willing to embrace AI as a predictive tool and an instrument to generate plans and take proactive action autonomously.
If your company has decided to approach Generative AI or has challenges with improving supply chain resilience, we'd be happy to tell you more in a call or send you information via email. Contact us now; we are the right people.
As we embark on the age of artificial intelligence, the saying, "garbage in, garbage out," has never been more relevant. Generative AI, a powerful branch of artificial intelligence, is transforming various industries by generating new content based on existing data. Yet, to unlock its full potential, businesses must understand one thing: the quality of data fed into AI models is paramount.
AI can inadvertently create biased, unrepresentative, or even toxic outputs without it. Generative AI disrupts legacy content and media creation, such as ChatGPT and Dall-E. However, these AI models are like sponges, absorbing data to learn, understand, and generate new content. Therefore, the data input to train these models must be vast, diverse, and relevant. If the data is not relevant, representative, and complete, the output mirrors these shortcomings, leading to problems such as algorithmic bias, misinformation, and even legal issues.
Nevertheless, managing and understanding data can be daunting for many businesses. Recent findings indicate that due to its complexity, 41% of business leaders need help comprehending their data. Such a lack of understanding can be a roadblock in leveraging the immense power of generative AI. A data strategy is crucial for businesses intending to implement AI. Yet, only 35% of companies recognize the integral role of a data strategy in facilitating AI.
Generative AI requires substantial quantities of data for training. Therefore, businesses must collect, store, and analyze data accurately and efficiently. Furthermore, companies must remain vigilant about the quality and relevance of their data, ensuring its freshness and correct labeling.
Generative AI, in essence, mirrors the data it is trained on. Inaccurate or biased data may produce harmful outputs, damaging a brand's reputation and causing legal complications. Accurate, diverse, and high-quality data, on the other hand, fuels AI's ability to create unique, innovative, and valuable outputs, resulting in increased productivity and profitability.
In conclusion, data management is the key to harnessing the true power of generative AI. While the task may seem overwhelming, resources and tools are available to help businesses navigate their data journey. By understanding their data and ensuring its quality, companies can unlock the full potential of generative AI, driving their growth and innovation to new heights.
In BabelusAI, how we collect and label data to train our models is the center of our AI. If you want to know how BabelusAI can help you make sense of your company's supplier data, contact us now.
One of the most fascinating elements of the ChatGPT breakthrough is the time it takes to search for information online, especially when it is not critical training. This is just one way how ChatGPT beats Google.
The challenge of searching on Google is that once you type in the keywords, an infinite number of results appear, which you must open one by one. By the way, each piece of content is biased toward the interest of the site.
So, it takes many hours, weeks, or even months to get a decent result. However, the advantage is that the information is in real-time.
ChatGPT, on the other hand, analyzes the available data and provides a result without the need to search the web, simplifying the work of analyzing information in seconds.
But ChatGPT is not a search engine and does not scan the Internet for real-time information. The model was trained on a large dataset of text from the Internet and other sources until 2021, which serves as a cutoff point for its knowledge. It is a linguistic model that generates text from the input given to it, and its responses are generated based on the patterns it learned from the training data.
Babelus' technological approach is quite similar to that of GPT, except that we do offer a smart search engine, and the information is updated periodically: Our goal is to save the time spent searching for supplier data and provide the company with a knowledge center.
Now find out how BabelusAI outperforms ChatGPT and Google when it comes to searching for supplier information.
Since our very beginning, our goal has been to prevent users from wasting endless hours searching the Internet and e-procurement by category by offering an efficient solution for searching and finding information about suppliers.
Searching within e-procurement systems with the company's supplier base is limited because it is executed in clicks and relies on the supplier tagging their category. In addition, e-procurement is not in the position to answer a query in natural language, nor does it offer technical information on suppliers beyond the category.
As the data contained in the supplier database is limited, the company does not get to know its suppliers in-depth and is forced to consult its suppliers' websites, study catalogs, or study technical specifications.
Babelus visits the supplier's website, extracts the information, and publishes it in our repository, and a profile is created within Babelus, where even technical documents can be added. The profiles are curated by experts who perform the labeling, key in Machine Learning. All training is 100% supervised.
Then, when a user does a search, Babelus analyzes the website data and attached documents in real-time to make a match from a natural language command.
The technology even suggests what data could enhance the information and then returns a list of providers that are in that range and meet the conditions.
This technology is useful for non-experts, as they do not need the end-user in the initial phase, but also for experts, who can obtain keywords from tens of thousands of suppliers.
In any case, if you would like to know more about how BabelusAI, based on Machine Learning and generative artificial intelligence (OpenAI), can help your company save hours and get new suppliers, contact us today.
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