Five Ways AI and Analytics is Revolutionizing Supply Chain Management
With massive transformations in global markets, AI and machine learning have become important assets for companies since they have the power to transform the way companies in the food and beverage industry deliver customer experiences. These technologies offer huge opportunities to companies to revolutionize critical aspects of their business processes. In the food and beverage industry, producers, retailers, and restaurants are rapidly changing their business strategies to incorporate innovative technologies to meet consumer demands, stay ahead of the competition, and provide an enhanced experience. Through the implementation of AI and machine learning technologies, retailers in the food and beverage industry can transform business processes and directly enhance the customer journey. In this article, we have summed up some of the interesting benefits of AI and machine learning that retailers in the food and beverage industry can leverage to improve customer engagement as well as ROI.
What is natural language processing?
Natural language processing (NLP) refers to the ability of computer programs to decipher human language as it is spoken. NLP is a component of artificial intelligence that teaches computers to use language in the same way humans do. This technology relies heavily on machine learning to analyze and use patterns in data to enhance a program’s understanding of speech. Natural language processing is gradually going mainstream now with Siri, Google, and Alexa communicating with users and easily decoding the language spoken by them.
One of the key challenges in developing NLP is that traditionally computers require humans to “communicate” to them in a programming language that is unambiguous, precise, and highly structured, or through a specific and clearly enunciated voice commands. Human speech, on the other hand, is not always precise. It is often ambiguous, and the structure of the language can depend on many complex variables, including regional dialects, slang, and the social context.
Business applications of natural language processing
In the business scenario, natural language processing is becoming an integral element of communication. The applications of natural language processing can be leveraged by companies to increase the efficiency of their documentation processes, optimize the accuracy of documentation, and identify the most relevant information from large databases. Here are some of the key business applications of natural language processing:
Chatbots to optimize HR
Chatbots are the newest form of natural language processing system used by modern companies. Initially, chatbots were used majorly by B2C companies, who have to cater to customer queries and requests directly. Over the years, companies have realized the potential of this technology and have started using it for operations that include optimizing HR. These bots can reply to employee queries such as “How many paid leaves do I have left” or “when does my insurance need renewal”. Some employee-friendly companies are also using bots to take employee polls on workplace satisfaction to the snacks they want in the office pantry.
Gaining customer insights is one of the key requisites for businesses to know where they stand in the market and the improvements that need to be made. NLP is a great tool for businesses to identify and analyze the responses of customers regarding the messages published to the business on social media. It helps to identify the tone of the message or the emotional state of the writer of the post. It is implemented through a combination of statistics and NLP. Specific values are assigned to the text (positive, negative or neutral) and, in turn, efforts are made to identify the underlying mood of the users.
Ensuring customer satisfaction is one of the most challenging tasks for most companies. This is where the applications of natural language processing can be put to use to provide a better user experience. NLP is an effective way for companies to gain deeper insights into audience tastes, preferences, and perceptions. It makes use of speech separation, where artificial intelligence is used to identify each voice to the corresponding speaker and answer each of the callers separately. This will ensure personalized customer care, which consequently results in enhanced customer satisfaction.
For businesses to be relevant in the market, they should be up-to-date with the dynamic industry trends and innovations. The application of natural language processing can easily help monitor market intelligence reports and extract the information necessary for businesses to formulate new strategies. This also provides exhaustive insights into critical factors such as tender delays, employment changes and status of the market, and extracting information from large repositories.
Customer targeting and advertisement
Identifying customers’ needs is a crucial aspect of serving the customers effectively. One of the key applications of natural language processing is that it makes it possible to reach out the right patron of a company’s product. It matches the right keyword with the text and reaches out the right prospects. This is a great way of intelligent placement of advertisements at the right time, in the right place, and for the right audience.
Quanztig is a global advisory firm specializing in leveraging analytics to help businesses in strategic decision-making. Our top clients include several Fortune-500 companies for who have relied on our analytics solutions to gain strategic insights on the market dynamics and execute the right tactics to mark a stronger foothold in the market.
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The manufacturing sector has been an aggressive user of robotics and mechanization for decades. Artificial intelligence is clearly the next step for companies in this industry to improve productivity, production line, and tooling utilization, and minimize the production cost per unit. With the increasing application of AI technology into various functions, the future of artificial intelligence seems to be a bright one in the manufacturing space. The use of artificial intelligence in the manufacturing sector will take the functioning of machinery to the next level from merely doing what they are originally programmed to do. Such machines can now mimic human actions and display enhanced decision-making capabilities. Moreover, by showcasing better sensory capabilities; and, off the factory floor, predicting what will be needed and when the future of artificial intelligence in the manufacturing sector is already becoming feasible in emerging markets. Employing AI in manufacturing plants enables companies to completely transform their proceedings.
Future of AI in manufacturing
New manufacturing markets
As far as the manufacturing sector is concerned, capital margins are often higher when compared to the profit margins. This has fueled the shift of manufacturing facilities to lower-wage countries, where the costs of labor are low. Here, capital investments in AI and automation were hard to justify for companies in the manufacturing sector. However, the rising -living standards in low-wage countries like India and China have opened new doors for the future of artificial intelligence. In fact, China is already investing significantly in artificial intelligence for manufacturing and e-commerce. Though workers in the manufacturing sector are already lamenting the loss of jobs due to automation and machinery that work on AI technology, companies are now looking at ways to retain these workers. The workers are now being planned to be put to perform higher-level design, programming, or maintenance tasks. However, the real driver will be to develop applications for AI in manufacturing that not only automates tasks but also makes entirely new business processes feasible.
Patient outcomes have already taken the baton from products and services as the primary focus of healthcare providers. Medical companies are knuckling down to this trend and adopting advanced technologies like machine learning in healthcare to improve patient care and patient outcomes. Indeed, the use of machine learning in healthcare is slowly but steadily revolutionizing the healthcare industry. This technology has been playing a pivotal role in diagnosing diseases and other medical issues, which is one of the major healthcare industry challenges. While the healthcare industry is being transformed by the ability to record massive amounts of information about individual patients, the enormous volume of data being collected is impossible for human beings to analyze. Machine learning provides a way to automatically find patterns and analyze unstructured data. This enables healthcare professionals to move to a personalized care system, which is known as precision medicine.
Benefits of Machine Learning in Healthcare
Thanks to advanced technologies like deep learning and machine learning, computer visions are now one of the most remarkable breakthroughs in the healthcare industry. Top companies in the medical industry are trying to integrate cognitive computing with genomic tumor sequencing to help develop advanced precision medicines. Furthermore, by using machine learning in healthcare, it is also possible to detect diabetic retinopathy and macular edema in the photographs of the retinal fundus.
Artificial intelligence has taken over the world incredibly fast. AI technology is being used across all industries today; thereby, reducing the dependency on human labor and minimizing the errors and delays made in operations. Artificial intelligence is a broad term which incorporates capabilities ranging from image recognition software to robotics. The maturity level of each of these technologies differs from one another. But the catch here is that introducing any advanced technology into an industry comes with its own set of challenges. Artificial intelligence problems are often overshadowed by their numerous benefits; however, these challenges cannot be completely ignored. It is always better for companies to think of the flip side of a technology to understand the gaps and challenges involved.
Varying development approach
One of the most prominent artificial intelligence problems is that of the development approach. The development phase is quite different in the case of artificial intelligence. Most of the time, developments in AI technology are all about identifying data sources and then gathering content, cleansing it, and then curating it. Such an approach requires different skills and mindsets, as well as different methodologies. In addition, AI-powered intellectual systems must be trained in a particular domain.
Highly dependent on data
Artificial intelligence heavily relies on data for learning. They depend on enormous amounts of high-quality data from which to observe trends and behavior patterns. Furthermore, AI systems quickly adapt to improve the accuracy of the conclusions derived from the analysis of that data. However, the massive amount of data required becomes one of the most challenging artificial intelligence problems for companies. Also, the datasets need to be highly representative and balanced, failing which, the system will eventually adopt bias that is contained in the data sets.
Next among the artificial intelligence problems is the difficulty in predicting the ROI and the improvements it may bring to a project. The outcomes of artificial intelligence are highly dependent on the data that has been fed into the system. It also requires a skilled team that can write or adapt to publicly available algorithms, select the right algorithm for the desired result, and combines algorithms as needed to optimize the result.
Threat to privacy
Like in the case of most technology, privacy breach is one of the major artificial intelligence problems. AI technology that recognizes speech and has the capability to decipher natural language will theoretically be able to understand each conversation that takes place on e-mails and telephones. This means that if the system gets hacked, the user’s data could get leaked easily. Also, any malfunction in the system could also result in the loss of an enormous amount of data, some of which cannot even be recovered.
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The world was in awe and threw in a lot of praise for Google’s new AI voice assistant Duplex which is able to place a call to a human on behalf of another human. This just goes to show the level of advancement AI has achieved this year. However, Google Duplex is just one demonstration amongst numerous other tech trends, areas where artificial intelligence has been making headways. Every day a new headline is made with AI affecting certain aspects of life. But what is happening behind the scenes, in development facilities where researchers and techno heads are constantly striving to beat the levels achieved by previous AIs? Here are some of the top artificial intelligence tech trends this year.
Deep neural networks have the ability to mimic human brain by learning from images, audio, and text data. They have been in use for more than a decade, however, there’s a lot still to be discovered and learn how a neural network learns and how can they be made efficient. Deep learning is getting smarter though, instead of feeding hundreds and thousands of data points, today’s systems can give an accurate output by factoring only few hundred data points.
Deep reinforcement learning
You learn from your own mistakes is probably the most realistic statements ever made. The developers of artificial intelligence have better understood this and are applying this principle of reinforced learning to their systems. This is the exact reason why the famous AlphaGo was able to beat a human champion. More recently, it beat a DOTA champion in a very complex game by teaching itself to play the game within two weeks. Researchers are relying more on this technique as it uses fewer data to train its models.
Augmented data learning
Machine learning works by accumulating a wide variety of data sources to train the system. However, unavailability of such kind of data poses a big problem for artificial intelligence systems. Emerging tech trends use new synthetic data and transfer a model trained for one domain to be used in another. Transfer learning or one-shot learning techniques are being used currently to teach AI systems without significant data sources. Similarly, it can address a wider variety of problems, which has less historical data.
Hybrid learning models
Machine learning takes in and processes data with a fixed set of rule and depends on metadata to have information in a certain format. However, they do not have a model for uncertainties like the way probabilistic or Bayesian approaches do. New hybrid learning models combine the two approaches to leverage the strengths of each one of them. Hybrid learning helps in solving various business problems with deep learning by factoring in uncertainty. Bayesian GANs, Bayesian deep learning, and Bayesian conditional GANs are some of the examples of such hybrid learning models.
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Artificial intelligence and machine learning have been making headlines for topics like driverless cars, predictive texts, personal assistant, and gaming. Although not much discussed, machine learning has been bringing about landmark changes in the area of industrial manufacturing. The manufacturing industry is always on the lookout to improve productivity and efficiency. They want to go beyond the gains achieved by automation and build on top of that. Machine learning’s core technologies do align seamlessly with complex manufacturing problems. Numerous algorithms are being developed which seek to learn continually and bring about optimizations in the manufacturing plant. So how will machine learning revolutionize the manufacturing industry?
Increased production capacity and decreased material consumption
Smart manufacturing systems make use of machine data including production capacity, daily production, and machine load. Such data is essential to perform a predictive data analysis, which uses machine learning to know the ideal production capacity and decrease the rate of material consumption.
Today, more and more companies are integrating their machine learning databases, apps, and algorithms into the cloud platform. Data generated from industrial equipment can be fed to predictive algorithms, which will then be assessed by remote experts. Such data is essential to generate predictive models to alert the local technician to perform a precautionary check and prevent any sudden machine downtime.
Improved supply chain management
The modern supply chain generates a massive amount of data. It is humanly impossible to analyze such data, so experts turn towards AI and machine learning to organize it into useful bits and pieces. AI and machine learning make it easier for companies to adapt to dynamic market conditions. As a result, many vendors have been providing cloud-based supply chain management solutions, which collect and analyze data from different sources, including weather forecasts, historical data, news feed, and social media. Such data can be processed to enhance performance in all areas of supply chain including transportation, warehousing, packaging, customer feedback, and production.
Focus on customers
The customer is the king. It comes as no surprise that companies are aligning their business processes to address the needs of the customer. AI and machine learning can deliver exactly what people demand; thus, allowing for a high level of customization. Such technologies allow companies to build smart manufacturing processes that can seamlessly adapt to changing customer demands. Additionally, companies can collect data from smart homes to understand the latest consumer trends and design products accordingly.
Players across the manufacturing industry are pouring big chunks of money into machine-learning based quality assurance. Till today, companies rely on sensors and computers to remove low-quality products from their assembly line. Machine learning will provide a seamless quality control over the entire manufacturing process. Manufacturers can easily identify defects with efficiency and accuracy, and thus optimize its production and maintain customer satisfaction.
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It’s just impressive how the human brain learns new information and uses it to solve problems. Such behavior is evident even in children as they learn from their environment and have their own way of interpreting things. Computers have been trying hard to emulate such intelligence and today, they are getting close. Such AI applications can positively impact our lives and are already present in some form around us. For instance, one of the most common AI applications can be seen in the form of Apple’s voice assistant Siri or Netflix’s content recommendation engine. These AI systems can learn from your behavior and assist you in solving your queries. At some level, AI integration is already a crucial part of our lives. Just imagine a system which can think the way as humans do, but perform millions of calculations per second. The amount of problems they can solve can be limitless. And it comes as no surprise that companies such as AIBrain, Anki, Banjo, and iCarbonX are investing heavily in this technology to provide solutions in the banking sector. So what are the key AI applications that benefits the banking sector?
AML pattern detection
Anti-money laundering (AML) activities have been troubling governments all across the world. To crack down such activities, it is essential to think like them and develop countermeasures to tackle such issues. People involved in AML operate skillfully by depositing small sums of money which don’t trigger bank reporting requirements. AML detection is one of the most prominent AI applications in the banking sector as it can assign AML threat score to detect such fraud. By profiling each customers banking transactions, AI systems can learn users banking behavior and detect anomalies to crack down on AML activities.
Customer support is one of the crucial areas that ensures customer satisfaction in the banking sector. However, most of the customer queries are repetitive in nature, and it costs banks a lot to serve such customers. Uncontrolled spending can increase banks operations expenses and subsequently increase their cost of service. Chatbots are a welcome addition to this list of AI applications in the banking sector as it can simulate human chats. Such systems identify the content and emotions in the message and give an appropriate reply or even solve customer queries. After engaging in a lot of real-time chats, the system can learn to provide more accurate responses through machine learning. Bank of America, JPMorgan Chase, Capital One, Master Card, and many more banking companies are already using this technology to improve customer satisfaction.
Many people make millions of dollars in micro trading. Such people deal with a single stock and buy and sell shares multiple times within a single day. Such trading requires people to process vast amounts of information. AI systems can replicate such transactions by making accurate calculations to trade and turn in a profit. Numerous reports state that 70% of the trading today is carried out by automated AI systems.
One of the key AI applications in the banking sector has been in the area of fraud detection. It has done so with superior results. AI systems process multiple inputs and learn from user behavior to distinguish regular transaction from that of fraudulent ones. Although fraud attacks have become more sophisticated, AI and machine learning systems have always been in the driver’s seat. AI and machine learning skims through all transaction data to identify visible patterns and detect anomalies to identify fraud. Even the early data analysis technique FICO Falcon fraud assessment system has been refined to tackle modern-day fraud.
Similar to how content recommendation engine works seamlessly to suggest you the music or video you like, players in the banking sector can use customer recommendation. Using past data on users and how they behaved towards various offers and promotions from the bank, AI systems can recommend suitable users based on their history with the bank. Such customer recommendation systems help banks to grow their revenue stream and match customers to particular products.
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iPhone X’s Face ID was greeted with a lot of hype when it was announced. The facial recognition technology has the capabilities to differentiate the owner of the device from others just by scanning the face. In the same year, Google Pixel had an inbuilt app called Google Lens, which could scan images and return relevant and accurate results. So how exactly do computers know what it’s looking at? Much of this can be attributed to computer vision, which utilizes neural networks, machine learning, and image recognition to make accurate human-like decisions regarding images. However, knowing what the picture is can be impressive, but what significance does it offer in changing the technological landscape?
Applications of computer vision and machine learning algorithms
It may be possible to direct the self-driving cars to drive in a particular lane or apply brakes when an obstacle appears in their proximity. But one major challenge for autonomous vehicles is its ability to identify road signs, lane markings, traffic signals, and hand signals. Advanced computer vision and machine learning algorithms can quickly detect whether the obstacle nearby is a boy crossing the road or just some object which can be circumvented. Additionally, it can also understand fellow drivers’ hand signals and make necessary adjustments.
Computer vision enables users to understand the content of the image by using powerful artificial intelligence tools, which can quickly categorize images across thousands of categories. These image searches are so powerful that it can list out all the objects within a single picture and also identify objects shot through multiple angles. Image search can help companies identify explicit or offensive content, so they can block them before they create a nuisance. Google Cloud Vision API and Microsoft Azure Computer Vision API are two of the most significant players in this area.
Today, the recommended products section is primarily driven by simple algorithms, which classify product type and display other goods that are from the same category. However, with advancements in computer vision and machine learning algorithms, retail companies can now show products that have a similar design, color, or style. Visual product discovery can recommend a large number of products and find similar goods in a separate category. For instance, a customer may like a sneaker which might have been placed in the sports shoe category. This way, artificial intelligence tools can enhance customer experience.
People may be impressed by how accurately the new iPhone X can detect faces. However, that is just the tip of the iceberg in terms of computer vision. Computers are far more capable than just recognizing faces. They can identify and decipher facial expressions and ascertain whether the person is feeling happy, sad, or perplexed. Such technologies are mostly used in content testing, where advertisers capture the reaction of the audience to their ads.
Understanding non-verbal cues, body language, and gestures is a crucial aspect of human behavior, and that is what sets it apart from machines. However, the line between humans and machine regarding this aspect is blurring. Advancements in computer vision and artificial intelligence means that computers can understand human gestures. Such technology is embraced by companies such as PointGrab, which offers home appliance with smart gesture recognition technology, allowing users to control the appliances by using hand gestures.
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