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We can consider Artificial Intelligence as cognitive intelligence as it can mimic human intelligence and perform tasks without human interventions. And the task enablement takes place with cues from machine learning that changes and improves its actions by learning patterns. AI has rapidly shifted its base into our day-to-day lives by surprise, but the public sector is yet to deploy its cognitive brilliance. But, there are many ways the government can make use of AI solutions to the fullest, and reap real potentials out of this component. We will discover the most comprehensive use cases of AI in the public sector that are truly relevant. Government acts can impact our living. Today, the government has enormous data. And the public sector can tap into AI potentials and bring about a transformative change the following way. 1. Crowd Analytics Crowd analytics is by nature intuitive and intelligent tool to interpret data collected from the free and natural movement of the crowd in any place to determine their behavior. As private sectors such as retailers and businesses use crowd analytics to make insightful decisions about customer behavior and bring in techniques to increase efficiency and promote better customer service. However, the government can harness crowd analytics to improve its people’s needs. But how? Government can improve public services by mapping crowd data in line with the growing demand of citizens’ needs. As they build their platform, it enables them to connect with citizens at a deeper level via an easy-to-use interface. By tapping into the power of data from citizens, the government can be able to reduce risks, boost the performance of their workforces, and optimize scarce resources to help its people. The government can also make use of traffic information and develop a real-time traffic ecosystem to avoid congestion, accidents, control speed, and other street hazards. It is all possible for sensors, bar codes, and cameras that keep a watch on our every step. As is the case with auto insurance bodies, it can monitor their customer’s driving behavior using GPS, and use the data in combination with behavioral economics to throw insights into accurate pricing. 2. Dialect Classification Dialect classification is part of general language identification to differentiate between accents of the specific language spoken. This AI strategy that infuses machine learning is key to deriving the correct language specification due to the complexity of the linguistic similarities between dialects. The AI system uses acoustic and natural language processing to build a dialect classification system. Then neural network systems extract dialect embeddings from the acoustic signals and present accurate results about the language being spoken by finding similarities and dissimilarities between languages and dialect or accent. The outcomes can be later utilized for automatic speech recognition. With the new classification of voice data, it can open up new dimensions for government in various ways. Using dialect classification data, the government can find useful information from a user’s purchase history; know their demographic and geographic data. This can be handy in identifying the users’ age, gender and even ethnicity, race of group, although it is regarded as discriminatory. But, leveraging this type of data helps policing units to determine a user’s connection with a terrorist group. On the other hand, dialect classification can work in sync with surveillance capabilities, and improve oversight for the purpose of immigration control. 3. Disaster And Emergency Management AI-powered systems can already predict several variables present in stock markets, customer service, trading and even health care. A similar way, AI can be of great use in the public sector in reducing the threats from disaster and emergency incidents. If the government can employ AI capabilities the right way, it is easier to harness data and predict varying degrees of norms related to natural disasters. This prediction can help gauge accurate scenarios about the disaster or critical events so that we can take appropriate measures to save thousands of lives and restrict further property damage. How does it work? AI-integrated emergency incident detection tools can predict the occurrence of natural disasters ahead of time, allowing disaster recovery personnel enough time to prepare strategies and deal with the incidents with improved rescue operations. Upon realization of the occurrence of the natural disaster of any type, it sends notifications to the system administration and helps take adequate measurements to improve operations that could make a difference between life and death. AI can be programmed with seismic data so as to enable the system to detect magnitude and patterns of the earthquake. It also identifies the location of the earthquake and aftershocks. To predict the likelihood of floods, AI can be trained with rainfall history and flood simulations to predict the occurrence of future rainfall and flood. So, using AI for disaster and emergency management, the government can make the right use of resources for preventive maintenance and repairs. 4. Face Recognition Facial recognition is a system integrated with AI to foster greater efficiency at law enforcement agencies and provides a means to ensure safety for its citizens. The tool is there to identify and verify people’s identity in line with face detection and face match. Other than establishing the identification of a person or entity, it is used to detect emotion as well. From this perspective, it means a lot for law enforcement. This facial recognition case study is an apt resource to improve your understanding of the technology and its application. Use by law enforcement The foremost use of facial recognition is to ensure safety and security for people by fighting crime and terrorisms. So, it can be at its best in detecting and preventing crimes. The technology is used to issue identity documents in merge with other technologies such as biometrics. A face match is done using this technology to compare the image on the passport with the real holder’s face. It is also used at police checks to ensure crime-free zones. Conclusion According to IDC, AI applications are estimated to grow at a CAGR of 54% across government organizations. It is also expected to leave its mark in the education sector with its growth prospects during 2017 and 2021. It is high time you make use of this technology in the right proportion and achieve its benefits. For any AI-based technology and applications, you can seek assistance from SynergyLabs- an expert in AI analysis and application implementation. Feel free to talk to us about your queries.

Data is abundant, but the challenge for the government is how and where to implement Artificial Intelligence capabilities to make an immediate impact. Although AI in the public sector is in its infancy stage, the widespread application of this machine learning tools that yield data-driven results and help maximize ROI and can make a difference for the government functions. There are some more ways you can find to make use of AI in the public sector and reap the immediate results. Incredible AI Applications In Public Sectors Yes, it is true you need a robust IT infrastructure to implement the AI models, which is expensive right at the moment. But, you can find real opportunities that matter. As AI possibilities are peeping through the hole, the public sector needs to make the right decisions to reap its benefits. i. Predicting Social Unrest and Geopolitical Events We can illustrate social unrest for religious reasons dated back to 2002, which is known as the infamous Gujarat Riots. This religious-based unrest caused massive damage to businesses, properties and lives. Imagine another recent event. The tension between the USA and Iran. Where could it lead to? The geopolitical crisis between these two countries is at rife, which is going to impact the other countries too in the next two months. But, the impact may be even worse in the coming months. Maybe, wider war with Iran. Had it not been in the Information age, it would have been tough to predict the retaliatory steps from both nations. And for human analysts, they could have missed important signs. Here is AI. We can certainly predict some events about the current situations and forecast social unrest and instability prior to three to five days ago. The most striking thing about AI is that it can derive insights into disparate social data and predict what might happen next. The technology uses deep and machine learning to extracts patterns and dynamics, and anticipate future threats. So, by leveraging open source data sets from surveillance, it parses data and prepares for riots and political unrest in the society. ii. Real-Time Media And Social Intelligence Analytics Social media intelligence is critical to informing decisions. It offers scopes to build actionable insights sourced from social media data. The key idea is to derive data about consumer behavior and explore ways to serve them better. However, the toolkit of social media intelligence analytics can be leveraged more broadly. · One of the major use cases of real-time media and social intelligence analytics is crisis communication. It has already changed how the government reacts during the emergency. With an effective communication method during an emergency via social media, it can improve outreach during crisis, and avoid risks. · Another use of social intelligence analytics is deriving information about citizen behavior and retrieving their feedback. This analysis can help offer future service delivery. · Research and development can apply this technique as well to detect future product needs. iii. Sentiment Analysis Generally, sentiment analytics extracts textual data to confirm if it is positive, negative or neutral. Leveraging machine learning and the natural learning process, it assigns scores to different variables in a text. The government can use sentiment analytics in combination with its organizations’ structured data to execute automated sentiment monitoring and analysis at local, provincial and state levels. The action would probably help them target the right people to deliver the message and respond to citizen’s complaints. In addition, using geo-fencing capabilities, organizations can target specific location-based citizens to solve their issues. With a dashboard view of real-time information derived from the sentiment analysis, the government can improve response to critical events, and even dispatch faster resources if necessary. iv. Social Media Bots Social media bots are programmed automatically to promote engagement in social media. These bots are designed or engineered to mimic human intelligence or act in a more autonomous way. There is a range of use cases of social media bots in the public sector. · Social media bots can influence the results of elections. As per reports, before the presidential election in 2016 in the U.S, social media bots were responsible to prompt as much as 20% political decisions. · It collects valuable information about citizens by providing real-time reports for analytics · It is also used to influence financial market decisions. For example, it may be doing rounds on social media to spread bad or manufactured news about a corporation. This is done to influence stock market prices. Conclusion It is indeed crucial to put forth effective and efficient public services while allowing the best application of resources and expertise. AI so far can be leveraged to accomplish the purposes in the public sector as well. The time is just right to foster forward-thinking and bring about a change. SynergyLabs, a dynamic and enthusiastic AI assistance provider that possesses high caliber skills can complement your business needs no matter how complex or simple they are. Get in touch to have a discussion.

Is AI anything new? No, it is not. It has long been there. But, today the scopes of AI have changed over the years. Thanks to the abundance of data and advances in algorithm coding. However, unlike the private sector, AI adoption is still nascent in the public sector, although AI applications can be leveraged for a wide number of use cases. AI alone can transform the way the public sector and educational institutions execute their operations. When accomplished strategically, AI can help these constituents unlock valuable potentials of the data in the following way: · Reduction in repetitive tasks · No wastage of talent and motivation · Improved labor efficiencies · Higher concentration in tasks that need creativity and oversight Since the government workforce is a little older as compared to the private sector, AI can help a better transition to abolish the traditional view of government work like that of boring meetings and form fill ups. With AI becoming more ubiquitous, you can find many applications of Artificial Intelligence to be relevant inside and outside of the government organizations. These public sector use cases are readily driving performance improvements. Let’s check with them. AI Applications We would highlight some of the specific use cases, although quite common in nature, they are capable of offering immediate benefits. 1. Agent-based simulation for decision-making Owing to agent-based simulation, it refers to the computational science that concentrates on independent active components of a system that interact with each other. In agent-based simulation modeling, active components are known as agents. The role of ABM is to detect interactive agents and their behavior. And these agents could be anything from people to vehicles, equipment to products and companies to entities. · How does the model work? ABM establishes a relationship between agents depending on environmental variables, and it runs the simulation. It then measures interactions of independent behavior and brings out the dynamics from the model. SynergyLabs uses ABM with different key variables in one AI-based platform to extract efficient results. Hence, ABM is a novel and innovative way to leverage it for your organization. And Public policy management is a field that can benefit from agent-based simulation. Nonlinearity is the base of complex systems like policy management, where the dynamics and nonlinear behaviors are tough to capture due to the presence of heterogeneous agents. As a result, decision-making is tough. To date, public policy assessors failed to define efficient public policy management process despite using several methods. However, simulations allow for dynamics and nonlinearity captures to solve the problem, and make decisions efficient and useful for management organizations. Benefits it can offer are; · Exhaustive interpretation of complex theories · Insightful observation from interactions between different entities and strategic methodologies · Examining new ideas · Anticipating the impact of policy · Developing a new theory · Determining the need for an improved approach for decision making 2. Behavioral Analytics Data analytics is critical to behavioral analytics that provides insights into people’s behavior or actions involved with online purchasing, social media activity, and gaming. The idea behind data analytics is to identify possibilities to optimize scopes and foster certain business outcomes. Behavioral analytics deals with demographic and geographic data, and sometimes more than this to uncover additional data. The primary objective of this phenomenon is driving conversions by creating and iterating patterns and dynamics from customer data. The same application can be employed across different governmental bodies to unlock key potential, improve efficiency and transparency in public management. How it helps: The government can make use of a large volume of data and acquire more insights using data analytics · Predicting Behavior Making predictions based on the available data is useful to determine a careful balancing act much ahead of an important public meet or convention. For example, a music concert is likely to take place in a place. So, based on the public space data, you can detect the number of visitors, gauge pressure in a specific area, identify busy or less busy locations and decide about the probabilities of congestions in an area. Thanks to data analytics, it helps deploy necessary police security in an area where it is highly needed. Other than this, predicting crime and fraud, improving emergency response, increasing cost-savings are some essential applications of AI. 3. Converting Paperwork into Digital Asset We know most of the government functionaries are too much engrossed with tedious and iterative paper work or content management. But, the problem with these papers is their non availability when needed. There comes the need of converting paperwork or any content type into digital assets. The content can comprise anything from images, documents, audio/video clips, and creative files. These are valuable resources of any organizations and digital asset management can help you centralize, organize, and make use of these files anytime anywhere you want through a centralized server. The operation runs on cloud computing, an integral part of AI and the conversion from paperwork to a digital asset is done via Intelligent Character Recognition and Optical Character Recognition. In addition, an advanced AI metadata tagging helps find files faster while giving the designated person access to important files to improve efficiency. 4. Crime Reduction and Prevention The government must make efforts to prevent crime to ensure safety for its people. AI here offers a great contribution to crime reduction and prevention. For crime detection, AI can offer real-time information about a crime. It uses machine learning to predict when and where a crime is most likely to occur. The system uses historical data to predict future crime. On the other hand, AI offers better insights into the scopes of preventing crimes by identifying patterns accurately. It rightly complies with the principle to prevent crime in the first place to avoid major threats to life and property and ensure security. Conclusion: SynergyLabs’ case study can highlight how AI can be of great use to improve efficiency and increase productivity in organizations. The similar way, government organizations can rethink the scopes and benefits of AI so as to bring a massive transformation that reinstates trust and credibility to government endeavors. SynergyLabs offers AI-based consultancy to organizations of every type. If you have any query, we would love to connect.

Shopping malls must provide their retailers with a comfortable environment to rent their space and for customers to visit them. It is no easy task for them to streamline everyday visitors because of the complex layout of the mall and its management. Mall Management System by Synergy labs provide effective retail solutions to automated customer analytics and give insights to the retailers. Our mall management system provides real-time data, by using AI technology, for demographics of the mall, people and vehicle count, heat mapping with various other performance solutions. Benefits: Increase retail avenues Enhance marketing across shopping malls and retail shops Lease efficiently in a data-driven manner Plan shopping mall operations guided by data analytics Run surveys and collect feedback Build shopping mall CRM People and Vehicle Counting Technology All the shopping mall managers can calculate the number of vehicles and footfall present at any given time. They can find the peak hours and visiting trends for each day of the week. With our AI-based analytics solutions, you can use insights to improve staff schedules and restack all the mall supplies beforehand. Visiting trends can be used by all staff to prepare adequately for the busiest hours of the day or week. It will ensure a comfortable environment for visitors to return. People Counting Technology will provide you with analytics for low-traffic times for which you can improve marketing strategies to lure customers. The technology allows you to analyze the performance of various strategies and optimize them for future gains. Heatmap Technology Heatmap provides the means for all the mall administrators with in-depth visitor analytics. You can use the analytics to know the movement of people around the mall and the sections they visit the most, and their convenience of navigating through the mall. You can plot visitor’s journeys throughout and identify congestion points to improve the overall layout. Our Heatmap Analytics help you place advertisements alongside high footfall paths to increase viewership or place attractive displays along less-taken paths to improve visitor frequency. The Heatmap data can also be used to outline the rent retailers pay for their spaces as prime locations attract a large customer base and higher rents. The same data can be used to attract prospective tenants. Demographic Analytics Our Demographic Analytics segregates mall visitors according to their age and gender for their mood analysis while moving around the space. The more diverse the demographics are, the more attractive it becomes for retailers to occupy the space. You can show demographics to your new occupants to know their target audience, also, let your current occupant stores highlight the selling opportunities appealing prominent demographics. Also, the mood analytics of visitors help retailers know the pain point and provide quick solutions. Mall Management Solution by Synergy Labs provides shopping malls and retailers to measure visitor behaviour in their stores. The solution help occupant stores predict future behaviour to optimize and personalize the shopping experience. Book a demo for a better understanding of incorporating Artificial Intelligence to improve shopping mall management and shopping experience of your customers. 4.Energy Analytics Today, Suburban malls have evolved into multifunctional shopping temples. To give unique experience to customers, shopping malls are using AI technology for energy-efficiency and optimization. Smart energy management systems help optimize heating systems and have become indispensable for improving overall shopping mall experience. Synergy Labs provide an efficient Smart Energy Management System for retailers and malls to improve their shopper’s experience that leads to an increased footfall and helps attract them more. Benefits: Keep track of energy consumption of your entire facility Track energy reduction and keep a history for creating a baseline Identify individual machines consuming excess energy for better-optimized systems Track and forecast energy savings and reduce overall energy consumption Improve your shopper’s experience Mall Management Solution by Synergy Labs provides shopping malls and retailers to measure visitor behaviour in their stores. The solution help occupant stores predict future behaviour to optimize and personalize the shopping experience. Book a demo for a better understanding of incorporating Artificial Intelligence to improve shopping mall management and shopping experience of your customers.

When it comes to video and audio data mining, it is relevant to uncover the real meaning of data mining. First, we should mention that the term is associated with machine learning and artificial intelligence technologies. Data mining is a process that interacts with a massive set of data. In this perspective, it unravels interesting patterns from unknown data structured. The same may apply to audio/video data mining as well. Today, users can access a large volume of multimedia data generated from the use of information technology and easy availability of multimedia systems. Hence, the amount of audio video data available today is growing exponentially. Video falls under the multimedia category that contains a variety of data comprising text, image, visual, audio and meta-data. Today, audio video mining holds a major place in different applications across security and surveillance, medicine discovery, education, entertainment, and sports. The key objective of video data mining is to extract data from video sources and discover and define patterns and dynamics. Here, we will discuss different use cases of audio and video data mining in businesses. Let’s find them. Traffic control management Be it traffic control or traffic violation monitoring, video data mining or analytics can be used to reach a better decision making regarding the traffic management. Live feed is extracted from the cameras installed upon the traffic posts. This live input is added to the database system to process it. As per the available data extracted from the video, the traffic control system will control the devices attached to the systems. If it detects anything suspicious on the road, such as the speeding driver or miscreant activity, the system alerts the system administrator via the analytics dashboard. Other progress reports on the road also monitored and dispatched via a separate database. 2. Vehicles monitoring procedures The video processing via video data mining can be leveraged to control various public vehicles or transport at different busy locales. AI-based video processing is one of the useful tools to monitor vehicles. Vehicle traffic department can use a video- based vehicle monitoring system. This is used to monitor the traffic flow on highways as to determine the exact speed and travel time of transports and accurate toll values. The CCTV cameras provide images of the moving transports, which are analyzed with a video image processor. The vehicles detected by the image sequencing tool are regarded as external communication output. The same technology is used for incidence detection. It offers a better use case for video-based transport monitoring. So, images captured from the CCTV cameras can be processed to detect vehicle breakdowns, road accidents, and bad road conditions. Another example of use case of video-based data mining is to monitor the activity of pedestrians and even lightweight vehicles such as cycles and motorcycles. In this case, the video stream can be applied to determine the exact variable of pedestrian movements. 3. Enhanced Security with Live Video Streaming Enhancing security and safety of people is a primary objective of surveillance. The main objective is to track behavior, activities and other data to ensure safety. A video processing system embedded with surveillance is important for monitoring and also security. Provided an access control, it works in line with video processing surveillance and provides useful information about a person entering the premises through a live video stream feed. Using AI-based facial recognition technology, it assigns unique identifiers to persons so as to enable them to enter any areas of the premises with an authorization level access. Intrusion detection is another useful use case of this tool. The live video image processor is programmed to detect centroid of the intended object. If it identifies such, it is suspicious in nature. 4.Health Status Monitoring Surveillances embedded with CCTV video streams can help detect health status of patients in hospitals as well. A camera that captures video streams of an infant is ideal for detecting the infant’s respiratory troubles using an Eulerian video magnification and optical flow algorithms. The extracted information from the camera can be used later to offer better treatment options for the ailments. In the business front, audio/video data mining provides large scopes of use cases. They are as follows as below; 5. Customer Demographic Data on Hand In order to extract data for discovery of customer demographic information, speech recognition technology is widely used. The technology leverages extensive data-mining process on your audio/video resources and identifies different range of information about a customer. It involves gender, age, emotion and sentiment, language and many more. Leveraging this kind of robust customer information helps launch highly infused user-specific marketing campaigns. As a result, your business can improve customer support, user experience and enhance productivity. 6. Automated Transcription Of Audio/Video Data Small businesses can benefit from audio/video data mining that helps transcribe different unstructured data extracted from video and audio. This results in conversion of accurate texts from audio/video sources, enabling business to find important information relevant to increase productivity and efficiency. At the same time, the whole process helps you automate a number of activities such as complaint analysis, demographic analysis, and legal compliance and more. 7. Understanding Customer opinion accurately Your customer opinion matters most as they determine success or failure for your business. Using AI-based data mining process that involves speech recognition can help you interpret an exact and accurate meaning of what your customers feel about your product and service. In most cases, it performs extensive data mining on the recorded calls. This gives you more scopes to establish a better strategy for customer support and unique customer experience. Conclusion Remember, AI-based technologies and data mining techniques are not mere a buzzword. They are readily disrupting every industry with its powerful capabilities that help businesses take data-driven decisions. This is unique in terms of mitigating business risks and leveraging more scopes to improve business efficiency and productivity. SynergyLab, an expert AI consultant is a pioneer that leverages the right AI tools and skills to yield the right business results. For further information on AI and machine learning services, you can feel free to get in touch with us.

Every human behavior is based upon decisions that prompt ecological populations to decide various activities. The decision involves what to eat, when to eat, where to move, and whether to travel or not. Well, every individual being follows certain rules to regulate their decision making process based on the theory of the population models. However, these models are supposed to be inaccurate to foster real-time decisions on human actions. In addition, certain factors influence the decision making process. They include the environment with its risks and rewards, the complex social parameters, lack of experience and learning about the environment. Here, enters the agent-based simulation (ABS) or Agent-based modeling (ABM). It involves simulation technique and a model that parses mannerisms of actions and interactions between individuals and the environment in a program. In agent-based simulation model, it refers to the independent decision-making bodies known as agents. As is with decision-making, each agent examines its situations and makes decisions using some rules. Let’s understand agent-based simulation or Agent-based modeling (ABM). What is Agent-based Modeling? It is an effective simulation modeling process to be used in different types of applications including real world business problems. In agent-based modeling, the whole procedure is orchestrated upon agents that carry out autonomous decision-making known as agents. Agent-based modeling gives out more accurate results about competitive interactions between agents using computational theories that explore dynamics. The scope is limited with the pure mathematical processes. To have a simple definition about agent-based modeling, it means a collection of autonomous agents and relation between them. Even a simple agent based model can parse complex behavior patterns of consumers. Most often, it can provide valuable information about dynamics and enable unanticipated human behaviors to come up. A wide range of tools such as neural networks, machine learning techniques, and evolutionary algorithms are used in this application to parse information. This AI-based model offers a real-time application which is so beneficial for every industry. Its flexible implementation is an underlying reason for its popularity. We will first explore a number of benefits of the technique and they discover its application of use case by various industries. Benefits Of AI-Based Agent-Based Modeling There are three core benefits of ABM. a. ABM drives emergent phenomena b. ABM defines a natural system accurately c. ABM is flexible ABM’s emergent phenomenon is a powerful process that precedes the other two benefits. · ABM captures emergent phenomena The interactions between individual entities result in the emergent phenomena. As the definition goes, the system is not held liable for any causative incidents because of the interactions between the systems or parts. With emergent phenomena, it has decoupled properties not associated with the part. For example, a traffic jam that results from the drivers’ faults, is identified as a different reason not related to vehicles that cause it. Under emergent phenomena, the scenario is tough to discern and predict. · ABM Defines A System With A Natural Definition ABM seems to produce the most probable reality about scenarios involved with traffic jams, stock markets or polling. For example, it is of more use to assume shoppers behavior in a supermarket than describing their density. It also helps companies work with real data about their users retrieved from pane data and customer survey. · ABM Offers Accurate Assumptions ABM uses a flexible model to test strategies and discern a real cause of something that happens or something about to happen and more. Also, it is a key predictive modeling technique used for business analytics. Based on the benefits it offers, we would find out some key use cases of AI-based Agent –based modeling simulation. Application areas of Agent-based Modeling Simulation ABM’s emergent phenomenon has become progressively accepted tool to predict difficult and counterintuitive situations in various moments. 1. Improving Evacuation Resulting From Herding Behavior Crowd stampede can occur in any place such as temples, shopping malls, movie theatres and many. Generally, it happens due to panic that results in fatalities. Many times, such incidents come about in an overcrowded place for fire breakout or rush for seats. More common disasters are quite frequent during mass gatherings in pop concerts, sporting events and many. When people are self-obsessed during such incidents, they lose control over their actions, leading to irrational herding. As an overall result, it results in overcrowding and blocks easy escape routes. In this situation, Emergent Phenomenon is applicable since it addresses panic behavior of people resulting from the complex human behavior and individual interactions. Based on the theory extracted from emergent phenomenon, ABM can offer insightful suggestions on mechanisms of panic and jamming. Using neural network and machine learning, Agent-based modeling can parse panic and jamming behavior of herding people, and offer simulation results that can help reduce harmful accidents and suggest optimal escape strategy. 4. Evaluating Market Risks The stock market is a dynamic place where incessant interactions happen between different agents such as investors, issuers, law makers, and economics and policy makers, resulting in emergent phenomenon. Every stock market tends to new trading policies in compliance with regulatory systems. This is quite high-risk, which can generate a negative response from market shares, investors, and issuers. ABM simulation can help understand the market more efficiently, and help gauge the impact on tick-size reduction. The simulation model gauges different factors under many conditions. This enables stock markets regulators to have better understanding of different strategies, observe the market behavior in response to changes, and offer warning prior to the occurrence of unwanted financial consequences faster. The agent-based modeling simulation uses neural network and other artificial intelligence techniques to design a regulatory system to prevent financial damage, and improve consistent performance. Improving Customer Retention It is true that acquiring a new customer costs you five times more than the existing customer. It makes sense to increase loyalty and trustworthiness with your existing customer. Using different strategies for customer retention can help you reach your goal. When it comes to customer retention, it is important to pay attention to the customer churn as well since they are integral to each other. ABM helps develop an improved platform to discover influence on customer churn and offer better strategies for customer retention. Conclusion ABM is more than a simulation tool, it helps reduce operational risk and develop ideas to rebuild the organization strategies. We expect to have a wide application of ABM simulation across various organizations and days are not too far when we can see its routine application in audits too. For any suggestions on ABM simulations and other AI-based technologies, you can contact SynergyLabs. It is an expert and renowned AI consultant to drive your business and offer you more opportunities beyond your imagination. Feel free to contact us.

The unprecedented volume of data and AI are the biggest use case to improve the operational efficiency in Logistics. The industry is dependent on many moving parts that can create operation jolt in the supply chain. However, the application of AI combined with big data; the complex traits of logistics can be transformed. When leveraging AI and machine learning, Logistics can improve the streamlining of factory functions or optimization of the routes. The level of transparency AI offers, it can enhance the efficiency grade for both logistics and supply chain. As per a study, 98% of the logistics operations are interested to implement AI-based technology at their operations as they facilitate data-driven decision making, which is central to bringing about the operational success of the supply chain activities. By leveraging AI, it enables logistics to improve some of the key operations and improve cost-savings. Let’s check the best use cases of AI for logistics operations. Accelerated last-mile shipment The last-mile delivery is a complex activity, costing logistics up to 28% of the total cost of the delivery. The most common challenge for this phenomenon is the unavailability of the customers, resulting in an item to remain undelivered. Additionally, the delivery person must take extra care to prevent any damage to the item. The last-mile delivery is quite exhaustive. However, using AI-based capability, logistics can use last-mile analytics enabling them to predict what is going to happen with the delivery from start to finish. Embedded with sensors and GPS, the delivery vehicle constantly streams data to the warehouse data system, and alerts them about the exact delivery time. This improves the process of delivery by alerting customers about its expected delivery time while helping the warehouse optimize the delivery strategies. Improved delivery of perishable goods It is a long-time problem for logistics to deliver perishable goods. Using AI and IoT, the delivery persons can get a better understanding of their products and ways to avoid loss due to perished items. It is efficient for logistics that carry ice cream and desserts. Using temperature sensors inside the delivery vehicle, it is easier to monitor the state of the weather and direct the vehicle to another route if it senses damage to the product. Enhanced Transparency AI helps logistics decide the real-time and accurate delivery time of the product, thus preventing unnecessary expenses related to goods canceled. This transparency also helps logistics make better decisions regarding how many shippers they actually need to ship products. AI and big data are bringing new possibilities in the arena of logistics. Besides, the Internet of Things combined with AI can improve forward-thinking and help businesses reduce costs and improve customer experience, while increasing customer satisfaction. SynergyLabs is a renowned AI consultant to help you implement the next big AI project for your business.

Artificial intelligence is a buzzword in the current scenario of logistics management. Its widespread implementation across various verticals is so apparent today. The exciting implementation of AI technologies by Amazon to opt for automated warehouse solutions, last-time delivery drones by leading retail stores like Walmart and Amazon- all have augmented applications. Complex skills required to accomplish logistics jobs can also be transformed using this technology. As logistics planners are geared up for leveraging this too to execute high-skilled activities, it contributes to active result-oriented business results through cost-reduction, time- saving, and elimination of manual errors. We can breathe a sigh of relief as Artificial Intelligence can transform the way freights move across the geographies. We cover five interesting and exciting use of AI in logistics. Damage detection with computer vision When it comes to transferring cargo around the world, paying attention to damage reduction is central to improving productivity in logistics. And it is computer vision-based AI has provided us with state-of-the-art technology to bring about a change to the vision of how we tend to serve customers. In context to damage identification in logistics, the technology has become so relevant to reduce damage and avoid customer churn. Renowned Logistics giants are using this technology to identify damage much before it is likely to happen. The computer vision-based AI technology enables damage identification in multiple ways. By leveraging this technology, you can trace the damage depth, the type of damage, and take an actionable approach to reducing further damage to your service. The entire process happens faster than ever before. AI-integrated tools can improve the efficiency of the freight carriage train wagons and prevent damages to ensure uninterrupted service. To carry the process, tools are programmed to fetch data from the installed cameras along the train tracks. These cameras capture images of the wagons, process them using AI-vision capabilities. As a result, it improves the accuracy rate up to 90% and reduces the damage to the wagons. Besides the recognition of damage, the computer vision AI can help unload a stack of inventory less than 30 minutes. Logistics Robots to foster automation Research established that the worldwide sales of supply chain and logistics are expected to grow $22.4 billion by 2021. And this is augmented by the use of robotic process automation. Using AI-based robotic process automation, it is easier to trace and move inventories in the warehouse. It also improves the efficiency of moving and sorting oversized packages at the warehouse facilities. In a process to put robots into action, they are programmed with deep learning algorithms to make autonomous decisions for a dozen works with locating, identifying, picking and optimizing work. Robots can ease the process of picking and accomplish the task in less than .2 seconds and move the parts to the expected location. Improved demand and network planning Networking and predictive demand planning is key to boosting the efficiency of the logistics using the capabilities of AI. Leveraging this tool enables a better understanding of accurate demand forecasting and networking planning and helps execute proactive operations across the logistics channels. As you get to predict the expected future occurrences much earlier, it can improve the optimization of the vehicles by directing them to the locations where the demand is higher. With AI, it is easier to analyze data to its fullest potential and improve the assessment of future risks and enhanced techniques to avoid risks, and reduce operational costs. Logistics can better use resources and maximize benefits using AI. These days, advanced logistics services are using a wide range of parameters to air freight. Building a model with the machine learning-based internal data can help predict the average duration of daily transit. This helps gauge the exact duration of delivery per week in advance if they are going to fall or rise. The internal-based machine learning system can improve the prediction of the air freight delivery status depending on key factors like weather and operation failure. AI capabilities are a good technique to safeguard against the risks as well. A system orchestrated with machine learning and natural language processing can better understand the conversation taking place online platforms and social media channels. This helps analyze data and discover the real meaning of the sentimental elements of the matters, resulting in a better understanding of future risks. Thereby, AI capabilities help logistics assess material shortage on time and find real issues in the site. Small logistics operations can benefit from the machine learning-based systems by using them with their existing solutions. Optimization of the future performance Logistics can execute highly accurate predictions and optimize future performance better than ever before using AI capabilities. Leveraging big data in combination with AI, they can bring transparency to the overall logistics operations. It also helps improve the future performances of the supply chain and logistics. As per the study, 81 percent of logistics operations and 86% of third-party logistics prefer using big data as a core competency tool for their supply chain operations. This is a complex and diverse sector that depends on a wide variety of parts and vehicles. Big data helps improve the supervision capabilities across all the operations. As this technology helps improve route optimization, it is expected to help save millions of fuel annually. Besides, it is a more efficient tool to ferry last-miles deliveries to the destination without affecting the customer experience. Most of the time, logistics do not have access to user data or figure to implement. There come algorithms to extract structured or unstructured data from different sources to enable the identification of issues and establish better transparency across the business. For instance, shipment data can be handy to predict precise deductions about unknown quantity. AI is competent to work only with 5-10% of accurate shipment data to establish some metrics. Using this data, it helps detect accurate amounts of quantity to be loaded in the vehicles. Thus, it helps optimize the use of vehicles to the fullest potential. This industry-changing phenomenon is simply groundbreaking. The most interesting fact about AI in the industry of logistics and supply chain is that they can do more than these expected activities explained in this blog. SynergyLabs is a tech company that uses machine learning and natural language processing to offer AI consultancy services. To transform your business in the overall operations of logistics, you can get in touch.

Artificial intelligence is so obvious in various sectors today. And logistics is one such area that can leverage this technology to its advantage to the fullest scopes. For say, there are so many manual jobs involved in the supply chain and logistics that are time-consuming, error-prone and expensive including warehouse sorting, picking, driving, last-mile delivery persons. Well, you get Artificial Intelligence to these core activities, the result is different. However, we should remember that Artificial Intelligence cannot work alone unless combined with human intelligence. And the integration between human intelligence and Artificial Intelligence fosters the Augmented Intelligence. So, this tool can facilitate the performance of a dozen activities by reducing operational costs, preventing manual errors and encouraging employees to execute more complex activities. While these are some common phenomena of Artificial Intelligence, there is another use case of AI-based Augmented Intelligence, which can add value to the logistics and supply chain operations. And this is called an intelligent predictive alert that enables improved logistics planning. What is predictive alerting? As the name suggests, it is an intelligent alert system. To be precise, it is an efficient and smart way to provide predictions of specific events using machine learning and Artificial Intelligence. The technology teaches machines to learn from the data and reflect some useful patterns and metrics to make actionable predictions. The machine regularly processes data and provide useful information based on its learning. Similarly, it is the most reliable way to track your data and make useful decisions about the work process. As you are able to set up customized alerts using machine learning and artificial intelligence, it facilitates better tracking of your business performance with ease. Problem with the existing planning model In logistics, managers of planners must understand the complex work culture of the industry, including customer requirements, business rules and managing data quality issues. The whole practice becomes so complex and you must bring technological improvements in the planning area. Today, the logistics industries require following 2-3 monitors with the transport management system and Excel together on the other hand. It clearly indicates decisions are made based on manual assumptions and experiences of the planners. However, this prevents the optimization of the planning by 10%. As a result, you are likely to miss key information that really matters for your business operations. Generally, an Excel contains job roles of at least 10-20 persons under one supervisor. And if he leaves or skips his responsibility for the day, it affects the operation and poses huge threats to the logistics. There is no scope for transparency with Excel and it causes reservation of capacity for logistics leaving it frustrated with empty trucks, empty containers and unnecessary line hauls. Now, you can gauge the trouble of this mismanagement. But, Machine Learning and Artificial Intelligence, rather augmented intelligence can help prevent this situation with intelligent predictive alerts. How can it help you? The tool can work along with human planners. It processes plenty of data such as available data and recent data. It can take data-driven fall-back decisions when the planner is not available. This flexibility helps logistics prevent the accomplishment of repetitive work. For say, selecting a cargo vehicle may take more than minutes for humans as they need to make decisions through different variables such as routes and schedules of the vehicles. But, this sorting is easier through AI, and the final call is so easy with human intervention. For more effective optimization of augmented intelligence, planners can use intelligent alert based predictive analytics. This is so useful for freight management since it helps retrieve information about real-time positions and estimated time of arrival of vehicles. In doing the work, It needs satellite data to work with. Therefore, logistics planners can make impactful decisions about the vehicles and help improve logistics freight operations. Augmented Intelligent is a powerful tool to predict the vehicle’s functional capacity and help optimize the operation hours at an optimal level. SynergyLabs is a true pioneer of leveraging the best logistics technology integrated with AI and machine learning that offer better AI-based predictive optimization services from large to small scale logistics services.

A dozen factors decide the faith of successful logistics operations. Unless it maintains accuracy, timeliness, and resiliency, it fails to accomplish distribution and customer demands. But, logistics is not only about loading goods and delivering them at customer points. It has more to do with the interest of mankind. Reports suggest that the logistics industry is expected to grow to $1.2 trillion by 2020, meaning it needs a robust infrastructure to operate efficiently globally and meet customer demands faster. However, in the past thirty years, the circumstances were different from that of today. It had growing challenges, which big logistics operations have been able to overcome, while others still struggle today. Challenges of the Logistics and Supply Chain The optimization of supply chain and logistics is a big challenge for the business. It requires the brute force comprising larger warehouses, larger ports, bigger ships, and a large fleet of delivery vehicles to meet the growing demands. And it requires scaling supply as fast as possible. There lies the biggest problem for logistics and supply. It just adds to the complexity with a large number of stocks remaining idle at warehouse or ports for days and even months, leading to woes for recipients. It costs logistics and companies their repute, while idle stocks are expensive to bear. However, this deep-rooted inefficiency can be bypassed using AI-powered predictive analytics. Predictive analytics is one smart way to increase precision in tracking freight and packages using available data and improve efficiency. Well, there are more ways to use predictive analytics in supply chain and logistics to transform its environment. More interestingly, this phenomenon is a smart technique to reduce waste while increasing productivity. But, What Is Predictive Analytics? Predictive Analytics is an Artificial Intelligence-based technique that harnesses data from various sources, especially from data that the company already owned -historical data. The practice parses these data sets or algorithms to assign scores to various user segments. These scores actually point at some probabilities, rather than making absolute predictions about anything. Simply put, it applies the theory of probability using a massive volume of data, and derives data-driven results that hint at what is likely to happen in the future depending on the historical data. Today, supply chains and logistics can use Predictive Analytics to discover specific trends, patterns, and metrics by tracking historical data and establish predictions. Business today can leverage Predictive analytics for many reasons, such as inventory management, planning, and demand forecasting. Predictive Analytics can help build patterns to create a fast and agile response for future developments. Various Use Cases of Predictive Analytics in Supply Chain and Logistics Leveraging predictive analytics gives scopes to logistics to create solutions in time and improve productivity, and cost-savings. Predictive analytics has been a key phenomenon to harness real-time after-sales-service value for every industry. With AI implementing costs decreasing possibilities over time, it may foster a better possibility for you to leverage it. So, let’s find out essential use cases of machine-learning or AI-based Predictive Analytics to transform your logistics and supply chain. Predictive Pricing Many manufacturers still follow the old practice of pricing models based on Excel spreadsheets and cost-plus models. However, the problem with this infrastructure is that it creates confusion over different pricing structures of different parts and products at varied locations. This can affect customer experience and prevent profit-making at an optimal level. An attribute known as the k-nearest neighbors algorithm is used in predictive analytics to alleviate confusion. Then a set of factors, including weather conditions, location, seasonality, and demand can be mapped to harness real-time gestures and automatically set prices to adjust to the market. Predictive Demand Forecast No business can sustain an effective demand forecast model. Demand Forecasting is a process that predicts the trends of future sales based on historical data. It facilitates informed decision making about everything relating to warehousing needs, inventory planning and even marketing efforts. It’s a key attribute to making data-driven decisions about expected future sales. Not only does it help build robust predictions about the product demand, but it also helps gauge patterns of after-sales service success. This is an efficient method that helps enhance visibility across the supply chain to forecast accurate demands. Leveraging this technique, manufacturers can enhance service after sales without affecting costs. Machine learning and Artificial Intelligence-based predictive analytics use gradient boosted machines, support vector machines, and Neural Networks to help optimize stocks, thus eliminating overstocking. Predictive demand forecasting comprises four categories, including macro-level, micro-level, short-term, and long-term to be used to parse data depending on different lengths of time of the year, factors and conditions. This helps the supply chain increases uptime of the operation, reduces risks, and improves customer service. Predictive Maintenance 50% of services used by the supply chain are deemed ineffective as they are unlikely to be available when needed. This means indefinite product downtime, a decrease in revenues and poor customer experience. When it comes to predictive maintenance, it can detect an event before it is about to occur. IoT embedded devices and sensors, they make predictive analytics a top-notch service to improve situational awareness and prevent any emergency event. IoT and smart sensors can detect a failure of parts much before the real occurrences, proactively routing information to appropriate executives or repair centers to alert them and take appropriate action to prevent it. Predictive Maintenance helps the supply chain prevent excessive inventory and costs, avoid expenses related to operation disruption and downtime, and improve the process of part refill rates. And it improves customer relationship. To leverage the predictive analytics in the most efficient manner, SynergyLabs considers integrating all of the sales channel data with the data system and create a cohesive picture of the entire work process. Thus, we enable you to develop a platform to use predictive analytics across various work processes and help you make a data-driven informed decision. SynergyLabs, your trusted AI consultancy to accomplish stock optimization and deliver the right experience your customers want.

Artificial Intelligence or AI is no longer a buzzword for various industrial applications. Being the biggest technological trends, it is so omnipresent in every sector and disrupting the method of executing tasks in the traditional way. Well, new advances in computer vision, machine learning and deep learning, AI has also added a new dimension to utility and application services that also leverages advanced neural networks. The wonders of AI capability are ever- expanding. Not only does this tool extract data from terabytes of structured and semi-structured sources, but it is equally efficient in acquiring data from unstructured data sources. Then it parses them and identifies different sets of patterns and makes recommendations and predictions based on the analyzed data. So, AI offers better insights in understanding a pattern and helps us develop better solutions for smart applications that are accurate, independent, and real without human interventions. With thousands of use cases of AI being realized by industry leaders and more strategies are being developed for its deployment, the same holds true to the global energy market. How Energy Production Sector can use AI AI offers a better scope to be utilized in energy management and meeting demand supply by various industries. As this global utility sector is seeing a paradigm shift towards efficient energy production and preservation methods to meet the high demand for power supply by consumers, the industry works more on the decentralization and decarbonization. In addition, it is now every sector’s core responsibility to manage the imbalance in demand and supply while preserving it. For utilities, energy companies and grid operators; renewable energy production is the primary power generation medium in today’s context where fossil-based energy is likely to end soon. Hence, they want to use AI capabilities to enhance easy access to renewable energy and increase thier efficiency across different sectors. AI technology in sync with IoT and big data can help improve management of the grids for renewable power generation while balancing its demands and supply. Besides improving renewable power production and its supply, AI also aids in the management of energy in different services such as malls, hotels, retails and many other sectors. AI in energy management The sustainable challenges for the energy world have always been there. By optimizing the energy at an optimal level, any industries can contribute to better preservation of energy and efficient application without creating wastes. As explained earlier, AI is opening new opportunities for various services and industries to tap into unmapped data and connect it to the decentralized energy resources. So, industries can leverage AI capabilities to optimize the use of energy across various sectors and give us real opportunities to address the challenges of the environment. When used with the core energy system of any organization, AI capabilities in combination with machine learning and deep learning algorithms can easily drive insights into the operations of the energy operations. It then parses the data and suggests an actionable approach to energy management while helping you save costs on unnecessary energy use. It is a real-time approach to reduce energy wastes and build new energy-saving opportunities by optimizing every industry’s energy consumption using untapped data. SynergyLabs utilizes AI-based technology to let users leverage it to monitor data and other factors in real-time that impact energy consumption. Working on the data from the energy system, our tool offers some recommendations and helps build a model for the staff to create and implement the most efficient strategy to enhance energy efficiency. Our tool is a customized solution that works in sync with the managers and energy managers of the concerned space. It helps monitor the performance of the system and optimizes energy in a cost-effective manner. This use case of AI is essential to drive growth at businesses, create an efficient energy-proof environment that allows getting more and increased cost-savings. For AI-based digital solutions at your organization, you can contact us. We are happier to help you with our robust and advanced AI and machine learning tools.

The supply chain is a diverse and complex domain and manufacturing industries must align with its workflow to remain competitive. High calibrated competencies are required to sync and manage multiple activities during warehouse management, inventory management and product delivery. Even a small technical glitch and machine downtime can cost you billions of dollars in revenue loss and time to fix the issue on time. But, technology appears to transform the way supply chain has been managed. Today, the explosion of data is all time high. And it is fast keeping pace with various industries. Artificial Intelligence and Machine learning together have long contributed to digital transformation in supply chain. According to experts, these two phenomena are expanding its boundary to offer more tangible uses cases in the coming years.  Experts believe they are highly competent to deliver high performance and drive real business results for supply chain management. AI adoption in Supply Chain in the coming years The scope of AI is ever-expanding and it is triggering with the evolution of new digital era. Exploring the latest enablement by AI in the supply chain As we go further in searching potential uses cases of AI, we have come across the latest findings below. Predicting Customer’s Behavior Customers are whimsical. They may step back from purchasing even if the order is about to be delivered. This makes your logistics put up a huge workload and time being wasted. This volatile order pattern can lead to miscommunication between your team and loss of unnecessary productivity loss. More often, an unstable customer behavior is hard to predict due to a surplus of orders from the online retailers. Hence, predictability of volatile order volumes is a challenge for many companies. But, AI and ML give freedom to predict the volatile nature of the customer behavior much earlier at optimal level during such situations. This way, you can avoid time waste and reduce manual error to invest more resources for business improvement. Sensing Market scenarios Observing the market patterns and its behavior is a key to remaining in the business and offering better service to end-users. AI is capable of harnessing real data from external casual resources such as weather, industrial production and employment history. As it processes the data from these sources, this application can better gauge the market conditions and assess the growth drivers. Leveraging its sensory competencies, AI can reshape the capabilities of supply chain by improving capital expenditure and product portfolio. Mitigating the risk of chargeback It is customary to demand chargeback from brand owners in case of delay in delivery of products. As a result, brand owners have to pay hefty penalties for missed On Time in Full deliveries. With access to advanced AI integrated with deep learning, it is easier to shuffle through essential data involving number of order placed, order types, location and type of shipment. This helps unearth real cause of charge back while reducing disputes among peers. On the other way around, it is helpful to analyze the cause of failure. Increasing Fleet efficiency In supply chain, on time product delivery to the destination matters the most. It takes just a minute to make or break your credibility towards winning a customer trust. However, it is always unpredictable what is ahead on the route while it is en route to delivery. In such a scenario, an AI driven GPS tool enables better optimization and navigation of the route for your fleet. It helps you access the most efficient route for product delivery by processing customer, driver and vehicle data using machine learning. As a result, it is possible to cut through the most trafficked area and uneven road conditions. Simultaneously, it helps you save time, money and reduce the wear and tear of your truck tires. As per reports, it is believed that using such advanced AI enabled GPS for supply chain delivery; you can save an estimated $50 million per year. Increasing accuracy in tracking of arriving and departing orders It is essential in the supply chain to track the path of order so as to keep the warehouse loaded with fresh product line.  As manual errors are likely during path of order arrangement, pallets cannot be positioned properly. Items not moved for long in the warehouse are pushed further in the back and replaced with the fast moving items. This can be a challenge for retailers for not putting older products out of the warehouse. AI algorithms can predict the arrival and departure of the product in and out from the warehouse more easily. This is useful in assisting employees put the pallet in the correct order and release product as per their shelf life.  Companies can become smarter using AI in their supply chain. With the ever increasing volume of cloud and AI algorithm intelligence, supply chain is on the verge of digital representation. Challenges are there as they still adjust to their existing infrastructure. But, if you are really keen to render a real-world platform and predict business challenges, AI can boost your operational goal. With AI driven decision making, business can gain unprecedented speed and scale its business amid the continuous market shifts.  We at synergylabs take care of your priority and do exactly what fits your domain. Connect with us today. Team Synergy