Embedded Systems have extensive applications in Household, Consumer and Industrial Automation systems.
We build embedded systems with our experience and understanding in embedded processing, sensors, wireless and wired communication.
We develop and integrate embedded software and firmware (microcontrollers, processors, gateways, sensors) into a host of IoT and M2M devices.
We design cross-platform and native mobile apps that facilitate on-the-go access to data captured by smart devices and behave as a remote control for IoT solutions.
We help clients configure IoT devices, analyze sensor data via BI tools, manage IoT systems remotely and onsite and display it via responsive dashboards.
We execute edge computing on IoT devices instead of traditional cloud computing to ensure lower latency rates and quicker data offloading time.
We develop custom IoT software that interacts with smart IoT devices and ensures that software supports the hardware functionality seamlessly.
We provide industry-specific IoT software development services to meet client expectations.
We develop IoT-enabled apps that enable real-time fleet management and greater transportation efficiency for the logistics industry.
We offer IoT application development services for the healthcare industry that allow remote monitoring, deliver better patient care and capture patients’ vitals via IoT devices.
We provide IoT development services for smart homes & workplaces that directly transform our lifestyles.
We develop IoT applications for retail companies that help analyze customer behavior, provide a better in-store shopping experience and boost revenue.
We develop IoT-enabled applications for the manufacturing industry that can facilitate the production flow, automatically monitor development cycles, and manage warehouses and inventories.
We build IoT systems for monitoring crops and field conditions in real-time.
The manufacturing industry has faced growing challenges in recent years. The sector is looking at a skills gap that is about to get larger, as 22% of skilled manufacturing workers are due to retire by the end of 2025. Manufacturing and related industries are also struggling to improve on existing inventory and supply chain management, especially in the face of recent disruptions caused by the pandemic. Finally, manufacturing needs to keep up with customer expectations for high-quality, reliable products at an affordable cost.
Computer vision, or CV, can alleviate the biggest challenges facing the industry. CV, a subset of artificial intelligence, allows computers to take in information from digital images and then make decisions based on that information. When applied correctly, this means that CV allows computers to carry out the kinds of repetitive tasks that skilled workers would otherwise be doing. CV can improve efficiency and attention to detail while freeing up workers’ time for other tasks.
The future of industrial work is clear. Industry 4.0 will be highly automated, with faster production timelines and a smart warehouse, allowing for rapid production and distribution of products. It will also require a highly-skilled, educated workforce that knows how to use and operate cutting-edge technology, as well as safer workplaces so that employees can perform at their peak. Clearly, this represents a number of changes to what has been a fairly traditional sector. Computer vision can help the industry to bring about those changes quickly and smoothly.
The Internet of Things (IoT) is a key component of smart factories. Machines on the factory floor are equipped with sensors that feature an IP address that allows the machines to connect with other web-enabled devices. This mechanization and connectivity make it possible for large amounts of valuable data to be collected, analyzed and exchanged.
Cloud computing is a cornerstone of any Industry 4.0 strategy. Full realization of smart manufacturing demands connectivity and integration of engineering, supply chain, production, sales and distribution, and service. Cloud helps make that possible. In addition, the typically large amount of data being stored and analyzed can be processed more efficiently.
AI and machine learning allow manufacturing companies to take full advantage of the volume of information generated not just on the factory floor, but across their business units, and even from partners and third-party sources. AI and machine learning can create insights providing visibility, predictability and automation of operations .
The demands of real-time production operations mean that some data analysis must be done at the “edge”—that is, where the data is created. This minimizes latency time from when data is produced to when a response is required. For instance, the detection of a safety or quality issue may require near-real-time action with the equipment.
Manufacturing companies have not always considered the importance of cybersecurity or cyber-physical systems. However, the same connectivity of operational equipment in the factory or field (OT) that enables more efficient manufacturing processes also exposes new entry paths for malicious attacks and malware.
The digital transformation offered by Industry 4.0 has allowed manufacturers to create digital twins that are virtual replicas of processes, production lines, factories and supply chains. A digital twin is created by pulling data from IoT sensors, devices, PLCs and other objects connected to the internet. Manufacturers can use digital twins to help increase productivity.
Embedded sensors and interconnected machinery produce a significant amount of big data for manufacturing companies. Data analytics can help manufacturers investigate historical trends, identify patterns and make better decisions. Smart factories can also use data from other parts of the organization and their extended ecosystem of suppliers and distributors to create deeper insights. By looking at data from human resources, sales or warehousing, manufacturers can make production decisions based on sales margins and personnel. A complete digital representation of operations can be created as a "digital twin."
The smart factory’s network architecture depends on interconnectivity. Real-time data collected from sensors, devices and machines on the factory floor can be consumed and used immediately by other factory assets, as well as shared across other components in the enterprise software stack, including enterprise resource planning (ERP) and other business management software.
Smart factories can produce customized goods that meet individual customers’ needs more cost-effectively. In fact, in many industry segments, manufacturers aspire to achieve a "lot size of one" in an economical way. By using advanced simulation software applications, new materials and technologies such as 3-D printing, manufacturers can easily create small batches of specialized items for particular customers. Whereas the first industrial revolution was about mass production.
Industrial operations are dependent on a transparent, efficient supply chain, which must be integrated with production operations as part of a robust Industry 4.0 strategy. This transforms the way manufacturers resource their raw materials and deliver their finished products. By sharing some production data with suppliers, manufacturers can better schedule deliveries. If, for example, an assembly line is experiencing a disruption, deliveries can be rerouted or delayed in order to reduce wasted time or cost.
Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. Manufacturers are integrating new technologies, including Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their production facilities and throughout their operations.
These smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyze data and allow for better decision making. Even higher value is created when data from production operations is combined with operational data from ERP, supply chain, customer service and other enterprise systems to create whole new levels of visibility and insight from previously siloed information.
This digital technologies lead to increased automation, predictive maintenance, self-optimization of process improvements and, above all, a new level of efficiencies and responsiveness to customers not previously possible.
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