Let’s take Smart Decision to Revolutionize the Manufacturing Industry  


Let’s take Smart Decision to Revolutionize the Manufacturing Industry  

revolutionize the manufacturing industry

Uncover valuable insights by turning simple data into smart data

The Customer:

The manufacturing industry includes not only the plant floor but also the warehouse, quality-material flow throughout the manufacturing process, and the product supply network. To achieve and maintain manufacturing excellence, one must have command and insight. Getting the right information to the right people at the right time results in making better decisions in every aspect of manufacturing to generate better brand value.

Business Challenge:

Forecasting future demand is difficult for manufacturers. They are miscalculation of how many items they should sell in the coming months or years. As a result, their products fail to meet customer demand, and their sales suffer. Besides that manufacturing industry is facing a shortage of workers. They deal with lower sales. It is affecting their revenue. Overhead production costs, higher employee compensation for unskilled people, and customer dissatisfaction. Manufacturers frequently struggle to identify potential leads, Businesses focus on unpromising opportunities and fail to follow up with high-potential leads. When it comes to implementing automated solutions, manufacturers appear to be careless. They are hesitant to adopt new technology due to heavy costs.


Manufacturers can now use innovative software programs and artificial intelligence to digitalize many methodologies which previously required human intervention. With a renewed emphasis on IoT and a heavy reliance on predictive maintenance, almost every surface will be transformed into a sensor for data collection, providing makers with real-time insights. The ability to collect data from multiple sources, combined with increasingly powerful cloud computing capabilities. It enables manufacturers to cut and cube data in ways that provide a comprehensive understanding of their business — essential as they work to reevaluate forecasting and planning models and develop a successful strategy to overcome the challenges. Data enables businesses to tailor their products and services to precisely what the customer desires. Big data is user-friendly, affordably priced, and adaptable enough to serve companies now and in the future.

Business Benefits:

MSRcosmos- An expert of Big data solution

  • Better decision making
  • Assist to understands customer’s needs
  • Ensures hiring of appropriate employees
  • The use of automated services reduces costs.
  • Big data tools save a significant amount of time.
  • Eliminated or reduced invoice-processing errors.
  • Enables to stay competitive in a fiercely competitive environment.

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Make Digital Worker as Your Colleague

Make Digital worker as your colleague

Business and society will continue to be rapidly digitized and virtualized. As we enter a new year, however, the need for sustainability, ever-increasing data volumes, and increasing compute and network speeds will help to recover the work as the primary drivers of digital transformation.

With growing technology, every organization is embracing automation. A robot is an entity that performs human actions without human intervention: It is an efficient method for achieving accuracy in the successful completion of a task. For a successful transformation, a comprehensive, end-to-end view of the automation opportunity is required. It should prioritize automation activities based on their business value, ease of implementation, and risk.

The RPA is a method for integrating automation and redistributing resources across future deployments. It reduces human efforts.  The task completion process is simplified by using automation of the various tasks, allowing businesses to utilize that time to complete other tasks. It contributes significantly to the market’s fierce competition by improving performance.

RPA combined with cognitive technologies such as Artificial Intelligence (AI), machine learning, speech recognition, and Natural Language Processing (NLP) can withstand extreme tasks and make decisions without human intervention. It instills in employees the fear of losing their jobs. The average knowledge employee cooperating on a back-office process is subjected to a slew of tedious, routine tasks for which they have no coding knowledge. Conventional automation necessitates programming and API knowledge in incorporating multiple systems on a single platform.

RPA does not require any programming skills or direct access to the application database. Traditional automation does not allow for as much customization as RPA does.

The most significant advantages of RPA are it is a no-code programming system. It has the ability to use information resources to improve employee productivity, spend less time waiting on IT, and devote more time to achieving business goals.

 RPA Blueprism

Reduced costs: No-code lets companies save time and money by waiting on IT to create business applications with conventional programming approaches.

Increased agility: Businesses will respond to emerging market conditions and business circumstances more quickly while better preparing for the future.

Decreased training: This reduces costs and shortens the time to value for business users, who can work at their own pace without having technical skills.

Better applications: No-code programming aids in the development of better applications which allows the user to make changes as needed for iterative improvements.

Integration ability: No-code platforms excel in integrating with a wide range of other platforms and systems by allowing complex steps much easier.

Faster development: No-code platforms help IT and business teams work together. It allows developers to create enterprise-grade applications based on their specifications.

RPA will undoubtedly impact different parts of the organization, including the front and back offices, but the number of jobs lost will not be the primary value proposition; cost savings will be a factor.  RPA handles many of the mundane, repetitive tasks that are typically assigned to employees. Still, the automation process requires human intervention. It is highly possible that it will affect segments of jobs and/or departments. But, it offers employees to be assigned higher-value tasks.

In recent years, RPA technologies have advanced significantly. It allows operations centers to achieve the high quality and stability required for sensitive, customer-facing processes.

SMEs, in particular, benefited from RPA by increasing employee and customer satisfaction. Automation of structured and repetitive processes across the organization increase productivity and rescue operation time. It drives market growth over the forecast period.

RPA will remain a hot market, earning a place in the toolkits of IT managers as an easy way to streamline various business operations and make better use of raw data. Forrester predicts that the RPA industry will grow from $250 million in 2016 to $2.9 billion in 2021. According to Grandview Research, the RPA automation market will be worth $25.56 billion by 2027!

Finally, RPA is not intended to replace human workers. In fact, it is intended to make humans’ working days easier and, in many cases, to change the work that people do.

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IoT trends 2021: Managing workforce of future

IoT trends 2021

In a world where face-to-face interaction is currently limited, contact between devices, tools, and toys can help us stay connected. Current world conditions have undoubtedly been affecting IoT trends.

During the forecast period, the global Internet of Things (IoT) market is expected to grow at a CAGR of 25.4 percent, from $381.30 billion in 2021 to $1,854.76 billion in 2028.

IoT is implemented in Agriculture, healthcare, electricity and power, telecommunication, Banking, Supply chain, Media and Entertainment, and many more industries. They are taking advantage of IoT devices. The use of IoT devices is steadily expanding.

What makes IoT trends in today’s world?

  • Guaranteed security and uptime
  • Accelerate the time to market for your connected offerings.
  • Reduce risk and expense by using IoT connectivity as a service.
  • Immediately implement a future-proof solution around the world.

IOT Trends 2021

Security: The number of Internet-connected devices has increased significantly, and this trend expects to continue in the coming decade. As a result, extra precautions taken by network operators can easily prevent intruders from entering the network, making IoT security the most modern IoT trend.

AI-ML-BIGDATA:  AI, smart devices, and Big Data will significantly contribute to security risk mitigation. So far, a few companies have implemented the AI Internet of Things. AI-powered analytical solutions have the ability to aggregate massive amounts of high-quality data and information, process it in real-time, and derive actionable insights.

Data Analytics: The powerful integration of Big Data, Artificial Intelligence, and IoT devices will enable users to make important and effective business decisions with ease based on the data analytics information and insights. The Internet of Things is about more than just analyzing behavior and blowing out data; it is also about speedy data processing and making recommendations based on those findings.

Blockchain Technology:  Blockchain technology, also known as distributed ledger technology, emerges as an appropriate tool for ensuring data security during encryption techniques as well as peer-to-peer contact without the use of intermediaries. It is one of the top IoT trends that address major IoT scalability and security issues.

IoT application: IoT application: Internet of Things applications and use cases are evolving at a rapid pace. Currently, its apps revolve around smart homes, smart grids, wearables, smart cities, industrial settings, and so on. With the rise and development of this technology in the coming years, the Internet of Things will reach more business and industry settings, ushering the world into a more digital age.

Edge computing: Businesses today are overflowing in data, massive amounts of data can be obtained daily from sensors and IoT devices acting in real-time from remote locations and hostile operating environments almost anywhere in the world. The Internet of Things devices send all of the data they collect to the cloud for analysis and insight extraction; this work is done directly on the devices themselves. The adoption of edge computing will become more important for Internet of Things devices in order to overcome cloud computing drawbacks such as latency issues and low bandwidth encountered in the real-time data processing. Edge computing is a cost-effective and accurate data processing method for IoT devices.

IoT testing: Today, there is a greater need to provide better and faster services. There is a high demand for data access, creation, use, and sharing from any device. The goal is to provide greater visibility and control over a network of interconnected IoT devices. The Internet of Things testing is ready to integrate with other technology to make life smarter and easier. Smart sensors, wearables, and connected devices will continue to change the way healthcare is delivered, from automated homes to telemedicine assistance for the elderly and disabled. Furthermore, in situations where the risk of virus infection is high, it will be used to avoid unnecessary contact.

The Internet of Things appears to be well established in our industries and daily lives. The Internet of Things has primarily altered the interaction model between intelligent solutions, real-world objects such as home appliances, and electronic gadgets, assisting us to improve our daily lives. As technology enters its golden age, more and more businesses will see IoT as a valuable tool, resulting in widespread adoption.

2022 intends to be another year of turmoil and unpredictability around the world, and IoT technology will undoubtedly provide practical solutions to the various issues and challenges faced by people working on the fringes.

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When Information Technology and Operational Technology Collide

When Information Technology and Operational Technology Collide

Many organizations these days want to build up smart factories due to the advantages they bring to their organization, but they need to first have a proper understanding of what makes smart factories so unique. Organizations must learn that along with its advantages, smart factories also bring in risks and threats that come up due to the convergence of environments – both virtual and physical.

In setting up smart factories, there is a necessary convergence of Information Technology (IT) and Operational Technology (OT), and these need to integrate with the Industrial Internet of Things (IIoT). With a set up like this, now the smart factory is capable of monitoring threats in real-time, interoperability and virtualization. However, with these advantages comes the threats to the expanded attack surface. Cyberthreats now become even more dangerous as they can damage physical equipment and systems. An example of this is the attack surface of industrial robots. In some cases, the software running on industrial robots might be outdated and even more vulnerable.

IIoT is the possible solution to all these threats as it can play an important role in creating and running these smart factories. In order to let IIoT do its job properly, it must be properly embedded into the architecture of the smart factory.

Data-driven Smart Factories

As the title of this section suggests, smart factories are driven by data. How much data can be utilized directly depends on how much raw materials the smart factory has in storage, how quickly the machines can work in production, where the deliveries need to be made and other such factors. This also depends on the type of industry that is utilizing the smart factory.

Thanks to big data, smart factories can create a virtual copy of their physical operations. This allows for prediction of possible outcomes and autonomous decision making. If an organization is not ready for the large volumes of data required to carry out these procedures, it is not ready for a smart factory. The organization should identify the different types of data that will be used and be able to chart out its course. This means from collection to transfer to processing to storage. Charting the course also means to note all the entrance and exit points.

For example, an employee can move the data from their office premises to the smart factory through USB file transfer. An employee can take out terminals for servicing. However, all the tools used should be without any viruses. So, it is not just important to train employees in security protocols but also check that the tools being used are not bugged while they are constantly connected and reconnected to the smart factories.

Channels of Communication

The data being used is shared or communicated through the network that connects the smart factory. These network devices and the cloud might have several vulnerabilities which can be easily exploited if there are no proper cybersecurity measures in place. There might also be denial-of-service (DoS) attacks on the network or malware infections that can be prevented by using the proper cybersecurity measures.

If there are certain IT systems within the organization that are not connected to the smart factory, they should be updated to avoid entry point attacks. They should be properly monitored so that the organization is alert and aware of any incoming threats and can beat them easily.

For those network communication channels used within the smart factory, including those involving industrial control systems (ICSs), the organization must keep a proper note of these channels so they can easily pinpoint the areas of exposure to threats. The organization must be aware of the kind of information being transferred through these channels so that sensitive information channels can have stricter security measures in place. Using strong firewalls, encryption methods and authentication can also avoid intrusion into external channels of communication.


Indeed, security should not only be a major concern in the case of smart factories, but it should also be periodically updated and maintained. All the parts of the factory must be updated with the latest patches and firmware to avoid intrusion.

Where there is convergence of Information Technology (IT) and Operational Technology (OT) in smart factories, there should be a layered security approach that will protect network endpoints and the cloud. This means that every component of the smart factory is protected, especially where there are converged systems.

It is important to note the role of individual employees here. Since employees directly interact with the machine and data, there should be standard operating procedures (SOPs) in place on how to handle the machine and data without disrupting them or weakening security. There must be policies in place regarding how to handle the equipment and systems. For this, representatives from Information Technology (IT) and Operational Technology (OT) departments should be included.

However, these security measures must be implemented at the design phase itself and not at a later stage of building the smart factory. Security in smart factories must always be the first thought. Weak defenses can leave room for threats and cybersecurity risks and negate the profits of the organization in implementing the smart factory in the first place. In fact, if the organization implements smart factories without security in place at the design stage, it might have to spend lots of money in product recall due to unseen threats and risks resulting in a poor outcome.

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Traditional Automation v RPA – Who Wins?

Traditional Automation v RPA

It seems silly to classify technology as traditional or non-traditional, but that is where we are in the automation industry. Traditional automation is the automation of a repetitive task. While both traditional automation and RPA help to automate processes, traditional automation is found in enterprise production lines where parts are assembled. To conduct traditional automation processes, we require application integration at the database stage. It can take months to setup traditional automation process.

On the other hand, robotic process automation (RPA) is used when there are lots of repetitive and rule-based tasks to automate. It is a software and there are RPA tools that are used to create and integrate “robots” built in software that can replicate actions from human beings. These “robots” need a set of rules and pre-defined activities to conduct their tasks. They can be set up to execute many tasks and processes in a serial and logical order without any human intervention.

Let us look at the differences between RPA and traditional automation:


Traditional automation tools such as selenium can be used to automate any processes conducted in web-based applications. QTP on the other hand can be used to automate desktop applications as well as web applications. Load runner is used for performance test automation. Another example of traditional automation is that it helps to log in and log out of the work system everyday when enabled. Each tool under traditional automation serves a specific purpose.

However, RPA works differently to this. Using the RPA software, one can automate any function or process of the business. It works despite the platform being different, so unlike traditional automation, it does not need different platform-based tools. Just like traditional automation, RPA can automate tasks on the web applications, desktop applications, mobile applications and even mainframe applications. It can even automate tasks running on virtual machines. Perhaps there could be a version of RPA which could automate testing as well. A clear example of RPA is the automation of bank account creation. While most companies use back-office executives for this task, others use RPA.

Traditional automation vs RPA

How It Works

Traditional automation tools work on the data layer. They execute the instructions fed into the programme of the tool. They do not look to mimic any human actions like RPA. While RPA not only mimics human actions, but it is also capable of taking the decisions to perform actions once it learns the instructions given to it. RPA goes through the learning phase, after which it becomes capable of decision-making. The decisions are minor.

Programming and Domain Knowledge

In order to get traditional automation tools to work, its user needs to learn programming skills to automate the functions of the tools. Each automation tool is built using different programming languages and the user must be aware of them to use the tools. The user also needs to understand the language syntax. On the other hand, the user does not need to remember language syntax or have programming skills when using RPA. It is like following a flowchart where the user simply needs to understand the RPA functionality rather than any technical language.

For traditional automation, the user must also have domain knowledge of the functionality under testing conditions. Usually, a human tester will define all the scenarios of the automation and then a dedicated automation tester will write the script. This is also a human. Similarly, for RPA, user should have a strong base of understanding when it comes to processes and the domain itself. However, there is no separate person who will define the process and write the script.


Traditional automation cannot work on systems without the APIs to run them. RPA can. So, RPA “robots” can be created to fulfil certain needs that can be as specific as needed. Combining RPA with calendar, email clients, CRM and ERPs is a great way to synchronize information and generate automatic responses. So, in terms of customization, RPA wins!


RPA is used by businesses to eliminate the need to run boring and repetitive tasks by humans. When a bot does this repetitive work, humans are free to work on something that requires more skill, creativity, and effort. Indeed, RPAs can be used to eliminate errors and generate precise outcomes. Businesses can then focus on a better Return of Investment (ROI).

However, setting up RPA is going to cost way more than setting up traditional automation at the beginning. In the long run, the maintenance and effort needed to run these two clearly show that RPA is the cheaper option. In the long run, traditional automation costs much more than RPA.

Using Traditional Automation and RPA Wisely

While RPA looks like it comes out on top, it cannot fully replace traditional automation. RPA might be the better choice for a lot of applications, but traditional automation still has its place among niche systems and tasks. Traditional automation, for example, would help transfer files between systems more efficiently and quickly than RPA.

However, if a business needs to automate a process in their system such as data access, invoice creation and order processing, it can use RPA. This is because RPA works on the UI and helps desk job employees work faster and more efficiently. It can work 24×7, something that a human employee cannot do.

Hence, though RPA might look like the better option, traditional automation is not out of the running yet and it might never be.

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Hadoop and NoSQL for Data Storage and Management

Hadoop and NoSQL for Data Storage and Management

Managing Big Data Using Hadoop 

Hadoop is an open-source framework used to store and processes big data. Being a distributed file system it allows concurrent processing and fault tolerance by storing data on inexpensive commodity servers that run as clusters. Hadoop is adopted by organizations to manage and analyze Big Data in a structured and unstructured format. In this digitally evolving world, with a lot of data being analyzed every minute, Hadoop has become the go-to data management tool for businesses. A lot of top-tier organizations include Hadoop in their operations for efficiency. 

Storing and analyzing a massive amount of data coming from a variety of sources becomes an unmanageable and unachievable task. Hadoop helps companies solve this problem by managing raw data efficiently and cost-effectively, performing robust analytical data management and analytical tasks. Behind this ability of Hadoop lies the contribution of core components that influence the activities of Hadoop as necessary. Organizations typically limit themselves from collecting different forms of data. Thus, with Hadoop, organizations can store any desired form of data, irrespective of its structure. It gives organizations more room to gather and analyze data to gain maximum insights on market trends and consumer behaviors.   

Hadoop proves to be a cost-effective option because it is open-source in nature. Hadoop tools are highly efficient in collecting and processing large amounts of data. They are known to be cost-effective measures for storing and processing a huge pool of data. Organizations can remarkably save by applying Hadoop tools. Data storage that could cost up to $50 thousand will only cost a few thousand by applying Hadoop tools. According to researches, there is going to be increased adoption of Hadoop in the coming years. Rather than replacing other databases, Hadoop will become an important part of data ingestion in the digital world.
Hadoop and NoSQL for Data Storage and Management

 Major Business Benefits of Hadoop 

Hadoop is playing a notable role in today’s life. Any sector can adopt this technology as per business requirements. Below are the uses and benefits of Hadoop. 

Customer’s Requirement Understanding 

Sectors like Finance, Telecom, Retail, and E-com, etc. can make use of Hadoop to find out the requirements of the customers by analyzing bid amount of data and extracting important information from these large amounts of data. Thus, businesses can use this technology to find out the exact customer for their product. They can also keep a keep a track of users for their previous journey according to the information collected and based on the customer interest, organizations can provide better recommendations to their customers. 

Understanding and Optimizing Business Processes 

Hadoop can optimize the performance of the company in various ways. For example, an E-com can customize its stocks using predictions came from different sources, and based on this company can make the best decision to improve their business. Many organizations use this technology to monitor the behaviour of their employees. This helps lots to take HR decisions in case of issues between the employees.

Future Proof 

Since Hadoop allows concurrent processing by storing data on inexpensive commodity servers that run as clusters, when data is sent to a particular node in a cluster, it allows for the data to be replicated to other nodes in the cluster. So, even if the data gets lost or destroyed, there will always be a copy available on the other node.  

Complete Security and Authentication 

Hadoop restricts access to only the trustworthy employees of the organization. Thus, ensures the extensive security of the system. The security parameters if Hadoop works like a shield against threats from outsides. By using Hadoop, organizations can always ensure safety in their operations when compared with enterprises that use rudimentary methods for security. 

What is NoSQL? 

NoSQL also referred to as Not Only SQL is a different framework of databases. The requirement to analyze data in huge volumes, coming from variety of sources has given rise to NoSQL. It can be used in real-time data analysis and IoT. Many organizations are deviating towards NoSQL as a cloud-friendly solution to their big data challenges. The choice between NoSQL and relational DBMS (RDBMS) depends upon your business’ data requirements. NoSQL proves to be a beneficial option for organizations whose data workloads are more towards processing and analyzing of large amounts of varied data. 

Benefits of NoSQL  


Growing storage and computing capacity is simply a subject of adding more commodity servers for NoSQL databases. It gives an effectivestructure that scales out horizontally. 


Because of the accelerated and voluminous rise in data, NoSQL incorporates a wide array of different database technologies for performance and processing. NoSQL offers advantages such as high performance, scalability, and availability. 

Flexible Schemas 

NoSQL databases have flexible schemas. Instead of applying the schema to writing, NoSQL applies the schema to reading. This makes it uniquely suitable for today’s high volume, high variety of data, and online applications. Thus, it makes businesses more agile and more flexible in storing, retrieving, and processing huge amounts of data.  

Are NoSQL and Hadoop Competitors? 

Hadoop and NoSQL are both closely associated with big data. They are both great for managing large data sets and handling a variety of data formats. It might seem that they are competitors, but the truth is, they are not. Although they are both used for big data handling, both are designed for different workloads. Hadoop is suitable for analytics- and historical archives use cases, whereas NoSQL is fit for operational workloads. In simple terms, NoSQL is a distributed database infrastructure that handles a massive amount of big data, whereas Hadoop is a file system that allows for huge parallel computing. 


The big data explosion is causing organizations to store, manage, and analyze data in a better way for competitive advantages. With all the above benefits, the combined strength of Hadoop and NoSQL can be a powerful and effective solution for the company’s current and future big data needs. 

MSRcosmos with its extensive expertise helps you architect the right solution for your big data needs by addressing the scale and agility needs of your modern applications.  

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