Machine Learning & AI Archives - Crafsol-Technology Solutions

GenAI Adoption and Compliance for Streamlined Integration

In a digital age of profound change, companies adopt GenAI as the competitive advantage and as a tool for optimization and innovation. As machine learning is a subset of artificial intelligence, the creation, forecasting of outcomes, and betterment of decision-making processes encompass the applications of things by GenAI. Focus has shifted from fully tapping the technology to adhering to a set of regulatory standards and ethics for gen AI across various organizations.

This brings new compliance issues, which need to be dealt with by the organizations considering this adoption. Effectively getting over the obstacles is crucial for organizations to manage the full potential of this GenAI, while minimizing risks associated with data privacy, bias, and ethics. In this blog, we’ll steer businesses toward a healthy GenAI adoption process with compliance strategies that integrate it seamlessly and responsibly.

Understanding Generative AI and Its Business Applications

Generative AI refers to a method of generating new content based on algorithms and deep learning models, including text, images, music, and even code. GenAI creates nearly any form of creative output with the help of NLP, GANs, and machine learning to mimic human patterns.

Examples of GenAI in Practice:

Content Generation: Applications like ChatGPT, for instance, which powers the companies open AI; with its ability to produce human-like texts in generating human-like texts to use for chatbots, automated responses to consumer questions on customer service, and content marketing.
Image Generation: Platforms like DALL·E and Midjourney enable the GANs to generate very real and precise pictures from text-based descriptions, thus revolutionizing fields in graphic design, advertising and e-commerce.
Music Composition: GenAI is used by the Amper Music and AIVA for composing original music tracks. It can help content creators, game developers, and marketers to have entirely new soundtracks for their projects.

GenAI touches industries across the board. Customer support evolves through AI chatbots employed by companies such as Zendesk, offering instant answers and personalized experiences. At the same time, content generation platforms derive from GenAI, using it to compose advertisements, social media posts, and reports. In data analysis, GenAI predicts market trends, enhances decision-making, and automates repetitive tasks so that businesses can focus on strategic growth.

The Challenges in the Adoption of GenAI

Though full of potential, GenAI still has some challenges in its implementation. Some of the primary concerns include the following:

Technical Hurdles: The implementation of GenAI in the existing business dynamics will require solid infrastructure, high-quality data, and expert professionals. For instance, companies like Netflix use their GenAI systems to build recommendation engines, but achieving accurate personalization at scale heavily depends on the investment in data infrastructure and machine learning competence.
Ethical and Regulatory Issues: The adoption of GenAI has also been hindered by risks of bias and lack of transparency of systems. Facial recognition software such as used by Clearview AI appears biased against certain races and ethnicities, or was criticized for allowing racial biases to exist; thus, greater transparent, fair AI systems are needed. There is also a considerable data-privacy concern, especially in case the system deals with sensitive customer information.
Industry Standards Compliance: Companies depend upon their respective industry standards with respect to data utilization, security, and transparency. For instance, financial institutions implementing AI for fraud detection alert systems have the compliance burden of the Sarbanes-Oxley Act in the US and the European General Data Protection Regulation (GDPR), among other regulations. The customer information would need to be processed responsibly and securely by AI algorithms.

Integration of GenAI and Compliance

Compliance is very important so that GenAI systems do not fall out of the law and ethics framework. The significant regulatory frameworks which govern GenAI deployment include:

General Data Protection Regulation: This EU regulation emphasizes data privacy. Companies have to ensure the privacy of personal data they collect through AI systems. Companies like Facebook and Google were penalized under GDPR for their improper handling of user data.
California Consumer Privacy Act (CCPA): CCPA requires businesses operating in California to be transparent in collecting data and provides consumers with control over their information. The impact of CCPA was reflected when Apple decided to alter its app store privacy policies under CCPA.
The AI Act: Set to Introduce Regulation of AI Technologies from the European Commission in Order to Make Them Reliable, Transparent, and Used Properly These regulations point towards the high-risk sectors like healthcare and finance.

This will lead to massive fines, reputational loss, and demise of consumer trust, so compliance is in the DNA of any long-term success in GenAI initiatives.

Compliance Best Practice for GenAI

To achieve streamlined integration, GenAI has best practice compliance as follows:

Data Governance: Ensure the quality, security, and ethical sources of data used in AI models. Data anonymization techniques should be applied to conceal sensitive information, and resultant datasets need to comply with relevant data protection laws. For example, Microsoft Azure AI gives integrated data security and compliance features that help businesses manage their AI data safely.
Bias Detection and Mitigation: Thus bias in AI may lead to unfair outcome. Organizations, such as IBM, are working ahead on this challenge by designing AI fairness toolkits to help businesses test for biases and improve them either by means of re-sampling, data augmentation, or adversarial training.
Transparency and Explainability: To gain the trust of the consumer and fulfill the current regulatory requirements, there is a need for transparency and explainability in building explainable AI (XAI) models. For example, FICO, the global leader in credit scoring, has been using explainable AI techniques for building a model that enables its customers to explain how the factors can affect their scores.
Continuous Monitoring: AI must be continuously monitored and audited to ensure it is up to date with the ongoing changes in standards and regulatory norms. Companies such as Salesforce use AI lifecycle management tools to maintain fresh models, maintaining fidelity to requirements for compliance.

Framework for Easy Integration of GenAI

GenAI implementation in business operations is not easy; it requires a structured approach within an organization if businesses are interested in doing so.

Assessment and Planning: Start with evaluating the needs of your organization, defining clear objectives on how GenAI will be used, determining data requirements, possible use cases, and expected outcomes. This is what Airbnb did in developing personalized search algorithms with GenAI, so users are better understood by the company.
Teams must collaborate: The integration of GenAI involves data scientists, compliance officers, IT specialists, and legal teams. And, ultimately, this multi-disciplinary approach-precisely the approach that Johnson & Johnson has pursued with regard to the technical and compliance side of its AI-driven drug discovery efforts-will make all the difference.
Agile Methodologies: Agile methodologies help in doing iterations quickly, fast deployment, and adaptive response to problems. Agile approach helps refine AI models and lets the organism update real-time feedback just like Spotify, which continually updates its recommendation algorithm.
AI Lifecycle Management Tools: These AI lifecycle management platforms can observe the performance of the model, usage of data, and also check whether it remains in compliance. They help in managing scalability and ensure that GenAI models evolve based on changing business needs, as seen in platforms like DataRobot and H2O.ai.

Future Trends in GenAI and Compliance

As the GenAI technology is continually developing further, new trends are surfacing that will make it shape its future:

Emphasis on Ethics of AI: In such a scenario, the culture of responsible AI practices will gain more importance with an increasing tendency toward openness, accountability, and right AI development. Self-proclaimed tech giants such as Google have appointed boards for AI ethics meant to oversee AI initiatives for them to become aligned with the principle of responsible AI.
Global Regulatory Alignment: The globally aligned approach by which the AI regulations are becoming set, realizing global standards for AI compliance. Institutions like the European Union have been on the frontline in trying to present what will be “non-harmful, reliable, and harmonized” regarding any regulation for different AI technologies through the European Commission’s AI Act.
AI-Powered Compliance Solutions: AI itself will bring compliance processes to a streamlined web. In the future, there may be a more significant use of automated tools for regulation reporting, data analysis, and risk assessment to make organizations compliant with minimal human interference. For instance, Cognizant has leveraged AI to develop its compliance platform to track regulatory changes automatically.

Conclusion

This really offers the best opportunities for enterprises to innovate and be ahead of the curve. However, when it comes to compliance, non-compliance is the major risk factor as concerns legal risks, customer data, and the development of trust in AI systems. Through application of best practices on data governance, bias detection, transparency, and continuous monitoring, GenAI will be put into business operations in the streamlined way.

Organizations, therefore, have to keep innovating with change in AI regulations and update strategies to the mark of compliance. Thus, companies will realize the full power of GenAI by fueling innovation, ethics, and sustainable growth.

Artificial Intelligence (AI) in Healthcare

Dr. AI – How it is impacting healthcare? and Covid-19 in China?

Technological change is rapidly sweeping the world today. On one hand there is the digitalisation of many services, and on the other hand, there is Artificial Intelligence which is redefining various aspects of life. From education to production and logistics, AI is transforming the way we work, play and live. The field of medicine has not remained untouched by technology. Digitalization is already reshaping the age-old doctor-patient relationship, and now, Artificial Intelligence is poised to bring tectonic shifts in medicine and healthcare.

More than 32,000 potential cases of Covid-19 have been detected by using AI software to expedite diagnosis.

Chinese hospitals are deploying artificial intelligence to detect visual signs of the pneumonia associated with Covid-19 on images from lung CT scans. As of February, around 34 hospitals in China were using software to diagnose Covid-19 cases. The software used data fro over 5000 previous diagnosis to successfully detect Coroavirus differences in CT scans with an accuracy of 96% within 20 seconds.

Here’s a look at how AI is changing medicine

Digital Diagnosis

With systematic integration of AI, Deep Learning, and chat-bots, consultation has really advanced to the next level. AI enabled chat-bots are able to ask relevant questions and help doctors with correct and detailed information to provide accurate diagnosis in the shortest possible time. With Deep Learning processes even complicated and unstructured inputs can be transformed into accurate information and utilised to achieve a quick resolution. On a lighter note, AI can save doctors from tasks such as writing notes and reading scans, allowing them more time to connect with the patient.

Medicine Selection

After consultation, AI further helps doctors to quickly and efficiently select medicine to prescribe. It could offer pointers about whether the medicine is available near the patient’s location. AI could help quickly diagnose and treat common childhood conditions. It’s expertise in recognising patterns could also help predict whether an individual could develop Alzheimer’s. Similarly, AI could play a vital role in the treatment of potentially terminal illnesses such as cancer.

Robotic Operators

The use of tools such as robotic arms for surgeries goes to show how robotics has impacted medicine. The entry of AI into the operation theatre could further improve the efficiency and accuracy of surgeries. It could help doctors choose the right tools, and provide real-time data and risk-assessment while the surgery is in progress

Follow-up

Many a time, the follow-up visit to the doctor becomes more of a hassle for the patient than the actual treatment itself. Doctors find it difficult to devote enough time to follow-up and patients struggle to get an appointment. AI-enable chat-bots could help resolve many such issues with follow-up. As data about treatment is stored digitally and could be accessed easily, AI-enabled bots could instantly solve the patient’s problem. This could help the follow-up checkups hassle-free for both patients as well as doctors.

These are just a few areas of opportunity where AI could revolutionize medicine. There is a lot of ground AI expected to cover over a period of time. More accuracy is to be achieved while ensuring operational efficiency. Being a veteran player in the healthcare sector, Crafsol is committed to working towards the betterment of the healthcare industry in every way. The company’s leadership has deep expertise in healthcare and pharmaceutical domains and aims to play a pivotal role in driving innovation in this field.

Integrating Machine Learning to your SAP HANA Platform

Artificial Intelligence is one of biggest drivers of today’s digital world. AI has already become the part of our everyday life.

Machine Learning is a subset of AI that empowers the system to automatically learn and improve from experience without being explicitly programmed. The success of Machine Learning depends on the quantum of data. It requires large sets of data augmented with a whole range of algorithms.

As technology advances, enterprises are migrating or have already moved to SAP ERPs, so for better success it is best to sync Machine Learning with your SAP database.

Running Machine Learning algorithms where your data resides—in the database—can help reduce latency and alleviate other delays that arise when copying data to another server.

The tango between Machine Learning and SAP

SAP HANA Predictive Analytics

SAP Hana’s Predictive Analytics provides machine learning capabilities through in-memory database. SAP Hana predictive analytics library (part of AFL) provides data analysts and developers with automated, wizard-driven machine-learning capabilities and it can create algorithms to build predictive models and provide better controls for the data scientist.

PAL also has several algorithms that ML learns and continuously updates for dynamic predictions.

Open-Source Machine Learning

SAP Hana also facilitates open source machine learning frameworks, R integration is one of them. R is an open-source programming language designed for statistical computing and advanced analysis. Users can write R code in HANA and then mix and match those programs with PAL Algorithms.

Main advantage here is that businesses can utilize the power of SAP HANA and user-expertise for scalability as well as performance through R scripts

HANA inbuilt Machine Learning Integration

SAP’s Extended Machine Learning Library (EML) provides machine learning inference on data at rest or in motion. ML models can be built in tens or flow using python packages to build complex deep learning integration.

Deep learning brings in ability to solve complex inspection and build algorithms, Identifies every defect outside if the set tolerance and processes prediction at very fast pace.

Crafsol Technology Solutions has in-depth experience in SAP implementation and has also worked on some of the challenging Machine Learning projects, If you wish to take best advantages of ML from SAP platform, get in touch with our experts.

How SMEs are benefiting from Data in ‘Big’ way?

Data has a pivotal role to play in today’s technologically advancing world. Data is deriving valuable insights and speeding up decision making for businesses. Efficient use of big data can thus change the way businesses operate.

Estimates suggest that by the end of 2020, there will more than 40 times more data as compared 2015. Multinationals and large conglomerates are already taking advantages of big data. However, small and medium enterprise are also making efforts to utilize the potential of big data.

Big Data and SMEs

Due to their infrastructure, processes and systems, large organisations are better placed to generate and utilize big data to gain better insights. However, it is a struggle for the SMEs. As the volume of their business is small, the volume of data they generate also tends to be small. Big Data can still offer a number of benefits to SMEs. It can help SME make their businesses more agile, analyze and predict in consumer’s behavior and plan their product accordingly.

What could SMEs do using Big Data?

Analyze markets and forecast its trends
Predict price fluctuations
Define buyer personas and profile customers for marketing
Run and manage marketing campaigns
Improve and develop product design

Challenges in choosing Big Data

When it comes to SMEs, biggest challenge is uncertainty about return on investment. Many a times, SME go for average IT infrastructure which reduces their efficiency. Even in cases where SMEs can invest they may not know how to choose the right technology. In such matters, two points need to be considered.

Selecting the right tools
Selecting the right implementation partner.

A knowledgeable and experienced implementation partner plays key role in the success of Big Data. A partner who has experience in leading the projects at scale of SME can understand the challenges faced by SMEs and create meaningful difference with desired end-results for small businesses.

Talking about tools, open source tools like Hadoop, or Spark can act as boon to SMEs. Hadoop can process large sets of structured as well as unstructured in nature. processes in real-time for creating delightful insights for SMEs. Crafsol helps businesses to create easy and efficient digital insights engines to gain an competitive edge.

Get in touch with our expert team to avail our services.

Machine Learning & AI for Cyber Security

In last decade we have seen great development is Machine Learning, Artificial Intelligence and its impact in everyday lift of Human ecosystem.

AI and ML typically helps machines or systems to automatically learn and improve from experience without being explicitly programmed. ML can build algorithms on data that is classified or unclassified, with partial information or no information been provided.

Cyber security also is one of the area where AI and ML can play pivotal role. lets take an example, if your computer or machine is made able to to decide what is good for it and what is bad for it while accessing, this way one machine itself finds new Malware or Anomalies when it faces for the first time.

AI and ML can be used to build this capability, it can identify and safeguard the systems from ever changing cyber world. Traditional security tools may run short to point out the control the cyber attacks.

Building the system that can fight threats

Malware or Cyber attacks always evolve themselves, hacker constantly build upon their previous works, adding new features, upgrading abilities to crack current systems that are blocking its attacks.
AI coupled with ML learns from the information available from previous attacks (data) and build algorithms to predict, identify and control future ones which can be similar category or style

Layered Defence

Basic principle of Cyber security is creating a defence that layered and is in depth. Keeping everything updated such as constant updates, scanning computers every time and everything user accesses is one thing.

Quickly scanning the content user is trying to access, identifying if it is good or bad is one element that every cyber security tool has in build right now, however time involved in doing it is quite time consuming.

this is exactly AI and ML can make the difference, A properly-trained AI/ML model can deliver decision on good or bad files in just few milliseconds.

Resource optimization

Applying machine learning and artificial intelligence to improve cyber security saves an organisation a considerable amount of time and money that would have otherwise been spent by cyber security experts.

Machine Learning quickly excess large pool of data instantly and learn and analyze from it. Systems generate lot of alerts and unautomated attention can buildup lot of work security team. ML can learn from historical data and create resolution on its own.

Artificial intelligence and machine learning is all set to become one of the front runner of next-generation security, enabling elevated degrees of cyber security.

Crafsol ML and AI services builds highly advanced and customized solutions. We are focused on improvisation of the algorithms after the machines have undergone some experience in identifying objects based on defined attributes which makes our approach unique.

EHS – Disruptive Technologies that changing the way it operates!

Awareness about Environment, Health and Safety has been incremental over last couple of decades and lot has been invested proving EHS issues. Tracking injuries, transportation mishaps, leading indicators, and related environmental incidents are not easy.

Identifying the reason behind occurrence of particular incident, developing preventive measure is one of the key challenge for the companies.

However, the age we live in is poised to take that transformation several notches up, Huge investments in technologies like AI, ML and Big Data are being made by companies to achieve operational efficiency, these can very well important role in EHS as well.

In this blog, we will look at how ML, AI and Big data helps companies to improve their EHS department and which areas it can positively impact.

Artificial Intelligence:

AI is practically automating many tasks, and execute these task in effective and reliable model. This has released EHS executive to more valuable tasks.
Alongside, advanced AI systems are capable of integrating company’s data monitoring system, understand regulatory norms, and generate suggestions, prediction and actions to take for better decision making.

Big Data:

EHS has huge data, large amount of data is generated from sensors, control and monitoring systems and interpreting the same was big challenge in earlier days. With Big Data, companies get deeper insights and understanding of their employees, their behavior, operations, incident occurrence patterns, etc based on which EHS departments within a company can take preventive action, optimize reporting to improve performance of EHS department.

Machine Learning:

Machine Learning is everywhere in the businesses now, playing an important role all over different departments, in EHS as well, ML has great role to play for example: Industrial Hygiene, ML can offer a practical method to process data to build a predictive modelling resource, delivering a significant improvement in efficiency.

Let’s look at some of the factors where these technologies can be of great help

Occupational Safety

Big data & machine learning offers lot of insights when it comes ti identifying several risk factors connected to accidents, mishaps and incidents. Based intensity of risk a corrective actions can be planned. This proactive outlook helps companies to improve safety as well as productivity.

Risks analysis

Predictive analytics coupled by artificial intelligence, ML helps companies in identifying areas of risks, typical behavioral issues, typicality in mishaps etc. Utilizing predictive technologies enables management to be more proactive and opportunistic in the way they manage their operations. ML provides insights on the past data, and build realistic assumptions about when is it likely to happen.

Waste Management

Managing wastes at factories is a laborious job, ML helps to identify when a high quantity of waste is generated during the production day. Alongside, it also helps manufacturing units to visualize the location, quality, type of waste that will be generated

These just few benefits listed here, while the list can long and long like predictive maintenance, EHS Audits etc. At Crafsol we strongly believe these disruptive technologies are changing the way EHS can operate.

Interested in learning more? Get in touch with our experts to know more.

Optimising Customer Support Services with Machine Learning

Enterprises have been using Machines to improve efficiency and productivity for long, though it may be advance machinery or shifting to robotics or cobots to further improvement, the need to increase efficiency is consistent. Machine Learning is the next thing which is helping enterprises to use the machines to improve and work more efficiently.

Customer Support is undoubtedly one of the biggest cost centers in modern-day operations, for big or medium scale businesses.

And Customer Support is also considered as one of the most complex parts as well.
Well does it really need to be one? the straight answer is ‘No“

Machine Learning is already set to bring in a major role in resolving challenges in a customer support function.

What is Machine Learning (ML)

ML is one of the parts of artificial intelligence (AI), it is a set of techniques that provides systems or computers an ability to automatically learn without being actually programmed. It can read the data whether it may be structured or unstructured and discover insights.

How does it help in Customer Support

When it comes to Customer support ML provides a higher level of convenience to the customers and efficiency to the support staff.
CS means a lot of data, of which the major part is an unstructured one. This data that is gathered through everyday conversion and communication which contains deep insights, ML when used intelligently can be helpful in understanding the customers, their thinking, wishes and so on.

How is Machine Learning used in the CS environment?

Here are quick top picks

Understanding the Intent

Most of the time your agent does not know why is customer contacting you or where probably he is asking for help.

Machine Learning can help CS agents predict the same. Machine learning collects data about a customer’s previous browsing, geo-events, contact pattern, and other online behaviors. That allows agents to plan and prioritize the customers need. In a way your agent provides personalized services to the connecting customer, assuring elevated user experience.

Timesaving – a win-win situation

Well, what is mostly observed by the researchers, people prefer getting in touch with customer support is through chat or messaging app option. that’s where chatbot comes in the picture.

While, chatbots are something that is equally disliked by people, when used intelligently it can have a greater impact. for instance, if a chatbot can extract what exactly the customer is looking from the text types, further tag it correctly, to direct it right customer support specialist, can bring in a win-win situation for both.

it saves time for the customer, saves time for an agent, reaches the right SME and improves the overall experience

Predictive Service

With IoT, companies are now able to practically track their devices even while the customer is using it. For example, a Smart Machine connected to the internet can send back a signal to the manufacturer when a fault or unusual operation takes place. The manufacturer can get back to owning the company to inform arising issues with the required solution.

Virtual Assistance

Virtual assistance is helping customers during their journey, providing insights to customers as well as feeding the analytic program at the back-end for future reference.

ML can help a enterprises to virtually assist the customer right from buying stage to finding out resolution when he is facing any issue. ML can also help in pushing a product which customer has checked or liked based on his online behavior, it can trigger alerts or updates to the customers.

While these are only a few things listed here, there is a long list where ML can help in optimizing Customer support.
To list a few – customer data capture, classification, proactive emailing, perform root-cause analysis of repetitive instances, reduce interaction time.
Crafsol with its ML and AI services has strong experience of building custom solutions for enterprises.

Get in touch to know more how we can help you in your fasten your customer support services.

https://www.zendesk.com/blog/machine-learning-used-customer-service/
https://freshdesk.com/customer-support/machine-learning-optimizing-customer-support-blog/
https://www.forbes.com/sites/forbestechcouncil/2019/04/24/using-machine-learning-to-improve-customer-service/#298ccf985892
https://www.cognizant.com/perspectives/how-machine-learning-can-optimize-customer-support

Pharma 4.0 – A Wave in Making!

Industry 4.0 has brought lot of change in many sectors like transportation and logistics, manufacturing, aviation, and oil and gas production. New waive that is coming up is Pharma 4.0 i.e. Implementation of Industry 4.0 in pharmaceuticals

Understanding Pharma 4.0

Similar to Industry 4.0, Pharma 4.0 refers inter-connectivity, automation, integration of AI and ML to make it possible to gather and analyze data across machines, enabling faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs.

Why to move towards pharma 4.0

Remaining competitive, ensuring to keep market place in tact is pushing pharma manufacturers to improve productivity ensure better control on monitoring. harma 4.0 technology allows for continuous, real-time monitoring of manufacturing processes so any drift away from specified parameters can be predicted and rectified before it turns into a deviation, avoiding the associated down time and loss of product.

Wining over existing control strategies

Pharma industry always had automated process control right from beginning of 90s. But this relatively old system which only alerts when already damage is done. Pharma 4.0 on other hand is proactive in nature. By using sensors and using continuous monitoring techniques, integrated with AI and analytics provides predictive analysis and a world of other business insights that are currently unavailable because they’re buried in unstructured, dispersed, incomplete data.

Role of Big Data

Big data analytics draws data from sources that have traditionally been disconnected from one another, and looks for relationships and trends that were previously undetectable. For example, data from the online inspection system described above can be combined with that from equipment maintenance and engineering systems to streamline maintenance schedules; combining production data with that from sales and dispatch systems can streamline production planning.

How will this transform to a waive

Although pharma is very precautious industry, which requires tighter control and is coupled with very strong statutory norms. However, still Governments, regulators are taking every possible step help enterprises to move towards Pharma 4.0 soon Pharma 4.0 is be waive in itself

Crafsol with its leaders, who have strong understanding of pharmaceutical industry, is ready and keen to help companies to start Pharma 4.0, get in touch with our team now.

How to choose the right IoT Platform?

Choosing right IoT Platform can be a challenging task, coupled with confusion due to thousands options available. Choosing the right IoT platform isn’t as easy as it seems at first. There are numerous categories of IoT systems to consider, all of which offer more or less advanced options depending on your needs.
In this blog we are trying to guide to choosing the best IoT platform for your needs.

Factors to consider

While selecting an IoT platform, one has to consider some factors, These factors will help you narrow down applicable platform, like:

Reason of implementing IoT
What will be your scability needs
What kind of support you need
Does platform that you are selecting adheres to good security parameters

Keep in mind, platform that you are selecting is going to be huge investment and you need to be extra sure before doing it. While above factors help you narrow down the selection process, There are 3 – 4 aspects you need to consider or evaluate before choosing the platform

ROI

While implementing any new tool in the business ecosystem, first point that come in mind is end result and ROI. In this aspect one needs to ensure proper road map is created, even before the platform is selected. Performance metrics are well defined and attributed to respective KPI in the implementation process

Application Industry

Probably even more important point than ROI which needs attention is ‘Application‘ Requirements will drastically change based on application industry. Just to take example medical field will require 100% reliability considering the criticality attached to it. Offshoring will require consistent operations and global connectivity. considering similar requirement for your application than selecting a platform makes real sense.

Security

One needs to take security very seriously while Implementing IoT
IoT devices have become a massive and popular target for cyber attack and during migration when data is in in-transit mode it is even more vulnerable. Unsecured IoT can lead to major risks such as financial losses, info leaks, reputation losses

Bandwidth and Cost

Well, while implementing we may not find this important, however it is very critical aspect when it comes to operational success later on. For ex. Some businesses need lot of data transmission where as some would need lot of storage. Bandwidth will also important with respect of getting desired result of the IoT. One needs to consider current requirement, growth expectancy and capability for Platform to support both.

Data Delivery

if your desired platform is just going to deliver data from a single or two sources, than its point which needs your attention. Consider a situation where your delivery source had a crash, like power outage, connectivity errors or any other case, your system will be unable to work or work in negative manner. Select a platform which offer multiple delivery methods like cloud, on-premise or edge.

Choosing an IoT platform is not easy, but taking the time to research and compare different services could be a game-changer. Crafsol has inherited capability to understand your business action as per define road map and KPI

Get in Touch

5 Trends Industry 4.0 that will drive Manufacturing in 2020

Industry 4.0 and allied technologies are already driving a huge change when it comes to Manufacturing. Although technologies in manufacturing are advancing at very fast pace industry 4.0 is already to set to take it to the next level.

Industry 4.0 has already benefiting manufacturers by increased visibility into operations, substantial cost savings, faster production times and the ability to provide excellent customer support.
As Competition is growing, The only way manufacturers can stay ahead of competitors and win market share is getting best out of technologies. Those companies who want excel and not just survive are on set to ripe best out Industry 4.0

In this month’s Newsletter we take a look at 5 trends that drive manufacturing in 2020

Digital Twins

Although digital Twin technologies have been around 2003, real utilization is up when IoT came into the picture. Digital twins are virtual replicas of physical devices that data scientists and IT pros can use to run simulations before actual devices are built and deployed. They are also changing how technologies such as IoT, AI and analytics are optimized.

IoT

Manufacturers are already leveraging the benefits of IoT by connecting their existing manufacturing infrastructure to internet using unique devices. In 2020 more and more companies even from SME’s will invest in IoT to leverage more informed decisions to achieve increased efficiency, improved safety, meeting compliance requirements, and product innovation.

Increased use of Cobots

Markets have welcomed Cobots positively and some research show that by 2025 investment in cobots will be 12 billion USD. Cobots create opportunities for manufacturers to improve their production lines, increasing productivity while keeping employees safe. Cobots are compact and affordable and easy to use.

AR & VR

Assistive technologies, such as augmented reality (AR) and virtual reality (VR), will continue to create mutually beneficial partnerships between man and machine that positively impact manufacturers. These technologies help businesses work more effectively while also making them more efficient. Helping in several ways like product design, production line development, driving OEE improvements, technical and engineering support, training, team collaboration, inventory management, and more.

Smart Factories

Businesses, to stay competitive, are striving hard to make their production lines more efficient, effective and utilize the resources to fullest all this to achieve better productivity.

The smart factory represents a leap forward from more traditional automation to a fully connected and flexible system—one that can use a constant stream of data from connected operations and production systems to learn and adapt to new demands.

While above is just a quick list of trends that will be seen in next year, it does show the opportunities that exist in 2020 and beyond for manufacturing companies with a forward-thinking and innovative outlook.

Want to know more about how Crafsol can help you?
Get in Touch.