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Internet of Things (IoT) sensor trends reflect a rapidly expanding IoT market.

Emergen Research reports that the global sensors in the IoT devices market will reach a market size of $205 billion in 2028, a compound annual growth rate of 30.8%. The firm attributes much of this growth to increased adoption of technologies like wearable medical devices and the evolution of smart factories and manufacturing automation. 

IoT sensors are used to monitor a wide range of qualities related to a virtually endless list of potential applications, from heart rate monitoring to livestock health in the agricultural sector. Among other qualities, IoT sensors can monitor:

Temperature

Humidity

Pressure changes in gases and liquids

Proximity (for example, monitoring and reporting on the number of open rest area spaces for semi-trucks)

Levels of fluids and other materials

Acceleration

Velocity

Chemical presences 

Infrared health sensors that monitor blood pressure and other health markers

Optical sensors in smartphones, autonomous vehicles, and more 

This article will take a look at five IoT sensor trends helping to drive this sector’s growth:

See more: The Internet of Things (IoT) Sensor Market

artificial intelligence (AI) of things (AIoT) combines the technologies of AI and IoT by embedding AI into IoT components. Connected sensors and actuators that include AI help reduce network latency, improve privacy, and deliver real-time AI-driven insights to the cloud and edge computing servers.

AI-enhanced IoT goes beyond providing data; this kind of IoT can actually trigger actions as sensors deliver data. Examples include robots used in manufacturing, autonomous vehicles, real-time retail analytics, and smart thermostats.

As reported by Deloitte, IDC predicts that soon, AI will support all “effective” IoT initiatives and that without AI enhancements, data from IoT deployments will hold only limited value. 

See more: The Artificial Intelligence Market

IDC reports that the manufacturing segment has invested nearly $200 billion in IoT spending — a figure twice as high as the second-largest IoT vertical market, consumer IoT. Smart factories play a significant role in these investments.

IoT-enabled “smart” manufacturing (sometimes referred to as Industry 4.0) gives operators and producers much more visibility into their assets, processes, and resources. Data from sensors and machines are communicated to the cloud by IoT sensors and devices and then analyzed by IoT software platforms. Ultimately, these insights can help companies improve overall revenue. 

Known collectively as “healthtech,” wearable medical devices deliver sensor-gathered data insights about patients to their medical providers. This technology gained more traction with patients and medical professionals alike during the widespread COVID-19 pandemic lockdowns in 2023 and 2023. 

Insider Intelligence reports that more than 80% of consumers are willing to wear fitness or medical technology. 

Among the many dozens of medical wearable IoT devices, blood pressure monitors have emerged as especially effective. Omron Healthcare, for example, launched its HeartGuide wearable in 2023. 

Modeled after smartwatch designs, the device measures blood pressure and activities like steps taken and calories burned. The information is stored in memory and later transferred to a corresponding medical app, where the data can be shared with medical providers. 

IoT wearable healthtech devices are changing how we receive health care and how medical professionals, health insurance companies, and service providers make decisions.

See more: The Internet of Things (IoT) in Health Care

Advances in microprocessor technology allow IoT sensor manufacturers to create smaller, cheaper, and faster chips for use in connected devices. One prominent example is Qualcomm’s investment in this technology sector. 

Some high-end IoT sensor chips can even run robots that pull items in warehouse environments. 

The newest IoT sensors are helping companies manage the massive volumes of data required to perform business intelligence (BI) and big data analytics by communicating with edge computing servers. This is an improvement over cloud-based IoT, where servers are often located far from sensors, resulting in higher levels of latency and much slower data processing. 

IoT sensors can deliver analytics algorithms to edge servers, enabling data processing to occur locally or to aggregate data before sending it on to a centralized site for deeper analysis or storage. 

Sensors working within edge computing environments are vital for use cases requiring autonomous decision-making in real time (like health monitoring devices and self-driving cars). Thus, latency can introduce potentially dangerous real-world consequences. 

IoT sensors connected to edge servers can also keep systems up and running, even when a network connection is lost because of the distributed nature of edge computing, where no single server is integral to continuous connectivity.

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5 Best Internet Of Things Development Platforms In 2023

Here are Internet of Things platforms that are booming

According to the latest study, the number of Internet of Things and connected devices is likely to increase to 75million by 2025. The IoT devices are still yet to grow driving businesses to seek the best IoT product solutions. Did you ever think of the best IoT development platforms?  We are here with the 5 best Internet of Things development platforms in 2023.  

Google Cloud IoT

Google has launched its platform for the IoT development tools based on its end-to-end Google Cloud Platform. This is one of the world’s leading Internet of Things platforms. The Google Cloud has many services that bring value to linked solutions. The main features of Google Cloud IoT are AI and ML capabilities, data analysis in real-time, data visualization that is impressive and can track the location.  

Cisco IoT Cloud Connect

Cisco IoT Cloud Connect is created with mobile operators in mind. Cisco gives reliable IoT hardware, routers, gateways, and other devices. The main features of Cisco IoT Cloud Connect are its powerful industrial solutions, high-level security, edge computing, centralized connectivity, and data management.  

IRI Voracity

The IRI Voracity platform uses two engines Hadoop and IRI CoSort to process big data. It allows users to manage, discover, analyze, transform, and migrate data. The core features of IRI Voracity are a data governance portal that supports searching and sorts data in silos. The DB Ops environment allows you to manage all your databases from one place.  

Particle

Particle provides edge-to-cloud IoT development tools for global devices and hardware solutions. The main features of the Particle platform integrate with third-party services via REST API, cloud protected by a firewall, and can process data from Google Cloud or Microsoft Azure.  

Salesforce IoT Cloud

According to the latest study, the number of Internet of Things and connected devices is likely to increase to 75million by 2025. The IoT devices are still yet to grow driving businesses to seek the best IoT product solutions. Did you ever think of the best IoT development platforms? We are here with the 5 best Internet of Things development platforms in 2023.Google has launched its platform for the IoT development tools based on its end-to-end Google Cloud Platform. This is one of the world’s leading Internet of Things platforms. The Google Cloud has many services that bring value to linked solutions. The main features of Google Cloud IoT are AI and ML capabilities, data analysis in real-time, data visualization that is impressive and can track the location.Cisco IoT Cloud Connect is created with mobile operators in mind. Cisco gives reliable IoT hardware, routers, gateways, and other devices. The main features of Cisco IoT Cloud Connect are its powerful industrial solutions, high-level security, edge computing, centralized connectivity, and data chúng tôi IRI Voracity platform uses two engines Hadoop and IRI CoSort to process big data. It allows users to manage, discover, analyze, transform, and migrate data. The core features of IRI Voracity are a data governance portal that supports searching and sorts data in silos. The DB Ops environment allows you to manage all your databases from one place.Particle provides edge-to-cloud IoT development tools for global devices and hardware solutions. The main features of the Particle platform integrate with third-party services via REST API, cloud protected by a firewall, and can process data from Google Cloud or Microsoft Azure.Salesforce IoT Cloud focuses on customer relationships management. The main features of Salesforce IoT Cloud core functions are complete customer, product, and CRM integration, websites, services, and other support third-party products, and proactively resolve customer’s problems and needs.

51 Open Source Tools For The Internet Of Things

According to the market researchers at IDC, there were 9.1 billion Internet of Things (IoT) devices installed at the end of 2013. They expect that number to grow 17.5 percent each year and hit 28.1 billion in 2023, when the total IoT market could be worth more than $7 trillion.

The open source community has been at the forefront of this new trend, creating software and hardware designs that enable nearly anyone to experiment with IoT devices and applications. And the number of open source projects dedicated to IoT has been growing rapidly. Last year, we put together a list of 35 open source IoT projects, and this year, we’ve extended it to 51 tools.

Look five or ten years into the future, and which open source operating system will be dominant in the Internet of Things sector? That’s very hard to say at this point in the emerging IoT market’s life, but it’s clearly possible that one of the open source OSes on this list will be the winner. Will it be one of the already familiar names, or an underdog that is less well known? Take a look at this list and make your best guess.

The Internet of Things conjures an image of millions – billions – of sensors across a vast physical area. But this sprawling network also requires a platform to support it. And in some cases, more than one platform. The following list details some of the pioneering open source platforms in the rapidly growing Internet of Things sector.

Middleware doesn’t get a lot of attention, but it provides valuable services by enabling the connection of disparate software components. This is particularly important in the diverse world of the Internet of Things, in which a vast universe of components must be tied together. The following open source middleware IoT tools – from AllJoyn to OpenRemote – are providing unsung but highly important support.

DeviceHive

DeviceHive is a machine-to-machine (M2M) communication framework for smart energy, home automation, remote sensing, telemetry, remote control and monitoring software and other IoT applications. It supports Java, C++, .NET, Python, Javascript, and other platforms.

IoTivity

Sponsored by the Open Interconnect Consortium, The IoTivity software allows for device-to-device connectivity. It is an implementation of the OIC’s standard specification. Operating System: Linux, Arduino, Tizen

InfluxDB

InfluxDB is a “distributed time series database with no external dependencies.” That makes it ideal for collecting data from IoT sensors; in fact, it can track data from tens of thousands of sensors sampling more than once per second. Operating System: Linux, OS X

Eclipse IoT Project

The Eclipse Foundation has a long list of IoT-related projects that include standards and development frameworks. The project also offers a wealth of videos, tutorials, sandboxes and other tools to help new IoT developers get started on their first projects.

KinomaJS

The Kinoma platform encompasses both hardware and software tools for prototying IoT devices and applications. KinomaJS, its JavaScript-based application framework, is available under an open source license. Operating System: Windows, Linux, OS X

M2MLabs Mainspring

Based on Java and the Apache Cassandra NoSQL database, Mainspring describes itself as “an open source application framework for building machine to machine (M2M) applications such as remote monitoring, fleet management or smart grid.” Features include flexible device modeling, device configuration, communication between devices and applications, data validation and normalization, long-term data storage and data retrieval. Operating System: Windows, Linux, OS X

Node-RED

This “visual tool for wiring the Internet of Things” simplifies the process of connect IoT devices with APIs and online services. It is built on chúng tôi and includes a browser-based flow editor. Operating System: Windows, Linux, OS X

OpenHAB

This Java-based open source home automation software promises a vendor-agnostic way to control all the IoT devices in your home through a single interface. It allows users to set up their own rules and control their home environment. You can download the software from the site or use it through the my.openHAB cloud service. Operating System: Windows, Linux, OS X, Android

The Thing System

The Thing System’s website says, “Today, you have to fight your things. They don’t talk to each other, the apps don’t work, it’s a tower of babel. Our solution — the Thing System — is open source. We’ll talk to anything, you can hack the system, it has an open API.” It supports a huge list of IoT devices, including those made by Cube Sensors, Parrot, Next, Oregon Scientific, Samsung, Telldus, Aeon Labs, Insteon, Roku, Google, Apple and other manufacturers. Operating System: Windows, Linux, OS X, others

Freeboard

This project promises “ridiculously simple dashboards for your devices.” It offers a widget-based, drag-and-drop development tool that makes it easy to track the data from your IoT devices. Both free and paid plans are available. Operating system: OS Independent

Exciting Printer

This unusual project makes it possible to create your own small, internet-connected printer. Want to talk to the project owners? You can send a message or draw a picture that will be printed on the Exciting Printer in their office.

Photo courtesy of Shutterstock.

5 Digital Manufacturing Trends To Watch In 2023

Industry 4.0 is reinventing how manufacturers do business, and this transformation isn’t happening by accident. It is the confluence of trends in both technology and labor, where growing amounts of data — especially from traditionally unconventional sources — are being corralled to solve two challenges in one shot: shortage of trained labor and the growing specificity of customer demands.

Data has the potential to revolutionize CRM, reboot the supply chain and increase employee productivity, dramatically improving efficiencies in every sector in the manufacturing pipeline. The trends seeded by big data in 2023 are going to gather increasing momentum in 2023. The results will manifest themselves in these five revolutionary digital manufacturing trends.

Customer-Centric Manufacturing

While the supply and demand theory holds as true in manufacturing as in other sectors, original equipment manufacturers (OEMs) have historically been slow to adapt to changing customer demand. According to a Forrester Report, manufacturing companies are stuck in the age of the product and let cost concerns dictate production more than customer experience. Most manufacturers, the report suggests, deliver products that are “engineering-led” and follow a “ship and forget” approach when it comes to customer interactions.

That is beginning to change. Traditional manufacturing workflows treated customers as appendages at the end of a production process that was focused on design, production, testing, distribution and sales. The smart factory is reworking that linear layout to include customers early in the design process to make manufacturing more customer-centric. With customers at the center, their specific demands indirectly control which batch process gets scheduled in the pipeline and when. This pattern is expected to garner speed in 2023. Increasing specificity of customer demand also means a fragmentation of the production process, where smaller runs delivering more specialty products will become increasingly commonplace.

The Rise of the Digital Supply Network

Traditional supply chains are linear: design, plan, source, make and deliver. Each arm of this chain is restricted in who it talks to, and therefore data intelligence from only one link or two informs next steps in the manufacturing pipeline.

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The digital supply network (DSN), on the other hand, is one where data forms the digital core of the network and informs every link of the supply chain in real time. The DSN delivers more agile smart factories with data from all the nodes driving production and replenishment. Since data from all nodes is completely transparent, each link can make intelligent decisions about next steps with new data that may not have been readily available before. Expect such digital interdependencies to continue to enmesh in 2023.

Increasing Use of Hybrid Computing

The rise of big data necessitates more computing muscle. While mainframe computing via in-house servers may have sufficed in the past, the sheer volume of data from embedded devices on the plant floor alone calls for solutions that need to be delivered cost-effectively and at scale.

Cloud computing, where all computing is routed to the centralized cloud, is one solution, but that creates another kind of headache: latency. Since some computations must be done onsite and in microseconds, moving all work to the cloud isn’t always wise.

Hybrid computing gives OEMs the best of all worlds: It embraces edge computing so that necessary functions (such as alerts as a result of predictive analytics) can be processed at the source, while the rest can be routed to the cloud. When computing for a team of robots takes place in the cloud, there is also the potential for shared information for one to “learn” from the other’s mistakes, yielding more efficiencies in the long term.

Move to Machine Learning

Machine learning, a subset of artificial intelligence where performance data is proactively used to create more intelligent models, will play a big role in manufacturing from predictive maintenance in asset management to inventory management. For example, data and information about failure models is fed into neural networks that “learn” from past behavior history to course-correct in the present and become more intelligent over time. Such machine learning automation can be critical in the smooth functioning of smart factories where labor is especially tight.

Collaborative Robots

Expect to see an increased use of collaborative robots (cobots) in manufacturing. According to the International Federation of Robotics, the market for collaborative robots is expected to hit a whopping $12.3 billion by 2025. Unlike regular robots which are large, often dangerous to use and programmed for only one task by engineers, intelligent robots work alongside humans and can be programmed by most factory workers to take on the most routine, tedious tasks and deliver with accuracy. This is especially good news as labor shortage looms. According to one Deloitte estimate, the U.S. manufacturing industry could be short of two million skilled workers over the next decade.

The growth of lean manufacturing and the smart factory as ushered in by big data will continue to spur new innovations in how humans and machines interact, delivering greater efficiencies in manufacturing for 2023 — and beyond.

Learn more about using mobile technology to modernize your factory with our free white paper.

Top 10 Big Data Trends Of 2023

2024 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2023, and is set to proceed with its rapid growth and reach $247 Billion by 2023. It’s the ideal opportunity for us to look at Big Data trends for 2023.  

Chief Data Officers (CDOs) will be the Center of Attraction

The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements. CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.  

Investment in Big Data Analytics

Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2023 may not be ready to go in 2023. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.) The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2023. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2023.  

Multi-cloud and Hybrid are Setting Deep Roots

In 2023, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.  

Actionable Data will Grow

Another development concerning big data trends 2023 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams. Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.  

Continuous Intelligence

Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data. Gartner predicts more than

Machine Learning will Continue to be in Focus

ML projects have gotten the most investments in 2023, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.  

Abandon Hadoop for Spark and Databricks

Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop. However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.  

In-Memory Computing

In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2023, in-memory computing will pick up fame because of the decreases in expenses of memory.  

IoT and Big Data

The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.  

Digital Transformation Will Be a Key Component

Top 5 Digital Transformation Current & Future Trends In 2023

The Covid-19 pandemic has accelerated digital transformation in every sector, and companies are forced to rethink their digital strategies. As a result, the investment in digital transformation is projected to reach $1.8 trillion by the end of 2023 and $2.8 trillion by 2025. It has become crucial for business leaders to stay aware of new trends and technologies and adapt to the changes accordingly.

This article presents 5 digital transformation trends and recommendations which can better prepare companies for their digital transformation journey. 

1. Emphasis on cybersecurity

The pandemic caused companies to rush into digital transformation, increasing their vulnerability and cybersecurity risk. 2023 had almost a record high of security breaches, which is why organizations are now paying more attention to improving their cybersecurity.

Companies are now leveraging AI (artificial intelligence), ML (Machine learning), and RPA (Robotics process automation) to improve their cybersecurity to mitigate this security threat.

Recommendations: 

Leveraging AI in cybersecurity through sophisticated algorithms can help detect malware and ransomware before it breaches the system. Trained ML models fueled with historical data can help detect and take actions against potential threats. However, even though AI and ML systems speed up malware and threat detection, they can also do false positives. Therefore, a combination of traditional methods with AI/ML would be a better approach.  

Additionally, companies need to change their overall strategy towards cybersecurity. Cybersecurity mesh is a trending new approach to implementing cybersecurity technologies in a modular way to achieve more flexibility and scalability to cater to the increasing complexity of businesses. Company managers should also add cyber security experts to the team who are experienced in mesh architectures for better results. 

RPA also has various implications in automating cybersecurity in your business. For more, check out our article on RPA in cybersecurity.

2. Preparing for 5G

As shown in Figure 1, the adoption of 5G technology is projected to increase in the future significantly. 

5G technology can provide more strength to companies through its 10-20 times higher speed, significantly larger device connectivity, and 10 times lower benefits. Companies can leverage 5G to speed up the implementation of digital solutions and significantly improve their internet-based software. 

Recommendations:

Company managers should focus on creating a strategic 5G plan to better prepare the business for future implementation. Another focus should be to create an implementation strategy that includes details regarding the current IT systems, relevant security considerations, and budget details based on ROI. Additionally, companies can also start educating their employees about the technology and the company’s expected results after the implementation.

Figure 1. Global 5G adoption forecast 2023 to 2026

 Source: Statista

Watch how Aerial Applications, a software company, can leverage 5G to improve their operations

3. Customer data platform (CDP) storm

Customer data is abundantly available for companies to use for optimal decision-making. CDP software is a unified customer database that provides easy access to real-time customer data. The pandemic has intensified the market of CDP, which is projected to reach more than $3 billion by 2025.

Recommendations: 

Businesses can leverage CDP to facilitate digital transformation by providing relevant data regarding customer transactions and behavior (in the form of customer profiles) and fueling the implementation and improvement of digital solutions. For example, automotive companies can combine customer and weather data to create an AI-based predictive model to provide driver accident risk insights. Prior to the implementation of CDP, managers should consider which systems already exist in the company so that seamless integration of CDP can be achieved. It is also important to clearly define your customer data strategy and the budget that you are willing to allocate for the CDP implementation project.

4. The boom of Artificial intelligence (AI)

AI has significantly impacted businesses and industries around the world. The AI market is projected to reach $126 billion by 2025 as companies of all sizes invest in the technology. Figure 2 illustrates the major investors in artificial intelligence worldwide. 

AI-based tools allow businesses to recognize trends, make optimal decisions, produce accurate forecasts, and improve operations. 

Recommendations:  

Considering your business needs is important before implementing AI-based tools. While some businesses have the resources to implement expensive AI solutions, others with limited financial resources can leverage AI-as-a-service or AI solutions by Google, Microsoft, and Amazon (AWS).

Implementing Generative AI in creative and R&D departments can also help companies create better content. For example, by using generative AI techniques in pharmaceuticals, a drug was successfully created to treat obsessive-compulsive disorder in less than 12 months. Gartner also predicts that more than 30% of new drugs will be discovered using generative AI by 2025. In addition, Generative AI can also be used in creating designs. For instance, jacobs engineering created a spacesuit using generative AI techniques. Company managers should focus on understanding and studying generative AI to identify potential implications in their business.

Figure 2. Major investors in AI technology

Source: Statista

Feel free to check our article on AI transformation for more.

5. Automating the business

Automation is another technology that is transforming businesses worldwide. More businesses are now investing in RPA (Robotic Process Automation) to enhance their business processes than ever before. As shown in Figure 3, the global RPA market is projected to increase to $13 billion by 2030, which is a ~1200% increase from 2023. Hyperautomation is another discipline that enables digital transformation in businesses.   

Test automation is another area that can create digital transformation by shifting manual testing to automated testing.

Recommendations: 

Companies can leverage hyperautomation to efficiently identify, examine and automate numerous business and IT processes. To effectively implement RPA in your business, check out Prepare and Implement RPA Successfully. 

To reduce automation maintenance costs, companies can implement autonomic systems to dynamically modify algorithms and optimize process automation behavior within a workflow.

However, implementing process automation can be expensive; therefore, companies need to have a complete understanding of the scope and ROI before deciding to implement and choose a solution.

Figure 3. Global RPA market size from 2023 to 2030

Source: Statista

Watch how DHL uses RPA to improve its supply chain and logistics operations

Further Reading

You can also check our sortable/filterable list of digital transformation consulting companies to find the fit that best suits your business needs.

If you have questions about digital transformation trends, let us know:

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.

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