Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimizing operating procedures and increasing productivity. The integration of IoT in this complicated setting is hindered by specific barriers that need a thorough examination. Prominent barriers to IoT implementation in the cold supply chain are identified using a two-stage model. After reviewing the available literature on the topic of IoT implementation, a total of 13 barriers were found. The survey data was cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This research applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among those barriers, "regularity compliance" and "cold chain networks" are key drivers for IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate the barrier interactions. In the second phase of this research, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system as a whole benefits. The research findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

If you have tapped into IoT's connectivity alone, you haven't explored even half of the technology's potential. The real power lies in data-centric IoT. The IoT is a giant network of connected things and people – all of which collect and share data about the way they are used and about the environment around them. That includes an extraordinary number of objects of all shapes and sizes – from smart microwaves, which automatically cook your food for the right length of time, to self-driving cars, whose complex sensors detect objects in their path, to wearable fitness devices that measure your heart rate and the number of steps you've taken that day, then use that information to suggest exercise plans tailored to you. There are even connected footballs that can track how far and fast they are thrown and record those statistics via an app for future training purposes.

As manufacturers seek to streamline their businesses, they want to gain more insight into the health of their equipment, with the goals of keeping it running smoothly, ensuring optimal performance, and reducing unexpected downtime. Automation has long been used ito help achieve these goals, and in recent years, advances in key technologies--including artificial intelligence (AI) and Internet of Things (IoT)--have allowed manufacturers to take advantage of more sophisticated industrial use cases for automation, such as bin picking, and collaborative and autonomous mobile robots. Today, new types of assets are taking automation even further, as machines such as robotic welding arms and injection molding machines use hundreds of sensors that combine with existing data sources to improve overall equipment effectiveness (OEE). In the case of a robotic arm, for example, the machine data spans a large number of sensor and actuator measurements from the robotic arm itself, and external sources that indicate other operational and environmental conditions (e.g., line speed, job style, ambient temperature and humidity). The process of joining these technologies with AI-based IoT technologies is playing an increasingly important role in delivering tangible business value throughout the manufacturing environment.

Emerging advanced technological solutions today have reached such a peak in growth that they are increasingly leaving deeper imprints on both the professional and personal lives of people around the world. According to CompTIA (via TechRepublic), among the emerging solutions offering the greatest business and financial opportunities in the digital age are artificial intelligence, the Internet of Things (IoT), and 5G networks. And while these technologies could transform the business landscape and how organizations operate on a daily basis, one of the most visible effects they have had so far is how they are (or could) transform the productivity and growth speed of corporations, small or large. One of the bigger players in the business arena today is my industry, AI. McKinsey noted that AI solutions can automate many tasks performed by humans.

And that's largely correct, in many cases, but it's increasingly not the whole story – IoT endpoints are getting closer and closer to the ability to do their own analysis, leading to simpler architectures and more responsive systems. It's not the right fit for every use case, but there are types of IoT implementation that are already putting the responsibility for the customising their own metrics on the devices themselves, and more that could be a fit for such an architecture. There are three main areas where letting the endpoint do its own data analysis – in whole or in part – is becoming increasingly common – smart cities, industrial settings and transportation. In smart cities smart cameras can do certain kinds of analysis right there on the device, helping planners understand pedestrian and motorised traffic patterns. The difference between doing analytics completely on an endpoint device or partially on a device is an important one, according to Gartner research vice president Mark Hung.

"Failing to plan is planning to fail." This popular adage, often attributed to Benjamin Franklin, is especially true as organizations seek to shift to new digital business models and develop IoT solutions in a rapidly evolving technology landscape. Without a clear vision and a model of the concrete steps needed to achieve success, organizations will continue to struggle with implementing IoT. Without a clear vision and a model of the concrete steps needed to achieve success, organizations will continue to struggle with implementing IoT. In fact, according to a recent Forbes Insights/Hitachi survey of more than 500 global executives, 57% say their current IoT initiatives are either not meeting expectations or aren't yet showing any clear signs of success.

The development of the internet of things has revolutionized the heavy industry, online shopping, localized data collection and virtually every other aspect of modern life and business. However, innovators are still struggling over the future of the IoT, and how they'll get there. While many see big data as the driving engine behind the IoT, savvy investors and entrepreneurs have shown that artificial intelligence is the real power of the interconnectivity phenomenon. Today's AI is increasingly capable of operating as a prediction engine, being used to foresee and exploit forthcoming market trends more so than being used as robotic labor. As the quality of AI's predictions continues to grow, companies in virtually every industry will come to rely on its accurate forecast more so than on big data analytics.