We have been talking a lot, lately, about the Internet of Things (IoT) and Artificial Intelligence (AI). So much so that it’s now difficult to differentiate the real from the not-so-real or purely ‘marketing’ IoT and AI. Data mining isn’t AI. Marketers have been doing it for a good three decades, and others likewise. It’s using intelligent correlations and cohorts to find patterns and latent needs. That’s not much that is artificial about the issue nor situation. There should be a new marketing codebook with these lines: “Thou shalt not cite IoT and AI in vain.” I don’t know how, but the salesperson calls my latest watch “AI enabled,” whether they have AI or not. The clock is not even smart; at best, it’s just digital. When you wipe off the not-so-real jargon and look at the actual applications of AI and IoT, they are aplenty. But how do we find what is actually true — in a world so taken with these terms? It’s simple. Just know the story behind the pitch. Does the product or solution improve over time? In a customer-facing scenario, does it customize itself to your language (maybe like the Amazon Echo). In a more enterprise setting, does it offer better/faster delivery routes for your logistics movement each time you use it? Does it incrementally better itself with a singular goal of improving the results, learning and adjusting? If yes (to any), then it’s AI. A system which learns on itself and tells right from wrong; A recent use-case comes to mind. The company I am associated with, LogiNext, used Kalman filters (algorithm). NASA made the Kalman filter famous when they used the algorithm in their effort to better direct satellites in near and outer space. According to a paper, right back from 1985, “The Kalman filter in its various forms has become a fundamental tool for analyzing solving a broad class of estimation problems.” The company in question used an updated iteration of the Kalman filter to fix vital tracking information of hundreds of trucks moving across the country. Hence, each tracking point was, then, accurate up to 3×3 yards. What’s the impact? Precise knowledge of where each truck is located. Where the truck will be in the future. And when this vehicle will reach the destination; down to the minute. The updated algorithm, with the layer of Kalman filter, learns from the tracking errors. It is essential as the tracking is hardware and network coverage dependent. It identifies patterns in the tracking data to understand what is ‘credible’ monitoring and what’s an error. The system would itself know which tracking data to use and which to ignore, growing the accuracy with continued functioning. In turn, this would ensure that the information going into the system for processing and route planning is accurate. More importantly, avoiding another case of ‘garbage in, garbage out.’ It would be more consistent with incrementally better plans each time it’s used. Here’s the IoT you can use, with complete logistics streamlining. Logistics is primarily a game of Service Level Agreements, SLAs. A company/carrier needs to adhere to these basic unit agreements, SLAs, or minimum viable service levels. It may be when a shipment leaves, the quality of the truck or environment for the cargo, the time when it needs to reach, etc. These SLAs are the code of conduct for carriers, drivers, and companies. They are specific to each shipment. SLA breaches are a serious affair and may result in delays and eventual penalties. So, with SLAs at the center stage, when you must track a package from perhaps LA to NY, you would expect a continuous flow of information regarding the location and state of your package, along with tracking the adherence to the all-important SLA, the ‘promised delivery time.’ How is your estimated time of arrival (ETA) looking as the package is exchanged between carriers, hubs, delivery centers, and the final mile couriers? It’s a dynamic logistical world where even local traffic and weather may become disruptors. If you simplify the entire end-to-end movement of your package – there’s the pickup, the hub-to-hub movement, and the delivery. It’s possible that all this would be dealt with different drivers, trucks, etc., changing multiple hands. How would you know if any of these drivers are more prone to speeding or delays? How would you know if the truck loaded with your package is well-equipped to handle it? All of the maneuverability allows logistic leaders to use AI right now. Here’s how IoT and AI help. It’s the system, an intricate-interwoven-intelligent ecosystem of software and devices where right from the moment the package leaves your hand; it’s tracking capture the unique id and driver details, aligning-in all possibilities, down to the climate in New Jersey a day from the end-delivery time. This system picks the best-suited driver and trucks for the package as per the promised timelines, nature of the package (perishable, fragile, sensitive, burdensome, etc.), route requirements and delays expected/predicted, hours of service for each driver (ELD/DoT compliances), etc. All the information is beamed-up into a single screen where a manager can view all his/her trucks across state lines, and the possibilities of any delays whatsoever. This monitoring empowers the manager (and the brand involved) to take on corrective measures and avoid final delays for the end-customer. Furthermore, this kind of detailed analysis and pin-point accuracy of multiple systems seamlessly talking to each other adds on a layer of predictability. Here the manager can efficiently predict, how many, trucks would continue to accommodate the possible load coming in, correctly. This is without having the need to dip into the spot markets. Conclusion? Only the beginning for IoT, AI, and yes — Machine learning, too. All this brings us to the summation of the main ‘gains’ of IoT and AI with real-world applications in logistics. 1.  Risk estimation – Cutting down on possible delays, SLA breaches, and service disruptions. 2.   Cost savings – Companies that can predict their carrying capacities (of trucks) precisely as per load variations (seasonal, regional, random aberrations), can plan better with their owned and market-sourced vehicles and boost their margins with favorable freight rates. 3.   Customer satisfaction – The ‘holy grail’ comes within grasp, as companies can reverse engineer the perfect delivery experience using AI (exhaustive delivery route permutations to get the quickest one, consistently), and deliver on time, every time. Perhaps it’s time we speak of AI and IoT as “tools,” which they are. They aren’t ‘magic’ solutions to each of our problems. Just last week my investment advisors told me that they could double my savings. When I asked them how they planned to do it, they quickly came back with ‘We’ll use AI.’ The funny part was that I wasn’t supposed to ask anything else. Well, I did, and now I am looking for better investment advisors. Moral: Don’t let the terms bog you down. Look beyond them to the real-world applications, and they may amaze you. The post Break the Mold with Real-World Logistics AI and IoT appeared first on ReadWrite. Read more.....

Every day we see headlines about new smartphones, voice assistants and other gadgets. These items take entertainment to the next level and help us be more productive. Devices are just the tip of the iceberg when it comes to unlocking the potential of the IoT. Conserve natural resources using connected devices. Consumers and businesses need to start thinking more about how they use the latest technologies to reduce their environmental impact. Connected devices conserve natural resources for the benefit of everyone. Consumers and businesses conserve natural resources with connected devices. Four essential conservation and environmental IoT is helping to solve air pollution, water waste, noise pollution, and wildfires. Reducing Air Pollution, One Sensor at a Time Conserves Natural Resources. The estimated economic cost of air pollution is $1.72 trillion, according to the OECD (Organization for Economic Co-operation and Development). Pollution is linked to a wide variety of medical issues in people and animals,  causing severe harm to plants. Over the last century, countries around the world have made significant policy changes to curb air pollution. Implemented more sustainable practices to conserve natural resources. Tracking air quality is fundamental to understanding how pollution spreads and the impact of it on communities. Businesses and governments with the correct data can make informed decisions about how to mitigate air quality issues. The problem with traditional air monitoring systems is that they are expensive and only capable of monitoring a limited range of parameters. The emergence of low cost, low maintenance sensors, and gateways have enabled cities and businesses to deploy air pollution monitoring solutions. Within a city’s massive scale, there can be an unprecedented insight into air quality. These insights can be seen by deploying sensors around roads, schools, industrial buildings, parks, and other areas. This IoT provides real-time measurements of air quality to map patterns. Issues are more easily identified, and cities can make more strategic business and policy decisions. Say Goodbye to Water Waste. Amid a variety of recent state-wide droughts, public service announcements have worked hard to educate the public. We are all learning the importance of conserving water and reducing water waste. However, even the most conscientious person might not be aware of water leaks in their home. How is that leak contributing to their water bill? For businesses, the problem of water leaks is magnified. Industrial, warehouses, or office buildings catapult the cost of water leaks. Connected devices send alerts. Damage from a ruptured pipe or other leaks cause the city and public works high costs when problems are left unchecked. Connected sensors are an easy way that consumers and businesses can keep tabs on water usage. Businesses and consumers need to better understand their water footprint and reduce water consumption. Sensors can monitor water usage, in addition to tracking a building’s levels of humidity and temperature. Real-time analytics revealing anomalies are quickly be resolved. Costco Wholesale is committed to slashing prices; it’s also make incredible strides in cutting down its water usage. When Costco tested out the Apana water management system, the company saw a 20 percent reduction in water use. The lowered usage resulted in a 22 percent reduction in their water bills. Costco has since rolled out this water management system across the rest of its North America locations. Curbing Urban Noise Pollution can be addressed through connected devices. Noise pollution is a persistent problem for residents in an urban environment. Municipal noise ordinances aim to reduce noise pollution with assessments of noise. But the monitoring is performed inconsistently, and primarily it’s only complaint-driven. Low-cost acoustic sensors enable continuous monitoring of noise in densely populated and trafficked areas. Cities now have access to valuable data to establish more effective noise regulations in places that need them the most. The city of Calgary, Canada, has undertaken several smart city initiatives, including deploying sensors in certain areas to autonomously detect noise. Calgary has partnered with The Urban Alliance and the University of Calgary to use machine learning to distinguish between different types of noise. The differentiator can monitor construction, traffic, gunshots, and music. They will track the noise against the time and location where the noise event occurred. These sensors are helping the city to detect when noise thresholds have been hit to better respond to noise issues and improve residents’ quality of life. Advances in Fire Detection Technology are Heating Up, Businesses can Conserve Natural Resources. Fires in the U.S. cause roughly $10 billion in property damage and injure or kill thousands of people every year. Historically, it’s been challenging to monitor wildfires as they occur over vast swaths of land and move very quickly. Cellular reception is often limited in those areas for wildfire danger. Wildfires have become more prominent and more commonplace in recent years, particularly in California. Firefighters are relying more heavily on technology that helps them detect fires sooner. When a fire is detected quickly, it changes the whole management dynamic. Early warnings are allowing firefighters an enhanced ability to keep people safe. In fire-prone areas, fire management agencies are turning to sensors equipped with AI to measure environmental metrics. Temperature, carbon dioxide levels, and wind direction provide vast amounts of information that combine to detect the fire and predict where it will head. Advanced monitoring systems in place reveal fires started deep within a forest. The detection times emerge quickly so firefighters can put it out, minimizing the environmental impact and damage. Most importantly, this tech is saving people’s lives. Building a Greener World; Businesses to Conserve Natural Resources. The choices businesses and consumers make every day have a significant impact on the health of the public and the environment. IoT devices will continue to play a pivotal role in identifying environmental issues and enabling us all to make changes to further our sustainability efforts. The post Businesses to Conserve Natural Resources Using Connected Devices appeared first on ReadWrite. Read more.....