By Sachin Kotasthane

In his book, 21 Lessons for the 21st Century, the historian Yuval Noah Harari highlights the complex challenges mankind will face on account of technological challenges intertwined with issues such as nationalism, religion, culture, and calamities. In the current industrial world hit by a worldwide pandemic, we see this complexity translate in technology, systems, organizations, and at the workplace.

While in my previous article, Humane IIoT, I discussed the people-centric strategies that enterprises need to adopt while onboarding IoT initiatives of industrial IoT in the workforce, in this article, I will share thoughts on how new-age technologies such as AI, ML, and big data, and of course, industrial IoT, can be used for effective management of complex workforce problems in a factory, thereby changing the way people work and interact, especially in this COVID-stricken world.

Workforce related problems in production can be categorized into:

1. Time complexity
2. Effort complexity
3. Behavioral complexity

Problems categorized in either of the above have a significant impact on the workforce, resulting in a detrimental effect on the outcome—of the product or the organization. The complexity of these problems can be attributed to the fact that the workforce solutions to such issues cannot be found using just engineering or technology fixes as there is no single root-cause, rather, a combination of factors and scenarios. Let us, therefore, explore a few and seek probable workforce solutions.

Figure 1: Workforce Challenges and Proposed Strategies in Production

1. Addressing Time Complexity

   Any workforce-related issue that has a detrimental effect on the operational time, due to contributing factors from different factory systems and processes, can be classified as a time complex problem.

   Though classical paper-based schedules, lists, and punch sheets have largely been replaced with IT-systems such as MES, APS, and SRM, the increasing demands for flexibility in manufacturing operations and trends such as batch-size-one, warrant the need for new methodologies to solve these complex problems.

   • Worker attendance

     Anyone who has experienced, at close quarters, a typical day in the life of a factory supervisor, will be conversant with the anxiety that comes just before the start of a production shift. Not knowing who will report absent, until just before the shift starts, is one complex issue every line manager would want to get addressed. While planned absenteeism can be handled to some degree, it is the last-minute sick or emergency-pager text messages, or the transport delays, that make the planning of daily production complex.

     What if there were a solution to get the count that is almost close to the confirmed hands for the shift, an hour or half, at the least, in advance? It turns out that organizations are experimenting with a combination of GPS, RFID, and employee tracking that interacts with resource planning systems, trying to automate the shift planning activity.

     While some legal and privacy issues still need to be addressed, it would not be long before we see people being assigned to workplaces, even before they enter the factory floor.

     During this course of time, while making sure every line manager has accurate information about the confirmed hands for the shift, it is also equally important that health and well-being of employees is monitored during this pandemic time. Use of technologies such as radar, millimeter wave sensors, etc., would ensure the live tracking of workers around the shop-floor and make sure that social distancing norms are well-observed.

   • Resource mapping

     While resource skill-mapping and certification are mostly HR function prerogatives, not having the right resource at the workstation during exigencies such as absenteeism or extra workload is a complex problem. Precious time is lost in locating such resources, or worst still, millions spent in overtime.

     What if there were a tool that analyzed the current workload for a resource with the identified skillset code(s) and gave an accurate estimate of the resource’s availability? This could further be used by shop managers to plan manpower for a shift, keeping them as lean as possible.

     Today, IT teams of OEMs are seen working with software vendors to build such analytical tools that consume data from disparate systems—such as production work orders from MES and swiping details from time systems—to create real-time job profiles. These results are fed to the HR systems to give managers the insights needed to make resource decisions within minutes.

2. Addressing Effort Complexity

   Just as time complexities result in increased  production time, problems in this category result in an increase in effort by the workforce to complete the same quantity of work. As the effort required is proportionate to the fatigue and long-term well-being of the workforce, seeking workforce solutions to reduce effort would be appreciated. Complexity arises when organizations try to create a method out-of-madness from a variety of factors such as changing workforce profiles, production sequences, logistical and process constraints, and demand fluctuations.

   Thankfully, solutions for this category of problems can be found in new technologies that augment existing systems to get insights and predictions, the results of which can reduce the efforts, thereby channelizing it more productively. Add to this, the demand fluctuations in the current pandemic, having a real-time operational visibility, coupled with advanced analytics, will ensure meeting shift production targets.

   • Intelligent exoskeletons

     Exoskeletons, as we know, are powered bodysuits designed to safeguard and support the user in performing tasks, while increasing overall human efficiency to do the respective tasks. These are deployed in strain-inducing postures or to lift objects that would otherwise be tiring after a few repetitions. Exoskeletons are the new-age answer to reducing user fatigue in areas requiring human skill and dexterity, which otherwise would require a complex robot and cost a bomb.

     However, the complexity that mars exoskeleton users is making the same suit adaptable for a variety of postures, user body types, and jobs at the same workstation. It would help if the exoskeleton could sense the user, set the posture, and adapt itself to the next operation automatically.

     Taking a leaf out of Marvel’s Iron Man, who uses a suit that complements his posture that is controlled by JARVIS, manufacturers can now hope to create intelligent exoskeletons that are always connected to factory systems and user profiles. These suits will adapt and respond to assistive needs, without the need for any intervention, thereby freeing its user to work and focus completely on the main job at hand.

     Given the ongoing COVID situation, it would make the life of workers and the management safe if these suits are equipped with sensors and technologies such as radar/millimeter wave to help observe social distancing, body-temperature measuring, etc.

   • Highlighting likely deviations

     The world over, quality teams on factory floors work with checklists that the quality inspector verifies for every product that comes at the inspection station. While this repetitive task is best suited for robots, when humans execute such repetitive tasks, especially those that involve using visual, audio, touch, and olfactory senses, mistakes and misses are bound to occur. This results in costly reworks and recalls.

     Manufacturers have tried to address this complexity by carrying out rotation of manpower. But this, too, has met with limited success, given the available manpower and ever-increasing workloads.

     Fortunately, predictive quality integrated with feed-forwards techniques and some smart tracking with visuals can be used to highlight the area or zone on the product that is prone to quality slips based on data captured from previous operations. The inspector can then be guided to pay more attention to these areas in the checklist.

3. Addressing Behavioral Complexity

   Problems of this category usually manifest as a quality issue, but the root cause can often be traced to the workforce behavior or profile. Traditionally, organizations have addressed such problems through experienced supervisors, who as people managers were expected to read these signs, anticipate and align the manpower.

   However, with constantly changing manpower and product variants, these are now complex new-age problems requiring new-age solutions.

   • Heat-mapping workload

     Time and motion studies at the workplace map the user movements around the machine with the time each activity takes for completion, matching the available cycle-time, either by work distribution or by increasing the manpower at that station. Time-consuming and cumbersome as it is, the complexity increases when workload balancing is to be done for teams working on a single product at the workstation. Movements of multiple resources during different sequences are difficult to track, and the different users cannot be expected to follow the same footsteps every time.

     Solving this issue needs a solution that will monitor human motion unobtrusively, link those to the product work content at the workstation, generate recommendations to balance the workload and even out the ‘congestion.’ New industrial applications such as short-range radar and visual feeds can be used to create heat maps of the workforce as they work on the product. This can be superimposed on the digital twin of the process to identify the zone where there is ‘congestion.’ This can be fed to the line-planning function to implement corrective measures such as work distribution or partial outsourcing of the operation.

   • Aging workforce (loss of tribal knowledge)

     With new technology coming to the shop-floor, skills of the current workforce get outdated quickly. Also, with any new hire comes the critical task of training and knowledge sharing from experienced hands. As organizations already face a shortage of manpower, releasing more hands to impart training to a larger workforce audience, possibly at different locations, becomes an even more daunting task.

     Fully realizing the difficulties and reluctance to document, organizations are increasingly adopting AR-based workforce trainings that map to relevant learning and memory needs. These AR solutions capture the minutest of the actions executed by the expert on the shop-floor and can be played back by the novice in-situ as a step-by-step guide. Such tools simplify the knowledge transfer process and also increase worker productivity while reducing costs.

     Further, in extraordinary situations such  as the one we face at present, technologies such as AR offer solutions for effective and personalized support to field personnel, without the need to fly in specialists at multiple sites. This helps keep them safe, and accessible, still.

Key takeaways and Actionable Insights

The shape of the future workforce will be the result of complex, changing, and competing forces. Technology, globalization, demographics, social values, and the changing personal expectations of the workforce will continue to transform and disrupt the way businesses operate, increasing the complexity and radically changing where, and when of future workforce, and how work is done. While the need to constantly reskill and upskill the workforce will be humongous, using new-age techniques and technologies to enhance the effectiveness and efficiency of the existing workforce will come to the spotlight.

Figure 2: The Future IIoT Workforce

Organizations will increasingly be required to:

1. Deploy data farming to dive deep and extract vast amounts of information and process insights embedded in production systems. Tapping into large reservoirs of ‘tribal knowledge’ and digitizing it for ingestion to data lakes is another task that organizations will have to consider.
2. Augment existing operations systems such as SCADA, DCS, MES, CMMS with new technology digital platforms, AI, AR/VR, big data, and machine learning to underpin and grow the world of work. While there will be no dearth of resources in one or more of the new technologies, organizations will need to ‘acqui-hire’ talent and intellectual property using a specialist, to integrate with existing systems and gain meaningful actionable insights.
3. Address privacy and data security concerns of the workforce, through the smart use of technologies such as radar and video feeds.

Nonetheless, digital enablement will need to be optimally used to tackle the new normal that the COVID pandemic has set forth in manufacturing—fluctuating demands, modular and flexible assembly lines, reduced workforce, etc.

Originally posted here.

Provisioning, managing and securing devices in an IoT product requires careful planning at the very start of the process. Rigorous evaluation of options, then a Proof of Concept helps determine the right solution. Once the POC has been approved, the IoT product moves to production. Then the real fun starts and many strategic considerations come into play. We can list them as follows:

• Robust and secure OTA software updates

• Security by design

• Scalability

• Automation

• Remote terminal management

• Device configuration, monitoring & troubleshooting

Robust and secure OTA software updates

Robust and secure OTA software updates are essential for keeping IoT devices secure as the software on these devices will become outdated during their lifetime and vulnerabilities are certain to arise if left in their initial states. Therefore a secure, risk-tolerant, and efficient update mechanism must be at the core of each product development team from the inception of the project to the end of its life.

How about a homegrown solution?

Homegrown solutions are less likely to be best-of-breed, can be hard to scale, can suffer from over customisation and scope creep, come at an inherently high cost and can be left in trouble if the star developers behind their creation suddenly jump ship and leave the organisation.  They also often lack the requirements needed to ensure security and robustness of software updates. Various open source solutions exist, but none provide an end-to-end solution and lack the overall functionality to make them enterprise-grade. Generic public cloud IoT stacks wish to cater to the entire IoT value chain but fail to deliver a purpose-built solution for software updates. Proprietary and platform solutions cause lock-in to specific cloud infrastructure, operating system, or development tools.

The common thread among all of these solutions is the lack of a fully optimized end-to-end OTA software update and device management infrastructure that can minimize risk, increase efficiency and enhance security and uptime.

Security by design

A device security breach incident can interrupt operations, damage systems, and negatively impact both virtual and physical processes. This translates into unhappy customers and lost business. As Colin Duggan, the Founder and CEO at BG Networks says in an interview with the Device Chronicle, “It is difficult to add security after the design has been completed. There are a number of reasons for this. Embedded systems have limited MHz, memory, and limitations of network interfaces on embedded processors. Security features can be added after the fact but usually will not close off all the vulnerabilities.” That is why it is so important to ensure security by design, in the very early stages of the product’s lifecycle.

IoT product security should be approached holistically with a framework that addresses the people, devices and process. To help IoT professionals make the right decisions concerning their product development, we designed a simple framework based on these factors and called it the Triangle of Trust:

Scalability

There’s a significant difference between managing a small number of embedded devices and having thousands or even millions of devices deployed in the field. Microsoft’s new IoT Signals report found lack of scalability as a leading cause for IoT project failures. Complexity is one of the greatest scalability issues. As such, choosing the right solution with the right architecture is important to safeguard the long-term management viability of your fleet of connected devices. More on the topic of IoT scalability can be read here.

Automation

When one of the arms of the Triangle of Trust fails, the other two are endangered. To prevent any risks arising from human mistakes, automating some of the processes is a solution that might save your business thousands of dollars. Mender.io is an OTA software update manager for Linux-based embedded devices, and it also offers a wide range of automations to securely manage these devices. One of the features that Mender offers is automatic retry of failed device deployments. Deployments to devices might fail for various intermittent reasons like loss of power, network or device usage. Automatic retry upon failures reduces device deployment error rates up to 90%. This translates to time and money savings managing deployments, and also leads to customers receiving the updates faster.

Remote Management

Remote management is a necessity for any kind of embedded device. Any company rolling out its IoT products needs to have control of its systems from a central location. SSH, secure tunneling and remote terminal access is preferred by service providers to VPN access as they can assure their customers of security when accessing and troubleshooting devices. Furthermore, the management involves grouping and accessing embedded devices, provisioning, configuring, and monitoring remotely and securely.

Seeing the necessity for not only secure over-the-air processes, but also for reliable ways of monitoring, provisioning, configuring, grouping, and accessing the embedded devices, the team behind Mender decided to expand their offering by the mentioned remote management features. Mender is open source software meaning there are many contributors to make it better and support a variety of customer hardware and software such as NVIDIA Jetson and NXP's family of iMX processors. It provides flexibility in choosing your infrastructure, software, and hardware from prototyping to production which means there is no vendor lock-in. Mender supports all device software updates from a full disk image to application updates with the freedom to customize the update and installation process to fit your workflow. It is also integrated with Google Cloud and Microsoft Azure IoT for easy device authentication. 

Device configuration, troubleshooting and monitoring

A proper device management set up should never be overlooked. Robust and secure device management is a necessary cornerstone for an IoT product and therefore you need to find a high quality solution. Once you deploy thousands or millions of devices into the field you’ll need to be able to configure them properly, gather the data, and quickly troubleshoot any arising problems. Many organisations treat these capabilities as an afterthought. Engineers realize that they need some kind of device management solution right before their deadlines and product releases, which results in rushed fixes being made, that may have serious implications for the robustness and security of connected devices.

Conclusion

In order to roll out a successful, secure, and robust IoT product a few things have to be taken into consideration before the release. To ensure security by design from the earliest stages of the product life cycle, the team behind the IoT product needs to find a solution for deploying secure and robust OTA updates, remotely monitor, configure, and troubleshoot the devices, and automate necessary processes in order to avoid human-made mistakes.

Flowchart of IoT in Mining

by Vaishali Ramesh

Introduction – Internet of Things in Mining

The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware; these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled. In the mining industry, IoT is used as a means of achieving cost and productivity optimization, improving safety measures and developing their artificial intelligence needs.

IoT in the Mining Industry

Considering the numerous incentives it brings, many large mining companies are planning and evaluating ways to start their digital journey and digitalization in mining industry to manage day-to-day mining operations. For instance:

• Cost optimization & improved productivity through the implementation of sensors on mining equipment and systems that monitor the equipment and its performance. Mining companies are using these large chunks of data – 'big data' to discover more cost-efficient ways of running operations and also reduce overall operational downtime.
• Ensure the safety of people and equipment by monitoring ventilation and toxicity levels inside underground mines with the help of IoT on a real-time basis. It enables faster and more efficient evacuations or safety drills.
• Moving from preventive to predictive maintenance
• Improved and fast-decision making The mining industry faces emergencies almost every hour with a high degree of unpredictability. IoT helps in balancing situations and in making the right decisions in situations where several aspects will be active at the same time to shift everyday operations to algorithms.

IoT & Artificial Intelligence (AI) application in Mining industry

Another benefit of IoT in the mining industry is its role as the underlying system facilitating the use of Artificial Intelligence (AI). From exploration to processing and transportation, AI enhances the power of IoT solutions as a means of streamlining operations, reducing costs, and improving safety within the mining industry.

Using vast amounts of data inputs, such as drilling reports and geological surveys, AI and machine learning can make predictions and provide recommendations on exploration, resulting in a more efficient process with higher-yield results.

AI-powered predictive models also enable mining companies to improve their metals processing methods through more accurate and less environmentally damaging techniques. AI can be used for the automation of trucks and drills, which offers significant cost and safety benefits.

Challenges for IoT in Mining 

Although there are benefits of IoT in the mining industry, implementation of IoT in mining operations has faced many challenges in the past.

• Limited or unreliable connectivity especially in underground mine sites
• Remote locations may struggle to pick up 3G/4G signals
• Declining ore grade has increased the requirements to dig deeper in many mines, which may increase hindrances in the rollout of IoT systems

Mining companies have overcome the challenge of connectivity by implementing more reliable connectivity methods and data-processing strategies to collect, transfer and present mission critical data for analysis. Satellite communications can play a critical role in transferring data back to control centers to provide a complete picture of mission critical metrics. Mining companies worked with trusted IoT satellite connectivity specialists such as ‘Inmarsat’ and their partner eco-systems to ensure they extracted and analyzed their data effectively.

Cybersecurity will be another major challenge for IoT-powered mines over the coming years

 As mining operations become more connected, they will also become more vulnerable to hacking, which will require additional investment into security systems.

Following a data breach at Goldcorp in 2016, that disproved the previous industry mentality that miners are not typically targets, 10 mining companies established the Mining and Metals Information Sharing and Analysis Centre (MM-ISAC) to share cyber threats among peers in April 2017.

In March 2019, one of the largest aluminum producers in the world, Norsk Hydro, suffered an extensive cyber-attack, which led to the company isolating all plants and operations as well as switching to manual operations and procedures. Several of its plants suffered temporary production stoppages as a result. Mining companies have realized the importance of digital security and are investing in new security technologies.

Digitalization of Mining Industry - Road Ahead

Many mining companies have realized the benefits of digitalization in their mines and have taken steps to implement them. There are four themes that are expected to be central to the digitalization of the mining industry over the next decade are listed below:

The above graph demonstrates the complexity of each digital technology and its implementation period for the widespread adoption of that technology. There are various factors, such as the complexity and scalability of the technologies involved in the adoption rate for specific technologies and for the overall digital transformation of the mining industry.

The world can expect to witness prominent developments from the mining industry to make it more sustainable. There are some unfavorable impacts of mining on communities, ecosystems, and other surroundings as well. With the intention to minimize them, the power of data is being harnessed through different IoT statements. Overall, IoT helps the mining industry shift towards resource extraction, keeping in mind a particular time frame and footprint that is essential.

Originally posted here.

In 2020 we saw retailers hard hit by the economic effects of the COVID-19 pandemic with dozens of retailers—Neiman Marcus, J.C. Penney, and Brooks Brothers to name a few— declaring bankruptcy. During the unprecedented chaos of lockdowns and social distancing, consumers accelerated their shift to online shopping. Retailers like Target and Best Buy saw online sales double while Amazon’s e–commerce sales grew 39 percent.¹ Retailers navigated supply chain disruptions due to COVID-19, climate change events, trade tensions, and cybersecurity events.  

After the last twelve tumultuous months, what will 2021 bring for the retail industry? I spoke with Microsoft Azure IoT partners to understand how they are planning for 2021 and compiled insights about five retail trends. One theme we’re seeing is a focus on efficiency. Retailers will look to pre-configured digital platforms that leverage cloud-based technologies including the Internet of Things (IoT), artificial intelligence (AI), and edge computing to meet their business goals. 

Empowering frontline workers with real-time data

In 2021, retailers will increase efficiency by empowering frontline workers with real-time data. Retail employees will be able to respond more quickly to customers and expand their roles to manage curbside pickups, returns, and frictionless kiosks.  

In H&M Mitte Garten in Berlin, H&M empowered employee ambassadors with fashionable bracelets connected to the Azure cloud. Ambassadors were able to receive real-time requests via their bracelets when customers needed help in fitting rooms or at a cash desk. The ambassadors also received visual merchandising instructions and promotional updates. 

Through the app built on Microsoft partner Turnpike’s wearable SaaS platform leveraging Azure IoT Hub, these frontline workers could also communicate with their peers or their management team during or after store hours. With the real-time data from the connected bracelets, H&M ambassadors were empowered to delivered best-in-class service.   

Carl Norberg, Founder, Turnpike explained, “We realized that by connecting store IoT sensors, POS systems, and AI cameras, store staff can be empowered to interact at the right place at the right time.” 

Leveraging live stream video to innovate omnichannel

Livestreaming has been exploding in China as influencers sell through their social media channels. Forbes recently projected that nearly 40 percent of China’s population will have viewed livestreams during 2020.² Retailers in the West are starting to leverage live stream technology to create innovative omnichannel solutions.  

For example, Kjell & Company, one of Scandinavia’s leading consumer electronics retailers, is using a solution from Bambuser and Ombori called Omni-queue built on top of the Ombori Grid. Omni-queue enables store employees to handle a seamless combination of physical and online visitors within the same queue using one-to-one live stream video for online visitors.  

Kjell & Company ensures e-commerce customers receive the same level of technical expertise and personalized service they would receive in one of their physical locations. Omni-queue also enables its store employees to be utilized highly efficiently with advanced routing and knowledge matching. 

Maryam Ghahremani, CEO of Bambuser explains, “Live video shopping is the future, and we are so excited to see how Kjell & Company has found a use for our one-to-one solution.” Martin Knutson, CTO of Kjell & Company added “With physical store locations heavily affected due to the pandemic, offering a new and innovative way for customers to ask questions—especially about electronics—will be key to Kjell’s continued success in moving customers online.” 

Augmenting omnichannel with dark stores and micro-fulfillment centers  

In 2021, retailers will continue experimenting with dark stores—traditional retail stores that have been converted to local fulfillment centers—and micro-fulfillment centers. These supply chain innovations will increase efficiency by bringing products closer to customers. 

Microsoft partner Attabotics, a 3D robotics supply chain company, works with an American luxury department store retailer to reduce costs and delivery time using a micro-fulfillment center. Attabotics’ unique use of both horizontal and vertical space reduces warehouse needs by 85 percent. Attabotics’ structure and robotic shuttles leveraged Microsoft Azure Edge Zones, Azure IoT Central, and Azure Sphere.

The luxury retailer leverages the micro-fulfillment center to package and ship multiple beauty products together. As a result, customers experience faster delivery times. The retailer also reduces costs related to packaging, delivery, and warehouse space.  

Scott Gravelle, Founder, CEO, and CTO of Attabotics explained, “Commerce is at a crossroads, and for retailers and brands to thrive, they need to adapt and take advantage of new technologies to effectively meet consumers’ growing demands. Supply chains have not traditionally been set up for e-commerce. We will see supply chain innovations in automation and modulation take off in 2021 as they bring a wider variety of products closer to the consumer and streamline the picking and shipping to support e-commerce.” 

Helping keep warehouse workers safe

What will this look like? Cognizant’s recent work with an athletic apparel retailer offers a blueprint. During the peak holiday season, the retailer needed to protect its expanding warehouse workforce while minimizing absenteeism. To implement physical distancing and other safety measures, the retailer  leveraged Cognizant’s Safe Buildings solution built with Azure IoT Edge and IoT Hub services.   

With this solution, employees maintain physical distancing using smart wristbands. When two smart wristbands were within a pre-defined distance of each other for more than a pre-defined time, the worker’s bands buzzed to reinforce safe behaviors. The results drove nearly 98 percent distancing compliance in the initial pilot. As the retailer plans to scale-up its workforce at other locations, implementing additional safety modules are being considered:   

• Touchless temperature checks.  
• Occupancy sensors communicate capacity information to the management team for compliance records.  
• Air quality sensors provide environmental data so the facility team could help ensure optimal conditions for workers’ health.  

“For organizations to thrive during and post-pandemic, enterprise-grade workplace safety cannot be compromised. Real-time visibility of threats is providing essential businesses an edge in minimizing risks proactively while building employee trust and empowering productivity in a safer workplace,” Rajiv Mukherjee, Cognizant’s IoT Practice Director for Retail and Consumer Goods.  

Optimizing inventory management with real-time edge data

In 2021, retailers will ramp up the adoption of intelligent edge solutions to optimize inventory management with real-time data. Most retailers have complex inventory management systems. However, no matter how good the systems are, there can still be data gaps due to grocery pick-up services, theft, and sweethearting. The key to addressing these gaps is to combine real-time data from applications running on edge cameras and other edge devices in the physical store with backend enterprise resource planning (ERP) data.  

Seattle Goodwill worked with Avanade to implement a new Microsoft-based Dynamics platform across its 24 stores. The new system provided almost real-time visibility into the movement of goods from the warehouses to the stores. 

Rasmus Hyltegård, Director of Advanced Analytics at Avanade explained, “To ensure inventory moves quickly off the shelves, retailers can combine real-time inventory insights from Avanade’s smart inventory accelerator with other solutions across the customer journey to meet customer expectations.” Hyltegård continued, “Customers can check online to find the products they want, find the stores with product in stock, and gain insight into which stores have the shortest queues, which is important during the pandemic and beyond. Once a customer is in the store, digital signage allows for endless aisle support.” 

Summary

The new year 2021 holds a wealth of opportunities for retailers. We foresee retail leaders reimagining their businesses by investing in platforms that integrate IoT, AI, and edge computing technologies. Retailers will focus on increasing efficiencies to reduce costs. Modular platforms supported by an ecosystem of strong partner solutions will empower frontline workers with data, augment omnichannel fulfillment with dark stores and micro-fulfillment, leverage livestream video to enhance omnichannel, prioritize warehouse worker safety, and optimize inventory management with real-time data. 

Originally posted here.

The benefits of IoT data are widely touted. Enhanced operational visibility, reduced costs, improved efficiencies and increased productivity have driven organizations to take major strides towards digital transformation. With countless promising business opportunities, it’s no surprise that IoT is expanding rapidly and relentlessly. It is estimated that there will be 75.4 billion IoT devices by 2025. As IoT grows, so do the volumes of IoT data that need to be collected, analyzed and stored. Unfortunately, significant barriers exist that can limit or block access to this data altogether.

Successful IoT data acquisition starts and ends with reliable and scalable IoT connectivity. Selecting the right communications technology is paramount to the long-term success of your IoT project and various factors must be considered from the beginning to build a functional wireless infrastructure that can support and manage the influx of IoT data today and in the future.

Here are five IoT architecture must-haves for unlocking IoT data at scale.

1. Network Ownership

For many businesses, IoT data is one of their greatest assets, if not the most valuable. This intensifies the demand to protect the flow of data at all costs. With maximum data authority and architecture control, the adoption of privately managed networks is becoming prevalent across industrial verticals.

Beyond the undeniable benefits of data security and privacy, private networks give users more control over their deployment with the flexibility to tailor their coverage to the specific needs of their campus style network. On a public network, users risk not having the reliable connectivity needed for indoor, underground and remote critical IoT applications. And since this network is privately owned and operated, users also avoid the costly monthly access, data plans and subscription costs imposed by public operators, lowering the overall total-cost-of-ownership. Private networks also provide full control over network availability and uptime to ensure users have reliable access to their data at all times.

2. Minimal Infrastructure Requirements

Since the number of end devices is often fixed to your IoT use cases, choosing a wireless technology that requires minimal supporting infrastructure like base stations and repeaters, as well as configuration and optimization is crucial to cost-effectively scale your IoT network.

Wireless solutions with long range and excellent penetration capability, such as next-gen low-power wide area networks, require fewer base stations to cover a vast, structurally dense industrial or commercial campuses. Likewise, a robust radio link and large network capacity allow an individual base station to effectively support massive amounts of sensors without comprising performance to ensure a continuous flow of IoT data today and in the future.

3. Network and Device Management

As IoT initiatives move beyond proofs-of-concept, businesses need an effective and secure approach to operate, control and expand their IoT network with minimal costs and complexity.

As IoT deployments scale to hundreds or even thousands of geographically dispersed nodes, a manual approach to connecting, configuring and troubleshooting devices is inefficient and expensive. Likewise, by leaving devices completely unattended, users risk losing business-critical IoT data when it’s needed the most. A network and device management platform provides a single-pane, top-down view of all network traffic, registered nodes and their status for streamlined network monitoring and troubleshooting. Likewise, it acts as the bridge between the edge network and users’ downstream data servers and enterprise applications so users can streamline management of their entire IoT project from device to dashboard.

4. Legacy System Integration

Most traditional assets, machines, and facilities were not designed for IoT connectivity, creating huge data silos. This leaves companies with two choices: building entirely new, greenfield plants with native IoT technologies or updating brownfield facilities for IoT connectivity. Highly integrable, plug-and-play IoT connectivity is key to streamlining the costs and complexity of an IoT deployment. Businesses need a solution that can bridge the gap between legacy OT and IT systems to unlock new layers of data that were previously inaccessible. Wireless IoT connectivity must be able to easily retrofit existing assets and equipment without complex hardware modifications and production downtime. Likewise, it must enable straightforward data transfer to the existing IT infrastructure and business applications for data management, visualization and machine learning.

5. Interoperability

Each IoT system is a mashup of diverse components and technologies. This makes interoperability a prerequisite for IoT scalability, to avoid being saddled with an obsolete system that fails to keep pace with new innovation later on. By designing an interoperable architecture from the beginning, you can avoid fragmentation and reduce the integration costs of your IoT project in the long run. 

Today, technology standards exist to foster horizontal interoperability by fueling global cross-vendor support through robust, transparent and consistent technology specifications. For example, a standard-based wireless protocol allows you to benefit from a growing portfolio of off-the-shelf hardware across industry domains. When it comes to vertical interoperability, versatile APIs and open messaging protocols act as the glue to connect the edge network with a multitude of value-deriving backend applications. Leveraging these open interfaces, you can also scale your deployment across locations and seamlessly aggregate IoT data across premises.  

IoT data is the lifeblood of business intelligence and competitive differentiation and IoT connectivity is the crux to ensuring reliable and secure access to this data. When it comes to building a future-proof wireless architecture, it’s important to consider not only existing requirements, but also those that might pop up down the road. A wireless solution that offers data ownership, minimal infrastructure requirements, built-in network management and integration and interoperability will not only ensure access to IoT data today, but provide cost-effective support for the influx of data and devices in the future.

Originally posted here.

by Philipp Richert

New digital and IoT use cases are becoming more and more important. When it comes to the adoption of these new technologies, there are several different maturity levels, depending on the domain. Within the retail industry, and specifically food retail, we are currently seeing the emergence of a host of IoT use cases.

Two forces are driving this: a technology push, in which suppliers in the retail domain have technologies available to build retail IoT use cases within a connected store; and a market pull by their customers, who are boosting the demand for such use cases.

However, we also need to ask the following questions: What are IoT use cases good for? And what are they aiming at? We currently see three different fields of application:

• Increasing efficiency and optimizing processes
• Increasing customer satisfaction
• Increasing revenues with new business models

No matter what is most important for your organization or whatever your focus, it is crucial to set up a process that provides guidance for identifying the right use cases. In the following section, we share some insights on how retailers can best design this process. We collated these insights together with the team from the Food Tech Campus.

How to identify the right retail IoT use cases

When identifying the right use cases for their stores, retailers should make sure to look into all phases within the entire innovation process: from problem description and idea collation to solution concept and implementation. Within this process, it is also essential to consider the so-called innovator’s trilemma and ensure that use cases are:

• Desirable ones that your customer really needs
• Technically feasible
• Profitable for your sustainable business development

Before we can actually start identifying retail IoT use cases, we need to define search fields so that we can work on one topic with greater dedication and focus. We must then open up the problem space in order to extract the most relevant problems and pain points. Starting with prioritized and selected pain points, we then open up the solution space in order to define several solution concepts. Once these have been validated, the result should be a well-defined problem statement that concisely describes one singular pain point.

In the following, we want to take a deep dive into the different phases of the process while giving concrete examples, tips and our top-rated tools. Enjoy!

Search fields

Retailers possess expertise and face challenges at various stages along their complex process chains. It helps here to focus on a specific target group in order to avoid distraction. Target groups are typically users or customers in a defined environment. A good example would be to focus your search on processes that happen inside a store location and are relevant to the customer (e.g., the food shopper).

Understand and observe problems

User research, observation and listening are keys to a well-defined problem statement that allows for further ideation. Embedding yourself in various situations and conducting interviews with all the stakeholders visiting or operating a store should be the first steps. Join employees around the store for a day or two and support them during their everyday tasks. Empathize, look for any friction and ask questions. Take your key findings into workshops and spend some time isolating specific causes. Use personas based on your user research and make use of frameworks and canvas templates in order to structure your findings. Use working titles to name the specific problem statements. One example might be: Long queueing as a major nuisance for customers.

Synthesize findings

Are your findings somehow connected? Single-purpose processes and their owners within a store environment are prone to isolated views. Creating a common problem space increases the chances of adoption of any solution later. So it is worth taking the time to map out all findings and take a look at projects in the past and their outcome. In our example, queueing is linked to staff planning, lack of communication and unpredictable customer behavior.

Prioritize problems and pain points

Ask users or stakeholders to give their view on defined problem statements and let them vote. Challenge their view and make them empathize and broaden their view towards a more holistic benefit. Once the quality of a problem statement has been assessed, evaluate the economic implications. In our example, this could mean that queueing affects most employees in the store, directly or indirectly. This problem might be solved through technology and should be further explored.

The result of a well-structured problem statement list should consist of a few new insights that might result in quick gains; one or two major known pain points, where the solution might be viable and feasible; and a list with additional topics that exist but are not too pressing at the moment.

Define opportunity areas

Map technologies and problems together. Are there any strategic goals that these problem statements might be assigned to? Have things changed in terms of technical feasibility (e.g., has the cost of a technology dropped over the past three years?). Can problems be validated within a larger setup easily or are we talking about singular use cases? All these considerations should lead towards the most attractive problem to solve. Again, in our example, this might be: Queuing is a major problem in most locations, satisfying our customers should be our main goal, existing solutions are too expensive or inflexible.

When identifying the right use cases for their stores, retailers should make sure to look into all phases within the entire innovation process: from problem description and idea collation to solution concept and implementation.

Ideate and explore use cases

When conducting an ideation session, it is very helpful to bring in trends that are relevant to the defined problem areas so as to help boost creativity. In our example, for instance, this might be technology trends such as frictionless checkout for retail, hybrid checkout concepts, bring your own device (BYOD) and sensor approaches. It is always important to keep the following in mind: What do these trends mean for the customer journey in-store and how can they be integrated in (legacy) environments?

Define solutions concepts

In the process of further defining the solution concepts, it is essential to evaluate the market potential and to consider customer and user feedback. Depending on the solution, it might be necessary to ask the various stakeholders – from store managers to personnel to customers – in order to get a clearer picture. When talking to customers or users, it is also helpful to bring along scribbles, pictures or prototypes in order to increase immersion. The insights gathered in this way help to validate assumptions and to pilot the concept accordingly.

Set metrics and KPIs to prove success

Defining data-based metrics and KPIs is essential for a successful solution. When setting up metrics and KPIs, you need to consider two aspects:

• Use existing data – e.g., checkout frequency – in order to demonstrate the impact of the new solution. This offers a very inexpensive way of validating the business potential of the solution early on.
• Use new data – e.g. measure waiting time – from the solution and evaluate it on a regular basis. This helps to get a better understanding of whether you are collecting the right data and to derive measures that help to improve your solution.

Prototype for quick insights

In terms of technology, practically everything is feasible today. However, the value proposition of a use case (in terms of business and users) can remain unclear and requires testing. Instead of building a technical prototype, it can be helpful to evaluate the value proposition of the solution with humans (empathy prototyping). This could be a person triggering an alarm based on the information at hand instead of an automatic action. Insights and lessons learnt from this phase can be used alongside the technical realization (proof-of-concept) in order to tweak specific features of the solution.

Initiate a PoC for technical feasibility

When it comes to technical feasibility, a clear picture of the objectives and key results (OKRs) for the PoC is essential. This helps to set the boundaries for a lean process with respect to the installation of hardware, an efficient timeline and minimum costs. Furthermore, a well-defined test setup fosters short testing timespans that often yield all needed results.

How IoT platforms can help build retail IoT use cases

The strong trend towards digitization within the retail industry opens up new use cases for the (food) retail industry. In order to make the most of this trend and to build on IoT, it is crucial first of all to determine which use cases to start with. Every retailer has a different focus and needs for their stores.

In the course of our retail projects, we have identified some of the recurring use cases that food retailers are currently implementing. We have also learnt a lot about how they can best leverage IoT in order to build a connected store. We share these insights in our white paper “The connected retail store.”

Originally posted here.

By Sanjay Tripathi, Lauren Luellwitz, and Kevin Egge

There are petabytes of data generated by intelligent, interconnected and autonomous systems of Industry 4.0. When combined with artificial intelligence tools that provide actionable insight, it has the potential to improve every function within a plant, i.e. operations, engineering, quality, reliability and maintenance.

The maintenance function, while crucial to the smooth functioning of a plant has, until recently not seen much innovation. Many among us have experienced the equipment downtime, process drifts, massive hits to yield, and decline in product reliability because of maintenance performed poorly or late. Yet, Enterprise Asset Management (EAM) systems – ERP systems that help maintain assets – remained as systems of record that typically generated work-orders and recorded maintenance performed. Even as production processes became mind-numbingly complex, EAM systems remained much the same.

IBM Maximo 8.0, or Maximo Application Suite, is one example of a system that combines artificial intelligent (AI), big data and cloud computing technologies with domain expertise from operating technologies (OT) to simplify maintenance and deliver production resilience.

Maximo 8.0 leverages AI to visually inspect gas pipelines, rail tracks, bridges and tunnels; AI guides technicians as they conduct complex repairs; it provides maintenance supervisors real-time visibility into the health and safety of their technicians. Domain expertise is incorporated in the form of data to train AI models. These capabilities improve the ability to avoid unscheduled downtime, improve first-time-fix rate, and reduce safety incidents.

Maintenance records residing in Maximo are combined with real-time operational data from production assets and their associated asset model to better predict when maintenance is required. In this example, asset models embody domain expertise. These models characterize how a production asset such as a power generator or catalytic converter should perform in the context of where it is installed in the process.

The Maximo application itself is encapsulated (containerized) using Red Hat’s OpenShift technology. Containerization allows the application to be easily deployed on-premises, on private clouds or hybrid clouds. This flexibility in deployment benefits IT organizations that need to continually evolve their infrastructure, which is almost every organization.

Maximo 8.0 is available as a suite that includes both core and advanced capabilities. A single software entitlement provides access to all capabilities. The entitlement provides access to the core EAM functionality of work and resource scheduling, asset management, industry-specific customizations, EHS guidelines, and mobile functionality. And it provides access to advanced functionality such as Maximo Monitor, which automatically detects anomalies in how an asset may be performing; Maximo Health, which measures equipment health; Maximo Predict, which, as the name suggests, predicts when maintenance is required; and Maximo Assist which assists technicians conduct repairs.

Originally posted here.

By Sanjay Tripathi, Kevin Egge, and Shane Kehoe

Each Industrial Revolution has been catalyzed by the convergence of technologies from multiple domains. Industry 4.0 is no different.

Machines were first introduced into a manual manufacturing process between 1760 and 1820.  But, it was the concurrent introduction of means to power machines that led to the First Industrial Revolution. An example is the first commercially viable Textile Power Loom which was introduced by Edmund Cartwright in England. It used water-power at first. But in two short years water-powered looms were replaced with looms powered with the steam-engines created by James Watts. The relatively smaller steam-engines allowed textile looms to be deployed in many sites enabling persons to be employed in factories.

Multiple innovations such as new manufacturing methods, electricity, steel, and machine tools ushered in the era of mass manufacturing and the Second Industrial Revolution. Henry Ford’s River Rouge Complex in Michigan, completed in 1928, deployed these modern inventions and was the largest integrated factory in the world at that time. The era of mass manufacturing subsequently brought about an explosion in the consumption of goods by households.

The Third Industrial Revolution improved Automation and Controls across many industries through the use of Programmable Logic Controllers (PLCs). PLCs were first introduced by Modicon in 1969. PLC-based automation and controls were introduced to a mostly mechanical world, and helped improve yields and decrease manufacturing costs. This revolution helped provide cheaper products.

Fast forward to the Industry 4.0 Revolution made possible by the synergistic combination of expertise from the worlds of Operating Technologies (OT) and Information Technologies (IT). The current revolution is bringing about intelligent, interconnected and autonomous manufacturing equipment and systems. This is by augmenting deep domain expertise within OT companies with IT technologies such as artificial intelligence (AI), big data, cloud computing and ubiquitous connectivity.

The widespread use of open protocols across heterogeneous equipment makes it feasible to optimize horizontally across previously disjointed processes. In addition, owner/operators of assets can more easily link the shop-floor to the top-floor. Connections across multiple layers of the ISA-95/Purdue Model stack provides greater vertical visibility and added ability to optimize processes.

The increased integration brings together both OT data (from sensors, PLCs, DCS, SCADA systems) and IT data (from MES, ERP systems). However, this integration has different impacts on different functions such as operations, engineering, quality, reliability, and maintenance.

To learn more about how the integration positively impacts the organization, read the next installment in this series to see how you can bridge the gap between OT and IT teams to improve production resilience.

Originally posted here.

By Patty Medberry

After 2020’s twists and turns, here’s hoping that 2021 ushers in a restored sense of “normal.” In thinking about what the upcoming year might bring for industrial IoT, three key trends emerge.

Trend #1: Securing operational technology (OT)

 IT will take a bolder posture to secure OT environments.

Cyber risks in industrial environments will continue to grow causing IT to take bolder steps to secure the OT network in 2021. The CISO and IT teams have accountability for cybersecurity across the enterprise. But often they do not have visibility into the OT network. Many OT networks use traditional measures like air gapping or an industrial demilitarized zone to protect against attacks. But these solutions are rife with backdoors. For example, third-party technicians and other vendors often have remote access to update systems, machines and devices. With increasing pressure from board members and government regulators to manage IoT/OT security risks, and to protect the business itself, the CISO and IT will need to do more.

Success requires OT’s help. IT cybersecurity practices that work in the enterprise are not always appropriate for industrial environments. What’s more, IT doesn’t have the expertise or insight into operational and process control technology. A simple patch could bring down production (and revenues).

Bottom line? Organizations will need solutions that strengthen cybersecurity while meeting IT and OT needs. For IT, that means visibility and control across their own environment to the OT network. For OT, it means security solutions that allow them respond to anomalies while keeping production humming.

Trend #2: Remote and autonomous operations

The need for operational resiliency will accelerate the deployment of remote and autonomous operations – driving a new class of networking.

The impact of changes brought on in 2020 is driving organizations to increasingly use IoT technologies for operational resiliency. After all, IoT helps keep a business up and running when people cannot be on the ground. It also helps improve safety and efficiencies by preventing unnecessary site visits and reducing employee movement throughout facilities.

In 2021, we will see more deployments aimed at sophisticated remote operations. These will go well beyond remote monitoring. They will include autonomous operational controls for select parts of a process and will be remotely enabled for other parts. Also, deployments will increasingly move toward full autonomy, eliminating the need for humans to be present locally or remotely. And more and more, AI will used for dynamic optimization and self-healing, in use cases such as:

• autonomous guided vehicles for picking and packing, material handling, and autonomous container applications across manufacturing, warehouses and ports
• increased automation of the distribution grid
• autonomous haul trucks for mining applications
• Computer-based train control for rail and mass transit

All these use cases require data instantly and in mass, demanding a network that can support that data plus deliver the speed required for analysis. This new class of industrial networking must provide the ability to handle more network bandwidth, offer zero latency data and support edge compute. It also needs security and scale to adapt quickly, ensuring the business is up and running – no matter what.

Trend #3: Managing multiple access technologies

Organizations will operate multiple-access technologies to achieve operational agility and flexibility.

While Ethernet has always been the foundation for connectivity in industrial IoT spaces, that connectivity is quickly expanding to wireless. Wireless helps reduce the pain of physical cabling and provides the flexibility and agility to upgrade, deploy and reconfigure the network with less operational downtime. Newer wireless technologies like Wi-Fi 6 and 5G also power use cases not possible in the past (or possible only with wired connectivity).

As organizations expand their IoT deployments, the need to manage multiple access technologies will grow. Successful deployments will require the right connectivity for the use case, otherwise, costs, complexity and security risks increase. With wireless choices including Wi-Fi, LoRaWAN, Wi-SUN, public or private cellular, Bluetooth and more, organizations will need to determine the best technology for each use case.  

Cisco’s recommendation: Build an access strategy to optimize costs and resources while ensuring security. Interactions between access technologies should deliver a secured and automated end-to-end IP infrastructure – and must avoid a “mishmash” leading to complexity and failed objectives.

As the end of 2020 fast approaches, I wish everyone a safe and healthy New Year. As you continue building and refining your plans for 2021, please consider how you can unleash these IoT network trends to reduce your cybersecurity risks and increase your operational resiliency. 

Originally posted HERE.

If in previous years many of the Internet of Things (IoT) predictions have failed, the year 2020 has been no exception. This time justified by the virus outbreak.

In my article " 2020 IoT Trends and Predictions: Be prepared for the IoT Tsunami" I wrote that we should be prepared for the Internet of Things (IoT) tsunami, but it won't be in 2020". I didn't imagine the special circumstances of the year "MMXX". Today, I do see clearly that Covid-19’s impact is difficult to ignore looking forward into 2021 and beyond. This pandemic is going to accelerate adoption in many industries that have been affected and will have to make some changes to how they operate.

The year 2020 has been a significant year in terms of the emergence of technologies leading to a much better space of IoT to flourish and grow.

I'm not going to make my own predictions this year. Although I have taken a responsibility for my followers to collect and publish the predictions of other recognized or enthusiastic voices of the IoT.

Here I summarize some of them. My advice is to keep relying on optimistic predictions as there are many Reasons to Believe in Internet of Things.

• Forrester - Predictions 2021: Technology Diverity Drives IoT Growth
  • Network connectivity chaos will reign. We expect interest in satellite and other lower-power networking technologies to increase by 20% in the coming year.
  • Connected device makers will double down on healthcare use cases. In 2021, proactive engagement using wearables and sensors to detect patients’ health at home will surge.
  • Smart office initiatives will drive employee-experience transformation. We expect at least 80% of firms to develop comprehensive on-premises return-to-work office strategies that include IoT applications to enhance employee safety and improve resource efficiency.
  • The near ubiquity of connected machines will finally disrupt traditional business. In 2021, field service firms and industrial OEMs will rush to keep up with customer demand for more connected assets and machines.
  • Consumer and employee location data will be core to convenience. In 2021, brands must utilize location to generate convenience for consumers or employees with virtual queues, curbside pickup, and checking in for reservations.
• CRN - 5 Hot IoT Trends To Watch in 2021 And Beyond
  • Changes In Real Estate Trends Will Push Smart Office Initiatives
  • The Internet Of Behavior Is Coming To Your Workplace
  • Location Data Will Become More Prominent
  • This Year’s Pivot To Remote Operations Will Expand Connected Assets
  • Connected Health Care Will Ramp Up In 2021
• The future of IoT: 5 major predictoins for 2021, based on Forrester
• TechoPedia - 6 IoT Predictions for 2021: What's Next? –
  1. An Increase in IoT Remote Workforce Management Products
  2. More IoT-Enabled Options for Smart Cities
  3. Improving Driving and Autonomous Vehicles
  4. The IoT Will Boost Predictive Maintenance
  5. The Connected Home over Internet Protocol (CHIP) Standard Will Become a Reality
  6. Market Enticements With Multipurpose Products
• Forbes - 5 IoT Trends To Watch In 2021
  1. Can You Turn Off Your Alexa? We'll likely see an increase in the security surrounding smart devices, including AI-driven, automated ability to scan networks for IoT devices.
  2. More Use Cases in More Industries - the IoT has the ability to mean big money for almost any industry.
  3. IoT Helping to Build Digital Twins - the IoT may be the perfect partner for the development of digital twins, for almost any application. Especially for things like construction, engineering, and architecture, that could mean huge cost and time savings.
  4. IoT and Data Analytics - the IoT is no longer just about monitoring behavior and spitting out data. It's about processing data quickly and making recommendations (or taking actions) based on those findings.
  5. Improving Data Processing at the Edge - With the confluence of 5G networks, an increase in IoT and IIoT devices, and a dramatic increase in the amount of data we are collecting, I don't see this trend going anywhere but up
• Security Today - By 2021, 36 bilion IoT devices will be installed around the world.
• IoT Agenda - Mitch Maiman - Intelligent Product Solutions (IPS)- IoT predictions for 2021–
  • Medical IoT
  • Radio frequency services
  • AI and augmented reality
  • Electric vehicles
  • Remote work
• IoT Agenda - Carmen Fontana, Institute of Electrical and Electronics Engineer -Top 5 IoT predictions for gworing use cases in 2021
  • Wearables will blur the line between consumer gadgets and medical devices
  • Consumers will be more concerned about data privacy
  • AI IoT products will be more accessible
  • Digital twin adoption will explode due to increased remote work
  • Edge computing will benefit from green energy investment
• IoT World Today - IoT Trends 2021: A Focus on Gundamentals, Not Nice-to-Haves. IoT trends in 2021 will focus on core needs such as health-and-safety efforts and equipment monitoring, but IoT in customer experience will also develop.
• Rockwell Automation Predictions for 2021
  • IT/OT Integration is critical for answering the $77 billion need for IIoT
  • Edge is the new cloud
  • Digital twins save $1 trillion in manufacturing costs
  • Pandemic promotes AR training as the new standard for a distributed workforce
  • Automation accelerates employee advancement through human-machine interface
• Top 5 IoT Predictions For 2021; What Future Holds?
  • Private IoT networks
  • Digital health
  • Cities would turn smarter
  • Remote offices
  • Improved location services
• online - 10 IoT Trends for 2020/2021: Latest Predictions According to Experts 
• J2 Innovations - Smart Building, Equpement and IoT Trends for 2021 –
  • Remote work and management
  • Changing the way we work
  • Flexible spaces
  • Digital processes- 2021 will see ever more processes becoming digital.
  • The convergence of IT and OT - The industry will continue to see a concerted push to integrate and leverage the vast amounts of valuable data derived from Operational Technologies (OT) into the Information Technology (IT) side of the enterprise
  • A new kind of interoperability - A good example of that is The Web of Things (WoT), which is an open source standard being pioneered by Siemens
• Krakul - IoT trends to expect in 2021 - Cloud service providers are the most prominent vendors within the IoT space. 2021 will also see the rise in IoT development partnerships. Brands, who not only require cloud transformation will need a hardware partner to ensure IoT devices perform to both consumer and business needs. Whether those IoT device applications will see use by consumers, businesses or the industry – the common concerns shaping IoT solutions for 2021 include:
  • integration
  • usability
  • security
  • interoperability
  • user safety
  • return on investment (ROI) for the business case
• Analysis Mason - predictions for business connectivity, communications, IoT and security in 2021 –
  • A major mobile operator will buy one of the IoT market disruptors.
  • A new deployment model for private LTE/5G networks will emerge – the public industrial network
  • Private networks will become a topic for financial sponsors.
• TBR (Ezra Gottheil)- 2021 DEVICES & COMMERCIAL IOT PREDICTIONS
  • AI in IoT will increasingly be encapsulated in specific functions like recognition and detection
  • Conversational user interfaces, based on voice or typed communication, will play an increasing role in business Solutions
  • THE EMERGENCE OF THE CHIEF DATA OFFICER ROLE WILL INCREASE ORGANIZATIONAL CLARITY, ACCELERATING IOT ADOPTION
• Predictions for Embedded Machine Learning for IoT in 2021 
  • From increasingly capable hardware to TinyML, embedded machine learning will make strides in 2021.
  • More capable microcontrollers combined with on-device machine learning at the edge is poised to develop further in 2021. These developments with further advances in video surveillance, manufacturing and more.
  • The impact of COVID-19 on the global supply chain, however, may stunt innovation and growth of embedded machine learning.
• Frost & Sullivan -Top 4 Growth Opportunities in the Internet of Things Industry for 2021 
  • Exponential growth of edge computing in public and private networks
  • Convergence between IT and OT to drive end-user concerns on IIoT security, privacy, and data protection
  • Emerging techs: convergence of IoT, AI, and blockchain
  • The future of retail post COVID-19

Key Takeaways               

In spite the global pandemic has influenced product introduction timelines, causing some things to be fast-tracked, while others lose priority, enterprises, consumers, and different stakeholders will continue to drive demand for new and improved internet of things applications, technologies, and solutions in 2021 across verticals and geographies.

IoT will continue to gain footholds, as people and enterprises become comfortable and familiar with the technology and it is incorporated into daily life in seamless ways.

You must not forget that by the year 2025 IoT devices installed worldwide would be 75.44 Billion.  That is a whopping number, which will relentlessly soar further, will give a positive impact on our lives and businesses alike.

I expect an exciting year for IoT advancements in 2021. And you?

In order to form proper networks to share data, the Internet of Things (IoT) needs reliable communications and connectivity. Because of popular demand, there’s a wide range of connectivity technologies that operators, as well as developers, can opt for.

IoT Connectivity Groups

The IoT connectivity technologies are currently divided into two groups. The first one is cellular-based, and the second one is unlicensed LPWAN. The first group is based around a licensed spectrum, something which offers an infrastructure that is consistent and better. This group supports larger data rates, but it comes with a cost of short battery life and expensive hardware. However, you don’t have to worry about this a lot as its hardware is becoming cheaper.

Cellular-Based IoT

Because of all this, cellular-based IoT is only offered by giant operators. The reason behind this is that acquiring licensed spectrum is expensive. But these big operators have access to this licensed spectrum, as well as expensive hardware. The cellular IoT connectivity also has its own two types. The first one being the narrowband IoT (NB-IoT) and category M1 IoT (Cat-M1).

Although both are based on cellular standards, there is one big difference between the two. That NB-IoT has a smaller bandwidth than Cat-M1, and thus offers a lower transmission power. In fact, its bandwidth is 10x smaller than that of Cat-M1. However, both still have a very long range with NB-IoT offering a range of up to 100 Km.

The cellular standard based IoT connectivity ensure more reliability. Their device operational lifetimes are longer as compared to unlicensed LPWAN. But when it comes to choosing, most operators prefer NB-IoT over Cat-M1. This is because Cat-M1 provides higher data rates that are not usually necessary. In addition to this, the higher costs of it prevent operators from choosing it.

Cat-M1 is mostly chosen by large-scale operators because it provides mobility support. This is something suitable for transportation and traffic control-based network. It can also be useful in emergency response situations as it offers voice data transfer.

The hardware (module) used for cellular IoT is relatively more expensive compared to LPWAN. It can cost around $10, compared to $2 LPWAN. However, this cost has been dropping rapidly recently because of its popular demand. 

Unlicensed LPWAN

As for the unlicensed LPWANs, they are used by those who don’t have the budget to afford cellular-based IoT. They are designed for customized IoT networks and offer lower data rates, but with increased battery life and long transmission range. They can also be deployed easily. At the moment, there are two types of unlicensed LPWANs, LoRa (Long Range) and SigFox.

Both types are amazing as they designed for devices that have a lower price, increased battery life, and long range. Their coverage range can be up to 10 Km, and their connectivity cost is as low as $2 per module. Not only this, but the cost is even lower than this sometimes. Therefore, they are ideal for local areas.

Weightless LPWAN

Although there are many variants of the LPWAN, Weightless is considered to be the most popular one. This is because the Weightless Special Interest Group, or the SIG, currently offers three different protocols. These include the Weightless-N, the Weightless-W, and the Weightless-P. All three work in a different way as they have different modalities.

Weightless-W

First off, we have the Weightless-W open standard model. This one is designed to operate in TV white space (TVWS). TV Whitespace (TVWS) is the inactive or unoccupied space found between channels actively used in UHF and VHF spectrum its frequency spans from 470 MHz – 790 MHz. For those who don’t know, this is similar to what Neul was developing before getting acquired by Huawei. Now, while using TVWS can be great as it uses ultra-high frequency spectrum, it has one downside. In theory, it seems perfect. But in practice, it is difficult because the rules and regulations for utilizing TVWS for IoT vary greatly.

In addition to this, the end nodes of this model don’t work like they are supposed to. They are designed to operate in a small part of the spectrum. As is difficult to design an antenna that can cover a such wide band of spectrum. This is why TVWS can be difficult when it comes to installing it. The Weightless-W is considered a good option in:

• Smart Oil sector.
• Gas sector.

Weightless-N

Second up we have the ultra-narrowband system, the Weightless-N. This model is similar to SigFox as both have a lot in common. The best thing about it is it is made up of different networks instead of being an end-to-end enclosed system. Weightless-N uses differential binary phase shift keying (DBPSK) digital modulation scheme same as of used in SigFox.

The Weightless-N line is operated by Nwave, a popular IoT hardware and software developer. However, while is model is best for sensor-based networks, temperature readings, tank level monitoring, and more, there are some problems with it. For instance, Nwave has a special requirement for TCXO, that is the temperature compensated crystal oscillator.

 In addition to this, it has an unbalanced link budget. The reason behind why this is bad is that there will be much more sensitivity going up to the base station compared to what will be coming down. 

Weightless-P

Finally, we have the Weightless-P. This model is the latest one in the group as it was launched some time after the above two. What people love the most about this one is that it has two-way features. In addition to this, it has a 12.5 kHz channel that is pretty amazing. The Weightless-P doesn’t require a TXCO, something which makes it different from Weightless-N and -W.

The main company behind Weightless-P is Ubiik. The only downside about this model is that it is not ideal for wide-area networks as it offers a range of around 2 Km. However, the Weightless-P is still ideal for:

• Private Networks
• Extra sophisticated use cases.
• Areas where uplink data and downlink control are important.

Capacity

Because of the fact that the Weightless protocols are based on SDR, its base station for narrowband signals is much more complex. This is something that ends up creating thousands of small binary phase-shift keying channels. Although this will let you get more capacity, it will become a burden on your wallet.

In addition to this, since the Weightless-N end nodes require a TXCO, it will be more expensive. The TXCO is used when there is a threat of the frequency becoming unstable when the temperature gets disturbed at the end node.

Range

Talking about the ranges, the Weightless-N and -W has a range of around 5 Km in Urban environments. As for the Weightless-P, it can go up to 2 Km.

Comparison

Weightless and SigFox

If we take the technology into consideration, then the Weightless-N and SigFox are pretty similar. However, they are different when it comes to go-to-market. Since Weightless is a standard, it will require another company to create an IoT based on it. However, this is not the case with SigFox as it is a different type of solution.

Weightless and LoRa

In terms of technology, the Weightless and LoRa. Lorawan are different. However, the functionally of the Weightless-N and LoRaWAN is similar. This is because both are uplink-based systems. Weightless is also sometimes considered as the very good alternative when LoRa is not feasible to the user.

Weightless and Symphony Link

The Symphony Link and Weightless-P standards are more similar to each other. For instance, both focus on private networks. However, Symphony Link has a much more better range performance because it uses LoRa instead of Minimum-shift keying modulation MSK.

Originaly posted here

Internet of Things (IoT) has revolutionized many industries including inventory management. IoT is a concept where devices are interconnected via the internet. It is expected that by 2020, there will be 26 billion devices connected worldwide. These connections are important because it allows data sharing which then can perform actions to make life and business more efficient. Since inventory is a significant portion of a company’s assets, inventory data is vital for an accounting department for the company’s asset management and annual report.

Inventory solutions based on IoT and RFID, individual inventory item receives an RFID tag. Each tag has a unique identification number (ID) that contains information about an inventory item, e.g. a model, a batch number, etc. these tags are scanned by RF reader. Upon scanning, a reader extracts its IDs and transmits them to the cloud for processing. Along with the tag’s ID, the cloud receives location and the time of reading. This data is used for updates about inventory items’, allowing users to monitor the inventory from anywhere, in real-time.

Industrial IoT

The role of IoT in inventory management is to receive data and turn it into meaningful insights about inventory items’ location, status, and giving users a corresponding output. For example, based on the data, and inventory management solution architecture, we can forecast the number of raw materials needed for the upcoming production cycle. The output of the system can also send an alert if any individual inventory item is lost.

Moreover, IoT based inventory management solutions can be integrated with other systems, i.e. ERP and share data with other departments.

RFID in Industrial IoT

RFID consist of three main components tag, antenna, and a reader

Tags: An RFID tag carries information about a specific object. It can be attached to any surface, including raw materials, finished goods, packages, etc.

RFID antennas: An RFID antenna receives signals to supply power and data for tags’ operation

RFID readers: An RFID reader, uses radio signals to read and write to the tags. The reader receives data stored in the tag and transmits it to the cloud.

Benefits of IoT in inventory management

The benefits of IoT on the supply chain are the most exciting physical manifestations we can observe. IoT in the supply chain creates unparalleled transparency that increases efficiencies.

Inventory tracking

The major benefit of inventory management is asset tracking, instead of using barcodes to scan and record data, items have RFID tags which can be registered wirelessly. It is possible to accurately obtain data and track items from any point in the supply chain.

With RFID and IoT, managers don’t have to spend time on manual tracking and reporting on spreadsheets. Each item is tracked and the data about it is recorded automatically. Automated asset tracking and reporting save time and reduce the probability of human error.

Inventory optimization

Real-time data about the quantity and the location of the inventory, manufacturers can reduce the amount of inventory on hand while meeting the needs of the customers at the end of the supply chain.

The data about the amount of available inventory and machine learning can forecast the required inventory which allows manufacturers to reduce the lead time.

Remote tracking

Remote product tracking makes it easy to have an eye on production and business. Knowing production and transit times, allows you to better tweak orders to suit lead times and in response to fluctuating demand. It shows which suppliers are meeting production and shipping criteria and which needs monitoring for the required outcome.

It gives visibility into the flow of raw materials, work-in-progress and finished goods by providing updates about the status and location of the items so that inventory managers see when an individual item enters or leaves a specific location.

Bottlenecks in the operations

With the real-time data about the location and the quantity, manufacturers can reveal bottlenecks in the process and pinpoint the machine with lower utilization rates. For instance, if part of the inventory tends to pile up in front of a machine, a manufacturer assumes that the machine is underutilized and needs to be seen to.

The Outcomes

The data collected by inventory management is more accurate and up-to-date. By reducing these time delays, the manufacturing process can enhance accuracy and reduce wastage. An IoT-based inventory management solution offers complete visibility on inventory by providing real-time information fetched by RFID tags. It helps to track the exact location of raw materials, work-in-progress and finished goods. As a result, manufacturers can balance the amount of on-hand inventory, increase the utilization of machines, reduce lead time, and thus, avoid costs bound to the less effective methods. This is all about optimizing inventory and ensuring anything ordered can be sold through whatever channel necessary.

Originally posted here

After so many years evangelizing the Internet of Things (IoT) or developing IoT products or selling IoT services or using IoT technologies, it is hard to believe that today there are as many defenders as detractors of these technologies. Why does the doubt still assail us: "Believe or Not Believe in the IoT"? What's the reason we keep saying every year that the time for IoT is finally now?

It does not seem strange to you that if we have already experienced the power of change that involves having connected devices in ourselves (wearables), in our homes, in cities, in transportation, in business, we continue with so many non-believers. Maybe, because the expectations in 2013 were so great that now in 2020 we need more tangible and realistic data and facts to continue believing.

In recent months I have had more time to review my articles and some white papers and I think I have found some reasons to continue believing, but also reasons not to believe.

Here below there are some of these reasons for you to decide where to position yourself.

Top reasons to believe

• Mackinsey continue presenting us new opportunities with IoT
  • If in 2015 “Internet of Things: Mapping the value beyond the hype” the company estimated a potential economic impact as much as 11,1 US trillions per year in 2025 for IoT applications in 9 settings.
  • In 2019 “Growing opportunities in the Internet of Things” they said that “The number of businesses that use the IoT technologies has increased from 13 percent in 2014 to about 25 percent today. And the worldwide number of IoT connected devices is projected to increase to 43 billion by 2023, an almost threefold increase from 2018.”
• Gartner in 2019 predicted that by 2021, there will be over 25 Billion live IoT endpoints that will allow unlimited number of IoT use cases.
• Harbor Research considers that the market opportunity for industrial internet of things (IIoT) and industry 4.0 is still emergent.
  • Solutions are not completely new but are evolving from the convergence of existing technologies; creative combinations of these technologies will drive many new growth opportunities;
  • As integration and interoperability across the industrial technology “stack” relies on classic IT principles like open architectures, many leading IT players are entering the industrial arena;

• IoT regulation is coming - The lack of regulation is one of the biggest issues associated with IoT devices, but things are starting to change in that regard as well. The U.S. government was among the first to take the threat posed by unsecured IoT devices seriously, introducing several IoT-related bills in Congress over the last couple of years. It all began with the IoT Cybersecurity Improvement Act of 2017, which set minimum security standards for connected devices obtained by the government. This legislation was followed by the SMART IoT Act, which tasked the Department of Commerce with conducting a study of the current IoT industry in the United States.
• Synergy of IoT and AI - IoT supported by artificial intelligence enhances considerably the success in a large repertory of every-day applications with dominant one’s enterprise, transportation, robotics, industrial, and automation systems applications.
• Believe in superpowers again, thanks to IoT - Today, IoT sensors are everywhere – in your car, in electronic appliances, in traffic lights, even probably on the pigeon outside your window (it’s true, it happened in London!). IoT sensors will help cities map air quality, identify high-pollution pockets, trigger alerts if pollution levels rise dangerously, while tracking changes over time and taking preventive measures to correct the situation. thanks to IoT, connected cars will now communicate seamlessly with IoT sensors and find empty parking spots easily. Sensors in your car will also communicate with your GPS and the manufacturer’s system, making maintenance and driving a breeze!. City sensors will identify high-traffic areas and regulate traffic flows by updating your GPS with alternate routes. These IoT sensors can also identify and repair broken street lamps. IoT will be our knight in shining, super-strong metallic armor and prevent accidents like floods, fires and even road accidents, by simply monitoring fatigue levels of truck drivers!. Washing machines, refrigerators, air-conditioners will now self-monitor their usage, performance, servicing requirements, while triggering alerts before potential breakdowns and optimizing performance with automatic software updates. IoT sensors will now help medical professional monitor pulse rates, blood pressure and other vitals more efficiently, while triggering alerts in case of emergencies. Soon, Nano sensors in smart pills will make healthcare super-personalized and 10x more efficient!

Top reasons not to believe

1. Three fourths of IoT projects failing globally. Government and enterprises across the globe are rolling out Internet of Things (IoT) projects but almost three-fourths of them fail, impacted by factors like culture and leadership, according to US tech giant Cisco (2017). Businesses are spending $745 billion worldwide on IoT hardware and software in 2019 alone. Yet, three out of every four IoT implementations are failing.
2. Few IoT projects survive proof-of-concept stage - About 60% of IoT initiatives get stalled at the Proof of Concept (PoC) stage. If the right steps aren’t taken in the beginning, say you don’t think far enough beyond the IT infrastructure, you end up in limbo: caught between the dream of what IoT could do for your business and the reality of today’s ROI. That spot is called proof-of-concept (POC) purgatory.
3. IoT Security still a big concern - The 2019 annual report of SonicWall Caoture Labs threat researchers analyzing data from over 200,000 malicious events indicated that 217.5 percent increase in IoT attacks in 2018.
4. There are several obstacles companies face both in calculating and realizing ROI from IoT. Very few companies can quantify the current, pre-IoT costs. The instinct is often to stop after calculating the cost impact on the layer of operations immediately adjacent to the potential IoT project.  For example, when quantifying the baseline cost of reactive (versus predictive or prescriptive) maintenance, too many companies would only include down time for unexpected outages, but may not consider reduced life of the machine, maintenance overtime, lost sales due to long lead times, supply chain volatility risk for spare parts, and the list goes on.
5. Privacy, And No, That’s Not the Same as Security. The Big Corporations don’t expect to make a big profit on the devices themselves. the Big Money in IoT is in Big Data. And enterprises and consumers do not want to expose everything sensors are learning about your company or you.
6. No Killer Application – I suggest to read my article “Worth it waste your time searching the Killer IoT Application?"
7. No Interoperable Technology ecosystems - We have a plethora of IoT vendors, both large and small, jumping into the fray and trying to establish a foothold, in hopes of either creating their own ecosystem (for the startups) or extending their existing one (for the behemoths).
8. Digital Fatigue – It is not enough for us to try to explain IoT, that now more technologies such as Artificial Intelligence, Blockchain, 5G, AR / VR are joining the party and of course companies say enough.

You have the last word

We can go on forever looking for reasons to believe or not believe in IoT but we cannot continue to deny the evidence that the millions of connected devices already out there and the millions that will soon be waiting for us to exploit their full potential.

I still believe. But you have the last word.

Thanks in advance for your Likes and Shares

Scott Rosenthal and I go back about a thousand years; we've worked together, helped midwife the embedded field into being, had some amazing sailing adventures, and recently took a jaunt to the Azores just for the heck of it. Our sons are both big data people; their physics PhDs were perfect entrees into that field, and both now work in the field of artificial intelligence.

At lunch recently we were talking about embedded systems and AI, and Scott posed a thought that has been rattling around in my head since. Could AI replace firmware?

Firmware is a huge problem for our industry. It's hideously expensive. Only highly-skilled people can create it, and there are too few of us.

What if an AI engine of some sort could be dumped into a microcontroller and the "software" then created by training that AI? If that were possible - and that's a big "if" - then it might be possible to achieve what was hoped for when COBOL was invented: programmers would no longer be needed as domain experts could do the work. That didn't pan out for COBOL; the industry learned that accountants couldn't code. Though the language was much more friendly than the assembly it replaced, it still required serious development skills.

But with AI, could a domain expert train an inference engine?

Consider a robot: a "home economics" major could create scenarios of stacking dishes from a dishwasher. Maybe these would be in the form of videos, which were then fed to the AI engine as it tuned the weighting coefficients to achieve what the home ec expert deems worthy goals.

My first objection to this idea was that these sorts of systems have physical constraints. With firmware I'd write code to sample limit switches so the motors would turn off if at an end-of-motion extreme. During training an AI-based system would try and drive the motors into all kinds of crazy positions, banging destructively into stops. But think how a child learns: a parent encourages experimentation but prevents the youngster from self-harm. Maybe that's the role of the future developer training an AI. Or perhaps the training will be done on a simulator of some sort where nothing can go horribly wrong.

Taking this further, a domain expert could define the desired inputs and outputs, and then a poorly-paid person do the actual training. CEOs will love that. With that model a strange parallel emerges to computation a century ago: before the computer age "computers" were people doing simple math to create tables of logs, trig, ballistics, etc. A room full all labored at a problem. They weren't particularly skilled, didn't make much, but did the rote work under the direction of one master. Maybe AI trainers will be somewhat like that.

Like we outsource clothing manufacturing to Bangladesh, I could see training, basically grunt work, being sent overseas as well.

I'm not wild about this idea as it means we'd have an IoT of idiots: billions of AI-powered machines where no one really knows how they work. They've been well-trained but what happens when there's a corner case?

And most of the AI literature I read suggests that inference successes of 97% or so are the norm. That might be fine for classifying faces, but a 3% failure rate of a safety-critical system is a disaster. And the same rate for less-critical systems like factory controllers would also be completely unacceptable.

But the idea is intriguing.

Original post can be viewed here

Feel free to email me with comments.

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By: Kelly McNelis

We have faced unprecedented disruption from the many challenges of COVID-19, and PTC’s LiveWorx was no exception. The definitive digital transformation event went virtual this year, and despite the transition from physical to digital, LiveWorx delivered.

Of the many insightful virtual keynotes, one that caught everyone’s attention was ‘Digital Transformation: The Technology & Support You Need to Succeed,’ presented by PTC’s Executive Vice President (EVP) of Products, Kevin Wrenn, and PTC’s EVP and Chief Customer Officer, Eduarda Camacho.

Their keynote focused on how companies should be prioritizing the use of best-in-class technology that will meet their changing needs during times of disruption and accelerated digital transformation. Wrenn and Camacho highlighted five of our customers through interactive case studies on how they are using PTC technology to capitalize on digital transformation to thrive in an era of disruption.

Below is a summary of the five customers and their stories that were highlighted during the keynote.

1. Royal Enfield (Mass Customization)

Royal Enfield is an Indian motorcycle company that has been manufacturing motor bikes since 1901. They have British roots, and their main customer base is located in India and Europe. Riders of Royal Enfield wants their bikes to be particular to their brand, so they worked to better manage the complexities of mass customization and respond to market demands.

Royal Enfield is a long time PTC customer, but they were on old versions of PTC technology. They first upgraded Creo and Windchill to the latest releases so they could leverage the new capabilities. They then moved on to transform their processes for platform and variant designs, introduced simulation much earlier by using Creo Simulation Live, and leveraged generative design by bringing AI into engineering and applying it to engine and chassis complex custom forged components. Finally, they retrained and retooled their engineering staff to fully leverage the power of new processes and technologies.

The entire Royal Enfield team now has digital capabilities that accelerate new product designs, variants, and accessories for personalization; as a result, they are able to deliver a much-shortened design cycle. Royal Enfield is continuing their digital transformation trend, and will invest in new ways to create value while leveraging augmented reality with PTC's Vuforia suite.

2. VCST (Manufacturing Efficiency, Quality, and Innovation)

VCST is part of the BMT Group and are a world-class automotive supplier of precision-machined power train and brake components. Their problem was that they had high costs for their production facility in Belgium. They either needed to improve their cost efficiency in their plant or face the potential of needing to shut down the facility and relocate it to another region. VCST decided to implement ThingWorx so that anyone can have instant visibility to asset status and performance. VCST is also creating the ability to digitize maintenance requests and the ability to acquire about spare parts to improve the overall efficiency in support of their costs reduction goals.

Additionally, VCST has a goal to reach zero complaints for their customers and, if any quality problems appear to their customers, they can be required to do a 100% inspection until the problem is solved. Moreover, as cars have gotten quieter with electrification, the noise from the gears has become an issue, and puts pressure on VCST to innovate and reduce gear noise.

VCST has again relied on ThingWorx and Windchill to collect and share data for joint collaborative analysis to innovate and reduce gear noise. VCST also plans to use Vuforia Expert Capture and Vuforia Chalk to train maintenance workers to further improve their efficiency and cost effectiveness. The company is not done with their digital transformation, and they have plans to implement Creo and Windchill to enable end-to-end digital thread connectivity to the factory.

3. BID Group Holdings (Connected Product)

BID Group Holdings operates in the wood processing industry. It is one of the largest integrated suppliers and North American leader in the field. The purpose of BID Group is to deliver a complete range of innovative equipment, digital technologies, turnkey installations, and aftermarket services to their customers. BID Group decided to focus on their areas of expertise, an rely on PTC, Microsoft, and Rockwell Automation’s combined capabilities and scale to deliver SaaS type solutions to their own industry.

Leveraging this combined power, the BID Group developed a digital strategy for service to improve mill efficiency and profitability. The solution is named OPER8 and was built on the ThingWorx platform. This allowed BID Group to provide their customers an out of the box solution with efficient time-to-value and low costs of ownership. BID Group is continuing to work with PTC and Rockwell Automation, to develop additional solutions that will reduce downtime of OPER8 with a predictive analytics module by using ThingWorx Analytics and LogixAI.

4. Hitachi (Service Optimization)

Hitachi operates an extensive service decision that ensures its customers’ data systems remain up and running. Their challenge was not to only meet their customers uptime Service Level Agreements, but to do it without killing their cost structure. Hitachi decided to implement PTC’s Servigistics Service Parts Management software to ensure the right parts are available when and where they are needed for service. With Servigistics, Hitachi was able to accomplish their needs while staying cost effective and delighting their customers.

Hitachi runs on the cloud, which allows them to upgrade to current releases more often, take advantage of new functionality, and avoid unexpected costs.

PTC has driven engagement and support for Hitachi through the PTC Community, and encourages all customers to utilize this platform. The network of collaborative spaces in a gathering place for PTC customers and partners to showcase their work, inspire each other, and share ideas or best practices in order to expand the value of their PTC solutions and services.

5. COVID-19 Response 

COVID-19 has put significant strain on the world’s hospitals and healthcare infrastructure, and hospitalization rates for COVID brought into question the capacity of being able to handle cases. Many countries began thinking of the value field hospitals could bring to safely care for patients and ease the admissions numbers of ‘regular’ hospitals. However, the complication is that field hospitals have essentially no isolation or air filtration capability that is required for treating COVID patients or healthcare workers.

As a result, the US Army Corp of Engineers has put out specifications to create self-contained isolation units, which are fully functioning hospital rooms that can be transported or built onsite. But, the assembly needed to happen fast, and a group of companies (including PTC) led by The Innovation Machine rallied to help design and define the SCIU’s.

With buy-in from numerous companies, a common platform was needed for companies to collaborate. PTC felt compelled to react, and many PTC customers and partners joined in to help create a collaboration platform, with cloud-based Windchill as the foundation. But, PTC didn’t just provide software to this collaboration; PTC also contributed with digital thread and design advice to help the group solve some of the major challenges. This design is a result of the many companies coming together to create deployments across various US state governments, agencies, and FEMA.

Final Thoughts

All of the above customers approached digital transformation as a business imperative. They all had sizeable challenges that needed to be solved and took leadership positions to implement plans that leveraged digital transformation technologies combined with new processes.

PTC will continue to innovate across the digital transformation portfolio and is committed to ensuring that customer success offerings capture value faster and provide the best outcomes.

Original Post Link: https://www.ptc.com/en/product-lifecycle-report/liveworx-digital-transformation–technology-and-support-you-need-to-succeed

Author Bio: Kelly is a corporate communications specialist at PTC. Her responsibilities include drafting and approving content for PTC’s external and social media presence and supporting communications for the Chief Strategy Officer. Kelly has previous experience as a communications specialist working to create and implement materials for the Executive Vice President of the Products Organization and senior management team members.