What if your everyday devices like washing machines, lights, speakers, TVs, and cameras could communicate with you, send information to you and work on your commands even from distant places? With technologies like IoT and Artificial Intelligence, it is possible to digitize homes and make everyday devices smarter. Read on to find out how.
While the earlier decade was all about data communication and internet proliferation, the economy of the next few years will thrive upon digitization of systems, where we will encounter implementation of smartness into everything i.e. smart home, smart city, smart appliances, smart retail, etc. The demand for smart home appliances is increasing due to numerous advancements and adoptions of digital technologies in everyday life. According to Sandler Research, the global at a CAGR of 23.48% during the period 2016-2020.
Adoption of digital technologies in home appliances
IoT solutions is one of the key focus areas of digital transformation projects in the consumer electronics and home appliances industry. With the addition of smart devices and appliances, and with capabilities in sensing, connectivity, and data transmission, people can interact, collect, and analyze highly valuable data to automate various operations at home, which were previously performed manually.
Let us take a look at how smart appliances enable the digital home:
Smart Appliances using Artificial Intelligence (AI)
Today, technology has evolved to such an extent that there’s a possibility to design meaningful collaboration between humans and machines, primarily due to advancements in AI. Moreover, digital assistants, also called virtual assistants, work on voice-controlled AI, which can do functions like searching the internet, making calls, and connecting to other devices. These assisted devices can be embedded into smartphones or can also be used as a standalone device. This is a great approach to enhance and automate with working of home appliances.
A few scenarios of utilizing AI in appliances are listed below:
● Smart Washing Machines
Using AI techniques in washing machines, washers can autonomously regulate the washing strength and detergent to be used according to the load weight and the type of fabric. It can also automatically send an alert when detergent is out of stock. By adopting such technologies, users can reduce approx 30% of detergent and power consumption while increasing the cleaning power and thus saving energy.
Another scenario where deep learning and interoperability of smart appliances can be used is to learn the daily schedule of the user and work accordingly. For instance, if the user sets a gym workout on his/her calendar, the washing machine will set gym clothes setting on when he/she returns. Apart from this, using the ‘fuzzy logic’ system, the machine can ensure that once we press the start button, smart sensors automatically detect the laundry load and water level.
● Smart Refrigerators
An efficient solution can be developed using AI technology, which can easily track all the activities through a connected mobile application. Also, round the clock troubleshooting and diagnostics can be offered by monitoring the energy usage and usage patterns of the appliance.
Using a deep learning algorithm, we can remotely monitor and recognize food items inside the refrigerator. The whole information can be automatically stored into an inventory list, which will let the user take a note of everything in the refrigerator from anywhere. In addition, it will let the user select any item in the fridge using AI assistance and view relevant recipes that can be made with that item. A smart fridge can also let the user create shopping lists, upload photos using smartphones, and check inside the fridge to get an idea about things that are out of stock. Users can also monitor the temperature inside the refrigerator and freezer.
In a smart home system concept, all appliances should be inter-connectable. So, when a user selects a recipe on the smart fridge, AI assistance will automatically communicate this with the smart oven and begin the pre-heating process.
● Smart Speakers
Smart speakers are the most trending devices that make use of machine learning and artificial intelligence. Technically, any speaker that is capable of doing anything beyond just sound can be labeled as a smart speaker. Wireless speakers such as Amazon Echo or Google Home feature voice recognition software, Bluetooth, NFC, and speakerphone. All of these capabilities can be controlled by mobile apps, making them smart. Smart speakers can be controlled using voice commands to do various tasks such as create a playlist, turn on reminders, make grocery lists, and even search the web.
● Smart TVs
We are all familiar with Smart TVs that are Wi-Fi enabled and work with streaming sticks. AI-powered TVs are gaining popularity nowadays, as they come with new features like voice command, which can understand the context of a conversation using Natural Language Processing (NLP). These NLP algorithms help in understanding the intent of the query before providing a search result. It also supports mobile assistance, where we can view the same content on smartphone and on TV, thus delivering a better user experience.
● Door Lock System
Using remote assistance and smartphone connectivity, we can see if the doors are locked and accordingly update the family or friends. We can be notified when someone opens the door using a key or phone. All these door locking functions work on mobile-enabled technology, which improves the overall safety of the home premises.
● Smart Camera
Smart cameras are the most important factor in home security since it can monitor the minute activities of home as well as the surroundings. With the advanced function of smart cameras, we can record & live stream the critical areas of the home, 24/7. Also, the motion detection feature of smart cameras personalizes and alerts any movements in the vision zone, based on the availability or non-availability of residents in the home.
The feasibility of remote connectivity and access through smart meters provides the functionality to track the electricity consumption and send information in real-time to a smartphone. We can remotely monitor electricity when appliances are not in use and cut off the power in order to save the electricity.
For example, a user can remotely control a smart washing machine via an app that alerts the user about the washing cycle progress, errors or threats, and the energy rate on demand.
In the washing and drying process, a user can monitor the cycle of a smart washer, energy consumption, and can get notified when the drying process is finished, using a remote application.
Using the smart grid technology, homeowners can save money by operating appliances during off-peak times, eliminating the need for the power plant to generate more electricity.
Baby monitor ensures that the parents keep an eye on the baby during naptime, or whenever they step out or are away from the baby for a moment. A baby monitor is a two-piece device with a transmitter that stays in the baby’s room. The receiver is with parents, who can then monitor the baby through sound or video.
Most of the baby monitors are now Wi-Fi enabled or support 3G/4G network. This allows the smartphones to safely and securely transmit data and allows parents to easily check the status of their baby.
Dust is the common origin of causing allergies across the world. In a recent study, 63 percent of people agree that their house is not as clean as they would like it to be. After spending 8 to 10 hours at work, people hardly find time to clean their house. Also, not many prefer to hire someone to get the cleaning done. Here robotic cleaners can be used. Robotic cleaners can automatically clean the tight and usually overlooked spaces that are hard to access in traditional ways. This reduces the chances of falling ill, thus saving money that would be otherwise spent on additional help or medical bills.
The largest barrier to smart home electronic adoption is technological fragmentation within the digitally connected home ecosystem. Currently, there are many standards, networks, and devices used to connect the smart home, creating interoperability problems and making it confusing for the user to set up and control multiple devices. Interoperability will be the key to making the smart home a reality and transforming the landscape of connected consumer products.
The term Digital Transformation means different things for different people. Some people might think of it as switching from manual processes to autonomous processes, while for others it might be about the insights that the data brings, which can help in making business decisions. What can Digital Transformation or moving towards Industry 4.0 do for the manufacturing sector? It can lead to enhanced production cycles, increased customization, a focus on reinforced products and better access to information for employees.
About a couple of centuries ago, the Industrial Revolution changed the face of the manufacturing sector in the US and Europe. The last time we saw a massive revolution in manufacturing was when Henry Ford introduced the concept of mass production to the automobile sector. Years later, we are about to witness how digital transformation brings significant changes to the manufacturing sector. With the advent of digital transformation, manufacturers are adopting smart technologies to move from mass production to customized production.
According to PwC, out of 2000 manufacturing companies, 86% expect to secure simultaneous gains from both lower costs and added revenue from their digitalization efforts in the next five years.
The typical image that comes to our mind of factories and smokestacks when we think about ‘industry’ needs to change. With manufacturers embracing digital transformation, the face of a factory or manufacturing unit is transforming. As transformational technologies like IoT, Cloud, ML and AI mature, it is time for manufacturers to move towards Industry 4.0.
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Digital Transformation for Smart Manufacturing
Smart Manufacturing, also referred to as Industry 4.0, is a balanced combination of traditional manufacturing techniques and recent technologies such as the Internet of Things, cloud computing, data analytics, and machine learning.
The digitalization of the manufacturing sector, or as we call it – the fourth industrial revolution – has several related terms. Some call it the Industrial Internet of Things, while others prefer the terms digital factory or smart factory.
Regardless of the term that is used to refer to Industry 4.0, its aim is to create an end-to-end digital ecosystem by digitalizing a large number of physical assets throughout the value chain.
Until now, components of a smart factory such as sensors, cloud infrastructure, and data analytics were only available to large-scale manufacturers due to the resources and cash they were capable of allocating. Now, all of these technologies are a lot more affordable, allowing mid-size and small manufacturers to benefit equally from transformational technology.
Data is at the center of the Industry 4.0. Collection of data, data analysis, and communicating that data using a wide range of state-of-the-art technologies underpins the improvements that the Industrial Internet of Things is expected to bring. Let’s explore what manufacturers can expect to achieve as they embrace digital transformation.
1) Enhanced production cycles
Consumer demands and expectations are continually evolving, so the ability to alter products according to customer feedback can give an edge to a manufacturer. With the advent of new technologies, it has become much easier to make last-minute changes to the production cycles.
Predictive analytics can help resolve a range of operational issues, allowing manufacturers to improve quality control, boost operational efficiency, and create a safer production environment. Manufacturers will be able to predict when a manufacturing machine is about to fail or needs maintenance. In addition, they will gain access to asset performance data, be able to identify the elements that are delaying the speed of production cycle, and get insights that might be helpful in taking important production decisions.
2) Increasing demand for customization
Before a few years customizing products would cost more and require more time to create. Mass production was not an option to manufacture customized products, as the time and cost involved in creating customized products in different batches would skyrocket. With the adoption of technology, manufacturers have developed the ability to create personalized products based on demand at a competitive rate.
The reason behind this is the ability to feed the data regarding the features of a personalized product into the production machines, which would then consider all the customization parameters while working on the production line. This seems to be applicable even for lower-priced products, thus discarding the one-size-fits-all approach for low range products.
The demand for customization might be a source of worry for many manufacturers out there. However, it presents an opportunity to establish a loyal customer base by delivering the products with exact functionalities and features that the customers are looking for. This is great news for many budding manufacturing companies to get some market attention swapped from the big players. Moreover, already established players should also give enough consideration to product customization and adopt the latest technologies to stay in the game.
3) Reinforced products
Converting a traditional factory to a smart factory will result in assets and products that can monitor their own performance, predict failures, as well as notify operators about the maintenance needs. Getting to know the exact areas where the production process needs improvement can help the manufacturers to enhance the product and make it more relevant to the target customers.
Machine learning is going to have a major contribution in predicting asset behavior by analyzing historic behavior patterns. Other technologies that are set to transform the global manufacturing industry are virtual reality and augmented reality. Virtual reality and augmented reality can revolutionize the way products are designed and can provide live demos to deliver a realistic experience of the product. This can allow manufacturers to test the product even before it hits the assembly line, which can help save a lot of time and money.
4) Information access to employees
Digitalization of an enterprise brings in collaboration tools and platforms that provide employees with easy access to information that they need with the flexibility to access it from anywhere and from any device.
Manufacturers can also expect to gain some real benefits by giving a bird’s-eye view of the entire supply chain to the employees through improved CRM, ERP, and Customer Experience Mapping. Access to a common master data that is updated in real-time will give all the employees – working across different parts of the supply chain – one common truth, which will be up-to-date and reliable. This will allow employees to make informed decisions regarding products, assembly line, and the marketing strategy.
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Getting to Industry 4.0 status
Digital transformation is undoubtedly crucial for the success of manufacturing as well as other industries. At this point, the CTOs and CIOs who have already begun the process for digital transformation in their organization know that it is not just another market trend, rather it is the only way to stay competitive and relevant in the current market. The points that were covered here will crop up as manufacturers embark on their journey towards Industry 4.0 or a digital factory.
eInfochips has helped several clients in their industrial automation projects. Starting from the strategy phase to planning, implementation, and managed services, eInfochips has accompanied companies in their digital transformation journey. If your business wants to adopt digital transformation technologies as a part of Industry 4.0 initiatives, get in touch with us.
The transportation and logistics industry is one of the most vulnerable sectors to cyber attackers. As more connected solutions are introduced to improve efficiency, securing these complex cyber-physical systems will require multi-layer security from Sensor to Cloud.
Rising global trade is fuelling logistics expansion, but there are growing pains
Beyond current short-term economic headwinds, World Economic Forum projects global trade volume to grow 4 times by 2050, with a value of over $68tn globally.
More goods transported (domestically and internationally) over large distances would lead to multi-fold growth in cargo movement. As per European Road Transport Research Advisory council, this would boost freight’s share in traffic on urban road networks, estimated 15% in EU by 2050.
Traffic congestions would mean delayed shipments, thus increasing cost of transport, greater carbon emissions and lower value throughput from supply chains. US Department of Transportation pegs this impact in escalating operation costs and fuel wastage is estimated around $28mn each year, just for US logistics industry.
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Developing economies are becoming growth hotspot
Developing economies in Africa and Asia have a large share in trade growth – both domestic and international.
Supply chain investment priorities are shifting
Historically, trade growth projections boosted corporate investments in capacity expansion in fleet size, warehouse space, and distribution operations. However, there is an emerging investment trend in digitalizing supply chain. These are seen across hardware (sensors, gateway devices, and firmware) as well as software (IoT, cloud, ML, and analytics) spectrum. Over the next decade, business value from autonomous trucks (US$30b) and drones (US$20b) is substantial.
Digitizing supply chains will help mitigate key business challenges
- Fleet monitoring and geo-fencing of trucks would ensure optimized routing of cargo through dynamic, unpredictable roads, and traffic conditions.
- Monitoring, analysing, and controlling on-board ambient conditions like temperature, humidity and airflow will help preserve perishable and high value cargo.
- Analysing driver behaviour, in cabin as well as on-road, through audio and video analytics will enhance safety, predict and minimize risks through focused driver training or automated driver assistance.
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Addressing new operational challenges
While we have addressed key business challenges, there can also be operational challenges that need to be addressed that will help in securing and improving connected logistics.
Simultaneous firmware + RTOS updates, real time device provisioning, and authentication across edge node population requires a robust remote device management system. Diverse sensors on commercial edge devices use multiple communication protocols like BLE, ZigBee, Wi-fi etc. to send data feeds that gateways need to process.
Most important, though, is an edge security mechanism, which provisions edge sensors securely, reliably, and gathers high fidelity data feeds, particularly for critical edge workloads like fleet tracking/ geo fencing and ADAS.
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Security is the glue that holds IoT value chain together
The transportation and logistics being one of the most targeted industries by cyber attackers, potential value erosion is also sizeable. Securing these complex cyber-physical systems involves multi-layer security from sensor to cloud.
- Edge sensors for parameters like vehicle health (fuel levels, tyre pressure, engine temperature), cargo ambient conditions (temperature, humidity) as well as driver behaviour need to be securely provisioned, data feeds acquired and communicated to cloud.
- Lack of securely acquired, high fidelity inputs i.e. tampered, intermittent, and generally unreliable sensor feeds increases risk of vehicle downtime, instances of undesired behaviour by driver and cargo condition deterioration.
- Gateways serve as communication hubs and conversely present a single point of failure for a set of connected devices.
- A security compromised gateway manifests in erroneous processing of multi-protocol communication sensor feeds, failed multicast communication leading to inaccurate Edge intelligence based analytics and device control actions not taken.
- Cloud data management platforms need to be equipped with appropriate level of security w.r.t multi-tenancy, partitioning, and encryption so that diagnostic and predictive analytics generated on device data feeds are accurate and generate actionable insights.
- User applications like data visualization and self-service analytics need robust security mechanism with secure user access management practices, feature level privileges so that data access is role appropriate.
Securing the weak link of IoT value chain – the Edge
Most organizations running IoT workloads rely on public cloud services to run their IoT data management workloads and end user analytical applications. Economies of business scale permit leading public cloud providers invest in state-of-the-art security infrastructure as well as extensive security policy frameworks, thus sufficiently securing the cloud part of IoT stack.
Edge security, though, is more complex, locally managed and thus contributes most to IoT security risks. Some ways organizations can secure their edge infrastructures for running IoT workloads are
- Public key based device certificates like X.509 so that sensors get discovered, authenticated, registered, and provisioned securely.
- Encrypting sensor state data in motion (feeds from edge to gateway, aggregated data pipe from gateway to cloud) as well as data at rest (device metadata store, edge intelligence rule repository) so that data is processed and device state threshold based actions are initiated at the edge appropriately.
- API and micro-services based 3rd party data/ application integration at edge and gateway layer so that context related enrichment of sensor data feeds yields more real time relevant and highly actionable insights.
eInfochips has significant IoT expertise and experience in building secure industry focused IoT applications, including some deployments in logistics and transportation sector as well – from sensor to cloud. To know more click here.
Internet of Things (IoT) began as an emerging trend and has now become one of the key elements of Digital Transformation that is driving the world in many respects. we are evolving to a more connected, digitized world. Leveraging Industry 4.0 technologies is a necessity if you are going to meet consumer’s demands and maximize efficiencies. Now is the time to redefine how we look at gathering and analyzing data across machines and the supply chain to enable fast flexible, and more efficient processes.
General Electric coined the term Industrial Internet of Things (IIoT) in late 2012.
While many of us are familiar with the Internet of Things used by Nike FuelBand, FitBits, Nest and Samsung as connected devices, there’s much more going on in connecting industrial devices in the world of IIoT.
The Industrial Internet is still at an early stage, similar to where the Internet was in the late 1990s. The IIoT, through the use of sensors, advanced analytics and intelligent decision making, will profoundly transform the way plants & factories connect and communicate with the enterprise.
Industries impacted by IIoT are Manufacturing, Aviation, Utility, Agriculture, Oil & Gas, Transportation, Energy, Mining & Healthcare.
One of the key opportunity that early adopters of the Industrial Internet are pursuing is the improvement of worker and equipment productivity, safety and working conditions in the factories.
The IIoT will revolutionize manufacturing by enabling the acquisition and accessibility of tons of data, at lightning speeds, and far more efficiently than before.
There are several challenges factories are facing:
Manual data collected by floor person in a shift has human delays, errors
It is not continuous and also not real time
Data is not comprehensive enough to do analysis and provide insights to senior management
One such framework available to factories is Intelligent Plant Framework provided by Covacsis.
The benefits are tremendous:
It collects real-time data from all the factory machines
It is completely automatic so no human errors
The data collected is comprehensive to provide actionable insights to the factory in charge
With predefined algorithms, the productivity and costs are calculated automatically and recommendations are made for improvement
While systems like MES can only synchronize the operations of the factory, IPF does the performance measurement and management.
Business benefits by implementing IPF:
Conversion costs are reduced by 20-30% from raw materials to finished goods
Production productivity is improved by up to 30%
Plug-n-play with minimal or no customization hence no impact on running factories
Implemented in 3-4 weeks compared to months and years of competitive products in the market
With solid experience of implementation in over 70+ factories and 15+ sectors across manufacturing such as Pharma, Chemical, Textile, FMCG, etc; IPF is a clear winner and the need of an hour for factories of future.
The path to Industry 4.0 is via Industrial Internet of Things IIoT and implementation of automation via IPF.
A new wave of technologies, such as the Internet of Things (IoT), blockchain and artificial intelligence (AI), is transforming cities into smart cities. Many of these cities are building innovation labs and zones as part of their new civic landscape. Smart city innovation labs are vital components of the smart city ecosystem (Figure One). They provide an organized structure for cities, communities, experts, and vendors to come together to create solutions. Successful solutions piloted in smart city innovation labs are then scaled and deployed into a city’s operations and infrastructure.
Figure One. Strategy of Things Smart City Ecosystem Framework.
Develop a well defined innovation sandbox. Every smart city innovation lab has an unique mission. That mission is specific to its community, capabilities, priorities, and surrounding ecosystem. However, it is easy to get distracted and work on the “next shiny object”, vanity projects and “me too” innovation pilots. These projects don’t add value, but take resources and focus away from the problems the lab was created to address.
Build innovation discipline and focus by defining a “sandbox” from the start and updating it annually. The innovation sandbox defines clearly what types of projects are in-scope and which ones are not. The criteria includes alignment with city or department priorities, problem set type, problem owner(s) or sponsors, budget availability, cost, resource requirements, and organizational jurisdiction.
Create procurement policies and processes for innovation projects. Innovation pilots fall outside the “sandbox” municipal procurement processes and policies operate in. These pilots may work with start-ups with limited operating history, use immature and evolving technology, or bought in non-traditional ways (“as a service”, loans, etc.). This mismatch leads to higher risks, extra work and long sourcing times. Due to this, many vendors choose not to work with cities.
Effective smart city innovation labs are agile and responsive. They employ new procurement policies and practices designed specifically for the unique needs of innovation projects. This includes simplified processes and compliance requirements, new risk management approaches, faster payment cycles and onboarding models.
Build a well defined plan for every innovation project. Many innovation pilots are “successful” during the pilot phase, but fail during the scaling phase. This is because the pilots were not fully thought out at the start. Some test a specific technology or solution, and not the approaches. Others test the wrong things (or not enough of the right things). Some are tested in conditions that are not truly reflective of the environment it will be deployed into. Still others don’t test extensively enough, or over a sufficient range of conditions.
Successful projects in smart city innovation labs involve extensive planning, cross-department collaboration, and a comprehensive review process throughout its lifecycle. They have well defined problem statements. They define a targeted and measurable outcome, a detailed set of test requirements and specific success criteria. While innovation projects contain uncertainty, minimize project execution uncertainties with “tried and true” project management plans and processes.
Continuously drive broad support for the lab. A successful civic innovation lab thrives on active support, collaboration and engagement from stakeholders across the civic ecosystem. However, many city departments and agencies operate in silos. Launching and having an innovation lab doesn’t mean that everyone knows about it, actively funnel projects to it, or support and engage with it.
Successful smart city innovation labs proactively drive awareness, interest and support from city leaders, agencies, and the community. This includes success stories, progress updates, technology briefings and demonstrations, project solicitations, and trainings. They engage with city and agency leaders regularly, host lab open houses and community tours. They conduct press and social media awareness campaigns. Regardless of the “who, how and what” of the outreach, the key is to do it regularly internally and externally.
Measure the things that matter - outcomes. There are many metrics that an innovation lab can be measured on. These range from the number of projects completed, organizations engaged, number of partnerships, investments and expenses, and so on. Ultimately, the only innovation lab metric that truly matters is to be able to answer the following question - “what real world difference has the lab made that justifies its continuing existence and funding?”.
All innovation lab projects focus on solving the problem at hand. It must quantify the impact of any solutions created. For example, many cities are monitoring air quality. A people counting sensor, mounted alongside an air quality sensor, quantifies the number of people impacted. Any corrective measures developed as a result of this project can now point to a quantifiable outcome.
Build an innovation partner ecosystem. A smart city innovation lab cannot address the city’s innovation needs by itself. A city is a complex ecosystem comprising multiple and diverse domains. Technologies are emerging and evolving rapidly. New digital skills, from software programming to data science, are required to build and operate the new smart city.
Successful smart city innovation labs complement their internal capabilities and resources by building an ecosystem of strategic and specialist partners and solutions providers, and subject matter experts. These partners are identified ahead of time, onboarded and then brought in on an as-needed basis to support projects and activities as needed. This model requires the lab to build strong partnership competence, processes, policies and the appropriate contract vehicles. In addition, the lab must continuously scan the innovation ecosystem, identify and recruit new partners ahead of the need.
Test approaches, not vendors or solutions. Real world city problems are complex. There is no magic “one size fits all” solution. For example, smart parking systems use sensor based and camera based approaches. In some cases, both approaches work equally well. In other cases, one or the other will work better. A common innovation mistake is to only test one approach or fall in love with a specific vendor solution and draw a generalized conclusion.
Effective innovation lab projects focus on testing various approaches (not vendors) in order to solve problems effectively. Given the rapid pace of technology evolution, take the time to identify, test and characterize the various solution approaches instead.
Employ a multi-connectivity smart city strategy. There are many options for smart city connectivity. These include, but not limited to cellular 3G/4G, Wi-Fi, LoRaWAN, SigFox, NB-IoT and Bluetooth, and so on. Use cases and solutions are now emerging to support these options. However, some smart city technologies in the marketplace work on one, while others work on more. There is no “one size fits all” connectivity method that works everywhere, every time, with everything.
To be effective, smart city innovation labs need to support several of these options. The reality is that there is not enough information to know which options work best for what applications, and when. What works in one city or region, may not work in another. Pilot projects test a possible solution, as well as the connectivity approach to that solution.
Make small innovation investments and spread them around. Open an innovation lab and a long line of solutions vendors will show up. Everyone has a potential solution that will solve a particular problem. Some of these solutions may even work. Unfortunately, there is not enough budget to look at every solution and solve every problem.
Focus on making smaller, but more investments around several areas. Overinvesting in one vendor or one approach, in a market where technologies are immature and still evolving, is not wise. Invest enough to confirm the pilot outcomes. A more detailed evaluation of the various solutions and vendors should be made when the pilot moves out of the innovation lab and into a formal procurement and RFP phase.
Simplify administrative and non-innovation workloads. While innovation pilot projects are challenging, interesting and even fun, administering and managing the projects are not. These unavoidable tasks range include managing inbound requests, proposals and ongoing projects. These tasks increasingly consume time and resources away from the core innovation activities.
Effective smart city innovation labs get ahead of this by organizing, simplifying and automating administrative activities right from the start. For example, SMC Labs reviews inbound proposals once a week and organizes follow up calls and meetings on a specific day once every two weeks. In addition, the lab uses a tracking and pilot management tool (Urban Leap) to track innovation projects. Administrative and management activities are unavoidable. However, advanced planning and tools help reduce the burden to keep the lab's focus on innovation.
Benson Chan is an innovation catalyst at Strategy of Things, helping cities become smarter and more responsive through its innovation laboratory, research and intelligence, consulting and acceleration (execution) services. He has over 25 years of scaling innovative businesses and bringing innovations to market for Fortune 500 and start-up companies. Benson shares his deep experiences in strategy, business development, marketing, product management, engineering and operations management to help IoTCentral readers address strategic and practical IoT issues.
According to global management consulting firm Bain & Company, long-term prospects for the industrial Internet of Things remain ambitious. However, many executives are resetting timeline expectations for reaching scale due to early adoption struggles. Notably, certain “darlings of IoT” like predictive maintenance have not lived up to the hype. And while Bain’s survey of 600 industrial customers shows increasing traction with ‘workhorse’ scenarios like remote monitoring and asset tracking, it exposes areas where many teams and vendors are struggling to deliver the goods. In the end, an iterative strategy focused on specific business outcomes remains critical.
Notably, Bain’s survey finds increasing concerns around integration with existing enterprise systems and data portability. Executives worry their visions for digital transformation will be restricted by internal skill gaps and proprietary vendor services. Understandably, they fear losing control of any data not managed by their own enterprise IT departments. Despite this, confidence remains high that an estimated 20 billion devices will be successfully connected by 2020.
Many executives feel the value proposition for industrial IoT is still emerging. For them, the ability to capitalize on this value and achieve better business results remains elusive. To address these challenges, Bain calls for organizations to build a new operating model and position themselves for long-term success in a connected world.
Recommendations for accelerating IoT adoption in the enterprise
First, Bain recommends industrial organizations choose specific, high-value use cases to tackle upfront. Prove out your ability to address security and other valid IT concerns. Then, adopt an iterative approach for demonstrating ROI and ease of enterprise integration.
Second, use experienced partners to address your gaps. Don’t try building everything yourself. Differentiation comes from the combination of acquired data with your industry-specific domain knowledge. We’ve seen manufacturing digital transformation initiatives stall out when internal engineering teams try to build their own IoT infrastructure. Software for collecting data (and system integration services) can be bought. Build your value, not your tools.
Third, don’t expect overnight success. You’re building up organizational capabilities and working with a new set of specialized partners. Commit to a realistic investment timeline and prepare for change. You’ll likely need to bring in new, more entrepreneurial talent to drive your connected business model. At a minimum, empower your existing teams to think differently. Remember, you’re not rolling out a new CRM application. You’re transforming your enterprise. Act accordingly.
Fourth, industrial IoT revenue starts at the top. Executives must ensure the entire organization is aligned for transition to the new operating model. This requires both vision and clear communication. Unsurprisingly, those responsible for existing products and revenue streams fear cannibalization. Furthermore, IoT initiatives take time to meet traditional P&L requirements. If executives don’t create an environment where the new operating model can take root, prevailing forces will prevent its maturation while competitors move ahead.
Prepare to scale the business
Eventually companies reach the point on their digital transformation journey where they’ve proven out their connected product technology and business concepts. Now what? Bain concludes with a method for assessing readiness to scale up your industrial IoT efforts.
To begin, how well do you understand the full potential of industrial IoT to your enterprise? IoT can dramatically impact the quality of manufactured products, service offerings, maintenance procedures, and other areas of your enterprise. But what will this cost, and what will revenue look like once the system is deployed to production and fully commercialized?
Never forget, your competitors aren’t standing still. You can be sure they’re working on their own industrial IoT initiatives. What is your plan to win in this new arena?
Additionally, scaling IoT requires incentives alignment and coordinated execution across the enterprise. Engineering, IT, service, sales, and business teams must work together for organizations to realize the benefits of digital transformation. Make sure everyone understands their part and is rowing in the same direction.
Bain summarizes their last recommendation with a sentiment that we refer to as “strategy over software.” By strategy, we mean not just a plan, but a comprehensive roadmap, organization structure, and business model across the enterprise to support the success of your industrial IoT initiative.
Digital transformation is a journey
As you start your journey, you’re going to need an industrial IoT platform. Whether it makes sense to build your own or buy one depends on a variety of factors. But digital transformation isn’t just about technology. As Bain notes repeatedly, it’s about so much more. Business models and sales strategies, along with clear user stories, team roles, and responsibilities are equally critical to successful IoT initiatives. Beyond a platform, an experienced digital transformation partner can accelerate planning, implementation, and successful commercialization of your connected systems.
Digital Transformation is now a number one priority for many businesses. Over the past two years, businesses have put increased focus on digitally transforming their brands from the inside out.
It is an ongoing process of change based on the market and the needs of the customers. To deliver this change successfully, there is a need to establish a clear vision with objectives & expected outcomes.
Simply put vision is a picture of how the organization will look like after stipulated time.
Importance of Vision:
· Provides the big picture and clearly describes what your organization will be like in several years
· Clarifies the right direction of change to ensure that everyone is moving forward
· Inspires everyone to take action in the set direction
· Synchronizes the action of different people. It provides self-sufficiency to individuals and teams while reducing conflicts.
There are some do’s & don’ts for setting up a vision:
· Develop a Vision that is in line with the company growth strategy.
· Connect with partners who support your vision, not only third-party technology vendors but your own customers and employees
· It should create the sense of urgency
· Link vision to specific goals in future
· Describe how the company will actually change
· How will you engage differently with customers?
· It remains only as floor branding and marketing
· Restricting the employees with set vision & its boundaries
· Vision is way too complicated, vague and lacking actionable initiatives
· Poor communication of the vision beyond the involved few stakeholders
· Setup the vision before analyzing current systems and operations
Vision brings in the cultural change that is required for Digital Transformation. People are extremely important in this roller-coaster ride.
When the digital vision is not clear, that affects the speed of adoption of both senior management and middle management. People will not act just because technology is ready.
Some successful vision statements, which helped companies in their digital transformation:
Google - To provide access to the world’s information in one click
Amazon - To be Earth’s most customer-centric company, where customers can find and discover anything they might want to buy online
Walmart - To be the best retailer in the hearts and minds of consumers and employees
GE - To become the world’s premier digital industrial company, transforming the industry with software-defined machines and solutions that are connected, responsive and predictive
Ikea – To create a better everyday life for the many people
Southwest Airlines - To become the world’s most loved, most flown, and most profitable airline
A top-down vision is a cornerstone & catalyst for digital transformation. These and many companies have created great vision statements to survive in this digital age.
- Easy to find: How easy it is to find a site or application
- Ease of use: How easy it is to use the site or application, how easy to learn
- Easy to access: How easy it is to access the site or application, easy to understand, easy to reach
- Usefulness: How useful the features and functions are and they meet my needs
- Elements of desirability: Will make users like the product’s looks and feel and visual appeal
- Credibility: How much users trust the site or application, creating the overall brand experience
What is a smart city? The answer depends on who you ask. Solutions providers will tell you it’s smart parking, smart lighting or anything to do with technology. City officials may tell you it’s about conducting city business online, such as searching records or applying for permits. City residents may tell you it’s the ease of getting around, or about crime reduction. Everyone is right. A smart city, built properly, will have different value for different stakeholders. They may not think of their city as a “smart”city. They know it only as a place they want to live in, work in, and be a part of. To build this type of city, you have to first build the smart city ecosystem.
A smart city is built on technology, but focused on outcomes
A scan of the various smart city definitions found that technology is a common element. For example, TechTarget defines a smart city as “a municipality that uses information and communication technologies to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare”. The Institute of Electrical and Electronics Engineers (IEEE) envisions a smart city as one that brings together technology, government and society to enable the following characteristics: a smart economy, smart mobility, a smart environment, smart people, smart living, smart governance.
But what does a smart city really do? Our scan of smart city projects worldwide showed that initiatives fell into one or more smart city “outcomes” (Figure One).
As a starting point, we define a smart city is one that uses technology extensively to achieve key outcomes for its various stakeholders, including residents, businesses, municipal organizations and visitors.
The smart city ecosystem framework
Figure Two shows our framework for a smart city ecosystem. A vibrant and sustainable city is an ecosystem comprised of people, organizations and businesses, policies, laws and processes integrated together to create the desired outcomes shown in Figure One. This city is adaptive, responsive and always relevant to all those who live, work in and visit the city. A smart city integrates technology to accelerate, facilitate, and transform this ecosystem.
Four types of value creators
There are four types of value creators in the smart city ecosystem. They create and consume value around one of the outcomes listed in Figure One.
When people think of a smart city, they automatically think of services provided by municipal and quasi-government agencies, such as smart parking, smart water management, smart lighting, and so on. In fact, there are three other value providers and users that co-exist in the smart city – businesses and organizations, communities, and residents.
Businesses and organizations may create services that use and create information to create outcomes for its stakeholders. Some examples of “smart” businesses include Uber and Lyft for personal mobility, NextDoor for information sharing, and Waze/Google for traffic and commute planning.
Communities are miniature smart cities, but with very localized needs. Some examples of potential smart communities include university campuses, office parks, airports, cargo ports, multi-dwelling unit (MDU) or apartment complexes, housing developments/neighborhoods, business districts and even individual “smart” buildings. They have needs for smart services that may be tailored specifically for their stakeholders.
Residents or individual citizens are also smart services providers in the smart city. A resident living near a dangerous street intersection can point a camera at the intersection and stream that information live to traffic planners and police. Residents place air quality measurement sensors on their properties to monitor pollution and pollen levels during certain times of the year, and make that information available to other community members. Residents can choose to make these smart services temporary or permanent, and free or fee based.
The Smart City is built on layers
A smart city is an ecosystem comprised of multiple “capability layers”. While technology is a critical enabler, it is just one of many foundational capabilities that every smart city must have. No one capability is more important than the rest. Each capabilities plays a different role in the smart city. These capabilities must integrate and coordinate with each other to carry out its mission.
Value layer. This is the most visible layer for city residents, businesses, visitors, workers, students, tourists and others. This layer is the catalog of smart city services or “use cases”, centered around the outcomes (Figure One), and offered by value creators and consumed by the city stakeholders.
Innovation layer. To stay relevant, value creators in the smart city must continuously innovate and update its services for its stakeholders. Smart cities proactively facilitate this through a variety of innovation programs, including labs, innovation zones, training, ideation workshops, skills development and partnerships with universities and businesses.
Governance, management and operations layer. The smart city creates disruption and results in digital transformation of existing processes and services. Smart city management models must integrate a new ecosystem of value creators and innovators. They must plan, support and monetize new business models, processes and services. They must upgrade their existing infrastructure and management processes to support “smart” services. Finally, they must measure the performance of the city with a new set of metrics.
Policy, processes, and public-private partnerships, and financing layer. The smart city doesn’t just magically appear one day. An entirely new set of engagement models, rules, financing sources, and partners are required to build, operate and maintain the smart city. Cities must develop a new set of “smart” competencies in order to get and stay in the “smart city game”.
Information and data layer. The lifeblood of the smart city is information. The smart city must facilitate this in several ways, including open data initiatives, data marketplaces, analytics services, and monetization policies. Equally important, they must have programs that encourage data sharing and privacy policies to protect what and how data is gathered.
Connectivity, accessibility and security layer. People, things and systems are interconnected in the smart city. The ability to seamlessly connect all three, manage and verify who and what is connected and shared, while protecting the information and users is crucial. The highest priorities for smart cities are to provide a seamless layer of trusted connections.
Smart city technology infrastructure layer. Most people automatically think of technology when talking about smart cities. The smart city technology infrastructure must scale beyond the traditional municipal users and support a new class of value creators, and city/user stakeholders.
Leveraging the smart city ecosystem framework
The smart city is a complex ecosystem of people, processes, policies, technology and other enablers working together to deliver a set of outcomes. The smart city is not “owned” exclusively by the city. Other value creators are also involved, sometimes working in collaboration and sometimes by themselves. Successful and sustainable smart cities take a programmatic approach to engage its stakeholders across the ecosystem.
Our research has found that many cities are not taking an ecosystem approach to smart city projects. This is due in part to smart city projects being managed by the Information Technology (IT) organization where their charter is on systems development and deployment. In contrast, more experienced smart cities manage their smart city programs through internal cross functional “Transformation” or “Innovation” organizations.
Regardless of where cities are in their smart city journey, they must get ahead of the “curve” with smart city projects. They begin by thinking in terms of building the broader ecosystem in order to create a sustainable and scalable smart city. Key next steps include:
- Understand the smart city ecosystem framework and tailor it to the realities of their specific city. Incorporate this model into the development of their smart city vision, strategy and execution plans.
- Relative to the smart city ecosystem framework, identify current capabilities and gaps across the various layers. Understand what is needed to support the four types of value creators.
- Evaluate existing and new smart city projects and initiatives against the ecosystem framework. Use this framework to identify what is missing from the project plans and what is needed to make the projects fully successful.
- Prioritize and develop competencies across the various ecosystem layers. A smart city requires new skills and competencies. Augment existing capabilities through strategic partnerships and contracting with service providers, as required.
Benson Chan is an innovation catalyst at Strategy of Things, helping companies transform the Internet of Things into the Innovation of Things through its innovation laboratory, research analyst, consulting and acceleration (execution) services. He has over 25 years of scaling innovative businesses and bringing innovations to market for Fortune 500 and start-up companies. Benson shares his deep experiences in strategy, business development, marketing, product management, engineering and operations management to help IoTCentral readers address strategic and practical IoT issues.
This post was co-authored with Renil Paramel, an IoT Innovation Catalyst, Strategist and Senior Partner at Strategy of Things.
Technologists and analysts are on a path to discovery, obtaining answers on how technology and the data collected can make our cities more efficient and cost effective.
While IoT may be seen as another buzzword at the moment, companies like SAP, Cloud Sigma, Net Atlantic and Amazon Web Services are working to make sure that for businesses, IoT is a reality. It’s companies with this willingness to change, adopt and invent that will win the new economy. Mobile phones, online shopping, social networks, electronic communication, GPS and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Most companies want their platform to be the foundation of everything it does, whether it is with big data, data analytics, IoT or app development. The same rub off phenomenon was emulated in Latin American countries like Brazil, Argentina, Mexico and European countries like Brussels, Italy, Germany, Denmark , Poland and Prague in recent times.
It is important to realize that technology is exploding before our very eyes, generating unprecedented opportunities. With easy access to cheap cloud services, smarter people came up with these platforms, and it has fundamentally changed businesses and created new ways of working. Mobile cannot be an afterthought. It needs to be integrated in everything you do and positioned at the forefront of your strategy. You have no valid reason to avoid migrating to the cloud. Cloud provides a ubiquitous, on-demand, broad network with elastic resource pooling. It’s a self-configurable, cost-effective computing and measured service. On the application side, cloud computing helps in adopting new capabilities, meeting the costs to deploy, employing viable software, and maintaining and training people on enterprise software. If enterprises want to keep pace, they need to emulate the architectures, processes and practices of these exemplary cloud providers.
One of the main factors of contributing value additions is the concept of a Smart City which is described as one that uses digital technologies or information and communication technologies to enhance the quality and performance of urban services, to reduce costs and resource consumption and to engage more effectively and actively with its citizens. We will interact and get information from these smart systems using our smartphones, watches and other wearables, and crucially, the machines will also speak to each other.The idea is to embed the advances in technology and data collection which are making the Internet of Things (IoT) a reality into the infrastructures of the environments where we live. We will interact and get information from these smart systems using our smartphones, watches and other wearables, and crucially, the machines will also speak to each other. Technologists and analysts are on a path to discovery, obtaining answers on how technology and the data collected can make our cities more efficient and cost effective. The current model adopted for IoT is to attract businesses to develop software and hardware applications in this domain. The model also encourages businesses to put their creativity to use for the greater good, making cities safer, smarter and more sustainable.
A few years ago like many others I predicted that Business models will be shaped by analytics, data and the cloud. Moreover, the IoT is deeply tied in with data, analytics and cloud to enable them and to improve solutions. The key goal is to ensure there is value to both customers and businesses. You can effectively put this strategy into action and build a modern data ecosystem that will transform your data into actionable insights.
Till we meet next time...
The best results will occur when technology and humans collaborate to create an entire ecosystem, which technology alone cannot achieve.
An early theme of digital transformation was the notion of selling services rather than products. A contract with the “thing maker” to circulate cooling fluid throughout my factory rather than a purchase order for me to buy the pumps and filters needed to do it myself, for example. The contract lets me focus on creating products for my customers rather than maintaining the machines making this possible. I don’t want to spend time on the process (pumps and filters), I just need the outcome (properly cooled machines) in the least distracting way possible to my core business of producing goods, medicine, energy, etc. The contract lets you, purveyor of the connected pumps and filters, build a closer relationship with me, streamline your business, and avoid competing in an increasingly commoditized space.
The fundamental shift happening today goes beyond providing guaranteed services rather than just hardware. Ensuring my lights stay on rather than selling me light bulbs solved your commodity hardware problem, but over time service offerings will face similar pressure as your competitors follow your connected product path and undergo digital transformations of their own. Your long term return on investment in IoT depends on more than keeping my lights on and water flowing. The value your IoT system creates for you depends on your IoT system’s ability to generate more business for me. There’s no such thing as a cheaper “good enough” replacement part when it comes to generating new revenue.
In healthcare for example, when your IoT system enables me to perform procedures in 24% less time, my clinics can perform 24% more procedures each day, increasing my revenue by 24% and delivering a 24% better patient experience. That’s what I’m looking for when I’m buying medical equipment. Depending on my corporate agility, the adoption and rollout of your connected machines may be a phased approach, following a progression of business outcomes. Asset Management means knowing the status of each device at all times and controlling them accordingly. This first step helps me see the potential value of incoming data and better understand my current utilization. Workflow Integration is connecting this information with my enterprise systems, which enables Predictive Maintenance and automatically alerts service technicians when a machine shows signs of impending failure. Where everything comes together and bonds me securely to your connected product service is Yield Optimization.
At this point your IoT system is collecting data from machines in my facilities as well as external data like weather and information from my other enterprise systems, correlating this information and uncovering patterns and ways for me to achieve more with less. Your “things” are now more than hardware installed in my facility performing physical tasks. They’re active components in a new System of Intelligence engaged in a loop of continuous learning and improvement.
This is true digital transformation, the creation of business value out of data collected and processed by your IoT solution.
Be ready for RPA storm coming in near future with the addition of artificial intelligence capabilities.
To paraphrase Geoffrey Moore, smart “thing makers” are investing in IoT solutions for their customers today in order to generate more revenue for themselves tomorrow. Traditional hardware vendors are being commoditized and replaced whenever a cheaper “good enough” option comes along. To thrive in the long run, your value must be “sticky”, embedded in your customer’s business, providing benefit to their customers as well. The “things” you sell now simply enable your customers to run their basic operations. Whenever a part breaks, customers make a decision to order a new one either from you or a competitor. How differentiated is your equipment from the rest of the market? Your business is constantly at risk.
What we’re seeing as a result are “thing makers” creating smart systems that empower their customers to not just operate, but to *optimize* their operations. These devices still perform their physical functions as before, but also collect and share a stream of data about their status and conditions in the world around them. It’s the data they produce, and the insights your system derives from this data, that enable your organization to offer far more valuable products and services to your customers that are not so easily replaced.
If you know the state of your machines at all times, you can build predictive maintenance and service models enabling guaranteed uptime and automatic replenishment. If your equipment never breaks or runs empty, your customer is unlikely to replace it with a competitor’s version.
If your products provide not just lighting and temperature control but also insights correlating usage patterns with time, weather, and utility data that reduce your customer’s costs, you can sell them this information for a percentage of these savings.
It’s the future. Your connected product system is part of your customer’s operating procedures, continuously generating insights for maximizing productivity. Improved asset utilization, faster turnarounds, synchronized workflows, and more. Smoother operations and reliable performance deliver better experiences for their customers, further expanding your customer’s business, because of your IoT solution. You don’t just sell “things.” You sell outcomes, which is what your customers really wanted in the first place.
That’s pretty smart.
Digital disruption is omnipresent, get on board or get thrown off the track.
Today’s consumer expects fresh food, whether it is in season or not, with an exotic dining experience.
Successful restaurants recognize that the easy path to their customers' stomachs begins in their minds. They need to grab customer's attention and entice them with a memorable experience in order to trigger repeat visits.
Here are some of the applications of Digital disruption in the restaurants & food service industry:
- Digital Signage to deliver eye-catching graphics to engage customers the moment they walk through the door
- Online reservations using mobile app & flexibility of customization of menu as per customer taste
- Chatbots: Restaurants are using virtual assistants to respond to customer inquiries and to process and customize customer orders. Taco Bell, Pizza hut have adopted chatbots to automate ordering process from a social media platform.
- Robots – Restaurants are using AI-driven robots to increase capacity and speed of food preparation and delivery.
- Recommendation engines – Developers are designing applications which use AI to help consumers choose meals & suggest foods based on their eating preferences.
- Wi-Fi enabled dining spaces for truly engaging customer experience
- Kiosks – Restaurants are integrating AI-driven self-service Kiosks to reduce customer waiting time and enhance the customer ordering experience.
- Pay by phone and flexible paying options
- Loyalty programs based on frequent visit
- Digital supply chains to accurate demand forecasting, inventory optimization, and cost reduction.
Restaurants generate vast quantities of data through software that controls everything from scheduling food delivery and shift staffing to taking reservations to managing vendors and inventory to paying bills.
Today almost every consumer is making dining decision on their smartphone. They have tried new menu item based on the mobile ad. Mobile payments have become the norm now in this industry. Customers would like to order quick meals via mobile and want to use mobile payments.
McDonald’s was the first store to accept Apple Pay.
Starbucks is a leader in digital transformation. Using more than 50mm Facebook fans & over 15mm Instagram followers at their disposal they mastered the social media engagement. First, they created an app to pay for coffee and food in their restaurants. Then they added the loyalty program, starting to craft hyper-personalized offers and experiences for their 24-hour connected customers. The company also developed new digital services to be enjoyed in their physical stores, achieving a highly praised omnichannel approach.
TGI Fridays, Wendy’s and other big names have all adopted digital technology to lure their customers.
OpenTable, GrubHub, and Zomato are some of the latest apps showcasing nearby restaurants with high-quality pics, presenting a menu with exotic pictures, price, ratings etc. you can also get offers, deals instantly.
The digital technology available to restaurants has streamlined the lives of restaurant owners much like smartphones have bettered our daily lives.
Digital has entered the restaurants and food industry through the front door and brings many exciting trends.
As consumers expect Apple to come up with a new iPhone every year that makes the earlier model obsolete, similarly they want fresh ways of serving food with fantastic dining experience which is made possible by Digital disruption.
- Image and Face Recognition: It understands the content of the image, classifies the image into various categories, detects individual objects and faces, detects labels and logos from the images.
- Language Translation: Translate text between thousands of languages, allows you to identify in which language any text that you need to analyze was written. Some APIs allows organizations to communicate with the customer in their language.
- Speech Recognition and Conversion: Today most of the customer service is handled by Chatbots with underlying APIs helping simple question and answer. Speech to text APIs are used to convert call center voice calls into text for further analysis.
- Text /Sentiment Analytics using NLP: With the rise of Social Media, consumers easily express and share their opinions about companies, products, services, events etc. Companies are interested in monitoring what people say about their brands in order to get feedback or enhance their marketing efforts. These APIs can identify, analyze, and extract the main content and sections from any web page. They further help in to analyze unstructured text for sentiment analysis, key phrase extraction, language detection and topic detection. There are some tools also helps in spam detection.
- Prediction: These APIs, as the name suggests helps to predict and find out patterns in the data. Typical examples are Fraud detection, customer churn, predictive maintenance, recommender systems and forecasting etc.
A lack of urgency is the greatest obstacle businesses face when considering the value of digital transformation. Proper planning is important but more than that execution as per the KPIs you select, is what take you through.
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