Guest post by Romain Wurtz, Chief Technology Officer, NarrativeWave

As companies engage in the implementation of analytics and data science applications, many challenges lie ahead. According to the Harvard Business Review, many data science applications fail due to poor goal definition, a lack of understanding of the key data, or a lack of focus on business value.

We believe the best route to data analytics and particularly analytics for the Industrial Internet of Things, must have several key elements:

Key Elements of Effective Analytics:

Builds upon your Subject Matter Experts’ existing knowledge. Allows engineers to use the platform and be part of the analytics process.

Enables automation of key processes.  Builds a solid foundation for more complex analytics (e.g. predictive).

This article takes a look at each of these elements in further detail and explores why they are important to driving value for your organization.

Having a platform built on your subject matter experts’ knowledge is the best starting point.

Your Subject Matter Experts (SMEs) and engineers have been building and maintaining your equipment for decades. Their expertise and knowledge is the best available expertise on how your equipment should be operated, maintained, and evaluated. Incorporating their knowledge to best evaluate data from the equipment and what that data means, is the ideal starting point for the application of analytics.

Analytics platforms using purely Machine Learning or Artificial Intelligence may lack insight on what the data means and the meaning of events within the data. Without human interaction or interpretation, more advanced analytics, such as predictions, have a difficult time achieving the desired outcome. Without a determined outcome, the process can take months to evaluate, and even then, the analytic effectiveness and accuracy can remain unknown and unproven.

We believe the best starting point for analytics is one that starts by using your own proven analytic methods as a foundation and then allows for a natural, building blocks approach.

Using a platform that allows engineers to be part of the process helps with the adoption of analytics.

Adopting new analytics and data driven business models is fundamentally about changing the way business has been done for many years. In an effort to make this transition, gaining adoption and trust of key players within your organization will significantly impact the success of a new program. Having a platform where SMEs can interact and engage, without having to be a data scientist or a developer, results in higher adoption and more impactful business outcomes for the organization.

Implementing a platform that automates current processes creates short-term and significant value.

In order to gain value from large data sets and sensor data, only a platform that starts to automate part of the process can create scalable value. Meaning, the platform must be able to interpret data, generate insights, and provide recommended outcomes for end users. Otherwise, it becomes just another way to visualize and explore data. This can add value on its own, but doesn’t reach the impact that automation provides. As noted earlier, building a system on your proven analytic methods, and then adding a layer of more advanced analytics, such as machine learning based predictions, is the best route to a highly accurate, automated platform.

Building a platform with a solid foundation of your experts’ knowledge is the best way to approach implementing an entire suite of analytics.

Building a platform configured by your own SMEs creates the optimal foundation for an entire range of analytics. Your experts can provide knowledge about significant areas such as:

The meaning of key data. How sensors are related to each other.

What constitutes an actionable event?  What constitutes a false alarm?

Exceptions to the rule.

Once this knowledge is part of an automated platform, adding a full range of analytics becomes more impactful. For example, knowledge of what constitutes a false alarm can lead to an insight describing what turned a false alarm into a valid alarm and what indicators are worth automatically tracking. By contrast, an approach that solely tries to use machine learning or AI techniques without these key understandings, can struggle with the “right” business outcome, accuracy, dealing with exceptions, and delivering significant value to the business.

Business Cases & Outcomes

These business case examples show how we at NarrativeWave impact customer’s operations, profitability, unplanned downtime, and workforce efficiency.

Improved Accuracy of Event & Alarm Analysis.

Challenge: The traditional workflow of diagnosing events or alarms on large industrial assets is a manual process for engineers. A manufacturer was looking for a solution that would increase accuracy and reduce the risk of costly human errors. 

Solution: NarrativeWave’s platform allowed the customer’s engineers to create detection models and equations through the SaaS platform. Currently, this manufacturer receives accurate and automated root-cause analysis of events in near real-time.

Impact: The software provided a 25% increase in accuracy of diagnosing events, which means a more consistent, predictable solution for this manufacturer’s engineers and clients.

Reduced Time Spent Diagnosing Alerts & Alarms

Challenge: Sensors on large industrial assets generate millions of data points per second. When an alert was triggered, engineers spent hours conducting redundant, manual research to diagnose the problem and produce an actionable report for clients. The diagnostic process can take up to 16 hours and technicians were struggling to keep up with the expanding service requirements. 

Solution: The NarrativeWave platform automated their manual processes, delivering an analysis, actionable insights, recommendations, and a report to their engineers in less than 3 minutes. This allowed their engineers to make near real-time decisions on what happened, why it happened, and what to do next.

Impact: The outcome resulted in a 95% time savings in diagnosing alerts and alarms, which reduced unplanned equipment downtime, improved workforce efficiency, and enhanced service contract profitability. This proved the opportunity for a multi-million dollar savings per year for this OEM, and better supported real-time service contracts.

Optimized Productivity of Skilled Engineering Labor

Challenge: More than 50% of all industry alarms are false positives, which still have to be diagnosed and solved. A customer was looking for a solution that would allow their engineers to optimize their workflow and spend less time servicing invalid alarms. 

Solution: The NarrativeWave platform automated the root cause analysis of events to produce actionable insights based on the manufacturer’s data. The outcome was an explanation of the event that occurred and guidance on what to do next, which was provided to the engineers within a few minutes.

Impact: The platform accurately and quickly invalidated false alarms, allowing engineers to focus more time on resolving valid alarms and serving their clients. For the first time, engineers were being leveraged in the best way to impact this manufacturer’s operations.

Increased Efficiency in Creating Detection Models

Challenge: A large enterprise client had a robust analysis setup with 3 detection models and 150 threshold variants. The client’s process for iterating detection models originally took 3–4 months and required engineers to rely on development from either a software engineer, data scientist, or an outside software vendor. 

Solution: NarrativeWave’s platform provided an intuitive pipeline, enabling their business users to quickly create, manage, and iterate their own detection models. The platform is user-directed, managed and utilized by the customer’s internal engineers, without the ongoing need of developers or data scientists.

Impact: The iteration timeframe has been dramatically reduced since using NarrativeWave. More importantly, this customer’s engineers can setup iterations on their own, allowing for immediate impact on the business operations and for their clients.

Enhanced Next Generation Knowledge Base

Challenge: Engineers have been detecting alarms individually for 30 or more years. While working with a major engine manufacturer, NarrativeWave found the detection process was not recorded, standardized, or made available to other engineers and management within the organization. 

Solution: The platform is setup to record the engineers’ knowledge and feedback, resulting in a platform that gets smarter over time. Engineers can customize the business analysis and recommendations to make them as accurate as possible, therefore creating an evolving knowledge base for SMEs. 

Impact: The outcome resulted in the manufacturer, for the first time, being able to capture their engineers’ knowledge. This increased collaboration between engineers, improved standardization, and allowed valuable knowledge to be visible across the organization.

Improved Fleet Health & Management

Challenge: Manufacturers and equipment operators currently lack visibility into assets across their entire fleet, making it difficult to identify poorly performing assets and best performing assets. 

Solution: With NarrativeWave, asset performance can be evaluated near real-time, enabling organizations to better manage critical assets and plan for future actions, all by the click of a mouse.

Impact: The platform-wide view provides significant time-savings of tracking and managing fleet health for equipment manufacturers and operators. Additionally, the platform reduces unplanned downtime and helps organizations prevent critical equipment failures.

Improved Predictive Analytics & Maintenance

Challenge: Manufacturers and equipment operators are interested in deploying predictive models for better asset maintenance and warranty support. Pure machine learning approaches lack a solid foundational basis and can be difficult to implement successfully.

Solution: With the NarrativeWave Knowledge Base, key information such as the meaning of events, the relationship of sensors, and what constitutes a valid alarm are already known. By applying machine learning techniques to a solid NarrativeWave foundation, predictive analytics is more effectively implemented. 

Impact: This approach provides a strategic method of utilizing predictive analytics and improves the outcome of implementing analytics. The result is a highly accurate, auditable platform rather than a pure “black box” approach.

Internet of Things (IoT) has generated a ton of excitement and furious activity. However, I sense some discomfort and even dread in the IoT ecosystem about the future – typical when a field is not growing at a hockey-stick pace . . .

“History may not repeat itself but it rhymes”, Mark Twain may have said. What history does IoT rhyme with?

 I have often used this diagram to crisply define IoT.

Even 10 years ago, the first two blocks in the diagram were major challenges; in 2017, sensors, connectivity, cloud and Big Data are entirely manageable. But extracting insights and more importantly, applying the insights in, say an industrial environment, is still a challenge. While there are examples of business value generated by IoT, the larger value proposition beyond these islands of successes is still speculative. How do you make it real in the fastest possible manner?

In a slogan form, the value proposition of IoT is ”Do more at higher quality with better user experience”. Let us consider a generic application scenario in industrial IoT.

IoT Data Science prescribes actions (“prescriptive analytics”) which are implemented, outcomes of which are monitored and improved over time. Today, humans are involved in this chain, either as observers or as actors (picking a tool from the shelf and attaching it to the machine).

BTW, when I mentioned “Better UX” in the slogan, I was referring to this human interaction elements improved by “Artificial Intelligence” via natural language or visual processing.

Today and for the foreseeable future, IoT Data Science is achieved through Machine Learning which I think of as “competence without comprehension” (Dennett, 2017). We cannot even agree on what human intelligence or comprehension is and I want to distance myself from such speculative (but entertaining) parlor games!

Given such a description of the state of IoT art in 2017, it appears to me that what is preventing us from hockey-stick growth is the state of IoT Data Science. The output of IoT Data Science has to serve two purposes: (1) insights for the humans in the loop and (2) lead us to closed-loop automation, BOTH with the business objective of “Do More at Higher Quality” (or increased throughput and continuous improvement).

Machine Learning has to evolve and evolve quickly to meet these two purposes. One, IoT Data Science has to be more “democratized” so that it is easy to deploy for the humans in the loop – this work is underway by many startups and some larger incumbents. Two, Machine Learning has to become *continuous* learning for continuous improvement which is also at hand (NEXT Machine Learning Paradigm: “DYNAMICAL" ML).

With IoT defined as above, when it comes to “rhyming with history”, I make the point (in Neural Plasticity & Machine Learning blog) that the current Machine Learning revolution is NOT like the Industrial Revolution (of steam engine and electrical machines) which caused productivity to soar between 1920 and 1970; it is more like the Printing Press revolution of the 1400s!

Printing press and movable type played a key role in the development of Renaissance, Reformation and the Age of Enlightenment. Printing press created a disruptive change in “information spread” via augmentation of “memory”. Oral tradition depended on how much one can hold in one’s memory; on the printed page, memories last forever (well, almost) and travel anywhere.

Similarly, IoT Data Science is in the early stages of creating disruptive change in “competence spread” via Machine Learning (which is *competence without comprehension*) based on Big Data analysis. Humans can process only a very limited portion of Big Data in their heads; Data Science can make sense of Big Data and provide competence for skilled actions.

To make the correspondence explicit, "information spread" in the present case is "competence spread"; "memory" analog is "learning" and "printed page" is "machine learning".

Just like Information Spread was enhanced by “augmented memory” (via printed page), Competence Spread will be enhanced by Machine Learning. Information Spread and the Printing Press “revolution” resulted in Michelangelo paintings, fractured religions and a new Scientific method. What will Competence Spread and IoT Data Science “revolution” lead to?!

From an abstract point of view, Memory involves more organization in the brain and hence a reduction in entropy. Printed page can hold a lot more “memories” and hence the Printing Press revolution gave us an external way to reduce entropy of “the human system”. Competence is also an exercise in entropy reduction; data get analyzed and organized; insights are drawn. IoT Data Science is very adept at handling tons of Big Data and extracting insights to increase competence; thus, IoT Data Science gives us an external way to reduce entropy.

What does such reduction in entropy mean in practical terms? Recognizing that entropy reduction happens for Human+IoT as a *system*, the immediate opportunity will be in empowering the human element with competence augmentation. What I see emerging quickly is, instead of a “personal” assistant, a Work Assistant which is an individualized “machine learner” enhancing our *work* competence which no doubt, will lead each of us to “do more at higher quality”. Beyond that, there is no telling what amazing things “competence-empowered human comprehension” will create . . .

I am no Industrial IoT futurist; in the Year 1440, Gutenberg could not have foreseen Michelangelo paintings, fractured religions or a new Scientific method! Similarly, standing here in 2017, it is not apparent what new disruptions IoT revolution will spawn that drop entropy precipitously. I for one am excited about the possibilities and surprises in store in the next few decades.

PG Madhavan, Ph.D. - “LEADER . . . of a life in pursuit of excellence . . . in IoT Data Science” 

http://www.linkedin.com/in/pgmad

This post original appeared here.

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Surprise! Operations and IT aren't getting along when it comes to IoT.

451 Research announced new survey results that show operational technology (OT) and IT stakeholders are not aligned on IoT projects. Sure will be harder to drive business results if this doesn't get fixed. Here are some key findings:

Research shows that IT and OT personnel are not well aligned on IoT initiatives, and they need to cross that divide for those enterprise IoT projects to prove viable.

• Only one-third of OT respondents (34%) said they ‘cooperate closely with IT’ on IoT projects from conception to operations.
• A relatively small group of respondents said they were in ‘active conflict’ with IT over IoT, OT professionals are four times more likely to characterize their relationship with IT that way.  
• More than half (55%) of the OT survey respondents currently deploy IoT within their organization, and 44% have successfully moved those projects from proof of concept to full-scale deployment.
• New operational efficiencies and data-analytics capabilities are driving successful projects; however, many IoT projects face roadblocks in the trial stage due to the IT and OT divide and budget, staff, and ROI concerns. 

Additional details in the graphic below. Want the full findings? 451 Research will happily sell it to you.

After years of evangelization waiting for the promises of the Internet of Things (IoT) to come true it seems that we are finally close to reaching the trough of disillusionment phase, we begin to forget all the hype generated so far and focus on reality. A harsh reality that involves selling IoT and not continue selling smoke anymore

THE TIME TO SELL IoT IS NOW

The sale of IoT is perhaps more complex than the sale of other disruptive technologies such as Big Data, Cloud or AI and maybe as complex as Blockchain today.  In the article “ Welcome to the first “Selling IoT” Master Class!” I commented how it should be  the evolution of M2M Vendors for sell IoT and how should be the evolution of IT Technology Vendors for sell IoT. However, many of these companies still have difficulty in forming and finding good sellers of IoT

The truth is that nowadays it does not make any sense to sell IoT as a technology. Enterprise buyers only want to buy solutions that provide measurable business outcomes while, in the other side, many IoT Vendors only want to sell their portfolio of products and services that have been categorized under the umbrella of IoT, either as quickly as possible or at the lowest possible cost.

During last 5 years, I have been analysing how IoT companies sell their products and services. Some of my customers (Start-ups, Device vendors, Telco Operators, Platform vendors, Distributors, Industry Applications, System Integrators) requested me to create IoT sales material to train their sales team about how to sell their IoT solutions and services. And sometimes I also helped Head Hunters or customers searching for IoT sales experts

Based on this varied experience I have launched this year a new service: “IoT Sales Workshops” to help companies train their internal teams in how to sell IoT. Here are some of the lessons I learned

• There is a time for act as an IoT Sales generalist and a time for act as an IoT Specialist.
• You need to adapt the IoT storytelling based on your audience.
• Being an IoT expert is not synonymous with being successful in selling IoT.
• You need to show how companies can get more out of IoT by solving a specific business problem.
• Make it easy for the customer to see the benefits of your IoT product or IoT service and what is the value you are adding.
• Given the complexity and specialization of IoT by vertical, explain companies the need to focus more closely at business cases, on their IoT business model as well as the ROI over three to four years before jumping into technology.
• You need to be patient because IoT selling is not easy and takes time align strategy and business needs with the IoT products and services you are selling.
• Build a strong ecosystem and make easy the customer the adoption of end to end IoT solution collaborating with your partners.
• Train your IoT Business and Technical experts to get better at telling stories. Design a new marketing and sales communications playbook. Keep it simple. Build your narrative from the foundation up – one idea at a time.
• If you want an IoT sales expert you need to pay for it (not expect miracles from external sales agents working on commission base).
• IoT Sales is a full-time job. You will not have time to other enterprise activities.
• Selling IoT to large enterprises is a teamwork process.
• Be Persistent. Do not expect big deals soon.
• Be Passionate, Be Ambitious, Be Disruptive to sell IoT.

Summary

I do not consider myself an IoT sales expert. And of course, neither a superman of sales. In fact, I have shied away from classifying myself in the role of a pure salesperson even though over time I have given a weight and value to this work that once seemed derogatory to me.

Sell IoT is not easy. In a few years we will have forgotten of the word IoT and we will be selling new hypes, but in the mean time you need to be prepared for disillusionment moments, long sales cycles and a lot of work with sometimes poor results. However, I do not know if will be 2020, suddenly if you persevere you probably will be awarded as the best IoT sales expert and you finally will earn a lot of money.

Be Persistent, Be Passionate, Be Ambitious, Be Disruptive to sell IoT

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• From the IoT Evolution to the Revolution of the Cognitive Age
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• Can the Industrial IoT Produce Energy Savings in Smart Factories?
• Can IoT Platforms Help Solve the Food Waste Problem?
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The rise of IoT is good because it has enabled humans to gather, process and understand vast sums of data. This understanding helps us observe the nature of Human existence in real time, both collectively and individually.

Supply chain management is an integral business process. It affects people in every industry from farmers in Food Supply Chain to manufacturers in Industrial Supply Chain. We are going to observe the up and coming technologies and how they are revolutionizing this fundamental business process.

How is IoT being used in today’s World?

The Understanding of Mass Human behavior at an individual level has enabled services and technologies to exist that cater to personalized needs.It is introducing a new genre of innovation in Mobile App Development.

Companies use this data to develop applications that can efficiently increase revenue by cutting liability costs because of Big Data Analytics and IoT prompting all investments.

Take for example the efficiency with which you can use GPS trackers and environment sensors to keep track of your inventory and the storing conditions of your goods. Asset Tracking has created transparency in the supply chain, providing manufacturers with scope for business customization.

The kind of granular data that can and is being generated using RFID tags and global SIMs can create efficient staffing practices. Also, addressing the availability of complementary resources at the right place and right time.

There is Beacon technology, which is Low Energy Bluetooth devices (BLE), capable of transmitting information over short distances. Bluetooth SIG (Bluetooth Special Interest Group) is pushing this wireless personal area network as a factory floor network.

BLE is being used to create an Internet of Things solution, for instance, take IoT Development companies that created apps that help in Airport Baggage Management all by using these BLE devices or Beacons.

Another great example can be that of Amazon Go. It uses computer vision, machine learning and AI to create a shopping experience where you can just walk in, pick up what you want and walk out.

You check into the store with your mobile Phone and through a technology they have developed called “Just Walk Out” you can shop and just leave. It is one of the best examples of an Internet of Things Company, using a variety of sensors and computer vision tracking working together over a secured shared network.  

How is IoT affecting the Supply Chain Processes (SCP)?

Gartner the leading research and advisory organization, recently released a study, showing a thirty-fold increase in Internet-connected physical devices by 2020. 

International Data Corporation (IDC) reports: Largest IoT segments in 2017: manufacturing operations: $105B

Just imagine the kind of data that will be generated when we could observe the real-time shopping habits of individuals, their waiting time in each aisle, their preference. And the rate at which products and services are sought will see an unprecedented rise.

We will be able to automate a system that will conduct targeted marketing and efficient manufacturing. Research shows three-quarters of all retail and manufacturing ventures beginning to transform their supply chain processes.

IoT is enabling a more bidirectional flow of communication. Now engineers can run efficient diagnostics using the most recent captured data enabling them to conduct remote repair, increasing machine uptime and better customer service.

Unlike previously available passive sensors, this generation of sensors can keep track of the state of products in shipment, such as external surrounding and execute actions. Also, it can monitor utilization of Machine and update cloud platforms that can, in turn, optimize performance and workflow.

IoT is playing an integral role in increasing the scope of digitizing the Supply Chain in the Agro-Industry. Modern farmers are now incorporating Cloud Platforms to keep track of their farm produce and fine-tuning storage conditions.

A much more inter communicative channel is being formed between the different talking heads of the Supply Chain. And the funny thing is IoT devices are guiding how the products reach the market and talking has nothing to do with it.

Industries are trying to create the process more transparent for the consumers, certifying quality checks and an invasive feedback process.

Fleet Management for industries that comprise of companies like FedEx and DHL. Driver headcount, maintenance, and fuel consumption can all be brought down using IoT cloud Platforms. These platforms take in enormous amounts of data about diverse variables like traffic models, weather reports etc. and chart out efficient routes and delivery itineraries.

Having a connecting channel among all the components of the supply chain enables vendors to form better relations amongst themselves and with the customers. This is done by linking the shipping companies to the on-ground delivery services to the shopkeeper, all in real time.

We generate a truly end to end offering by providing vendors with domain expertise in IP connectivity, cloud service, security, hardware, and positioning.

With the help of IoT, we can accurately forecast inventories; keep track of the expiry dates of products and restocking schedules. It can also be used for cutting on Downtime with smart sensors, which are assessing maintenance requirements around the clock, propagating positive revenue generation.

Fitting the factory floors and machinery with sensors helps the system to tail workflow efficiency and logistics short-comings and respective requirements.

The Industrial Internet of Things revolution is pushing entire businesses towards an approach of local connectivity. Many businesses are adopting tools like AT&T’s Low-Power Wide-Area Technology, which has smaller modules with extended battery life and capable connectivity even in underground environments.

This has also created a demand for developers who excel in creating IoT Applications. And lately, it seems IoT technology and software framework has become essential to the 21^st-century consumer market at par with Big Data Analytics and Management.

IoT compatibility is the need of the hour for businesses that want to stay ahead of the curve.One should investigate functional ways to integrate IoT technology and Applications into their Business Back-End and generate new streams of revenue.

Also, existing Businesses need to acknowledge the potential of IoT to redesign existing SCP. Building strong bridges to support the convergence of physical and digital supply chain.

In today’s market, SCP isn’t just for tracking your product. It’s an opportunity to gain an edge over your competitors and even establish your own brand.

I think so.  

If you run a manufacturing factory, you have just a handful of variables that let you cut costs. Chief among them is energy use. Energy conservation saves money obviously, but turning off one switch at a time compared to controlling thousands, that would be interesting.

A lot of efforts are taken to save energy historically, for instance, use of motion sensitive bulbs, limited time use of air conditioners, or cutting the number of shifts and functioning hours is another way to save energy costs. But those actions require productivity/OEE boosting focus of the facility rather than effect energy conservation. Energy conservation is a byproduct of those efforts.

Adding IoT on the other hand, can enable direct energy savings for the smart factory of today.

Many experts recommend IoT-based real time monitoring systems to bring optimum use of energy. But the issue is more nuanced than that. Sure, real time monitoring helps you track energy consumption, but that might not lead directly to energy conservation. For that, the realtime energy monitoring should lead to better predictions of energy usage and guide to implement right load level energy equipment.

The 2 components of electrical energy billing

Let us take an example of electrical energy. Usually, electrical energy billing has two components:  Demand charge and runtime/consumption related charges. Demand load is usually the peak load provided by the electricity service providers from the power grid. This usually has a hard and fast limit. Crossing it will prompt penalties of around 20 times the usual rates.

To avoid this, there are usually two options: Reduce the total load required by the machinery. Or ensure that the threshold limit is never reached.

The problem of motors

One of the major sources of electricity usage in the plant are the electrical motors and HVAC systems. They consume a large chunk of the power. A motor is considered under-loaded when it is in the range where efficiency drops significantly with the decreasing load. Most electric motors are designed to run at 50% to 100% of rated load. Maximum efficiency is usually near 75%. Below the 50% rated load, the efficiency tends to lower dramatically.

In many cases, operating motors are either overloaded resulting in overheating or under-loaded, working at most at 40% of their capacity. That causes huge spikes in energy consumption. Oversized motors have a higher initial cost and are very expensive to repair and maintain. Undersized motors don't perform well and prompt higher losses than properly sized electric motors. Same goes with air conditioners if their tonnage and room size or room dynamics aren’t suitable, it leads to higher energy consumption.

Addressing a Wide Range of Energy Consumers:
Apart from regular electrical consumption of motors and HVAC, IoT can address a wider energy sources and resources, including: 

• Air compressors, the source of air across plant.
• Boilers, serving as the main source of steam used across plants.
• Backup generators - an alternative electricity source in case of failure of the primary.
• Fuel, including diesel, coal, wood, solar, and batteries that are used to run above systems

How the Industrial IoT can help

In the pre-IoT era, the traditional energy management system would collect a sample of energy usage at an interval. The traditional EMS is good to get energy consumption data, but it does not help you with alerts in case of spikes, curating usage pattern, predicting the seasonal demand, or suggesting appropriate configuration. Pre-IoT era, the motor load test was a lengthy and cumbersome affair. Engineers used slip tests and electrical tests with a digital stroboscope. They had to spend hours with the equipment to obtain samples. Even then, the data collected was only a sample, and not real time. With the IoT in place, the analyses can occur on real time data from the motor. That makes the analysis quick, painless and more accurate. IoT brings realtime alerts, ability to predict energy demand, usage patterns and ways to optimize energy consumption.

With the right IoT platform, you can recommend the proper sizing needed for motors. That saves money on the original investment. IoT-based conditional monitoring ensures the motor never reaches its threshold limit. That means the motor lasts longer and suffers fewer failures.

The IoT-based monitoring system gives early warnings of electric motor vibration/temperature problems. Condition monitoring saves time from unplanned production outages. And the unnecessary stress of carrying out urgent repairs can be avoided.

Additionally, a properly designed IoT system can not only track the energy consumptions at distribution points throughout a smart factory, but with the help of smart meters, they can track energy consumption right from its source to all the way consumption point. Moreover it can help predict leakages or voltage drops at nodes if any.

The ultimate goal of the smart factory is a generating a real-time energy audit that traditional Energy Monitoring Systems (EMS) cannot provide.  IoT enabled energy monitoring can solve a lot of issues that are core to hindering a plant from real energy conservation efforts. That not only saves money but paves the way for true implementation of Industry 4.0. If you run a factory and are looking to cut energy costs, then IoT is worth a closer look.

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Here's the latest IoT Central Digest. Encourage your friends and colleagues to be a part of our community by forwarding this newsletter to them. They can join IoT Central here. You can contribute your thoughts on IoT here.  

Featured Resources and Technical Contributions

• New Developments in IoT connectivity +
• Podcast: Smart Cities and IoT
• Mobile World Congress 18 - Trapped in the Groundhog Loop
• Build, buy, partner? Strategic options for creating smart solutions
• IoT Minute Episode 72: Why IoT?
• It’s Time To Conceive For A Direct Integration Between The Physical World And Computer-Based Systems

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One of my LinkedIn contacts suggested me last year not to write more articles about MWC event. However, a couple of weeks ago talking with another contact that not attend this year told me he was expecting my article. So here it is, my fourth MWC article in a row since 2015. Am I a MWC addictYou can read previous articles here:

• 2015 - MWC 2015: Everything Connected, Tapas and Jamon
• 2016 - “GSMA need to think how to reinvent MWC”
• 2017-  “MWC- The Great Illusionists Show!”

Unfortunately, the Powerful GSMA rejected my ticket request as Analyst / Press (LinkedIn please help me next year) and of course I did not pay the prohibitive prices of Silver Pass, Gold Pass and Platinum Pass. At the end, conference sessions content is very generic and I can read free the content. I cannot justify the ROI for pay these tickets. Can you?

Avoiding the politics issues between Catalonia and Spain, it was the first MWC where the snow was probably the biggest surprise of the show. The snow and the rain did not allowed visitors to spend some time outside.

A painI do not know the final numbers, but I notice this year less attendants than 2017. No doubt GSMA will try to find excuses eg, political issues, but the reality is that the cost of the show do not convince to many usual large / medium / small companies. It is a fact that some big companies did not attend or send less delegates and use less square meters

Again, visitors that attend 1 or 2 days do not have had time to move to other parallel events like 4YFN.  Running from meeting to meeting, bad lunch as usual. I'm sure I've lost weight these days

The MWC18 has been an evolution of what we saw last 2 years. Not revolution. We need to wait another 5 years to see some notorious technological advances although GSMA should continue helping to create a better future

Before #MWC18

I was angry with the Search exhibitor page of the web . Please GSMA you have 1 year to improve. None exhibitor has included any product in the category of Blockchain or Internet of Things. Duplicates filters, etc. I read some LinkedIn post and articles and talked with people to plan my visit and capture their feeling this year.

• Dean Bubley posted a few days ago “7 reasons he will not go to MWC anymore”. For him, MWC is not productive, informative or fun.
• Matt Hatton, VP Research at Garner make Predictions for MWC this year: - Inappropriate use of blockchain - Inappropriate description of things as AI - Inappropriate use cases for 5G - Better focus on go-to-market for IoT - More apparent rigor applied to the whole POC process - Better specialization (rather than trying to sell "end-to-end IoT") So essentially my expectation is that vendors of all shapes and sizes are looking better on commercial than technological issues. In sharp contrast with previous years. Funny old world.
• Part I covered Analytics, AI, new radio and antennas, Part II NFV, cloud and open source news, plus the 5G delulge. and part III, The third part takes a close look at the network edge, looking at some major industry moves and at the MEC demos.
• Blockchain Makes an Appearance at Mobile World Congress 2018 - 5G will be less sizzle, more substance at MWC
• MWC is exploring Dubai or Paris as alternative to Barcelona. But, the big operators are committed to the defense of Barcelona as the headquarters of the Mobile: "It is the best place in the world".
• Ericsson launches marketplace to unlock the cellular IoT ecosystem
• IoT: Sensor-as-a-service, run by blockchain, is coming - Nokia is launching a blockchain-driven Internet of Things (IoT) sensor network for its telco equipment users to sell on.
• Que es y que no es Aura

During the #MWC18

• MWC Day 0 – Halcyon days.
• Four Highlights from MWC2018 by Nick Hum
• MWC18 Highlights Mobile World Live
• MWC 2018 – who are we trying to convince? – Share with me the idea that Mobile World Congress can feel like a geeky take on Groundhog Day.
• MWC 2018 – Reflections From An Airplane by Ingrid Wistrand, Deputy CEO of Tele2 IoT.
• Mercedes-Benz at the Mobile World Congress 2018: Focus on artificial intelligence
• Here the MWC18 Week in review

The euphoria of 5G has dropped – More info about 5G at MWC18 here “ Intel, Qualcomm Talk About Accelerating 5G Efforts at MWC 2018” 

IoT - The word that describes my feeling is disappointment. Although expected, something sad because the word IoT begins to lose brightness and disappear from the stands. The Pavillion 8.0 dedicated to the IoT, was not star this year. Do you really deserve to be exhibitors at the MWC

At least it was good to pulse the evolution and transformation of the IoT / M2M market. A new impulse will be necessary before 2020

Unfortunately, I could not attend any of the Top 7 IoT Activities at Mobile World Congress. Please tell me if any of it was worth it.

It was funny to hear how Operators trying to explain the use cases of Blockchain in Telco sector.

Artificial Intelligence, Connected Vehicles and Robots the starts of MWC18.  It was interesting discuss with some Operators about the practical potential of Machine learning, Artificial Intelligence (AI), Robots in this sector.  The conclusion in this article “ You Can't Teach an AI to Run a Telecom Network—Yet.

MWC18 was in my opinion the year of the Connected Intelligent Vehicle. Operators, Technology Vendors and Car Manufacturers need to cooperate to avoid a technology nightmare for future drivers/passengers.   

After #MWC18

I cannot resist to compare this congress with the Groundhog Day festivities. I make no secret of my discomfort for the continuous decisions of GSMA to make this show useless for many. My unpleasantness for the prohibitive cost of the tickets, hotels in the town, and the arrogant executives who attend the event as movie stars and finally for the many parallel events that I have missed or meetings of 15 minutes because I had spent hours daily walking by the walk sides of Fira Halls and my frustration for not finding some companies in the labyrinth of  the pavilions

Like Bill Murray in the movie, I discover year after year that MWC's events repeating almost exactly. I feel I am trapped in a time loop that probably most of you are aware of

I am glad if you have spent these days indulging in night parties, looking for new jobs or cheering you for the work you have in your great company.  Luckily for me, I do not return depressed, but my mind do not escape for some days to the MWC loop. Am I a MWC addict?

See you next year at MWC19 Barcelona

Thanks for your Comments and Likes

Earlier this week I recorded an interview with IoT Central Contributor and leading IoT recruiter Bill McCabe. We talked about smart cities and I revealed something you probably didn't know about me. Have a listen.