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Microsoft and TIBCO Team Up to Bring Machine Learning and AI to IoT and the Edge

July 18, 2020 TIBCO Spotfire

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In our continuing commitment to accelerate digital business transformation through the use of artificial intelligence (AI) and machine learning (ML), TIBCO unveiled the capabilities of TIBCO Spotfire^® and TIBCO^®Data Science to support Microsoft Azure Cognitive Services at a recent Build conference. Thanks to this combo of TIBCO and Microsoft technologies, sensor data, and log data can now be analyzed on edge devices with little to no internet connectivity. This means less lag time for analytics and hence, faster response times for businesses based on that intelligence, particularly in places with unreliable internet connections.

The need for intelligence at the edge without internet connectivity is growing fast. In fact, in a report by Gartner, they predict that “By 2022, more than 50% of enterprise-generated data will be created and processed outside the data center or cloud.” As more devices become intelligent, there will be a greater need for processing the data they collect outside of core systems and closer to the edge.

Think of factory floors, shipping containers at sea, and any other location where the internet is spotty. Now, in places with little to no internet connectivity, developers can bring the power of TIBCO visual analytics and data science solutions to detect and prevent anomalies. Anomalies help improve their organization’s operational efficiency and lower equipment costs.

“Increasingly, customers performing predictive maintenance must look for ways to leverage AI closer to where their data is generated for timely analytics and responses,” said Matt Quinn, Chief Operating Officer, TIBCO. So, for those who have focused on bringing AI to their core systems, it’s now time to think about how you can extend intelligence to your Internet of Things (IoT) and edge devices that might lack full-time internet connectivity.

For those who have focused on bringing AI to their core systems, it’s now time to think about how you can extend intelligence to your Internet of Things (IoT) and edge devices that might lack full-time internet connectivity. Click To Tweet

Some excellent use cases highlighting the power of intelligence at the edge where internet connectivity is questionable or shaky have been popping up in recent years including the University of Iowa Hospitals and Clinics who are proactively predicting infection rates using advanced analytics on data collected on medical devices. Their efforts saw a drop of 74 percent in onsite surgical infections as a result of making predictive decisions directly in operating rooms. Anadarko, an oil and gas exploration and production company that has been working with TIBCO for several years, increased the speed of their drilling projects by 10 times even though many of their drill sites are in the middle of the ocean or in underdeveloped countries where internet connectivity is spotty at best.

With so much data being generated at the edge, it’s not always practical to bring that data back to a central location for processing. You need the capabilities of AI and ML to bring better and faster decisioning to the edge without the limitations of, slow or no internet connectivity. For more information on how the TIBCO plus Microsoft use case might be able to help your company realize machine learning at the edge, please visit Microsoft Channel 9 for the demo given at the Build Conference, read the TechTarget News article or TIBCO press release for more details.

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The Need for Speed: Faster Data Access as Competitive Edge

June 5, 2020 Sisense

The cloud isn’t the future; it’s right now. In the Clouds is where we explore the ways cloud-native architecture, cloud data storage, and cloud analytics are changing key industries and business practices, with anecdotes from experts, how-to’s, and more to help your company excel in the cloud era.

The world of data is constantly changing and speeding up every day. Companies are storing more types of data from applications as well as the Internet of Things. This data flows into cloud-native warehouses where data teams manipulate it, allowing analysts to derive vital insights from it, and product teams embed those insights into products. Data is the bedrock on which the future of business is being built.

As the data that these businesses need to thrive continues to grow and its pace of change accelerates, it’s never been more important for employees at all levels of an organization to have fast access to actionable data in order to make strategic, operational, and tactical decisions. It’s both basic table stakes for success in the “new normal,” as well as a defining edge that companies can use to stay ahead of the competition.

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Saving lives in real-time

Easier access to fast-updating datasets isn’t just about making better decisions or powering the next killer app. It also can also save lives and change the way the world works.

“There are a wide range of scenarios where having super-fast access to real-time data can make a huge difference,” said Christelle Scharff, a professor and computer scientist based at Pace University in New York. “Fast access to data captured by video surveillance systems, for example, can improve security… It’s also the driving force behind autonomous cars. Our biggest industrial firms can use it for preventative maintenance — saving potentially millions of dollars. And almost all organizations can use it to avoid potential threats from security breaches and malware attacks.”

The success of COVID-tracing efforts will depend on fast access to multiple data sources.

George Thiruvathukal, professor of computer science at Loyola University

During our current pandemic, access to real-time data can also save lives. “Health officials are investigating how contact-tracing apps can help manage the ‘reopening’ after we begin to reopen the country after the COVID-19 lockdown,” said George Thiruvathukal, professor of computer science at Loyola University in Chicago. “The success of this will depend on fast access to multiple data sources.”

There’s an impact on customer expectations too. According to recent research from IDC, consumers are embracing personalized real-time engagements and resetting their expectations for data delivery. As their digital world overlaps with their physical realities, they expect to access products and services wherever they are, over whatever connection they have, and on any device. They want data in the moment, on the go, and personalized. As a result, IDC predicts that nearly 30% of the global datasphere will be real-time by 2025.

Pressure on infrastructure builds

As enterprises demand data infrastructures that can meet this growth in real-time data — and ultimately assist with their product differentiation strategy — the pressure put on product teams is huge.

Product teams are already having to manage the growing complexities that come with modern data environments.

Chandana Gopal, Business Analytics Research Director, IDC

“Product teams are already having to manage the growing complexities that come with modern data environments,” said Chandana Gopal, research director for business analytics at IDC. “Not only do they have to deal with data that is distributed across on-premises, hybrid, and multi-cloud environments, but they have to contend with structured, semi-structured, and unstructured data types. Multiple technologies to manage data at rest and in motion have compounded the challenge of managing data and making it accessible to decision-makers in the right time, in the right format, and in the right context.”

Managing cloud data is a key challenge for data and product teams who are tasked with connecting to a wide array of datasets stored in cloud-native warehouses and other locations. In a large-enough company, there can even be multiple clouds being operated by different divisions and teams. BI and analytics providers have had to design their platforms to serve up fast insights no matter where the data being analyzed resides, even partnering with third-party companies to make sure that their platforms can handle data from oft-used services like AWS, Google Cloud, and others.

Which makes sense, as customers searching for an analytics solution are also often grappling with their recently-purchased or possible cloud options:

“When customers come to us for their BI and analytics needs, in the same sentence they’re often telling us that they’re considering their cloud options,” said Erin Winkler-McCue, Lead for Strategic Partnerships & Special Projects at Sisense. “These conversations are no longer siloed. Customers want to know that our platform will work seamlessly with their chosen cloud vendor, even if that just means something as basic-sounding as making sure queries between the vendor and Sisense are optimized.”

The Need for Speed: Faster Data Access as Competitive Edge

The challenges these teams face become even more daunting when one looks towards the future, as new technologies like the internet of things, machine learning, 5G, and augmented reality will add a new level of demand. Forbes Insights data shows that in order to benefit from emerging technologies like these, 92% of CIOs and CTOs say their business will require faster download and response time in the near future. What’s concerning is that, despite recognizing this, just 1% of data center engineers believe their data centers are updated ahead of current needs.

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Competing priorities within companies

All of this results in a lot of friction within data-driven organizations. “Multiple technologies are required for managing, integrating, and controlling the flow and consumption of data from the edge to the cloud and all points in between. That’s without mentioning outdated metadata—the data about data that provides data intelligence,” said Gopal.

An upcoming skills gap might compound the problem: According to the Forbes Insights research, 37% of engineers say they will likely retire in the next 10 years.

Adding to this hurdle is the fact that some firms are led by executives that don’t understand or champion the importance of having contextual and timely data embedded into applications. Recent research by Exasol has found that less than half of decision-makers believe that those working in senior management (40%) or mid-management roles (32%) are very effectively informed of their organization’s data strategy.

Creating a path to success

Gopal believes that future success requires that data teams take a structured approach focused on people, processes, and technology in order to make data available to all.

“Data teams should identify short- and long-term data and analytics use cases that will demonstrate business value with input from stakeholders at all levels—both business and IT,” she said. “They should also identify data-related assets that will be required for the project and be realistic about time constraints. They should then look to deliver measurable value with short term projects to build business cases for more expensive or longer projects.”

Teams should look to deliver measurable value with short term projects to build business cases for more expensive or longer projects.”

Chandana Gopal, Business Analytics Research Director, IDC

From a technology perspective, the introduction of new technologies, such as 5G-enabled edge computing, will have an impact on IT staffing. According to the Forbes Insight report, almost three-quarters (74%) of C-suite executives believe staffing will be reduced or handled by external cloud or edge service providers. The ability to implement new technologies like these in the data center will be a competitive differentiator, as will better security (according to 43% of respondents), and bandwidth (according to 27%).

Ultimately, it’s clear that organizations need to act quickly if they want to succeed. “Continuous efforts to update the data center will be integral to business success,” states the Forbes Insight report. “Partnering with external third parties is a central part of the data center journey in the age of hyper-connectivity.”

These collaborations are happening between companies and their cloud providers and between platforms like Sisense and companies like Amazon and Google:

“Partnerships with companies like AWS, Snowflake, Microsoft, and Google are only becoming more important as the modern data landscape evolves,” said Erin Winkler-McCue, Lead for Strategic Partnerships & Special Projects at Sisense. “We feel like every customer is either already in the cloud or it’s only a matter of time until they contemplate setting up a hybrid- or full-cloud model.”

Gopal agrees that adopting new technology through new partnerships is key: “A new class of intelligent data operations platforms are emerging that can reduce friction, improve efficiencies with automation, provide flexibility and openness with policy and metadata-driven processes that can accommodate the diversity and distribution of data in modern environments,” she said. Equipped with these, product teams will be much better prepared for a new and exciting future.

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Lindsay James is a journalist and copywriter with over 20 years’ experience writing for enterprise business audiences. She has had the privilege of creating all sorts of copy for some of the world’s biggest companies and is a regular contributor to The Record, Compass, and IT Pro.

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Hailo partners with Foxconn to build edge device for AI inference

May 13, 2020 Big Data

AI startup Hailo today announced that it’s teaming up with Foxconn and system-on-chip provider Socionext to launch BOXiedge, an edge computing processing solution for video analytics. If the companies’ claims bear out, BOXiedge could deliver “market-leading” energy efficiency for AI inference, benefiting applications like industrial internet of things, smart cities, and smart medical.

BOXiedge is the successor to a mini server Foxconn teamed up with Network Optix to launch in January, which confusingly shares the same name. Unlike the previous server, this new BOXiedge can perform image classification, detection, pose estimation, and other tasks on footage from up to 20 cameras simultaneously thanks to SocioNext’s SynQuacer SC2AA chip and Hailo’s Hailo-8 processor, which features an architecture that consumes less power than rival chips while incorporating memory, software control, and a heat-dissipating design.

Under the hood of the Hailo-8, resources including memory, control, and compute blocks are distributed throughout the whole of the chip, and Hailo’s software — which supports Google’s TensorFlow machine learning framework and ONNX (an open format built to represent machine learning models) — analyzes the requirements of each AI algorithm and allocates the appropriate modules.

Hailo-8 is capable of 26 tera-operations per second (TOPs), which works out to 2.8 TOPs per watt. In a recent benchmark test conducted by Hailo, the Hailo-8 outperformed hardware like Nvidia’s Xavier AGX on several AI semantic segmentation and object detection benchmarks, including ResNet-50. At an image resolution of 224 x 224 pixels per inch, it processed 672 frames per second compared with the Xavier AGX’s 656 frames and sucked down only 1.67 watts (equating to 2.8 TOPs per watt) versus the Nvidia chip’s 32 watts (0.14 TOPs per watt).

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The edge AI hardware market is anticipated to be worth $ 1.15 billion by 2023, and Hailo — which raised $ 60 million in March — is hoping to beat rivals to the punch. Startups AIStorm, Esperanto Technologies, Quadric, Graphcore, Xnor, and Flex Logix are developing chips customized for AI workloads. Mobileye, the Tel Aviv company Intel acquired for $ 15.3 billion in March 2017, offers a computer vision processing solution for autonomous vehicles in its EyeQ product line. Baidu in July unveiled Kunlun, a chip for edge computing on devices and in the cloud via datacenters. And Chinese retail giant Alibaba launched an AI inference chip for autonomous driving, smart cities, and logistics verticals in the second half of 2019.

Foxconn is one of Hailo’s first publicly disclosed customers after NEC and ABB Technology. Previously, the startup said it’s working to build Hailo-8 into products from OEMs and tier-1 automotive companies in fields such as advanced driver-assistance systems (ADAS) and industries like robotics, smart cities, and smart homes.

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How to automatically add the edge width of a hand-drawn graph to the respective imported morphological graph in Mathematica?

March 24, 2020 BI News and Info

I am still relatively new to Mathematica, but with the great help of this forum, I managed to transform a doodled sketch into a graph, see attached file. I have used this code:

doodle = Import["filename.jpg"];
graph =
 MorphologicalGraph@
  SkeletonTransform@
   Thinning@Closing[ColorNegate@Binarize@doodle, DiskMatrix[2]];

However, what I would like to do now is to output a value for each edge that reflects how thick or thin the edge was (on average over its length) in the original doodle sketch. Is this possible at all? If so, how do I do it?

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Business Technology Platforms Give Midsize Companies A Competitive Edge

March 19, 2020 BI News and Info

Part 2 of the series, “Top Trends Impacting Midsize Businesses in the 2020s”

Technology advancements and marketplace changes are making it far easier for any business to make bold moves quickly. This new reality is certainly not lost on midsize companies, as they watch their larger contemporaries start to embrace speed and agility in their new business initiatives.

In response to this new environment, growing companies are facing a pivotal question: how can they keep their edge in innovation and customer service by tapping into intelligent technologies such as artificial intelligence, machine learning, and robotic process automation? One solution is the adoption of a business technology platform that simplifies and accelerates the delivery of new experience-centric applications, processes and systems to keep ahead of the pack.

IDC predicts that this will be one of the top trends for midsize companies in the 2020s. The IDC infographic, “The Roaring 2020s: Six Trends Impacting Companies in the Next Decade,” reveals that more than 25% of midsize companies will build a connected ecosystem of platform providers to accelerate digitization while using service providers and integrators to augment existing in-house capabilities.

An opportunity to redefine velocity and scale

Digital giants such as Google, Facebook, Airbnb, and Amazon may have left behind their “startup” or “midsize business” status long ago. Still, they do offer one critical lesson for every growing company. Their fast, sustainable growth likely points to the benefits of a technology platform, setting the foundation on which a multitude of products, services, capabilities, and experiences can be added in real time.

A business technology platform integrates with and extends application solutions, databases, analytics, and self-services into a harmonious landscape of technology solutions, providing simple and rapid business innovation. Unified and open enough to embed intelligence across integrated, modular applications, the advantages that the business technology platform offers can be visible across the enterprise – from online and physical stores to sales, marketing, manufacturing, logistics, and the supply chain.

But don’t be fooled: massive enterprises are not the only ones that can achieve such success with business technology platforms. Thanks to the choice of running their business technology platform on any of the affordable cloud providers and hyperscalers, midsize businesses can now leverage the same capabilities to not only protect but also strengthen their inherent competitive advantages.

Take, for example, c-Com. The growing German startup is orchestrating and simplifying the tool lifecycle management process of an international client base of manufacturers of automobiles, airplanes, and other complex products. By using a cloud-based business technology platform that features open-source technology, the business moves C-parts – such as nuts, bolts, screws, and commodity tools – and other resources around easily among business partners. And because c-Com is using a technology platform, they are delivering new business features and services to their customers quickly and easily, thereby staying ahead of their competition.

c-Com’s platform-enabled business strategy is paying off as it provides a uniquely valuable service that is saving customers a great deal of time and effort. In fact, large automotive and aerospace companies are either using, evaluating, or considering the service for themselves.

A rediscovery of the true advantage of midsize businesses

Most midsize companies are already on the path to adopting a business technology platform. They spend years building up their processes, supply chain, partnerships, sales and marketing channels, and customer base. Along the way, they create or invest in new applications or acquire the latest capabilities and integrate them into every aspect of the business.

With the introduction of platform thinking, decision-makers can manage such a growing application ecosystem in a manner that aligns well with the company’s mission, vision, and strengths. This approach allows businesses to maintain their competitive edge by evolving as the marketplace and customers demand – without disrupting existing operational experiences.

Discover how midsize companies are retaining their competitive advantage in today’s marketplace. Listen to an excerpt of our Webinar, “Winning in the 2020s: Six Trends Every Midsize Needs to Know,” with Timo Elliott, Global Innovation Evangelist from SAP and guest speaker Shari Lava, Research Director Small and Medium Business at IDC.

*Source: IDC infographic: “The Roaring 2020s: Key Trends Impacting Midsize Companies in the Next Decade,” sponsored by SAP.

This article originally appeared of Forbes SAP BrandVoice.

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Digitalist Magazine

4 Sales Presentation Innovations That Keep Viewers on the Edge of Their Seats

March 11, 2020 CRM News and Info

People have been giving presentations for thousands of years, from Moses with his stone tablets to Elon Musk revealing his grand plans to colonize Mars. While the elements of a great pitchman generally have remained the same over the past 5,000 years — conviction, charisma, credibility — today’s successful presenters do more than just get in front of an audience and talk. They meld together technology, data and multimedia to hammer home key talking points that keep viewers engaged throughout.

So how do you project the power of Moses in your next presentation? Those of us operating without divine intervention typically rely on PowerPoint as our stone tablets. PowerPoint has been around for more than 30 years and has changed not only the way we present, but also how we write and communicate.

PowerPoint allows anyone to create a professional presentation with relatively little prep time. As a result, it has proliferated and become the business norm. The pitfall is that too many of us rely on our slide decks to do too much, to the point that it overshadows the presenter and dominates the meeting.

For a really engaging, productive presentation remember that your slides are there to support you, not the other way around. Utilize the following tips to build a supplemental deck and presentation management strategy that will support your voice, reinforce your thoughts, and keep your audience on the edge of their seats.

1. Start With the Story

Tell a story, don’t sell a product. Sixty percent of people find generic sales pitches
to be irritating, according to Hubspot. Only 5 percent of attendees remember individual statistics, but 63 percent can recall stories.

We remember stories because they are about us — human beings with hardships, happiness and feelings. Speakers who connect with us in that way have a better opportunity to fully engage us.

That may sound challenging in a business environment where your material is very cerebral, like financial forecasts or charts conveying clinical data, but in those cases you can translate that data into its human consequences. Financial forecasts? Yay! You’re going to be rich (or going broke). Clinical data? You’re cured from that horrible disease. Life is good.

Money and health are two things we all feel with our hearts. When you are selling a consulting service, it’s great to talk about the experience your experts have, but remember to include how you can help your customer — make their jobs, their lives, whatever, better and easier.

2. Lead With Your Eyes and Body

Without even realizing it, we say more with our bodies than with our voices. How we stand — strong and upright projecting confidence, or slumped and hunched over projecting defeat — will do more to convince an audience than any of the words that come out of our mouths.

How we express ourselves, with eye contact and animation, also creates a bond. Former President Ronald Reagan was an expert at this. He rarely gestured with his arms. Rather, he used his eyes and his facial expression to talk with the American people.

Look your audience in the eye. If you are presenting to one or two people, that’s easy. If you are presenting to a large group, go across the room, one-by-one, pick a person and make eye contact for five seconds. (When you’re speaking, five seconds seems like an eternity. It’s hard, but not impossible.)

You probably won’t get to everyone in the room, but that’s not the goal. By keeping eye contact and making that connection, you become more relaxed and conversational. You will converse with individuals rather than orate at an anonymous audience. Sales success is as much about creating and maintaining relationships as it is about the products we sell.

What about Web meetings? Today, we are all logging in through our screens, whether phone or computer. Making eye contact and using body language might sound antiquated. It’s not. Do it anyway. Turn on your video camera, and even if it’s just a call in, remember that our voice, our tone, and our body language contribute to our overall manner and influence how people respond to us. Even if your audience can’t see you, a confident, yet relaxed manner will still have a positive effect.

3. Follow the Conversation

PowerPoint’s linear format has forced us into a rigid outline for presentations to keep everything in a strict order. The problem is that we don’t think in a linear order. The human mind likes to wander. Our audience’s mind is wandering as we’re speaking. So forcing them into a strict, linear outline with bullet points will bore them.

Diversions or questions at the wrong time can throw off a presenter’s game. Instead, presenters should get out of linear mode, go interactive, and let the presentation follow the conversation. Break up the slides with questions to the audience. Engage them, make direct eye contact, and ask a simple question, “Does this make sense so far?” or “Are you all following me?” or “What do you think?”

In doing so, you are turning your presentation into a conversation. You are breaking up the corporate monotony associated with PowerPoint. Above all, you are bringing your audience into your presentation so they become a vital part of the broader discussion. As a result, they are more invested in you and your message. Your message transforms and becomes their message. They sell themselves.

4. Use Analytics to Determine Messaging

Until now, we’ve talked about style, but substance matters too. Large organizations invest millions to create the right message, with the right graphics and brand. Despite that expenditure, 70 percent of content never gets used, and 90 percent never gets reused — a wasted investment. Why? Usually it’s only because your sales rep can’t find the slide or content needed while preparing the deck. Give your sales team substance.

Give them the content that helps them sell. Reporting and analytics can tell you what’s resonating in the field with customers and what’s not, so you can create more, better content going forward and retire content that’s not working.

If Bob just closed a major deal using his deck, see what elements he included and then make them standard in the decks of your other salespeople. Real-time data helps guide the type of content you should be creating for your salespeople to perform optimally, while helping you get rid of bad and unused content that’s just a cluttery distraction.

You don’t need divine intervention to be an effective presenter. You just need your company’s best content combined with your best self. This presentation management strategy allows your salespeople to worry less about slides, focus on their customers, and make a human connection that builds trust. You don’t need to be Moses to sell. You just need to be yourself.

AlexAnndra%20Ontra 4 Sales Presentation Innovations That Keep Viewers on the Edge of Their Seats
AlexAnndra Ontra is cofounder and president of
Shufflrr, and author of
Presentation Management: The New Strategy for Enterprise Content

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CRM Buyer

Predictive Analytics in Manufacturing: A Winning Edge

February 1, 2020 Sisense

The modern manufacturing world is a delicate dance, filled with interconnected pieces that all need to work perfectly in order to produce the goods that keep the world running. In Moving Parts, we explore the unique data and analytics challenges manufacturing companies face every day.

Building an accurate predictive analytics model isn’t easy. It requires a skilled data team, advanced tools, and enormous amounts of clean data from the right combination of inputs. It’s a difficult process, but an effective predictive analytics engine is an enormous asset for any organization.

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Big challenges, big rewards

Manufacturing companies are in a unique position regarding data: they create and capture tons of it every day. The process of producing goods is an enormous opportunity for data optimization. Raw materials need to be ordered, received, constructed, packaged, and shipped out for sale in the most efficient manner possible. Because the steps are repeated so many times through the process, a small edge created via predictive analytics in manufacturing will be magnified at every repetition to produce significant benefit.

Because of the cyclical nature of the manufacturing process, data-driven companies are building superior processes to create bigger and bigger advantages. Here are a few examples of companies using manufacturing analytics to win the future:

Predicting return rate

Skullcandy’s dive into predictive analytics started with the challenge of understanding return rates on new products. The logic was that if the team could predict certain features or aspects of a product that would lead to a return, they could optimize those policies around returning products. They used BigSquid to blend and analyze historical data related to returns and added their learnings to features and products on their roadmap. From there, the team could ask new questions of that dataset to understand the way customers were interacting with their products and ultimately build a better warranty policy for products before they were even released. This data was also useful for product managers, giving them a clear picture of what was making customers adopt Skullcandy’s products (or not).

Once the return rate questions were answered, the team focused their efforts on uncovering insights around reviews and warranty claims to generate insights about positive and negative drivers. Data like this is ideal for making decisions for product roadmaps. All those customer insights can be used in a number of creative ways to better focus resources and improve products.

Improve forecasts and maximize revenue

Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!

Gentex deployed Sisense to comb through millions of records after switching over from an outdated ERP system. They needed a platform that could churn through all that data quickly and deliver quick intelligence about both current and future revenues. Initially, Gentex created dashboards for their sales and operations teams that collected information about sales, quotes, and orders across the company. Those dashboards answered immediate questions about the current state of the business.

To answer more forward-looking questions, Gentex creates a sales forecast for an entire year using just a few months of data. They use predictive models to forecast revenues based on spending. They even incorporate trend data to improve accuracy over time. Currently, Gentex builds visualizations of year-to-date revenue data to forecast up to 15 months into the future.

Operating off those accurate forecasts, Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!

Improve inventory management with demand forecasting

Making a product that consumers want to buy is only useful if a company can find a way to get that product in front of the consumers who demand it. Several of today’s most cutting-edge manufacturers are blending historical customer data and external factors to predict demand for goods so they can increase production when demand will be high and decrease production when demand will be low. These companies aren’t just building for the future, they’re building the future.

The need to accurately forecast demand is crucial to these manufacturers. Assessing demand in real-time is ineffective since companies need to make decisions about demand far enough in advance to complete an entire production cycle and get that product in front of customers. With a solid predictive analytics model in place, manufacturers can create exactly the right amount of products (and the right variety of those products) to satisfy future customers.

These forecasts optimize sales revenue, but it also avoids unnecessary costs associated with producing, shipping, and stocking items that won’t sell. Accurate predictions are a win-win for any manufacturer.

Build your manufacturing business with analytics

Predictive analytics in manufacturing have gone from being science fiction to being a make-or-break addition to any company’s technology stack. Using a platform like Sisense for manufacturing analytics, combining internal and external information into a series of accurate forecasts is incredibly invaluable to any manufacturer. Improving any step of the manufacturing process is an advantage over the competition, but improving every step is a data-driven way to become an industry leader faster.

Predictive Analytics in Manufacturing: A Winning Edge

Adam Bonefeste is a veteran content marketing manager. When he isn’t writing copy, he’s probably reading books, running through San Francisco or getting lost in YouTube holes about math/logic problems.

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Taking A Peek Over The Mountain Edge

January 5, 2020 Humor

A stunning waterfall.

“Over the Edge.”
Image courtesy of https://imgur.com/gallery/mOfTxBE.

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Get 2020 vision about edge computing and 5G

December 20, 2019 Big Data

This article is part of the Technology Insight series, made possible with funding from Intel.
________________________________________________________________________________________

With 2020 predictions looming, there’s sure to be a fresh wave of hype around the edge and 5G. Now’s an ideal time to solidify and update your understanding, and explore how they’ll complement each other. If you’re processing payments, taking online orders, detecting fraud, in the financial services industry, or exploring machine learning, these two technologies can help keep you competitive in the coming months.

What and why

Edge computing is all about processing information from devices closer to where it’s being created, rather than shuttling it back and forth from the cloud. Together with 5G, computing at the edge paves the way for applications that wouldn’t have been possible before. Think augmented and virtual reality, where ultra-low latency keeps what you see in sync with what you do, or autonomous vehicles that need to make split-second decisions based on huge volumes of data.

Globally, IDC forecasts 150 billion connected devices (including RFID) by 2025, many of which will pump out data in real time. In 2017, real-time data was a mere 15% of all information created, captured, or replicated. In 2025, it’s expected to reach 30%. As a percentage, that might not sound transformational. But it’s an order of magnitude higher in raw capacity (from ~5 to ~50 zettabytes). Edge computing gives you the power to perform intelligent analysis based on that deluge of real-time data almost instantaneously, all while minimizing your bandwidth expenses.

Definition emerging

Yes, it’s nearly 2020. But the definition of “edge” still often varies depending on who you ask. From NIST to IEEE, models are still evolving. It could be the Raspberry Pi in a refrigerated big rig selectively sending sensor information to the cloud or the node processing game data for Google’s streaming Stadia platform. Although miles of proximity separate those two interpretations, they both put compute resources closer to the user.

Thanks to the collaborative, vendor-neutral 2018 State of the Edge report, there now exists a more explicit definition with some industry consensus behind it:

The edge is a location, not a thing.
There are lots of edges, but the edge we care about is the edge of the last-mile network.
This edge has two sides: an infrastructure edge and a device edge.
Compute will exist on both sides, working in coordination with the centralized cloud.

For clarity, the device edge includes end-points like phones, drones, AR headsets, IoT sensors, and connected cars; gateway devices like switches and routers; and on-premise servers. They’re all on the downstream side of your last-mile cellular or cable network. The infrastructure edge exists on the upstream side. That’s where you find your compute resources collocated with network access equipment and regional datacenters.

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Above: Edge computing exists along a spectrum, from the device edge to the infrastructure edge.

Image Credit: Intel

In our big rig example, the Raspberry Pi is on the device edge. Rather than chewing up bandwidth by continually transmitting environmental data, it processes locally and only phones home in the event of an emergency. Conversely, hyper-local datacenters streaming a 4K gaming experience at 60 frames per second live on the infrastructure edge. Although the device edge offers a narrow latency advantage, you’re obviously going to find much more powerful hardware further upstream.

Beyond the distributed infrastructure edge lies the core network and, ultimately, the cloud, which is more centralized and scalable. But by the time you get to the cloud, latency is much higher (and far less consistent).

Benefits of low-latency edge computing

It’s easy to point at low latency as the killer application of edge computing, particularly as cloud-based software strains under the limitations of physics. Data cannot move any faster than the speed of light, so requests to servers hundreds or thousands of miles away inevitably take tens or hundreds of milliseconds to fulfill. The difference isn’t perceivable as you scroll through your Twitter feed. But those numbers wouldn’t be acceptable to a surgeon operating remotely or a gamer in virtual reality. Above all else, processing at the edge shaves away latency to keep data relevant.

Above: Today’s Internet is ill-equipped for tomorrow’s applications, which will be bandwidth-hungry and latency-sensitive.

Image Credit: State of the Edge 2020

Edge computing also saves you from shuttling every bit of data back and forth between connected devices and the cloud. If you can determine the value of information close to where it’s created, you can optimize the way it flows. Limiting traffic to just the data that belongs on the cloud cuts down on bandwidth and storage costs, even for applications that aren’t sensitive to latency.

Reliability stands to benefit from edge computing, too. A lot can go wrong between the device edge and centralized cloud. But in rugged environments like offshore platforms, refineries, or solar farms, the device and infrastructure edges can operate semi-autonomously when a connection to the cloud isn’t available.

Distributed architectures can even be a boon to security. Moving less information to the cloud means there’s less information to intercept. And analyzing data at the edge distributes risk geographically. The endpoints themselves aren’t always easy to protect, so firewalling them at the edge helps limit the scope of an attack. Further, keeping data local may be useful for compliance reasons. An edge infrastructure gives you the flexibility to limit access based on geography or copyright limitations.

The edge is pervasive; 5G makes it better

Edge computing is not new. As far back as 2000, content delivery networks were being referred to as edge networks. But it’s universally accepted that as 5G coverage grows, edge computing is going to help address the high-bandwidth, low-latency requirements of modern applications with local, rather than regional, compute resources.

The technology underlying 5G will add speed, reliability, and flexibility to enterprise applications by getting compute resources closer to where data is being created. Information will move between 5G networks efficiently, rather than requiring a trip to the centralized cloud and back. As a result, we’re going to be looking at use cases that previously weren’t possible.

According to the 2020 State of the Edge report, the largest demand for edge computing comes from communication network operators virtualizing their infrastructure and upgrading their networks for 5G. Mobile consumer services running on those networks are going to rely on edge computing to enable streaming game platforms, augmented/virtual reality, and AI.

Smart homes, smart grids, and smart cities all share a proclivity for device edge platforms. As those use cases evolve and become more sophisticated, though, there will be demand for infrastructure edge capabilities, too. 5G’s provisions for ultra-reliable low-latency communications (URLLC) and massive machine-type communication (mMTC) mean the devices and edge can be even closer together, making their short connection more efficient.

Above: Traffic lights are networked and connected to edge gateway server, where their data can be collected and analyzed. As part of an edge network, they can feed data to map tools and reroute around congestion.

Image Credit: Moor Insights & Strategy

And who could forget about the autonomous automobile, the poster child for edge computing enhanced by 5G? Modern automobiles already utilize compute resources on the device edge for collision avoidance, lane-keeping, and adaptive cruise control. But as assisted and autonomous driving features become more sophisticated, infrastructure edge resources will be required to add intelligence that could only come from the surrounding environment. Good examples: rerouting a trip based on traffic miles ahead, communicating with other autonomous vehicles to accelerate from a stoplight in unison, or making split-second decisions to avoid unsafe situations.

The edge is still young

The empowered edge is one of Gartner’s Top 10 Strategic Technology Trends for 2020. However, several other concepts on its watchlist have roots in edge computing as well. Hyperautomation, which deals with the application of artificial intelligence and machine learning to augment human input, is going to rely on a foundation of low latency and unflagging reliability. The multiexperience is another example, incorporating multisensory and multitouchpoint interfaces dependent on high bandwidth and real-time processing. Of course, autonomous things are all about AI, 5G, and edge computing.

Above: The global annual CAPEX for edge IT and data center facilities is forecast to reach $ 146 billion in 2028 with a 35 percent CAGR.

Image Credit: State of the Edge 2020

Enabling those novel use cases will require substantial investment. A forecast model by Tolaga Research predicts a cumulative CapEx spend of $ 700 billion between now and 2028 on edge IT and datacenter infrastructure. As the computing pendulum swings from the centralized cloud to a distributed edge, opportunities abound, particularly in the maturing infrastructure edge. Understanding the impact of edge computing and 5G will allow you to provide seamless customer experiences, test new markets, and act on insights in real-time.

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Google’s Coral AI edge hardware launches out of beta

October 23, 2019 Big Data

Last March, Google took the wraps off of Coral, a collection of hardware development kits and accessories intended to bolster the development of machine learning models at the edge. It launched in select regions in beta, but the tech giant today announced that it’s graduating to a “wider” and global release.

All Coral products — including the $ 150 Coral Dev Board, the $ 74.99 Coral USB Accelerator, and the $ 24.99 5-megapixel camera accessory — are available for sale at electronics retailer Mouser and for large-volume sale through Google’s sales team. The company says that by the end of the year, it’ll expand distribution into new markets including Taiwan, Australia, New Zealand, India, Thailand, Singapore, Oman, Ghana, and the Philippines.

Coinciding with Coral’s general availability, the Coral website — which now lives at Coral.ai — has been revamped with better organization for docs and tools, testimonials, and “industry-focused” pages. Additionally, it links to a new set of examples aimed at providing solutions to common AI problems, such as image classification, object detection, pose estimation, and keyword spotting.

Lastly, Google says it’ll soon release a new version of the Mendel operating system that updates the system to the latest version of Debian (Buster), and it says it’s hard at work on updates to the Edge TPU compiler and runtime that’ll improve the model development workflow.

“We’ve received a lot of feedback over the past six months and used it to improve our platform,” wrote Coral product manager Vikram Tank. “Coral is also at the core of new applications of local AI in industries ranging from agriculture to health care to manufacturing … [It’s] already delivering impact across industries, and several of our partners are including Coral in products that require fast ML inferencing at the edge.”

For the uninitiated, the Coral Dev Board is a miniature computer featuring a removable system-on-module with one of its custom tensor processing unit (TPU) AI chips. As for the Coral USB Accelerator, it’s a USB dongle designed to speed up machine learning inference on existing Raspberry Pi and Linux systems.

TPUs are application-specific integrated circuits (ASICs) developed specifically for neural network machine learning. The first-generation design was announced in May at Google I/O, and the newest — the third generation — was detailed in May of last year.

The TPU inside the Coral Dev Board — the Edge TPU — is capable of “concurrently execut[ing]” deep feed-forward neural networks (such as convolutional networks) on high-resolution video at 30 frames per second, Google says, or a single model like MobileNet V2 at over 100 frames per second. It sends and receives data over PCIe and USB, and it taps the Google Cloud IoT Edge software stack for data management and processing.

Edge TPUs aren’t quite like the chips that accelerate algorithms in Google’s data centers — those TPUs are liquid-cooled and designed to slot into server racks, and have been used internally to power products like Google Photos, Google Cloud Vision API calls, and Google Search results. Edge TPUs, on the other hand, which measure about a fourth of a penny in size, handle calculations offline and locally, supplementing traditional microcontrollers and sensors. Moreover, they don’t train machine learning models. Instead, they run inference (prediction) with a lightweight, low-overhead version of TensorFlow that’s more power-efficient than the full-stack framework: TensorFlow Lite.

Toward that end, the Dev Board, which runs a derivative of Linux dubbed Mendel, spins up compiled and quantized TensorFlow Lite models with the aid of a quad-core NXP i.MX 8M system-on-chip paired with integrated GC7000 Lite Graphics, 1GB of LPDDR4 RAM, and 8GB of eMMC storage (expandable via microSD slot). It boasts a wireless chip that supports Wi-Fi 802.11b/g/n/ac 2.4/5GHz and Bluetooth 4.1, a 3.5mm audio jack, and a full-size HDMI 2.0a port, plus USB 2.0 and 3.0 ports, a 40-pin GPIO expansion header, and a Gigabit Ethernet port.

The Coral USB Accelerator similarly packs an Edge TPU and works at USB 2.0 speeds with any 64-bit Arm or x86 platform supported by Debian Linux. In contrast to the Dev Board, it’s got a 32-bit Arm Cortex-M0+ microprocessor running at 32MHz accompanied by 16KB of flash and 2KB of RAM.

On the subject of the camera, which is manufactured by Omnivision, it has a 1.4-micrometer sensor with an 84-degree field of view, 1/4-inch optical size, and 2.5mm focal length, and it connects to the Dev Board over a dual-lane MIPI interface. In addition to automatic exposure control, white balance, band filter, and blacklevel calibration, it features adjustable color saturation, hue, gamma, sharpness, lens correction, pixel canceling, and noise canceling.

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This article is part of the Technology Insight series, made possible with funding from Intel.
________________________________________________________________________________________

What and why

Definition emerging

Benefits of low-latency edge computing

The edge is pervasive; 5G makes it better

The edge is still young

Google’s Coral AI edge hardware launches out of beta

Recent Posts

Categories

Archives

Tag Archives: edge

Saving lives in real-time

Pressure on infrastructure builds

Competing priorities within companies

Creating a path to success

An opportunity to redefine velocity and scale

A rediscovery of the true advantage of midsize businesses

1. Start With the Story

2. Lead With Your Eyes and Body

3. Follow the Conversation

4. Use Analytics to Determine Messaging

Big challenges, big rewards

Predicting return rate

Improve forecasts and maximize revenue

Improve inventory management with demand forecasting

Build your manufacturing business with analytics

This article is part of the Technology Insight series, made possible with funding from Intel. ________________________________________________________________________________________

What and why

Definition emerging

Benefits of low-latency edge computing

The edge is pervasive; 5G makes it better

The edge is still young

Recent Posts

Categories

Archives

This article is part of the Technology Insight series, made possible with funding from Intel.
________________________________________________________________________________________