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IoT + Smart Edge Computing = Operations Intelligence

In the equation IoT + X = Operations Intelligence, what role does smart edge computing play?

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You don’t always need a sledgehammer to crack a nut.

The general premise driving the use of the Internet of Things (IoT) and data analytics to deliver intelligence is that the end actions usually have to be executed through some kind of blanket (often human) intervention. The shaky fallacy at the core of this idea is that it takes a sledgehammer to a nut in that even small adjustments to operating conditions requires a large investment of resources. Smart edge computing addresses this challenge and applies a solution that is more proportional to the size of the problem.

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Whether it’s a thermostat or a light switch or a card reader, most edge devices that control today’s commercial facilities are passive and wired devices, says Datta Godbole, the chief technology officer for Honeywell Building Technologies. Smart edge computing introduces a more efficient way of corralling the power of IoT to deliver operations intelligence. Smart edge devices can act on intelligence on the frontlines and save the heavy-duty computing for the cloud.

Smart edge computing helps companies, including facility management organizations, distribute computing needs more efficiently: you execute the small changes at the edge and save the heavy lifting for the cloud. “Time critical decisions are executed quickly without going to the cloud, while cloud computing is great for analyzing long-term trends through AI algorithms,” Godbole says.

Decisions at the edge

It is this “quickly” factor, the latency that is saved, that makes smart edge computing so valuable as part of the equation IoT + smart edge computing = operations intelligence.

Imagine a commercial building packed with fire and smoke detectors. Facilities management needs to maintain and periodically inspect these devices, which involves days of intensive work. What if instead the smoke detector could signal when it’s ready for maintenance – much like your car does? “In the future, all equipment in the building will be smart and can diagnose themselves and ask for help,” Godbole says.

The IoT part of the equation comes from the many sensors measuring a variety of parameters including temperature, humidity, light, foot traffic, occupancy and more. The introduction of IoT expands the working data set so management can more finely calibrate the final experience. “If we have IoT sensors that blanket a whole building, that conduct micro-measurements of every part of the building, we get a much truer picture of what’s happening in the building and you can control air conditioning or heating accordingly,” Godbole says.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

In a sense, IoT allows for both personalized comfort and efficiencies at scale. When an employee swipes her card and enters her workspace, what if IoT-embedded edge devices automatically gave her what she was looking for: a slightly warmer conference room, lighting that adjusted depending on where she was working and her favorite snacks lined up in the kitchen?

Foot traffic sensors and occupancy patterns in the long term can dictate heating and cooling requirements so management can optimize these over time.

The use of IoT in conjunction with smart edge computing will lead to a more efficient allocation of computing resources and better and faster decision-making. No longer do you need a sledgehammer for every problem, a fine scalpel will work even better.

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The importance of data access for digital initiatives

A new report from MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

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In a global survey of over 1,700 line of business employees in organizations with at least 250 employees, MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

The resulting report — The State of Business and IT Innovation — reveals four key ideas that IT leaders need to know in order to drive digital innovation forward.

These four key findings are:

  • Collaboration is key 
    • 68% of respondents believe IT and LoB users should jointly drive digital innovation.
  • Keep up the pace 
    • 51% expressed frustration with the speed at which IT can deliver projects.
  • Integration challenge
    • 37% cite security and compliance as the biggest challenge to delivering new digital services, followed by integration (i.e. connecting systems, data, and apps) at 37%.
  • Data access
    • 80% say that in order to deliver on project goals faster, employees need easy access to data and IT capabilities.  

“This research shows data is one of the most critical assets that businesses need to move fast and thrive into the future,” said MuleSoft CEO Brent Hayward

“Organizations need to empower every employee to unlock and integrate data — no matter where it resides — to deliver critical, time-sensitive projects and innovation at scale, while making products and services more connected than ever.”

Want to read through the whole report? Download it from MuleSoft

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Where is the financial value in AI? Employing multiple human-machine learning approaches, say experts

According to a new study, only 10% of organizations are achieving significant financial benefits with AI.

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AI is everywhere these days — especially as we work to fight the spread of COVID-19

Even in the “before times,” AI was a hot topic that always found itself in the center of most digital transformation conversations. A new study from MIT Sloan Management Review, BCG GAMMA, and BCG Henderson Institute, however, prompts a crucial question:

Are You Making the Most of Your Relationship with AI?

Finding value

Despite the proliferation of the technology and increased investment, according to the report, just 10% of organizations are achieving significant financial benefits with AI. The secret ingredient in these success stories? “Multiple types of interaction and feedback between humans and AI,” which translated into a six-times better chance of amplifying the organization’s success with AI.

“The single most critical driver of value from AI is not algorithms, nor technology — it is the human in the equation,” affirms report co-author Shervin Khodabandeh.

 

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From a survey of over 3,000 managers from 29 industries based in 112 countries — plus in-depth interviews with experts — the report outlined three investments organizations can make to maximize value:

  • The likelihood of achieving benefits increases by 19% with investment in AI infrastructure, talent, and strategy.
  • Scalability. When organizations think beyond automation as a use case, the likelihood of financial benefit increases by 18%.
  • “Achieving organizational learning with AI (drawing on multiple interaction modes between humans and machines) and building feedback loops between human and AI increases that likelihood by another 34%.”

According to report co-author Sam Ransbotham, at the core of successfully creating value from AI is continuous learning between human and machine:

“Isolated AI applications can be powerful. But we find that organizations leading with AI haven’t changed processes to use AI. Instead, they’ve learned with AI how to change processes. The key isn’t teaching the machines. Or even learning from the machines. The key is learning with the machines — systematically and continuously.” 

Continued growth

While just 1 in 10 organizations finds financial benefits with AI, 70% of respondents understand how it can generate value — up from 57% in 2017.

Additionally, 59% of respondents have an AI strategy, compared to 39% in 2017, the survey found. Finally, 57% of respondents say their organizations are “piloting or deploying” AI — not a huge increase from 2017 (46%). 

One of the biggest takeaways? According to co-author David Kiron, “companies need to calibrate their investments in technology, people, and learning processes.”

“Financial investments in technology and people are important, but investing social capital in learning is critical to creating significant value with AI.”

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Bringing DX to the food supply chain in a pandemic

In a new paper, supply chain stakeholders share how COVID-19 has affected the transformation of the sector.

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There’s little doubt that COVID-19 had a profound effect on the food supply chain.

As one example, just think back to roughly March of this year, when virus transmission was rapidly picking up speed. Remember the reports of food and beverage companies only producing their most popular or essential products? Or how it would take slightly longer than usual to restock certain products? What about the rush to integrate — or quickly improve the efficiency of — digital and e-commerce. 

Panning out a bit, think about food safety and quality professionals. The need to stay safe — and in many cases, stay at home — meant performing the very hands-on job of monitoring, auditing, inspecting at a distance, i.e. digitally. 

When the food supply chain was hit by storages, delays, breakdowns, and lockdowns, the end result was — like in so many sectors — a rapid digital transformation.

As The Food Safety Market — an SME-powered industrial data platform dedicated to boosting the competitiveness of European food certification — elaborates in a new discussion paper, “technology has played an important role in enabling business continuity in the new reality.”

The paper — Digital Transformation of Food Quality & Safety: How COVID-19 accelerates the adoption of digital technologies across the food supply chain — features industry experts from companies like Nestlé, Ferrero, PepsiCo, McCormick & Company, and more discussing the effects of the pandemic on the supply chain.

A few highlights from the paper:

  • John Carter, Area Europe Quality Director for Ferrero put the issue of food access into perspective at the start of his interview:

“The production of food defines our world. The effects of agriculture on our daily lives are so omnipresent that they can be easy to overlook; landscapes and societies are profoundly influenced by the need to feed our growing population. But much has been taken for granted. Only occasionally are we forced to consider: ‘where does our food come from?'”

  • Ellen de Brabander, Senior Vice President of R&D for PepsiCo provided insight on the cost benefits of digital transformation:

“The need for customization is a big driver for accelerating digital transformation and moving away from a ‘one size fits all’ approach. This means that the cost to develop and produce a product must be lower and digital technologies provide a clear opportunity here.” 

  • Clare Menezes, Director of Global Food Integrity for McCormick & Company brought up one area where digital tools need to go:

“There aren’t any areas where digital tools “fail”, but there is a need for tools that ‘prove out’ predictions around where the next integrity event will play out and how it could lead to quality or food safety failure. These tools are an obvious candidate for AI given the number of PESTLE factors that might come into play.” 

Want to read all of the interviews? Check out the paper here.

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