Pentagon and Joint Operations

Worked there long before such applications could be reasonably considered. How/why is quite interesting and inevitable.

Pentagon AI center shifts focus to joint war-fighting operations  in https://www.c4isrnet.com/

By Nathan Strout, , Jul 8, 2020

The Joint Artificial Intelligence Center is focusing its efforts on building AI for the Department of Defense's Joint All-Domain Command and Control concept. (Getty)

The Pentagon’s artificial intelligence hub is shifting its focus to enabling joint war-fighting operations, developing artificial intelligence tools that will be integrated into the Department of Defense’s Joint All-Domain Command and Control efforts.

“As we have matured, we are now devoting special focus on our joint war-fighting operation and its mission initiative, which is focused on the priorities of the National Defense Strategy and its goal of preserving America’s military and technological advantages over our strategic competitors,” Nand Mulchandani, acting director of the Joint Artificial Intelligence Center, told reporters July 8. “The AI capabilities JAIC is developing as part of the joint war-fighting operations mission initiative will use mature AI technology to create a decisive advantage for the American war fighter.”  ... ' 

Miro Managing Meetings and their Results?

For some time we talked about the desire for enterprises to manage meetings. Their planning, efficient operation and gathering of results.   Next steps.  The integration of needed expertise. Things like who needs to do what and when, and how that fit into the process.  And visually!   We even built such a system, mentioned here.     Especially now after the explosion of remote group interactions has been so improved.   Now the integration AI and Chat AI capabilities could further optimize meetings.    Saw several attempts, demonstrated but they were not adequate.  Could Miro be the right direction?  Investigating Miro.com and will report later.   Thoughts?    Let me know.  - Franz  

Examining Hub and Spoke for Post Pandemic

In MIT Sloan Review.   Thoughts on Post-Pandemic business operations.

Why Companies Should Adopt a Hub-and-Spoke Work Model Post-Pandemic

By Ben Laker

As the COVID-19 pandemic upended the traditional model of a corporate headquarters where employees congregate daily, it has also highlighted how companies can more effectively use schedules, space, and technology to be more productive. Copresence is no longer essential for productivity because more jobs than ever can be conducted and monitored virtually. In the U.S., for example, remote working has doubled during the past 12 months, with 1 in 4 employees situated entirely at home.

But a significant majority of businesses — 77% — believe the lack of social contact during work hours has compromised employee wellness. As a result, many organizations believe it’s time to reinvent the working environment with a middle ground between packed offices and the isolation of working at home: the hub-and-spoke office model. This setup — in which a company operates a centralized main office (hub) with more localized satellite offices (spokes) — is a fundamental driver of workspace mobility. Offering an attractive yet accessible hybrid of both home and office work, the model increases the options and flexibility for employees by including the home as an essential spoke. ... 

The hub-and-spoke concept is not new. The term derives from the airport industry, where instead of sending half-empty flights directly between smaller spoke destinations, airlines have passengers change flights at a central hub between the two airports. More recently, the term has come to refer to a more flexible workspace and working style, given that hub-and-spoke offices allow employees to work from either their city hub; a dedicated, strategic spoke location such as a regional workspace; or a personal home-based spoke .... '

Local Airport Optimizes Takeoff Operations with Machine Learning

 I see that our local airport is using this scheduling application.  A presentation on the method is linked to in the article.  Deep earning and scheduling using NVIDIA.  Useful example of operations application. Uses cameras for gathering data about operations.  " ...By installing cameras at several gates, airports can optimize the cleaning, restocking and servicing of planes — saving time for customers and costs for the airlines. ... "

Inception Spotlight: Assaia AI Ready for Takeoff at Kentucky Airport   By Isha Salian

Tags: Business Intelligence & Analytics, Computer Vision & Machine Vision, featured, Machine Learning & Artificial Intelligence, News, NVIDIA Inception

Discuss

Switzerland-based Assaia International AG, an NVIDIA Metropolis partner and member of the NVIDIA Inception acceleration platform for AI startups, is deploying a deep learning solution at Cincinnati/Northern Kentucky International Airport (CVG) to help airport employees monitor the turnaround time between flights. 

The Turnaround Control tool will help the airport work with its airline partners to improve turnaround transparency, identify situations that most often cause delayed flights, and notify employees of deviations from the schedule. 

“Assaia’s technology adds critical data points to CVG’s early-stage neural network for operational advancements,” said Brian Cobb, the airport’s chief innovation officer. “Structured data generated by artificial intelligence will provide information to make decisions, optimize airside processes, and improve efficiency and safety.”

The company uses NVIDIA Jetson AGX Xavier modules and the NVIDIA Metropolis intelligent video analytics platform to run image recognition and predictive analysis algorithms on video streams from multiple cameras around an airport. 

By installing cameras at several gates, airports can optimize the cleaning, restocking and servicing of planes — saving time for customers and costs for the airlines.

Assaia is also deploying AI solutions at London Gatwick Airport and Seattle-Tacoma International Airport. Watch a replay from the recent GPU Technology Conference for more:  ... " 

Deep Learning for Fleet Management

Interesting example,  worth examining.   Other operations examples?

New Deep Learning Systems Profoundly Disrupt Fleet Management Operations

Deep learning is having a profound impact on the future of fleet management through greater efficiency.

Posted by  Ryan Kh  in Smart data collective. 

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. Transportation and logistics is a prime example.

The transportation analytics industry is projected to be worth $27 billion by 2026. One of the biggest applications of this technology lies with using deep learning to streamline fleet management.

Fleet management is one area that is especially well positioned to benefit from the latest data-driven analytical tools, so here is a look at just how much positive disruption is being caused in this market at the moment.

Improvements to efficiency & sustainability

Businesses which operate fleets of vehicles, whether small or large, are under increased scrutiny with regards to the sustainability of their operations at the moment.

There are a number of ways to go about improving the eco-friendliness of business fleets, with the long term aim of many organizations being to migrate to fully electric vehicles, leaving fossil fuel powered incumbents in the past where they belong. .. ' 

Using Business Rules and Expertise

Via DSC, what looks to be a good podcast on this topic.  It has been a favorite approach of mine since the beginning.  Narrow machine learning methods can be very valuable, but to deliver them they have to be part of existing or proposed tasks or businesses.  Operationally embedded.   That requires real-life decision rules.  Access information to the podcast at the link below.  More on this topic to follow. 

Data Science Fails: Ignoring Business Rules & Expertise 

Nowadays, we have unprecedented access to data, plus the computing power and advanced algorithms to find correlations. We look at a cautionary case study of a cancer center that embarked on an ambitious plan to use AI to eradicate cancer. When AI is being asked to make decisions with significant consequences, such as life and death healthcare recommendations, it needs to be trustworthy. But if you don't follow best practices, if you don't include the knowledge of subject matter experts, and if you don't enforce business rules, your AI project will not be successful.

In this latest Data Science Central podcast, learn four AI governance practices that can help you achieve AI success.

Speaker: Colin Priest, VP of AI Strategy - DataRobot
Hosted by: Sean Welch, Host and Producer - Data Science Central .... 

Delta Airlines Develops Disruption Predictions

Example in the aviation industry, would seem to be much available data, consideration and implications  of inaccurate predictions?

Delta Develops Artificial Intelligence Tool to Address Weather Disruption, Improve Flight Operations    By Woodrow Bellamy III | January 8, 2020

Delta Air Lines CEO Ed Bastian used his keynote speech at the annual Consumer Electronics Show to discuss a new 2020s operational structure for the international carrier that will be driven by the use of a new artificial intelligence (AI) machine learning tool.

Under development at Delta’s operations and customer center, Bastian did not provide a specific product name for the technology, but instead called it a proprietary tool that will mainly be focused on helping passengers and flight crews overcome weather occurrences that impact the routes they fly on a daily basis. The keynote speech is a familiar strategy across all of the divisions of Delta, including their maintenance team whose predictive maintenance leadership gave a speech on how the airline is shifting towards the adoption of AI at the 2019 AEEC/AMC annual conference.  .... "

Operationalizing Analytics

Ultimately its always about how analytics will be used (implemented and operationalized)  Have seen many cases where the results have been lost in implementing them.   Here a Podcast that covers some of the related topics I am reading.

Operationalizing Analytics (Podcast)
by Phil Bowermaster In B-Eye-Network.

In this podcast, Tapan Patel, Senior Manager of Product Marketing at SAS, discusses the challenges organizations face when deploying and managing analytical models.  He also provides best practices for analytics governance. The interview is conducted by Phil Bowermaster, an independent consultant and analyst who writes and speaks about emerging technologies and the future. To listen to this podcast, click here.

Phil Bowermaster
Phil Bowermaster is an independent analyst and consultant specializing in big data, business intelligence and analytics. Phil is the founder of Speculist Media, which produces blogs, podcasts, and other social and traditional media exploring the role of technology, particularly data technology, in shaping the future. He works with select clients in developing and executing content strategies related to big data. Phil can be reached at phil@speculist.com.
Recent articles by Phil Bowermaster

Other Podcasts:

Artificial Intelligence: Improving Consumer Marketing (Podcast)
Data Science is No Longer Just for Data Scientists (Podcast)
Data Science: Democratization, Self-Service and Risk (Podcast)
HTAP Redefines the Data Management Landscape  ... '

Apple, Google and Amazon

More on this, good direction for assistants.  And security is a good place to start.   This is key way to make the average consumer invest in such infrastructure, the assurance that today and tomorrow they will work together.  Now that the market has been established, clean it up for design and operating value.  Further, there needs to be ways that skills/knowledge can effectively shared, and linked to new ideas for for their collective  use.  Microsoft?

Apple, Google, Amazon Decide to 'Play Nice' Over Smart Home Tech
BBC News
December 18, 2019

Apple, Google, and Amazon have announced a partnership to improve smart-home technology's ease of use by creating a new standard to ensure their smart products are compatible with all three companies’ smartphones and voice assistants. The three tech giants also will work with the Zigbee Alliance, and a successful effort could remove consumers and manufacturers' burden of favoring one smart-home technology over others. Draft specifications for the new standard are not expected before late 2020. The companies said current smart-home products should continue to operate after the new standard is implemented. The first product category to be targeted by the new standard will be smart home security products.  ..."

Rise of Operational Analytics

Free eBook that looks to be of interest.  Clearly this is not a new thing,  was a primary part of our early training.   New methods have emerged though, and the computational power is now far   superior, data is much more manipulable.

Real-Time Decision

Making and the Rise of Operational Analytics
What You'll Learn From This eBook  from O'Reilly

The need for faster decision making and insight into mission-critical operations are defining a whole new category of real-time, data-driven companies. Data by itself has no value, rather how we leverage the data to make decisions and respond to events is where the value is created. This is where Operational Analytics is born — a key business capability enabled by modern data platforms. This book helps you understand how to leverage operational analytics to enable your business to embrace real-time decisions and ultra-fast, actionable insights ..... " 

Consider the 'Last Mile' of Analytics

The term is usually used just for supply chain  applications,  but I like the broadening view here.  To describe key operations needed to complete a process for value.  Links to a number of articles that cover several areas. .

Resources to help you conquer the 'last mile' of analytics 
By Sarah Gates on SAS Voices 

Getting value from analytics is becoming top of mind for businesses. Organizations have invested millions of dollars in data, people and technology and are looking for a return on their investment. That requires operationalizing analytics so that it can be used for strategic decision making -- often referred to as the 'last mile' of analytics.

The key? Developing a ModelOps practice which aligns the culture, processes and technology to accelerate the analytics life cycle. If you're ready to conquer the last mile of analytics and cross the finish line strong, take a look at the resources I've gathered below: .... ' 

RPA Scaling Operations

Thoughts on 'Robotic Process Automation' , which reminds me of the considerable effort we did with 'knowledge systems' to improve process and results in the enterprise.  Trouble was, the approach became unwieldy to create and hard to maintain.    RPA is a better place to start, especially if you choose the domain and goals and processes involved carefully.

How is RPA Assisting Businesses in Scaling Operations?   By Mitul Makadia   

It is estimated by McKinsey & Co. that automation systems could well and truly, undertake the work of up to 140 million jobs by 2025.

Everest Group reports that Robotic Process Automation is likely to lead to a cost reduction of close to 65% with its potential to register data at the transactional level, thereby enabling decision-making which is swift, precise and predictive. Organizations that stick to a watertight RPA implementation strategy will soon outpace those who still depend on human capital for all their processes.

RPA in Business

RPA is gaining tons of traction for its promise of improving business efficiency, making employees more productive, and leading to an overall increase in profit. In spite of the benefits of RPA in enterprises for those who were the pioneers in implementing it, there are still some decision makers on the fence whether or not RPA is worth their time and effort.

Robotic process automation is a step by step undertaking that enables companies to automate routine, repetitive tasks and free their employees to focus on more fundamental ones. Besides this, there are numerous benefits to implementing it.

To separate the wheat and the chaff, we have compiled a comprehensive list of advantages businesses may enjoy as a result of using RPA.

How does RPA help enterprises? .... '

Operations and Predictive Analytics

Useful to think about operations.   Prediction implies we can adjust operations within a process based on future predictions.   Risk also come forward because of inaccuracy of predictions.

Predictive analytics in hybrid IT: The future of ops in TechBeacon   By David Linthicum, Chief Cloud Strategy Officer, Deloitte Consulting

The predictive analytics systems of today and tomorrow will change the way we do operations. We will know how system modifications will affect IT operations, security, and governance risks. We'll also learn how to automate forthcoming complexity in ways that are cheaper and less risky, and we will have the ability to proactively plan for three years into the future.

The growth of complexity in both on-premises and public cloud platforms, or in hybrid IT, is obvious to everyone at this point. Ops-related predictive analytics means the ability to leverage AI and big data in new, more efficient ways to deal with their increasing complexity. 

So, are you in? Most people in IT operations management, including cloud and traditional, see the value of systems that can literally predict the future. Apply that magic to IT Ops, and you have the ability to solve problems before they become known problems, perhaps problems that are never known to humans.

However, the costs of leveraging predictive analytics with ops are going up. This is true even with the use of the public cloud and its ability to leverage newer tooling and data sources. You'll need upgraded skill sets, expensive tooling replacements and upgrades, and, initially, more people at the helm.  ...  "

Procurement Operating Models

A need for more bots exploring and maintaining the current and predictive possibilities?  Risk analyses always attached and considered.

A Next-generation Operating Model for source-to-pay   By Samir Khushalani and Edward Woodcock  in McKinsey

A next-generation procurement operating model that capitalizes on advances in digital, data, and analytics delivers new levels of performance across the value-creation lifecycle. ... " 

Effective procurement has long been recognized as a source of a competitive advantage (Exhibit 1). But achieving excellence requires a concerted effort to align a variety of capabilities, insights, and activities in an integrated manner across the whole organization. 

Achieving the level of performance and cross-functional integration required for procurement excellence has never been easy. And the big trends that are reshaping so many aspects of modern business are only adding to the complexity of the challenge. Today’s procurement function must navigate new markets and new sources of supply. It must balance the benefits of global sourcing and standardization against the risks associated with complex logistics, and the need to tailor products and supply chains to suit the requirements of local markets. It must develop the agility to manage price volatility, shortages, and supply interruptions. Increasingly, it must cope with political uncertainty and global trade tensions.   ... " 

Ops 4.0 Continuous Improvement Cycle

Transformation that does not stop, and has to react to fast  changing context of many types.

In McKinsey: 

Ops 4.0 helps businesses understand their greatest challenges, and solve ones that previously seemed beyond reach. It also means committing to a transformation that never entirely ends.

The Operations 4.0 podcast: A new continuous-improvement cycle
By Yogesh Malik and Rafael Westinner

In this final episode of the Ops 4.0 podcast series, McKinsey partners Yogesh Malik and Rafael Westinner start by discussing how Ops 4.0 enables greater predictability and precision in identifying the root causes of problems. Consequently, a company can address questions that previously seemed too resource-intensive to consider—in 80/20 terms, shifting them from the “20” category (not worth tackling) to the “80” category (worth the effort). The conversation concludes with a summary of the cultural factors that help organizations succeed in adopting Ops 4.0.  ... "  

Analytics and Construction

Interesting example, which relates to lots of complex operational decisions to finish a project.  Worth a look to see what is being done here.

In McKinsey: 

How analytics can drive smarter engineering and construction decisions

Three applications illustrate how companies are beginning to embrace data-driven solutions while establishing a foundation for future initiatives. ... '

Opsgenie for Incidents in Process Modeling

In the process of looking at modeling processes that link to and handle incident driven operations, I was pointed to:

Atlassian: Opsgenie

Plan and prepare for incidents
Determine who should respond
Use templates to prepare messaging and communication channels to responders and stakeholders
Predefine collaboration methods including video conferences, and chat channels
Create status pages to communicate proactively to all stakeholders  ... "

Operations 4.0: Pilots

From McKinsey.  Thoughtful examination of essence and value of pilots

The Operations 4.0 podcast: Productivity and ‘pilot purgatory’
The value from Operations 4.0 comes from how it unleashes productivity gains across a wide range of measurements. But to achieve those results, businesses must do more than launch pilot after pilot. ... "

Wharton, MIT and BC Aim to Disrupt Global Supply Chain

Another example of verification and validation applications of Blockchain infrastructure, here in supply chain.   Such applications are an ideal experimental first step.   Useful details at the link. 

How a New Technology Can Disrupt the Global Supply Chain
Operations Management  In Knowledge@Wharton

An interdisciplinary team from MIT, Wharton and Boston College has created a new blockchain-based system that has the potential to disrupt the global supply chain. Called ‘b_verify,’ the system is designed to help small and medium-size enterprises — especially those in developing nations — get financing from lenders at potentially better terms while mitigating warehouse deposit fraud. The system brings greater transparency to a key part of the supply chain, which can have a big impact on global trade financing. B_verify introduces a series of blockchain technology innovations tailored to facilitate supply chain finance and operations management.

“The potential benefits are vast and global in scale,” said Gerry Tsoukalas, Wharton professor of operations, information and decisions, who was part of the team. Small and medium-size enterprises, he said, represent the backbone of many economies in the world, and they account for more than half of the jobs as well as a third of global GDP. But despite their scope and impact, these companies have a harder time getting financing than larger established firms. He said the World Bank estimates their global financing shortfall to be $2.6 trillion.

Small and medium-sized firms also find it difficult to get financing on terms as favorable as the ones big companies get because they usually lack the latter’s track record and reputation. Banks typically would charge higher interest rates or put more restrictions on loans to smaller enterprises because they are less certain of repayment. Add to the mix the propensity for fraud, especially in the developing world, and smaller firms get the worse end of the proverbial stick. “Obtaining loans at reasonable rates can be very challenging for small firms,” Tsoukalas said.   .... "

Cisco on AI Operations Srategy

Good thoughts.   Ultimately operational considerations will be required.

Cisco Intersight: AI-Driven IT Operations Strategy
In the Cisco Blog

Cisco launched our cloud-based platform for AI-driven IT operations (a.k.a. AI Ops), Cisco Intersight, last September. It already delivers significant benefits, and we intend to take it to the next level with artificial intelligence and machine learning.

Guest Blogger: Gautham Ravi, Director, UCS Product Management

A New Era in Operations Management

“Work smarter, not harder” is critical to improving IT efficiency. Organizations are adopting a multicloud strategy, so you need scalable and consistent management across data centers, private clouds, edge, and branch environments. Cisco Intersight delivers this consistent management, automation and policy enforcement across a variety of servers and hyperconverged infrastructure (HCI). It helps you work smarter by delivering proactive support and actionable intelligence through artificial intelligence (AI) and machine learning (ML), so that you can proactively manage complex environments and reduce risk.

Traditional IT operations management tools are deployed on-premise, they are vendor and device focused, difficult to maintain, and have limited ability to scale. We introduced a new era of systems management with Cisco Intersight. It provides the simplicity of software as a service (SaaS) with unlimited scalability. Intersight is enhanced by AI and ML to provide users with actionable intelligence.  .... "