Sections

How In-Memory Computing Could Transform Airlines: First, The Cockpit

Christopher Koch

View of the cockpit of European aircraft manuf...

View of the cockpit of European aircraft manufacturer Airbus’ A380, the world’s largest double-deck jet airliner (Image credit: AFP/Getty Images via @daylife).

In-memory computing sometimes seems like one of those overeager parking valets who literally sprints to the lot to get your car.

Yeah, in-memory is fast. Yeah, it puts lots of data idling at the curb, just waiting to be churned. But what’s the big rush on the big data?

That question has led me on a hunt for stories of how in-memory computing can do more than make data analysis faster. I want to see business  transformation, okay?

The first story I have for you is in airlines.

Airlines Can’t Prepare for the Unexpected

It turns out that airlines are essentially flying blind when it comes to keeping their expensive planes in the air. Inefficient Maintenance, Repair, and Overhaul (MRO) operations cause frustrating flight delays and safety hazards–not to mention wasted fuel and pollution.

I’ve been working with freelance technology journalist Stephanie Overby to interview three of SAP‘s airline industry experts: Wolfgang Ullwer, Sameer Deshpande, Phil Te Hau and Willem Gouws.

What they’ve told us is quite scary: Most airlines aren’t prepared to deal with the unexpected.

Big Data in Real Time Would Help

That’s because airlines use overnight batch processing to track MRO. That works for routine maintenance, but does little to address issues such as an unforeseen engine fault, or a tire that wears out prematurely. And it does nothing to help airlines predict when problems are likely to occur so that they can be fixed before anything bad happens.

The 40-Pound Bag of Problems

Bringing predictive analytics to aircraft maintenance won’t be easy, of course (I go into the challenges in more detail inthis post on the SAP Forbes blog). But there are some glimmers of hope appearing. I’ll be writing about them in a series of posts on this topic.

The first reason for hope is in the cockpit. For years, crews have lugged around a 40-pound case of documentation known as the flight bag. The flight bags contain that arch enemy of data analysis: unstructured data in the form of a pilot’s handwritten logbook entries, navigational charts, equipment manuals, and weather and radar maps. It is a terrible jumble of paper (you don’t want to know how many minutes you’ve lost the gate due to a few missing briefing papers). When the first electronic flight bags (EFBs)–ruggedized laptops–were introduced, they cost thousands of dollars, offered limited functionality, and did little to lighten the load.

The iPad Takes Flight

Then along came the iPad. (I know, it’s getting a little tiresome hearing about how wonderful the iPad is all the time–but it’s true.) Now that the U.S. Federal Aviation Administration has granted American Airlines the first approval to use iPads in all phases of flight (sorry, passengers, you’ll still have to keep yours stowed during take off and landing) its pilots can use them full-time, putting away the leather bag and paper documentation for good. (Hard copies of the airworthiness certificate and aircraft registration are still required.)

The tablets bring much-needed simplicity to the cockpit. For example, one click-update management for manuals and charts and the potential to cross-check critical flight data increase both efficiency and safety.

The Critical Link to the Ground

Yet what’s always been missing from the flight bag–even the electronic ones–is a link to what’s happening on the ground. By connecting tablets to enterprise systems, the cockpit could become another always-on node on the airline’s information network, sending back reams of data for real-time analysis. An airline maintenance engineer could access the latest from the pilot’s logbook while he goes about the business of getting us from here to there, planning work orders for quicker turnaround of that plane. Prognostics performed on data exchanged between EFBs and enterprise systems could ultimately lead to fewer equipment failures and safer air travel.

Sounds like a good argument for in-memory to me. What do you think?

Thanks to our subject matter experts for contributing their thinking to this piece. Here are links to their LinkedIn bios if you want to learn more about them: Wolfgang Ullwer, Sameer Deshpande, Phil Te Hau, and Willem Gouws.

Comments

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing.

Why 3D Printed Food Just Transformed Your Supply Chain

Hans Thalbauer

Numerous sectors are experimenting with 3D printing, which has the potential to disrupt many markets. One that’s already making progress is the food industry.

The U.S. Army hopes to use 3D printers to customize food for each soldier. NASA is exploring 3D printing of food in space. The technology could eventually even end hunger around the world.

What does that have to do with your supply chain? Quite a bit — because 3D printing does more than just revolutionize the production process. It also requires a complete realignment of the supply chain.

And the way 3D printing transforms the supply chain holds lessons for how organizations must reinvent themselves in the new era of the extended supply chain.

Supply chain spaghetti junction

The extended supply chain replaces the old linear chain with not just a network, but a network of networks. The need for this network of networks is being driven by four key factors: individualized products, the sharing economy, resource scarcity, and customer-centricity.

To understand these forces, imagine you operate a large restaurant chain, and you’re struggling to differentiate yourself against tough competition. You’ve decided you can stand out by delivering customized entrees. In fact, you’re going to leverage 3D printing to offer personalized pasta.

With 3D printing technology, you can make one-off pasta dishes on the fly. You can give customers a choice of ingredients (gluten-free!), flavors (salted caramel!), and shapes (Leaning Towers of Pisa!). You can offer the personalized pasta in your restaurants, in supermarkets, and on your ecommerce website.

You may think this initiative simply requires you to transform production. But that’s just the beginning. You also need to re-architect research and development, demand signals, asset management, logistics, partner management, and more.

First, you need to develop the matrix of ingredients, flavors, and shapes you’ll offer. As part of that effort, you’ll have to consider health and safety regulations.

Then, you need to shift some of your manufacturing directly into your kitchens. That will also affect packaging requirements. Logistics will change as well, because instead of full truckloads, you’ll be delivering more frequently, with more variety, and in smaller quantities.

Next, you need to perfect demand signals to anticipate which pasta variations in which quantities will come through which channels. You need to manage supply signals source more kinds of raw materials in closer to real time.

Last, the source of your signals will change. Some will continue to come from point of sale. But others, such as supplies replenishment and asset maintenance, can come direct from your 3D printers.

Four key ingredients of the extended supply chain

As with our pasta scenario, the drivers of the extended supply chain require transformation across business models and business processes. First, growing demand for individualized products calls for the same shifts in R&D, asset management, logistics, and more that 3D printed pasta requires.

Second, as with the personalized entrees, the sharing economy integrates a network of partners, from suppliers to equipment makers to outsourced manufacturing, all electronically and transparently interconnected, in real time and all the time.

Third, resource scarcity involves pressures not just on raw materials but also on full-time and contingent labor, with the necessary skills and flexibility to support new business models and processes.

And finally, for personalized pasta sellers and for your own business, it all comes down to customer-centricity. To compete in today’s business environment and to meet current and future customer expectations, all your operations must increasingly revolve around rapidly comprehending and responding to customer demand.

Want to learn more? Check out my recent video on digitalizing the extended supply chain.

Comments

Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is the Senior Vice President, Extended Supply Chain, at SAP. He is responsible for the strategic direction and the Go-To-Market of solutions for Supply Chain, Logistics, Engineering/R&D, Manufacturing, Asset Management and Sustainability at SAP.

How to Design a Flexible, Connected Workspace 

John Hack, Sam Yen, and Elana Varon

SAP_Digital_Workplace_BRIEF_image2400x1600_2The process of designing a new product starts with a question: what problem is the product supposed to solve? To get the right answer, designers prototype more than one solution and refine their ideas based on feedback.

Similarly, the spaces where people work and the tools they use are shaped by the tasks they have to accomplish to execute the business strategy. But when the business strategy and employees’ jobs change, the traditional workspace, with fixed walls and furniture, isn’t so easy to adapt. Companies today, under pressure to innovate quickly and create digital business models, need to develop a more flexible work environment, one in which office employees have the ability to choose how they work.

SAP_Digital_Emotion_BRIEF_image175pxWithin an office building, flexibility may constitute a variety of public and private spaces, geared for collaboration or concentration, explains Amanda Schneider, a consultant and workplace trends blogger. Or, she adds, companies may opt for customizable spaces, with moveable furniture, walls, and lighting that can be adjusted to suit the person using an unassigned desk for the day.

Flexibility may also encompass the amount of physical space the company maintains. Business leaders want to be able to set up operations quickly in new markets or in places where they can attract top talent, without investing heavily in real estate, says Sande Golgart, senior vice president of corporate accounts with Regus.

Thinking about the workspace like a designer elevates decisions about the office environment to a strategic level, Golgart says. “Real estate is beginning to be an integral part of the strategy, whether that strategy is for collaborating and innovating, driving efficiencies, attracting talent, maintaining higher levels of productivity, or just giving people more amenities to create a better, cohesive workplace,” he says. “You will see companies start to distance themselves from their competition because they figured out the role that real estate needs to play within the business strategy.”

The SAP Center for Business Insight program supports the discovery and development of  new research-­based thinking to address the challenges of business and technology executives.

Comments

Sam Yen

About Sam Yen

Sam Yen is the Chief Design Officer for SAP and the Managing Director of SAP Labs Silicon Valley. He is focused on driving a renewed commitment to design and user experience at SAP. Under his leadership, SAP further strengthens its mission of listening to customers´ needs leading to tangible results, including SAP Fiori, SAP Screen Personas and SAP´s UX design services.

Tags:

Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

Comments

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

Comments