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Get More Value From Operational Assets With Predictive Analytics

Pierre Leroux

Sharpening operational focus and squeezing more efficiencies out of production assets – these are just two objectives that have COOs and operations managers turning to new technologies. One of the best of these technologies is predictive analytics. Predictive analytics isn’t new, but a growing number of companies are using it in predictive maintenance, quality control, demand forecasting, and other manufacturing functions to deliver efficiencies and make improvements in real time. So what is it?

Predictive analytics is a blend of mathematics and technology learning from experience (the data companies are already collecting) to predict a future behavior or outcome within an acceptable level of reliability.

Predictive analytics can play a substantial role in redefining your operations. Today, let’s explore three additional cases of predictive analytics in action:

  • Predictive maintenance
  • Smart grids
  • Manufacturing

Predictive maintenance

Predictive maintenance assesses equipment condition on a continuous basis and determines if and when maintenance should be performed. Instead of relying on routine or time-based scheduling, like having your oil changed every 3,000 miles, it promises to save money by calling for maintenance only when needed or to avoid imminent equipment failure.

While equipment is in use, sensors measure vibrations, temperature, high-frequency sound, air pressure, and more. In the case of predictive maintenance, predictive models allow you to make sense of the streaming data and score it on the likelihood of failure occurring. Coupled with in-memory technologies, it can detect a machine failure hours in advance of it occurring and avoid unplanned downtime by scheduling maintenance sooner than planned.

This all means less downtime, decreased time to resolution, and optimal longevity and performance for equipment operators. For manufacturers, predictive maintenance can streamline inventory of spare parts, and the ongoing monitoring services can become a source of new revenue. And as predictive maintenance becomes part of the equipment, it also has the potential to become a competitive advantage.

Smart grids

Sensors and predictive analytics are also changing the way utilities manage highly distributed assets like electrical grids. From reliance on unconventional energy sources like solar and wind to the introduction of electric cars, the energy landscape is evolving. One of the biggest challenges facing energy companies today is keeping up with these rapid changes.

Smart grids emerge when sensor data is combined with other data sources such as temperature, humidity, and consumption forecasts at the meter level to predict demand and load. For example, combined with powerful in-memory technologies, predictive analytics can be used by electricity providers to improve load forecasting. That leads to frequent, less expensive adjustments that optimize the grid and maintain delivery of consistent and dependable power.

As more houses are equipped with smart meters, data scientists using predictive analytics can build advanced models and apply forecasting to groups of customers with similar load profiles. They can also present those customers with some ideas to reduce their energy bill.

Manufacturing

The manufacturing industry continues its relentless drive for customization and “lot sizes of 1” with innovations such as the connected factory, the Internet of Things, next shoring, and 3D printing. It’s also hard at work making sure it extracts the maximum productivity from existing facilities, which traditionally has been accomplished by using automation and IT resources. According to Aberdeen, the need to reduce the cost of manufacturing operations is now the top reason companies seek more insight from data.

Quality control has always been an area where statistical methods have played a key role in whether to accept or reject a lot. Now manufacturers are expanding predictive analytics to the testing phase as well. For example, tests on components like high-end car engines can be stopped long before the end of the actual procedure thanks to predictive analytics. By analyzing test data from the component’s ongoing testing against the data from other engines, engineers can identify potential issues faster. That, in turn, maximizes the capacity available for testing and reduces unproductive time. That is only one of the many applications manufacturers find for predictive analytics.

Innovations on the shop floor

Predictive analytics provides an excellent opportunity for COOs and operations managers to extract additional value from production assets. It can also be an opportunity to create critical differentiators in the way products are created and delivered to customers – by providing it as a paid service (predictive maintenance) or as insight (predicting future electricity consumption).

However a company chooses to use it, predictive analytics can be the key to beating the competition.

Gather more insights from MIT experts on how to differentiate your organization in The Digital Economy: Disruption, Transformation, Opportunity.

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Pierre Leroux

About Pierre Leroux

Pierre Leroux is the Director of Predictive Analytics Product Marketing at SAP. His areas of specialty include Data Discovery, Business Intelligence, Cloud applications, Customer Relationship Management (CRM), and ERP.

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.

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Daniel Newman

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

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Robots: Job Destroyers or Human Partners? [INFOGRAPHIC]

Christopher Koch

Robots: Job Destroyers or Human Partners? [INFOGRAPHIC]

To learn more about how humans and robots will co-evolve, read the in-depth report Bring Your Robot to Work.

Download the PDF (91KB)

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Christopher Koch

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. Share your thoughts with Chris on Twitter @Ckochster.

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Building A Business Case For Financial Transformation

Nilly Essaides

There’s constant pressure on the CFO from the CEO to do better—to innovate, and to transform the finance organization into both a leaner and a more forward-looking analytics hub that provides insight and foresight to the enterprise. CFOs today must:

  • Interpret numbers instead of reporting them
  • Deploy enabling technology to automate low-value work
  • Scout for business and growth opportunities
  • Work effectively with Big Data to turn their teams into the brains of the organization
  • Act as true partners to the CEO, business leaders, and board of directors

Defining the ROI for transformation

Transformation sounds great in theory, but to get finance to literally go beyond its form—not an easy feat—executives need to see a strong business case and a tangible payback. After all, finance is all about the ROI.

Here are some solutions CFOs can wrap their heads around to help drive change:

  • Manage competitive disruption. Today’s business environment is rife with competitive threats. My last post listed five ways financial planning and analysis (FP&A) in its future form can help companies battle these threats. The cost of not transforming the finance function into the fast-thinking, forward-looking brains of the enterprise is the opportunity cost of falling behind. It’s the risk of becoming irrelevant through the inability to foresee competitive threats, or of lacking an action plan for dealing with the potential impact of such pressures on the financial health of the corporation.
  • Streamline processes. Obviously, there’s the dollars-and-cents savings that come from streamlining processes, using new technologies, and breaking down internal silos. For example, in many organizations, forecasting processes occur in different departments. Merging these disparate processes into one and using a single technology platform can save enormous resources in terms of systems and time. It eliminates duplicate entries of data and the need to reconcile discordant information, or the need to later argue about which number is right. It creates a single version of the truth.

Even within finance, things can be improved. Often the processes of budgeting, forecasting, and planning happen in isolation in different time frames. And operational and financial planning occur in different cycles and levels. By syncing up these processes, companies can get rid of redundancies. What’s more important, they can discover efficiencies and improve the quality of the end product.

  • Eliminate waste and free up strategic time. New technologies are enabling the finance function to automate low-value work and free up executives’ time to focus on strategic thinking, developing partnerships with the business, and advising management on how to drive growth. The payback is smarter decisions (faster growth, higher investment returns) while lowering operating expenses.
  • Look forward. Finance and FP&A today are shifting their focus from yesterday to tomorrow, from what happened to what’s going to happen. Transforming their mindset is key to helping the business move forward. Using techniques and technologies like driver-based modeling and predictive analytics, finance is remaking itself and producing faster, more frequent and—most importantly—more accurate forecasts. It’s giving management the one thing that matters most: time to pull business levers to affect future financial results. The payback is higher sales, wider margins, and lower cost of operations.
  • Change the mindset. There’s no transformation of the financial organization without a transformation of the financial skill set of executives. The first-quarter Deloitte CFO Signal Survey indicated that CFOs expect to embark on a wide range of efforts to improve the performance of their teams before the end of 2016. While foundational finance skills remain a must, to transform finance into the “A-team” of the future, executives must possess business acumen, diplomacy skills, intellectual curiosity, technology savvy, and a degree of comfort with ambivalence. They have to be okay with making decisions without 100% of the information. One can argue that the return on soft skills is soft. But it also means being able to move fast and grab windows of opportunity. Not all business cases are based on cost savings.
  • Build an analytics hub. The biggest challenge for CFOs today is to transform finance into the analytical hub of the organization and leverage Big Data to drive smarter business decisions—both in terms of cost cutting and in giving the business units advice on how to market, sell, develop, and grow their operations. That’s how finance fits within the digital enterprise. Finance needs to funnel Big Data from all corners of the organization—and outside it—to leverage its unique central viewpoint. It must bring the information together and run it through advanced analytics models to come up with causal relationships that explain what business initiatives are really moving the needle, what steps the company can take to improve results, and what its customers are doing and are likely to do. Digitizing finance has a huge payback: It allows companies to stay competitive in a digital economy.

Is finance transformation worth the effort? That may be the wrong question. The question is, can companies afford not to transform their finance function and remain relevant now and going forward?

Learn how the FP&A team at CF Industries Holdings Inc. prioritized business partnering options and transformed the organization to optimally support strategic goals by establishing an integrated business planning process at the AFP Annual Conference session, Driving Finance Transformation Through Integrated Business Planning.

For more of my insights on FP&A, subscribe to the monthly FP&A e-newsletter from my company, the Association for Financial Professionals. You can also connect with me on LinkedIn or follow me on Twitter.

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Nilly Essaides

About Nilly Essaides

Nilly Essaides is the director of the FP&A Practice at the Association for Financial Professionals. She has over 25 years of experience in the finance field. Nilly has written multiple in-depth research reports on FP&A and Treasury topics, as well as countless articles. She also speaks at conferences and moderates financial executives' roundtables across the country. Nilly has published a book on best-practice transfer and process excellence with the APQC, "If We Only Knew What We Know."