Fluitec Wind: Leveraging the Power of Data
Analysis of Ivey Case 9B15E003
Fluitec Wind’s novel Tribo-Analytics platform is designed to help turbine operators utilize existing data streams to significantly reduce costs and guide decision making. As gearboxes have a 10 per cent chance of failure each year and replacements are expensive, predicting and preventing such failure is a constant source of stress for wind farm owners. Fluitec Wind has access to a wealth of untapped data resources that could be used to diagnose gearbox health using existing wind turbine data without the need for additional monitoring and sensor hardware. By aggregating data from tens of thousands of turbines globally, they have been able to create recommendations with unparalleled accuracy. Operators can identify at-risk components in advance and understand the causes and solutions to their elevated risks. Tribo-Analytics provides a platform through which operators can best forecast the health and reliability of their wind turbines.
Key Selling Points
Some of the primary selling points of Tribo-Analytics is the intuitive technology which will help in early detection of an impending gearbox. This will not only save on the cost of the gearbox change-out but will yield substantial savings in turbine downtime and repair costs — all without installing another piece of hardware on the device [1]. As the product by itself has low adoption barriers, Fluitec Wind’s customers can adopt the product inexpensively. Also, the robustness of the predictive analytics system helped to gain accurate information to foresee any turbine failures as well as configuration data across all major original equipment manufacturers globally [1]. Predictive analytics comprises of varied statistical trends and techniques ranging from machine learning and predictive modelling to data mining to efficiently analyze the historical data and information to process them to create predictions about the unknown future events. Historical data combined with SCADA and conditions monitoring systems, with technological advancements, the operations and maintenance costs reduced significantly.
With the growing tough economic conditions and a need for competitive differentiation, not only did the growing volumes and types of data produce valuable insights, it provided Fluitec Wind with an edge over its competitors.
With interactive and easy-to-use software on SaaS platform, customers can upload their data, create alerts and monitor the health of their wind turbines. Hence by providing an easy to use self-service environment, the customers can deal with data directly [1]. Besides viewing details at various levels, the customers can use the dashboard provided by Tribo-Analytics to gauge where they stand amongst their peer in a demographic. This will help Flutiec Wind gain the loyalty of their customers (Appendix A).
By using predictive analytics to predict a future event or trend, the company can create a strategy to position itself to take advantage of that insight. All these factors, in turn, mean that the customers can higher return on investments in the longer run.
Justifying Investments in Predictive Analytics
A recent survey-based report has found a trend in companies spending an estimated $36 billion on data storage and infrastructure alone and is expected to be doubled by 2020 [2]. They are investing in big data infrastructure because they believe that it offers a positive return on investment. Some of the cases which will drive this positive ROI from big data are:
· Better predictions of machine failure: Smoothly operating supply chains are vital for stable profits. As previously stated, predicting mechanical failures of the turbines in advance can reduce repair time and O&M cost. Turbine downtime imposes a cost to Fluitec Wind due to forgone productivity and can be particularly disruptive for both manufacturing supply chains and consumers. Executives in asset-intensive industries often state that the primary operational risk to their businesses is unexpected failures of their assets. Microsoft data scientists from the Cortana Intelligence Suite team can predict the probability of aircraft being delayed or cancelled in the future based on relevant data sources, such as maintenance history and flight route information [3]. A machine-learning solution based on historical data and applied in real-time predicts the type of mechanical issue that will result in a delay or cancellation of a flight within the next 24 hours, allowing the airlines to take maintenance actions while the aircraft is being serviced, thus preventing possible delays or cancellations.
Similar predictive-maintenance solutions are also built-in other industries — for example, tracking real-time telemetry data to predict the remaining useful life of an aircraft engine, using sensor data to predict the failure of an ATM cash withdrawal transaction and so on. Predictive analytics will make these supply chains less brittle and reduce the effects of disruptions for many goods and services.
· More precise demand forecasts: It is crucial to value accurate demand forecasts because inventory is expensive to keep on shelves and stockouts are detrimental to both short-term revenue and long-term customer engagement. Aggregated total sales are a poor proxy because any company needs to distribute inventory geographically, necessitating hyperlocal forecasts. the big data solution leverages the previously unused data point that people do a considerable amount of social inquiry and research online before purchasing a turbine. With the aid of predictive analytics tools, Fluitec Wind will be able to foresee, procure and fulfil orders, from routine system maintenance or replacement to installing a new turbine. Additionally, it would be possible to gauge the geographical areas with more demand.
The increased prediction accuracy, in turn, makes it possible to achieve a significant increase in operational efficiency: having the right inventory in the right locations. Processing search data and combining it with traditional sources is vital in creating a successful prediction.
· Better price sensitivity estimates: Using a single price is economically inefficient because part of the demand curve that could be profitably served is priced out of the market. As a consequence, Fluitec Wind will regularly be able to offer targeted discounts, promotions, and segment-based pricing to target different consumers. Such pricing strategies can be set in place by tracking consumers through smartphone connectivity and logging which customers enter the store, what type or size of turbines they look at, and whether they make a purchase. Machine learning applied to this data can algorithmically generate customer segments based on price responsiveness and preferences, which generally offers a large improvement on traditional demographic-based targeting.
Fluitec Wind’s online website too can have a distinct advantage in pursuing such an approach because they log detailed information on customer browsing. These price adjustments are a form of experimentation and, jointly with big data, allow companies to learn more about their customers’ price responsiveness.
· Radically new applications: The data obtained from SCADA and conditions monitoring, along with the historical data has the potential to revolutionize how certain impediments and failures and be diagnosed and dealt with. For example, taking massive data sets as inputs, coupled with clever designs that account for patient histories, can help how certain diseases are diagnosed and treated. Another example involves matching distributed electricity generation (e.g., solar panels on roofs) to localized electricity demand, unlocking huge value by equating electricity supply and demand with more-efficient generation.
Additionally, there lies potential to not only predict failures of the turbines, and also to foresee any weather anomalies. For instance, when the turbines are functioning at a significantly greater rate than usual, it could signify the onset of a windstorm. A suitable signal can be sent to the operator who can then issue a safety warning or an evacuation warning if need be. The value of radically new applications is challenging to understand ex-ante and speculative by nature. It is reasonable to expect losses for many firms, due to uncertain and higher risk investments, with a few firms earning spectacular profits.
Product Benefits Evaluations
Albert Einstein once said, “Know where to find information and how to use it; that is the secret of success.” Finding value in data equals success. Therefore, the predictive analytics equation can be written as [7]:
Data mining + business knowledge = predictive analytics => success
Predictive analytics empowers an organization by providing three advantages (Appendix B):
- Vision: Predictive analytics will lead you to see what is invisible to others — in particular, useful patterns in your data.
- Decision: Consistent and unbiased insights to support your decisions.
- Precision: Using automated tools saves time and resources, reduces human error, and improves precision.
One way for Fluitec Wind to ensure their product adds tangible business value is by focusing on the following [6]:
1. Income statement improvements that:
- Reduce cost or increasing margin by using data analytics to find real opportunities. Helps Fluitec to avoid guessing which opportunities might be real and cease addressing ideas that are counterproductive.
- Increase revenue or gross sales by using data analytics to find under-served customers, poorly served markets, or emerging product trends. Quit over-analyzing sales data and financial transaction data.
2. Balance sheet improvements that:
- Increase the ratio of revenue to marketing expenditure. Although expenditure impact data available from Google and Facebook has helped significantly, data analytics can reduce the waste further.
- Reduce inventory value. Any warehouses or storage units which still carry a surprising amount of dead stock and too much float for active products. Data analytics can help reduce wasted capital.
It is also important to track these tangible business value Fluitec Wind is achieving. Failure of which leads to the following problems [6]:
1. Stakeholders have no way of knowing if the business value assertions of the project sponsor or the project team are being exaggerated or not.
2. The end-user community asserts the business value is due to their excellent work and not due to the new data analytics capabilities delivered by the project team.
These problems undermine the belief that data analytics is contributing value. It will be possible to claim that data analytics can to add tangible business value, only when these values are tacked. For example, when adding an incremental revenue, then this amount should be tracked on a daily, weekly, or monthly basis.
Nurturing an Analytic Approach
As data has become increasingly important, companies are embracing the changing business environment by adding new senior roles such as Chief Data Officer and Chief Analytics Officer (Appendix C). Additionally, it is crucial to have future-thinking leadership throughout the organization and as well as data-focused talent. Hiring data-focused talent in areas such as sales and marketing positions, i.e. in roles one does not typically heavily associate with data can have massive impacts on the future of the organization. To make such a company shift happen efficiently, leadership from human resources must be brought to the planning table, so they understand the business requirements in each of the business areas.
With data accessible to the whole organization, management should take it a step further by encouraging experimentation. To make this initiative more efficient, employees should be shown how to test and measure the data against a training data set and draw inference from the study and should be emboldened to constantly new means and methodologies pertaining to their industry. In this way, with measures of accountability and reward in place, employees and decision-makers have the incentive to make data-driven decisions. When a new customer request comes in, with the support of the management, the data-savvy employees will be able to take on the challenge, thereby utilizing the already present resources and eliminating the need to hire new talent every iteration.
Managers generally lack the extensive expertise of data science in mining and analyzing data using technical tools. In the same way, data scientists do not have the extensive knowledge managers have in solving business problems [9]. Through effective communication techniques, both parties must work together and seek to understand each other for data to create positive outcomes for the organization and its decisions. Targeted business leadership training emphasizing communication combined with the technical knowledge is instrumental to the Fluitec Wind’s success.
Creating a Culture for Data Science to Thrive
Businesses that place great importance on using data for a competitive advantage will emerge as tomorrow’s successes. Without the right data-driven talent and culture, the organization will be left behind in a business landscape in which other organizations embracing data are winning [11].
Creating a data-driven culture is more than just having a team of data scientists. To enable data scientists to do their job, management at Fluitec Wind must be willing to invest time, money and the necessary resources into building infrastructure, acquiring technology and providing relevant training. This includes a management team that understands the methods of big data, data analytics and data science.
It is vital to keep in mind that the results are only as good as the decision-makers themselves. Since the future of work revolves around data, organization-wide access to data and insights must be provided to facilitate decision making. Then, decision-makers do not have to wait for analysis or reports by the data science team. With the data exposed to more employees, more perspectives could increase fresh and groundbreaking insights on a given business challenge. Additionally, management must direct the whole effort of managing data science toward problem-solving endeavours.
Data analysis provides insights that must be acted upon to make the process worthwhile. There should be complete objectivity in discussing these ideas, and they must be complemented by the expertise and experience of the management team.
Improving the Accuracy
By constantly collecting data from the turbines, SCADA and conditioning monitoring, an integrated forecasting suite can be set up. An integrated forecasting suite means that data moves seamlessly among applications and users, making collaboration easier and faster. Advanced data visualization augments data discovery and exploration to derive rapid insights from huge volumes of data and faster answers to complex questions. Even those with limited analytical prowess can quickly zero in on areas of opportunity, or concerns, in vast amounts of data. The shorter the time from initial forecast development to execution, the more accurate the forecast.
For instance, Salt River Project is the second-largest public power utility in the US and one of Arizona’s largest water suppliers. Analyses of machine sensor data predicts when power-generating turbines need maintenance. [4]. SRP uses SAS to minimize generator downtime and anticipate future demands on the power grid. The technology has allowed the agency to achieve far greater granularity with data forecasting, notes Corby Gardiner, principal economist for the SRP. In the past, analysts examined models that were only able to forecast at a day, week or month level. Today, they are able to examine patterns at an hourly level. The system uses data from 8,760 different markets and relies on millions of observations to deliver far more precise energy forecasting capabilities. “By analyzing all this data, we gain an accurate picture of our actual generation, so we have a fine-tuned understanding of maintenance requirements,” says Steve Petruso, Senior Software Developer at SRP. About sixty batches of SAS jobs are run every morning and the data is directed the appropriate systems, including the respective data marts. [8]. As seen here, over time, SRP collected more data from numerous data sources enabling them to add include more variables consequently improving the accuracy of the predictive system. The same holds true for Flutiec Wind.
Another data source which can be essential is the customer’s feedback and their activity. By analyzing the behaviour of the customers, Fluitec Wind will be able to predict their future procurements such as purchasing more turbines to expand their wind farms or even specific parts for the wind turbines. From the feedback Fluitec Wind receives from their customers, they will be able to improve the technology they process subsequentially improve the accuracy of the system.
Appendix A:
Fluitec Wind Tribo-Analytics Dashboard
Working of Tribo-Analytics in synergy with SCADA, Conditions Monitoring and Historical Data
Appendix B
How predictive analysis empowers your organization
Predictive analytics empowers your organization by providing three advantages:
- Vision
- Decision
- Precision
Vision
Predictive analytics can provide you with powerful hints to lend direction to the decisions you’re about to make in your company’s quest to retain customers, attract more customers, and maximize profits. Predictive analytics can go through a lot of past customer data, associate it with other pieces of data, and assemble all the pieces in the right order to solve that puzzle in various ways, including
- Categorizing your customers and speculate about their needs.
- Knowing your customers’ wish lists.
- Guessing your customers’ next actions.
- Categorizing your customers as loyal, seasonal, or wandering.
Knowing this type of information beforehand shapes your strategic planning and helps optimize resource allocation, increase customer satisfaction, and maximize your profits.
Decision
A well-made predictive analytics model provides analytical results free of emotion and bias. The model uses mathematical functions to derive forward insights from numbers and text that describe past facts and current information. The model provides you with consistent and unbiased insights to support your decisions.
Consider the scenario of a typical application for a credit card: The process takes a few minutes; the bank or agency makes a quick, fact-based decision on whether to extend credit and is confident in their decision. The speed of that transaction is possible thanks to predictive analytics, which predicted the applicant’s creditworthiness.
Precision
Imagine having to read a lot of reports, derive insights from the past facts buried in them, go through rows of Excel spreadsheets to compare results, or extract information from a large array of numbers. You’d need staff to do these time-consuming tasks. With predictive analytics, you can use automated tools to do the job for you — saving time and resources, reduces human error, and improves precision.
For example, you can focus on targeted marketing campaigns by examining the data you have about your customers, their demographics, and their purchases. When you know precisely which customers you should market to, you can zero in on those most likely to buy.
Source: How Predictive Analytics Adds Business Value. (n.d.). Retrieved from https://www.dummies.com/programming/big-data/data-science/how-predictive-analytics-adds-business-value/
Appendix C
Data cultures are becoming pivotal as organizations develop more progressive digital business strategies and apply meaning to big data. The following six steps Chief Data Officers (CDO’s) can take to build a culture around the information that’s at the heart of today’s business and create a successful, thriving data cultures:
Map your organization’s data supply chain
Departmental silos of information are the nemesis of thriving data cultures. To promote the view of data as a flexible asset that’s usable by multiple departments, organizations need to educate employees on how the data they use daily ripples through other parts of the organization. Employees need to see the big picture. Mapping your organization’s data supply chain is a useful tool for gaining that 30,000-foot view.
The map tracks each data set’s path through the organization. The data supply chain map becomes a framework to which everyone within the organization can refer. It provides context for how data is used and how employees’ own data usage fits into the broader enterprise.
Data maps can also uncover “dark data,” or pockets of information that go largely unstudied, such as machine data and customer service call logs. Dark data is typically difficult to integrate and analyze due to technical issues such as formatting, variety and velocity.
Focus on the “art of the possible”
Awareness of data’s flexibility is the hallmark of any data culture. It leads to what we call the “art of the possible” — that is, a knack for spotting alternative uses for data.
Be transparent about data
Data becomes an asset only if its accuracy is trusted, its provenance is well established, and its security is safeguarded. But data also requires openness, even as it is protected from fraudsters and kept private for regulatory reasons.
Transparency extends even to data with accuracy issues. When confidence in data quality is low, or the data’s lineage cannot be established, the CDO organization can enhance the data’s value with suggestions for specific uses.
Develop reward-sharing mechanisms
Recognition can occur in many forms, including videos, blogs and special occasion gatherings, such as luncheons. Setting up a company portal to highlight data successes is another option. Rewards and recognition for data initiatives should also be included in formal corporate excellence programs.
Identify areas of friction within the organization
Data improves collaboration by keeping the departmental focus on facts. For example, tension often exists between product engineering and sales. Engineering’s objective is to freeze requirements so it can get products to market on time and within budget; for sales to meet its goal of boosting revenues, however, it prefers a more fluid approach to requirements in which it can funnel requests for additional features to engineering as it learns about them through customer discussions.
Elevate the conversation to focus on strategy and innovation
Openly discussing strategies and innovation goals provide employees with a clear view of data’s role in the company’s overall mission and reinforce their connection to the larger organization.
Source: How to Create a Data Culture. (n.d.). Cognizant 20–20 Insights.
References
[1] Riedl, C., & Hogan, T. (n.d.). Fluitec Wind: Improving Sustainability Through Predictive Analytics. Ivey Publishing. Retrieved from https://www.iveycases.com/ProductView.aspx?id=70970
[2] The Big Data Market: 2018–2030 — Opportunities, Challenges, Strategies, Industry Verticals & Forecasts. (n.d.). Retrieved from http://www.snstelecom.com/bigdata
[3] Jacob LaRivierePreston McAfeeJustin RaoVijay K. NarayananWalter Sun. (2017, April 24). Where Predictive Analytics Is Having the Biggest Impact. Retrieved from https://hbr.org/2016/05/where-predictive-analytics-is-having-the-biggest-impact
[4] Predictive Analytics: What it is and why it matters. (n.d.). Retrieved from https://www.sas.com/en_us/insights/analytics/predictive-analytics.html
[5] How to Create a Data Culture. (n.d.). Cognizant 20–20 Insights.
[6] Is data analytics adding business value for you? (n.d.). Retrieved from https://www.itworldcanada.com/blog/is-data-analytics-adding-business-value-for-you/401699
[7] How Predictive Analytics Adds Business Value. (n.d.). Retrieved from https://www.dummies.com/programming/big-data/data-science/how-predictive-analytics-adds-business-value/
[8] Making desert living cool and comfortable. (n.d.). Retrieved from https://www.sas.com/en_us/customers/salt-river-project.html
[9] Ahmed, A. (2019, February 11). The Importance of Internal & External Communication. Retrieved from https://bizfluent.com/info-8691408-importance-internal-external-communication.html
[10] Wind Turbines. (n.d.). Retrieved from https://www.fluitec.com/applications/wind-turbines
[11] Henderson, T. (2018, March 16). How Leadership Can Create A Data-Driven Culture And New Careers In The Future Workplace. Retrieved from https://www.forbes.com/sites/forbescoachescouncil/2018/03/16/how-leadership-can-create-a-data-driven-culture-and-new-careers-in-the-future-workplace/#463ce1d0450a
