2015 North American Machine Learning Analytics Platform in the Oil & Gas Industry Customer Value Leadership Award

Transcription

1 North American Machine Learning Analytics Platform in the Oil & Gas Industry Customer Value Leadership Award

2 Contents Background and Company Performance... 3 Industry Challenges... 3 Customer Impact and Business Impact... 3 Conclusion... 7 Significance of Customer Value Leadership... 8 Understanding Customer Value Leadership... 8 Key Benchmarking Criteria... 9 Best Practice Award Analysis for Mtell... 9 Decision Support Scorecard... 9 Customer Impact Business Impact Decision Support Matrix The Intersection between 360-Degree Research and Best Practices Awards Research Methodology Best Practices Recognition: 10 Steps to Researching, Identifying, and Recognizing Best Practices About Frost & Sullivan Frost & Sullivan We Accelerate Growth

3 Background and Company Performance Industry Challenges The global oil and gas (O&G) industry is striving to overcome its reliance on traditional risk management and asset management methodologies for decision making. Inefficient equipment and asset performance causes high operational downtimes and large maintenance and service costs. As a result of the upsurge in big data, the use of machine learning solutions and advanced analytical techniques to precisely identify and solve assetrelated issues at an early stage issues that were previously undetectable and unsolvable have become increasingly relevant. Integration of big data pools in conjunction with the computational logic of a machine learning platform unlocks the possibilities for prognostic analysis and provides decision makers with in-depth insights in any given situation. Furthermore, the generation of real-time alerts helps O&G companies avert any occurrences that might cost them heavily in terms of production and maintenance, repair, and overhaul (MRO) activities. Companies that adopt advanced machine learning technologies are able to analyze the realtime performance of their assets, preempt failure, improve asset lifespan, reduce maintenance costs, and increase production output. With such a setup, they find themselves best positioned to stay competitive in the dynamic North American market. Customer Impact and Business Impact Price/Performance Value San Diego-based Mtell has developed a smart machine learning software to facilitate the precise, timely, and prognostic assessment of a machine s operational condition in the O&G sector and in other process-intensive industries, such as water and wastewater, mining and transportation, to name just a few. Mtell s customer-centric approach enables users to take the right measures to address any performance-related issues in their equipment by proactively detecting asset degradation at its early stages. Mtell s unique solution notifies users about when and how a machine may fail. It does so significantly quicker than any other competing technology as it utilizes selflearning agents to monitor operations and pattern-recognition to scan real-time data. In contrast to its competitors in this space who provide only anomaly detection, their best-inclass solution uses a combination of anomaly-detection and pattern-recognition where knowledge is built from real-time sensor data. The evaluation of the new conditions is subsequently mapped against exact patterns. Mtell has further improved their technology such that individual machines can learn from their surroundings and transfer knowledge to other machines. The key differentiator here is its ability to detect small faults and minor degradations before they become major faults that have to be detected by other systems, such as modeling or vibration analysis techniques. This not only translates into a significant Frost & Sullivan We Accelerate Growth

4 leap in the operational efficiency levels of the customers assets and equipment, it also addresses the issues of cost and time by increasing equipment uptime. Based on requirement and application, customers can select either Mtell s Basis version or Mtell s Previse version. Mtell s Basis version houses a Boolean rules engine for simple condition monitoring and is primarily used as a bridge between the asset and the customers operations and maintenance systems. Mtell s Previse version offers a high-end machine learning software-based solution that uses adaptive self-learning agents to detect a fault imminent in the functioning of the equipment even before it has occurred and even before any other system is able to detect it. From a data gathering standpoint, Mtell s Previse version features data collation modules that fundamentally extract unrefined time series data stream from newly added sensors or from a data stream in the processes that are not archived by the on-site plant data historians. Collaboration between systems, work processes, and departments is imperative in order to optimize business performance and ensure sustained profitability. When Mtell s solution detects degradation at a very early stage, it facilitates decision-making processes across individual business units. For example, based on the alert generated, process engineers can reduce the load on an asset to make it last longer, operations managers can cease production until a more convenient time, MRO personnel can prioritize maintenance work, and production planners can shuffle assets and resources suitability to meet demand. Moreover, by its ability to seamlessly integrate with other enterprise asset management (EAM) systems to automatically issue work orders, Mtell delivers unmatched value to customers who are constantly on the lookout for a cost-effective centralized asset health decision-support platform. Customer Purchase Experience Unique selling points of Mtell s solution are its rapid deployment, ease of use, and the limited human involvement required post implementation. In contrast to manual data collection techniques and other rules-based models, Mtell s off-the-shelf machine learning platform seamlessly loads a large volume of sensor data onto its learning engine that preprocesses the data and prepares it for effective pattern recognition. Its smart machine learning-based solutions incorporate artificial software agents that are far easier to implement and produce by far the best results among other similar products. The customer can create multiple agents on a single asset and extract its precise data patterns. The first agent deployed is called the anomaly agent which tracks any change in operation; subsequent agents are called failure agents and are used to detect any kind of failure streaks or degradation of individual components. In case of a minor degradation, alerts are generated via or SMS messages and fed into a dashboard that has a state-of-the-art navigation system. Users can thereby track a work order without touching the maintenance system. Furthermore, as the platform is self-sufficient, self-learning, and self-improving, there is no need for expensive maintenance methodologists or data scientists to analyze the data and provide feedback. A case in point is a customer that created 400 agents on 40 Frost & Sullivan We Accelerate Growth

5 different asset classes without any help from Mtell. This is a significant feature when compared to other systems where an expensive data scientist is required to process and analyze tons of data. Mtell s simple and easy-to use-machine learning platform masks the inordinate complexity of operations that would previously have been performed by one person. By utilizing intelligent software agents that can be created in minutes to identify and proactively address the degradation of assets, Mtell is poised to revolutionize the maintenance decisionmaking processes. Customer Ownership Experience Committed to delivering a fulfilling ownership experience to customers, Mtell encompasses a 3P (performance, predictive, and prescriptive) approach to developing a smart machine for the future. While most competitors focus on monitoring current and historic machine trends to evaluate asset performance and predict future failure, Mtell s unique prescriptive approach facilitates degradation detection so that customers can take the steps required well in advance to mitigate or completely avoid failure. If there is a failure path detected, it is directly linked to the failure code in the asset management system and customers can determine which parts are failing, what parts are needed, and what skills are required to mitigate the problem. By focusing on degradation detection rather than failure detection, Mtell s machine learning platform has proven to be immensely successful in addressing customers specific pain points, such as environmental damage, personnel safety, loss of productivity due to machine downtime, or any other related compliance issues. Implementing Mtell s unique machine learning platform has resulted in a favorable practice in existing maintenance-management activities. Based on insights developed using Mtell s machine learning platform, users can modify maintenance plans and change maintenance goals to ensure that machines run longer, thereby reducing associated downtime costs. Mtell s state-of-the art platform mostly relies on agents to solve problems by monotonously monitoring a single event on one machine, whereas others rely on vast amount of partial data in many cases. A lot of the key data provided by Mtell s platform can be used to support other internal and external business operations that are normally not leveraged in other competing solutions. For example, the feedback garnered can be used for crossdepartmental collaboration projects, such as the transformation of current product design activities. Third-party insurance companies can also use Mtell s machine learning solutions to avoid catastrophes. In comparison to other solution providers in the market, Mtell provides customers with ample time (30+ days) to plan their maintenance activities. A key example that illustrates Mtell s finesse in reducing the time and costs associated with maintenance management is their solution implementation in a refinery. Mtell was able to analyze the refinery s 7-year failure record and execute its solution effectively within a short time span of 1-3 weeks, as opposed to a competitor that proposed a timeframe of weeks. Frost & Sullivan We Accelerate Growth

6 From an O&G application standpoint, Mtell s solution can be applied to a wide array of assets including pumps and compressors, motors and drives, electrical transformers, heavy haul trucks used in exploration activities, and other process equipment, such as chillers, heat exchangers, and boilers. Using Mtell s reliability-centered machine learning platform that can be applied across a number of O&G assets, customers can maximize their profit margins and avert risks that may interrupt production rates. Customer Service Experience Mtell s focus on delivering customers with a best-in-class service experience that can be illustrated by their easily deployable machine learning platform which sits within the boundaries of the customers firewall, allowing them for complete control over the health condition of each asset. While other techniques require external expertise to build, install, and analyze models, Mtell utilizes software agents to monitor and capture machine patterns and dimensions that identify normal and distinct degradation paths for each asset. The platform can analyze and cleanse data and also provide training for the agents through wizards where users can step in and get metrics on several perspectives of asset condition. The users can even tweak certain values to a larger percentage of the result, based on their specific requirements. Furthermore, Mtell s centralized control over asset performance allows customers to easily scale up production. Some of the other value-added benefits of Mtell s service offering that has allowed the company to gain a competitive advantage in this space include out-of-the-box integration with on-site work processes and systems to carry out successful implementation, pilot projects to build a business case prior to wide scale implementation, continuous improvement through upgrades, and extended technical support and training. In scores provided by customers during trial runs, Mtell has triumphed over a number of solution providers in the market on several occasions by wide margins. In the extremely competitive O&G industry, Mtell s diversified service offering, particularly its easily deployable software agents that detect minute-by-minute changes in any machine and correlate everything to detect a failure in advance, allows customers to establish an edge over their peers in the domain of machine operation and maintenance-related activities. Financial Performance Backed by its experience in the operational and maintenance domains and its superior value proposition of utilizing machine learning techniques to detect early degradation, Mtell was listed as one of the top 5 fast-growing companies in the July 2014 issue of San Diego Business Journal. The company recorded a remarkable rise of around 374% in its revenues during It has also been able to secure a position in the 2014 list of top 5000 companies in the United States published by Inc., and aims to reach out to a wider set of industry verticals that are on the lookout for machine learning and predictive maintenance solutions with next-generation capabilities that reliably extend the lifespan of equipment. Frost & Sullivan We Accelerate Growth

7 Growth Potential As the advanced maintenance analytics market (which includes predictive and prescriptive analytics) is expected to witness a steep CAGR of 55-65% over the next 5 years, Mtell s cutting-edge machine learning solution is poised to gain greater traction amidst peers who are currently focused solely on descriptive and anomaly-based analytics. In the current setup, there are two key factors that hinder growth for solution providers: i) the inability to maintain a strong enough workforce to bring in future business opportunities and ii) the lack of sufficient service staff to implement their solutions to a wide spectrum of customers. To eliminate these obstacles, Mtell has acquired competent partners who can identify and bring in value added business. In contrast to its competitors, Mtell has built a multi-sided platform that can be sold to a wide variety of customers including owneroperators in asset-intensive businesses, companies that provide remote monitoring and maintenance services for their customers, and original equipment manufacturers (OEM) who supply software and services along with machines. Such strategic collaborations and partnerships with key value chain participants ensure that end users do not have to rely heavily on Mtell for implementation services. Mtell s has a healthy collaboration with SAP and ThingWorx to align its strategy with the increasingly popular Internet of Things (IoT) trend. It also has a partnership with Infor, a leading enterprise asset management (EAM) company, to expand its customer reach. These are just a few of several strategic partnerships the company has entered into. The company s promise to significantly reduce equipment downtime and maintenance cost incurred by customers is expected to play a pivotal role in Mtell s penetration into different industries in the coming years. Mtell enjoys a strong presence in O&G industries across the globe, including Europe and the Middle East. While Mtell continues to expand the scope of its strategic partnerships across the world, the company follows the direct sales plus partner approach in the North American space. Conclusion Mtell is poised to revolutionize the asset health domain through its design of leading-edge machine learning software-based solutions with advanced prescriptive analysis capabilities that guarantee early detection of degradation. In contrast to other remote monitoring and condition monitoring offerings in the market, its best-in-class scalable machine learning platform provides customers accurate alerts on when and how a machine might fail. Moreover, it also facilitates self-learning of machines and minimal human involvement owing to the deployment of software agents. Its ability to allow seamless changes and modifications on existing maintenance plans due to its interoperability with existing maintenance systems has proved to be phenomenally useful to customers managing complex asset-intensive industries. Frost & Sullivan We Accelerate Growth

8 BEST PRACTICES RESEARCH With its strong overall performance, Mtell has earned Frost & Sullivan s 2015 North America Customer Value Leadership Award for their Machine Learning Analytics Platform in the Oil and Gas Industry. Significance of Customer Value Leadership Ultimately, growth in any organization depends upon customers purchasing from your company, and then making the decision to return time and again. Delighting customers is therefore the cornerstone of any successful growth strategy. To achieve these dual goals (growth and customer delight), an organization must be best-in-class in three key areas: understanding demand, nurturing the brand, and differentiating from the competition. Understanding Customer Value Leadership Customer Value Leadership is defined and measured by two macro-level categories: customer impact and business impact. These two sides work together to make customers feel valued, and confident in their products quality and long shelf life. This dual satisfaction translates into repeat purchases and a high lifetime customer value. Frost & Sullivan We Accelerate Growth

9 Key Benchmarking Criteria For the Customer Value Leadership Award, Frost & Sullivan analysts independently evaluated two key factors Customer Impact and Business Impact according to the criteria identified below. Customer Impact Criterion 1: Price/Performance Value Criterion 2: Customer Purchase Experience Criterion 3: Customer Ownership Experience Criterion 4: Customer Service Experience Criterion 5: Brand Equity Business Impact Criterion 1: Financial Performance Criterion 2: Customer Acquisition Criterion 3: Operational Efficiency Criterion 4: Growth Potential Criterion 5: Human Capital Best Practice Award Analysis for Mtelligence Corporation Decision Support Scorecard To support its evaluation of best practices across multiple business performance categories, Frost & Sullivan employs a customized Decision Support Scorecard. This tool allows our research and consulting teams to objectively analyze performance, according to the key benchmarking criteria listed in the previous section, and to assign ratings on that basis. The tool follows a 10-point scale that allows for nuances in performance evaluation; ratings guidelines are illustrated below. RATINGS GUIDELINES The Decision Support Scorecard is organized by Customer Impact and Business Impact (i.e., the overarching categories for all 10 benchmarking criteria; the definitions for each criteria are provided beneath the scorecard). The research team confirms the veracity of this weighted scorecard through sensitivity analysis, which confirms that small changes to the ratings for a specific criterion do not lead to a significant change in the overall relative rankings of the companies. Frost & Sullivan We Accelerate Growth

10 The results of this analysis are shown below. To remain unbiased and to protect the interests of all organizations reviewed, we have chosen to refer to the other key players as Competitor 2 and Competitor 3. DECISION SUPPORT SCORECARD FOR CUSTOMER VALUE LEADERSHIP AWARD Measurement of 1 10 (1 = poor; 10 = excellent) Customer Value Leadership Customer Impact Business Impact Average Rating Mtell Competitor Competitor Customer Impact Criterion 1: Price/Performance Value Requirement: Products or services offer the best value for the price, compared to similar offerings in the market Criterion 2: Customer Purchase Experience Requirement: Customers feel like they are buying the most optimal solution that addresses both their unique needs and their unique constraints Criterion 3: Customer Ownership Experience Requirement: Customers are proud to own the company s product or service, and have a positive experience throughout the life of the product or service Criterion 4: Customer Service Experience Requirement: Customer service is accessible, fast, stress-free, and of high quality Criterion 5: Brand Equity Requirement: Customers have a positive view of the brand and exhibit high brand loyalty Business Impact Criterion 1: Financial Performance Requirement: Strong overall financial performance in terms of revenues, revenue growth, operating margin and other key financial metrics Criterion 2: Customer Acquisition Requirement: Customer facing processes support the efficient and consistent acquisition of new customers, even as it enhances retention of current customers Criterion 3: Operational Efficiency Requirement: Staff is able to perform assigned tasks productively, quickly, and to a high quality standard Frost & Sullivan We Accelerate Growth

11 Criterion 4: Growth Potential Requirements: Customer focus strengthens brand, reinforces customer loyalty and enhances growth potential Criterion 5: Human Capital Requirement: Company culture is characterized by a strong commitment to quality and customers, which in turn enhances employee morale and retention Decision Support Matrix Once all companies have been evaluated according to the Decision Support Scorecard, analysts can then position the candidates on the matrix shown below, enabling them to visualize which companies are truly breakthrough and which ones are not yet operating at best-in-class levels. DECISION SUPPORT MATRIX FOR CUSTOMER VALUE LEADERSHIP AWARD High Mtell Competitor 2 Competitor 3 Business Impact Low Low Customer Impact High Frost & Sullivan We Accelerate Growth

12 The Intersection between 360-Degree Research and Best Practices Awards Research Methodology Frost & Sullivan s 360-degree research methodology represents the analytical rigor of our research process. It offers a 360-degree-view of industry challenges, trends, and issues by integrating all 7 of Frost & Sullivan's research methodologies. Too often, companies make important growth decisions based on a narrow understanding of their environment, leading to errors of both omission and commission. Successful growth strategies are founded on a thorough understanding of market, technical, economic, financial, customer, best practices, and demographic analyses. The integration of these research disciplines into the 360-degree research methodology provides an evaluation platform for benchmarking industry players and for identifying those performing at bestin-class levels. 360-DEGREE RESEARCH: SEEING ORDER IN THE CHAOS Competitive Benchmarking Availability of Capital Demographics Buying Behavior Growth Strategies Economic Trends Industry Expansion Industry Evolution New Vertical Markets GeoPolitical Stability Growth Implementation Technology Obsolescence Emerging Technologies CEO Sustainability Disruptive Technologies New Business Cultures Smart Cities Career Development New Applications Segmentation Industry Convergence Country Risk Branding and Positioning Crowd Sourcing Competitive Strategy Needs and Perceptions Capital Investments Emerging Competition Frost & Sullivan We Accelerate Growth

13 Best Practices Recognition: 10 Steps to Researching, Identifying, and Recognizing Best Practices Frost & Sullivan Awards follow a 10-step process to evaluate award candidates and assess their fit with select best practice criteria. The reputation and integrity of the Awards are based on close adherence to this process. STEP OBJECTIVE KEY ACTIVITIES OUTPUT 1 Monitor, target, and screen Identify award recipient candidates from around the globe Conduct in-depth industry research Identify emerging sectors Scan multiple geographies Pipeline of candidates who potentially meet all bestpractice criteria 2 Perform 360-degree research Perform comprehensive, 360-degree research on all candidates in the pipeline Interview thought leaders and industry practitioners Assess candidates fit with best-practice criteria Rank all candidates Matrix positioning all candidates performance relative to one another 3 Invite thought leadership in best practices Perform in-depth examination of all candidates Confirm best-practice criteria Examine eligibility of all candidates Identify any information gaps Detailed profiles of all ranked candidates 4 Initiate research director review Conduct an unbiased evaluation of all candidate profiles Brainstorm ranking options Invite multiple perspectives on candidates performance Update candidate profiles Final prioritization of all eligible candidates and companion best-practice positioning paper 5 Assemble panel of industry experts Present findings to an expert panel of industry thought leaders Share findings Strengthen cases for candidate eligibility Prioritize candidates Refined list of prioritized award candidates 6 Conduct global industry review Build consensus on award candidates eligibility Hold global team meeting to review all candidates Pressure-test fit with criteria Confirm inclusion of all eligible candidates Final list of eligible award candidates, representing success stories worldwide 7 Perform quality check Develop official award consideration materials Perform final performance benchmarking activities Write nominations Perform quality review High-quality, accurate, and creative presentation of nominees successes 8 Reconnect with panel of industry experts Finalize the selection of the best-practice award recipient Review analysis with panel Build consensus Select winner Decision on which company performs best against all best-practice criteria 9 Communicate recognition Inform award recipient of award recognition Present award to the CEO Inspire the organization for continued success Celebrate the recipient s performance Announcement of award and plan for how recipient can use the award to enhance the brand Frost & Sullivan We Accelerate Growth

14 STEP OBJECTIVE KEY ACTIVITIES OUTPUT 10 Take strategic action Upon licensing, company may share award news with stakeholders and customers Coordinate media outreach Design a marketing plan Assess award s role in future strategic planning Widespread awareness of recipient s award status among investors, media personnel, and employees About Frost & Sullivan Frost & Sullivan, the Growth Partnership Company, enables clients to accelerate growth and achieve best in class positions in growth, innovation and leadership. The company's Growth Partnership Service provides the CEO and the CEO's Growth Team with disciplined research and best practice models to drive the generation, evaluation and implementation of powerful growth strategies. Frost & Sullivan leverages almost 50 years of experience in partnering with Global 1000 companies, emerging businesses and the investment community from 31 offices on six continents. To join our Growth Partnership, please visit Frost & Sullivan We Accelerate Growth