5 High-Impact Use Cases of Big Analytics for Optimizing Field Service Processes Improving Field Service Efficiency and Maximizing Eqipment Uptime with Big Analytics and Machine Learning Field Service Profitability Part Order Optimization Trbine Service Calls Pmp Failre Prediction Trbine Shtdown Prediction
Table of Contents Unlocking Competitive Advantage with Big Analytics and Machine Learning...3 Exploring the Vale of Big to Optimize Field Service Operations...4 Simltaneos Analysis Across Silos: Vital to Sccess...4 How Maana Can Help...5 Exploring 5 High-Impact Field Service Use Cases...6 Field Service Profitability...6 Part Order Optimization...9 Pmp Failre Prediction (Oil and Gas)...10 Trbine Service Calls...11 Trbine Shtdown Prediction...12 Why Maana...13 A Fndamentally New Approach to Big Analytics...13 The Most Comprehensive, End-to-End Big Analytics Platform...13 Powerfl Core Capabilities...14 Understanding and Integration...15 Statistical Understanding and Analytics...15 Soltion Modeling...15 Operationalization of Insights into Line-of-Bsiness Applications...15 Learn More...15
Unlocking Competitive Advantage with Big Analytics and Machine Learning Technology plays a vital role in field service operations today. Most likely, yor organization is already sing a variety of platforms and applications to nderstand cstomer histories; track agreements, SLAs and service consmption; schedle appointments; provide call-center based spport; captre and share knowledge; manage parts inventory; and more. And assming yo are monitoring eqipment and other assets sing bilt-in sensors, yo are captring streaming data abot the crrent stats of assets. Collectively, these systems generate hge volmes of data in a variety of formats: think sensor data generated by eqipment, text data from technician notes and job reports, strctred data stored in systems like ServiceMax and SAP and voice data captred throgh call centers. In most cases, these types of strctred and nstrctred data reside in mltiple, disconnected data silos. The qestion is, how can yo harness all of this data to optimize field service assets and processes? According to a recent Accentre/GE stdy, a growing nmber of indstrial companies are sing big data analytics and the Internet of Things (IoT), sometimes referred to as the Indstrial Internet, to realize greater efficiency and profitability in two major areas: asset and operations optimization. For instance, by gathering and analyzing vast volmes of machine sensor data, companies can implement predictive maintenance of eqipment, which can dramatically increase eqipment ptime and redce costs. Even small improvements can yield big savings, as jst 1% improvements in OPEX can reslt in hndreds of millions of dollars in savings. For example, General Electric is sing the Indstrial Internet to increase the fel efficiency of a gas trbine fleet. Jst a one percent increase in fel efficiency will save the company pwards of $6 billion in fel savings per year. 1 And a 1% improvement in oil recovery is worth 80 billion additional barrels per year. That eqals billions of dollars in additional revene. Or avoiding jst one day of downtime in an offshore platform can prevent $7 million per day in lost prodction. For Fortne 500 companies, jst 1% improvements in OPEX can reslt in hndreds of millions of dollars in savings. Given these kinds of potential impacts, it s no srprise that in a recent Accentre srvey, nearly 90% of companies indicated that big data analytics is either their top priority or among the top three. 2 And nearly three-qarters believe that big data analytics has the power to shift the competitive landscape for their indstry in the next year. 1 http://www.tilities-me.com/article-3972-ge-drives-smart-tilities/ 2 www.accentre.com/s-en/insight-indstrial-internet-competitive-landscape-indstries.aspx 3
Exploring the Vale of Big to Optimize Field Service Operations In the area of field service management, big data analytics, IoT and machine learning technologies have the potential to optimize key assets and bsiness processes by: Predicting eqipment failres by analyzing streaming data and alerts from sensors, historical asset break/fix records and other data Redcing nplanned downtime by enabling proactive maintenance that heads off isses Redcing the nmber of nnecessary parts ordered and retrned by helping technicians identify the correct parts needed to complete specific kinds of jobs Matching the right technicians to each job by analyzing skill sets and experience levels needed to complete jobs efficiently and accelerate cstomer payment Simltaneos Analysis Across Silos: Vital to Sccess Bt to realize these benefits, yo need a way to analyze all of yor fieldservice related data simltaneosly. Most organizations are swimming in data, bt their analytical tools can only analyze data in silos or a limited nmber of data sorces. This severely limits the kinds of insights, correlations and patterns that analysis can yield, and ths the potential bsiness vale of those insights. Take analysis of data related to field service management, for example. These processes are ripe for transformation throgh strategic se of big data analytics. Most indstrial firms are generating massive volmes of data from the field service atomation, work order management, fleet management, sensors on eqipment and other soltions they have already deployed. 3 Yet one of the biggest challenges of field service organizations is the vast nmber of data silos, where data related to service and maintenance resides, from HR and travel to training, text docments, sensor data, time series data and more. Only a big data soltion that can take all of this data and analyze it simltaneosly can ncover nexpected correlations and patterns across silos of data. This is what yields insights that can be sed to improve processes, make better decisions and more. 3 Big Can Make A Big Difference In Field Service. Field Technologies magazine. By Brian Albright. Jly 24, 2014. 4
How Maana Can Help Maana is the analytics platform that operationalizes big data insights into line-ofbsiness applications. Or team has been working with Fortne 500 companies to help them optimize their field service assets and bsiness processes. This ebook, which is based on or experience working with indstrial manfactring cstomers arond the world, explores the top five high-impact se cases of big data analytics for enhancing field service efficiency. For example, for offshore and major trbines, or cstomers are seeing reslts sch as: 67% redction in nnecessary parts ordered for field service maintenance 5% redction in major asset downtime As illstrated in Figre 1, Maana s patented technology analyzes data across mltiple silos to give a holistic view of assets and bsiness processes and generates insights from data in mltiple silos simltaneosly. In addition, it enables cstomers to operationalize big data insights and recommendations into line-of-bsiness applications, which empowers thosands of employees to make better decisions every day. As a reslt, companies can optimize key enterprise assets and bsiness processes. Enterprises Using Maana Can: Gain a holistic view of assets and bsiness processes from data across mltiple silos Job Reports Sensor Repair Records Maintenance Records Alarm Trip Call Center Weather Operationalize big data insights and recommendations into line-of-bsiness applications for thosands of employees to make better decisions Optimize key enterprise assets and bsiness processes with insights from data in mltiple silos simltaneosly Figre 1: Maana provides a holistic view of assets and bsiness processes to be optimized. 5
Exploring 5 High Impact Field Service Use Cases The following se cases all based on real Maana cstomer experiences illstrate some of the ways that indstrial and healthcare eqipment companies are sing Maana s analytics platform to optimize field service assets and bsiness processes. As shown in Figre 2, these se cases focs on: Improving field service profitability Predicting eqipment failres and proactively addressing root cases to prevent nplanned downtime Redcing the nmber of nnecessary parts ordered by field service engineers DATA SILOS 010 00110 11010 Log/Event MAANA USE CASES 1% improvement in OPEX represents 100s of millions of dollars Docments Part Order Optimization Improve cstomer resoltion Hadoop Application Field Service Profitability Field Service Redce nnecessary parts ordered Warehose Redce nplanned down time Predictive Maintenance Time Series Figre 2: Maana spports a variety of se cases for field services organizations. Field Service Profitability A Fortne 50 company that manfactres and maintains indstrial eqipment wanted to increase the effectiveness and profitability of its field service organization. Using manal ETL processes to create a dashboard, management was only able to track a limited nmber of key performance indicators. The data they needed to gain insights, plan jobs more effectively and assign resorces optimally was hosed in 10 disparate silos and applications, making it nearly impossible to access and analyze them together. So they were nclear exactly what factors were impacting field service profitability and effectiveness and what changes they cold make to reach these goals. 6
This company needed a big data analytics tool that cold analyze data across silos simltaneosly and generate insights that wold help them increase billable hors and redce overall costs throgh efficiency improvements. Analyzing Simltaneosly Across Silos To meet these needs, the company sed Maana's analytics platform. As illstrated in Figre 3, Maana crawled 10 different data sorces, sch as: SAP ERP for revene and invoicing data ServiceMax field service software Travel Hb for data on dates and costs of technician travel to cstomer sites Applied time reports for time per field engineer HR data, sch as each technician s location, their tenre and years of experience Training completed by field engineers Net Promoter scores for measring cstomer satisfaction Operations activity reports Cstomer data in Salesforce.com Weather data FIELD SERVICE PROFITABILITY SAP Using insights from Maana, the company can: Determine factors that drive job efficiencies Identify correlations between job efficiencies and field engineers Recommend the right field engineer to staff each job type ServiceMax Salesforce Applied Time Report Self- Directed Learning Training Net Promoter Score Weather Operations Activity Report BUSINESS BENEFITS Determine factors that drive job efficiencies Identify correlations between job efficiencies and field engineers Recommend the right field engineer to staff each job type Operationalize Maana recommendations directly in their field service software Identify recrrent repairs made to the same eqipment Operationalize Maana recommendations directly in their field service software Identify recrrent repairs made to the same eqipment Travel Hb Figre 3 : Field Service Profitability Use Case 7
Identifying Root Cases of Inefficiencies Maana analyzed mltiple data sorces simltaneosly to identify hidden correlations and nexpected insights. The first phase of analysis focsed on the relationships between data on field service engineers, the geographic location of cstomers and their major eqipment and the efficiency of engineers when working on different prodct lines. Maana s analysis provided the company with a clear nderstanding of: The location of field service engineers employed by the company The skill set, years of tenre and hors of training for each field engineer by region and city Correlations between field service efficiency and prodctivity by region relative to amont of annal training and years of tenre each engineer has with the company Maana analyzed all of this information simltaneosly to generate insights into job inefficiencies. For example, Maana fond tens of thosands of dollars in nbillable travel time; as the company lacked sfficient service engineers in certain regions and was paying to fly staff from other regions to these job sites. By hiring new staff in these areas with the right skills and experience, they cold eliminate most of these costs and service cstomers faster and more effectively. In addition, Maana identified geographic areas where the company had invested heavily in training of new technicians, bt that investment did not translate to higher cstomer satisfaction. The analysis revealed that technician experience not jst training was critical to efficiency and effectiveness, and that region had the lowest tenred field engineers. By ensring that the local staffing mix for all geographies inclde sfficient nmbers of experienced field engineers, the company can now sbstantially increase job efficiency and cstomer satisfaction. Finally, the company sed Maana to create a data-driven process for assigning technicians to each job. Maana analyzed what skills and experience levels had historically correlated with the fast, efficient completion of different types of jobs on each kind of prodct. The new matching process ensres the right field engineer is assigned to the right job, increasing efficiency, profitability and cstomer satisfaction. Understanding Barriers to Profitability Maana also identified barriers to field engineer prodctivity and profitability by correlating service profitability by type of service call and by prodct type. This analysis revealed which prodct lines generated the most service problems or repeated service calls and why. For example, the company learned that: After a major software pgrade to a specific trbine model, the company experienced a spike in the nmber of alarms (de to downtime) and cstomer calls to the call center 8
Certain prodct lines were generating the majority of service tickets or repeated service calls Profitability varied by type of field services (for example, fixed rate service vs. horly billed services) The next phase of this project will translate key findings into operational recommendations that are expected to increase field service profitability by $6- $10 million per year. Part Order Optimization A mlti-billion dollar division of a Fortne 100 company that manfactres and operates medical diagnostic eqipment wanted to improve field service efficiencies by redcing nnecessary parts ordered by field engineers associated with cstomer service calls. Unnecessary parts ordered were tying p cash to the tne of $30 million per year. By ensring workers broght the correct parts with them to complete each job, the company cold boost profitability and cstomer satisfaction. Maana plled in data from XELUS, the company s service inventory management and demand planning software, and SAP ERP to nderstand the root case of why 55% of parts ordered were nnecessary. Frther analysis identified that a sbset of field engineers jst 5% located in Asia PAC had a 95% retrn rate on parts ordered for maintenance and repairs of medical eqipment. To minimize ftre parts retrns, Maana was sed to optimize the parts ordering process for field engineers. Maana crawled mltiple data silos to identify and recommend the parts that wold most likely be needed to address specific device and cstomer isses. Recommendations are based on an analysis of past service isses and the parts that were sed to sccessflly address similar service calls. Now field technicians can verify the correct, complete parts to order for each job, as well as additional parts related to the main parts that may be needed, ths minimizing part retrns. At the same time, technicians can resolve cstomer isses on the first visit. Maana enabled a mlti-billion dollar division of a Fortne 100 company to redce nsed parts ordered by field engineers from 33% to 11%. PART ORDER OPTIMIZATION SAP XELUS OPERATIONALIZED INSIGHTS Redced nsed parts ordered by field engineers from 33% to 11%. Identified root case of why 55% of parts ordered were nnecessary Enabled frad discovery and investigation Detected policy and bsiness rle violation Figre 4: Part Order Optimization Use Case 9
Maana s insights and recommendations have redced part orders that go nsed from 33% to 11%. The company has also realized additional savings throgh redctions in cstomer service trips, part retrns and inventory and shipping costs. Pmp Failre Prediction (Oil and Gas) Oil pmps are critical to the efficiency of oil wells. When a pmp fails in an oil well, it s very costly to remove and replace the part. It involves not only stopping oil prodction bt also mobilizing costly, specialized personnel and eqipment to pll the pmp ot of the well and replace it with a new one. These pmps are designed to work in harsh environments like oil wells, bt choosing the right pmp for a specific well s geologic and environmental condition is very complex. The right pmp can operate for a longer period of time withot costly interventions. So it s no srprise that a Fortne 20 company wanted a soltion to help maintenance employees choose the right pmp and anticipate failres likely to occr, in order to implement an effective predictive maintenance strategy. Maintenance experts sed Maana to collect data regarding existing pmp operations from a variety of sorces (sch as rn and pll reports, pmp failre reports, pmp sensor data, and high freqency data flows) and analyzed it to predict the likelihood of pmp failre. Mch of the data related to the inspection of failed pmps was entered by people in the field describing what they saw when they retrieved failed pmps from wells. In addition to the langage-based data, the company also had highly detailed sensor data from pmp operations. Maana was able to se both machine data and hman langage to help the company nderstand the cases of pmp failres. Maana crawled and indexed these disparate data sorces, broght them together and provided an interactive exploration environment that the company s sbject matter experts can se to validate varios hypotheses related to failres and their cases. Maana helps indstrial manfactring companies dramatically improve eqipment downtime and redce well risk and reliability. PUMP FAILURE PREDICTION Rn & Pll Reports High Freqency 0100101 0 011010 1101010 BUSINESS INSIGHTS Validate failre hypothesis Predict potential pmp failre Identify failre cases Failre Analysis Figre 5: Maana helped a cstomer accrately predict pmp failres and implement an effective predictive maintenance strategy. 10
With Maana s ser-gided machine assisted discovery, the company gained a holistic view of pmps that allowed them to: Choose the right pmp to be installed in each kind of well Validate failre hypotheses Predict ftre pmp failres Identify the cases of failres Armed with these insights, the company can better choose the correct pmp for each well and perform predictive maintenance that dramatically redces pmp failres and prodction downtime. Trbine Service Calls This Fortne 50 company soght to better nderstand the nderlying reasons for cstomer service calls. To accomplish this, the company needed to gain more insights from data stored across mltiple, disparate data sorces. Maana was deployed to identify the isses that prompted cstomer service reqests. Maana crawled and indexed mltiple data sorces, sch as global installed base data, trbine trip data, controller alarm data, parametric time series data and field service data from their ServiceNow system. The project involved over 600 million alarm vales and approximately 1,200 trip events. Maana sed natral langage processing (NLP) to identify the reasons why people called cstomer spport. Maana s machine learning capabilities enable sbject matter experts to gain insights into the cases of trbine alarms. To do this, Maana fond and identified correlations between historical patterns in cstomer spport calls and trip events. The correlations showed obvios bt previosly nverified relationships between trips and spport calls for varios generator models. These insights enabled the company to better nderstand the nderlying triggers for cstomer service calls and determine which prodct lines and regions incr the most isses. These insights enabled the company to better nderstand the nderlying triggers for cstomer service calls and determine which prodct lines and regions incr the most isses. 11
TURBINE SERVICE CALLS ServiceNow Global Installed Base Trbine Trip Controller Alarm BUSINESS BENEFITS Establish correlations between cstomer service tickets and trips/alarms Identify what isses prompt cstomer service contacts Determine which prodct lines and regions incr the most isses Parametric Time Series Figre 6: Maana crawled diverse data sets to identify triggers for cstomer service calls. Trbine Shtdown Prediction A Fortne 100 company operating hndreds of trbines arond the world began experiencing an average of two nplanned shtdowns per day. They wanted to redce nexpected interrptions and mitigate prodction loss by anticipating potential shtdowns. Maana enabled the company to navigate volmes of controller alarm data created by its 350 generators. As shown in Figre 7, this data was then combined with trbine specification data and trbine trip data to bild a model that predicts imminent trips and alerts staff sing alarms. The model draws correlations between historical patterns in spport calls and trip events. Now the company can detect nwanted events that cold lead to trbine shtdown within 24 hors, gain insights into trip events and ltimately redce the occrrence of nexpected shtdowns. Maana enabled the company to navigate volmes of controller alarm data created by its 350 generators. TURBINE SHUTDOWN PREDICTION ON 350 UNITS Trbine Specification Trbine Trip Controller Alarm BUSINESS BENEFITS Identify alarm featres associated with trips Gain insight into trip events Gain insight from a large volme of time series data Parametric Time Series Figre 7: Maana crawled diverse data sets to predict trbine shtdowns. 12
Why Maana? A Fndamentally New Approach to Big Analytics Maana is the analytics platform that operationalizes big data insights into line-ofbsiness applications. Or soltion makes it easy for enterprises to trn data that s scattered across mltiple silos into continos insights in jst days. Enterprises are optimizing key assets and bsiness processes with Maana by operationalizing big data insights and recommendations into line-of-bsiness applications so that thosands of employees can make better decisions. We have taken a fndamentally new approach to big-data analytics by sing or newly patented Emergent Semantic Graph and nified index strategy to search across all data silos and data types and find data relevant to specific assets or bsiness processes. Maana simltaneosly analyzes these context-specific data to provide a holistic view of the asset or bsiness process to be optimized. Or end-to-end software platform rapidly solves the most complex data analytics challenges of Fortne 500 companies, withot the need for large nmbers of professional services experts or data scientists. Companies can se Maana to easily organize their siloed data into new knowledge for iterative data discovery and insights. Going beyond data discovery, they can se Maana to bring big data insights as recommendations directly into line-of-bsiness applications, enabling thosands of employees to make smarter, day-to-day decisions. The Most Comprehensive, End-to-End Big Analytics Platform Companies can se Maana to easily organize their siloed data into new knowledge for iterative data discovery and insights. Maana s end-to-end analytics platform sits between enterprise data silos and line-of-bsiness applications. Maana crawls, indexes, mines, enriches, joins, classifies, analyzes, clsters, connects and correlates data from varios data silos simltaneosly to create a dynamic knowledge strctre of an asset or bsiness process. This knowledge strctre provides a holistic view of assets and bsiness processes to be optimized. As shown in Figre 8, Maana s comprehensive, end-to-end analytics platform enables enterprises to analyze data in mltiple silos simltaneosly and dramatically accelerate from months to days the time it takes to get from raw data to continos insights. 13
Maana Platform Mltiple Sorces Related to an Asset 010 00110 11010 Log/Event Docments DATA A N D Mine S TAT I S T Classify I C A L U N D E R S TA N D I N G Join Semantic Graph Insights into Line-of- Bsiness Applications Hadoop Crawl Orchestration Indexes Figre 8: Maana provides a comprehensive, end-to-end Big insight platform. Maana s ser gided, machine-assisted approach makes it easy and intitive for sbject matter experts to find and draw correlations between data from disparate sorces. Sbject matter experts can qickly find answers throgh search and exploration, drill-down, filtering and interactive visalization fnctions that accelerate the time it takes to get from raw data to continos insights often by an order of magnitde. Powerfl Core Capabilities Maana s comprehensive platform spports the following capabilities: Application Warehose Time Series S O L U T I O N Correlate Analyst Connect M O D E L Search nderstanding and integration Analyze I N G A N D Scientist Clster E V A L U A T I O N Domain Expert Maana s ser gided, machine-assisted approach makes it easy and intitive for sbject matter experts to find and draw correlations between data from disparate sorces. Statistical nderstanding and analytics Soltion modeling Operationalization of insights into line-of-bsiness applications 14
Understanding and Integration Maana examines varios data types to nderstand data shape, connection and relevant information and provides key capabilities for sers to easily prepare and join varios data types for iterative data discovery. Maana s powerfl knowledge representation integrates strctred, semi-strctred, nstrctred, time series, sensor, events, mltimedia and categorical information from mltiple sorces. Its flexible data and knowledge modeling fnctions bild a dynamic, evolving knowledge graph of key assets and all related information. Maana s dynamic insights are continosly refreshed and extended with incremental data sorces and pdated data. Statistical Understanding and Analytics Maana enables sers to formlate soltions throgh advanced machine learning algorithms and analytics sing techniqes sch as temporal co-occrrence, which finds relationships between events that happen together in time, or clstering of temporal entities based on configrable distance metrics. Maana ses topic modeling to clster the reslts, and its similarity search capability finds similar entities based on exemplars and a set of ser-defined featres. Maana s advanced analytics also inclde atomatic featre extraction, simlation and predictive models. Soltion Modeling Maana enables programmatic data access, maniplation and data enrichment sing langages and tools that data scientists are already familiar with, sch as Spark, Scala, Python and R. Its cstom machine learning algorithms leverage featres from the nderlying data to easily bild a predictive model. Maana s soltion modeling also enables time series analysis and distribted compte to rapidly analyze large volme of data. Operationalization of Insights into Line-of-Bsiness Applications Maana provides ongoing insights and recommendations directly into the line-of-bsiness applications so that thosands of employees can make insight-driven decisions. Once embedded into the line of bsiness applications, Maana learns and adapts from the actions of sbject-matter experts and provides new and additional insights and recommendations. In addition, Maana s Exective Analytics measres the improvement in KPIs that a bsiness sees as a reslt of Maana s ongoing operational recommendations. Learn More To learn more abot Maana, please visit: http://www.maana.io/technology/ or contact s to reqest a live demo. www.maana.io 530 Lytton Avene Site 252 Palo Alto, CA 94301 @Copyright 2016 Maana Inc. All rights reserved. 11100 NE 8th Street Site 360 Belleve, WA 98004