dredging the value out of data analytics - caterpillar.doc

8/17/2019 Dredging the value out of Data Analytics - Caterpillar.doc

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DREDGING THE VALUE OF DATA ANALYTICS

R.Bradenham1, M. Bacelar 2, B.Ritscher 3, S. Santelises4

ABSTRACT

The marine engine technology is on its way to move from pre mechanical to more and more

electronic controlled engines. !ngines e"ipped with common rail and dal fel systems are availa#le today andspar$ ignited gas engines are pcoming fast. %ith this change, a lot of improvements to engine performance andemissions are achieved. &n the other hand it can #ring some challenges to the operator ' crew in nderstanding the principles and new ways in performing tro#leshooting and falt finding in case of alarms and diagnostics. Besidesengines, also the vessels are #ecoming more electronic and connected. There are many opportnities for the marineindstry to ta$e advantage of newly availa#le data and increase asset and operational performance. &ther indstriesli$e (erospace have moved this way over the past 1)*2) years and have demonstrated significant gains in relia#ility,safety, efficiency and prodctivity + all of which cold #e applied to the marine indstry as a whole and to thedredging sectors specifically.

Remote Monitoring has #een availa#le to the maritime indstry for a while #t not the vale creating analyticstechnology that creates cstomer vale ot of raw data. t has #een reported a lot that cstomers and operators aresffering from each and every system manfactrer trying to #ring their own remote system to the vessel. t drives

the clear need to the indstry to spply integrated soltions that have the capa#ility to commnicate with severalsystems and ta$e vales from these, compare to others and create intelligent, analy-ed and vala#le data.

(s these $inds of new technologies will #ring some challenges it will on the other hand give the possi#ilities leadingto the present day of how technology, specifically analytics and connectivity, are starting to impact the marineindstry. The potential vale can #e significant, with fel savings, increased ptime days of prodction/, optimi-edmaintenance, redced failres, associated repair costs, increased transparency into safety, greater prodctivity,streamlined operations, decreased costs of compliance administration, etc. This paper will go throgh somee0amples of how data analytics have #een and can #e sed to create vale for vessel owners and operators.

Keywords: ata analytics, remote monitoring, indstrial internet, internet of things, predictive analytics, prognostics, health assessment, relia#ility centered maintenance, condition #ased maintenance, condition #asedoperations, pattern recognition, machine learning.

EXECUTIVE SUMMARY

sing data analytics to improve ship#oard operations and maintenance has the potential to create #illions of dollarsof vale in the marine indstry today and even more in the ftre. This ndstrial nternet of Things oT/ concept,connecting machines and sing atomated data analytics along with domain e0pertise to optimi-e operations andmaintenance, has already created significant vale in many indstries li$e power generation and commercialaviation and is now #ecoming a reality for the marine indstry. %hile the opportnity across indstries will e0ceed1) trillion dollars per year in the ne0t 1 years 1, the opportnity for owners and operators to redce costs, improvefel efficiency, and increase ptime and relia#ility is appro0imately 2) #illion dollars today for the entire marineindstry and will e0ceed ) #illion dollars #y 2)3).2

1

lo#al Sales and Bsiness evelopment Manager, Marine (sset ntelligence, 5aterpillar nc. )32 Rose r,Site 1)), 6irginia Beach, 6( 22314. T7 81*2)2*944*1:4, !mail7 #radenham;ro#;e


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This white paper focses on the overall marine indstry and the dredge segment in particlar. %hile the valeacross the entire marine indstry is massive D2)B S today, growing to D)B S #y 2)3)/, when we loo$ atthe dredge indstry we can ma$e that vale clear on an individal vessel #asis. Aor a high vale asset sch as amega*ctter with capacity of at least 23,)))$% and a typical 5ape0 of 2))M !ro 3, the vale cold easily #e inmltiple millions of dollars per year in higher prodctivity'tili-ation, redced downtime, redced maintenancee0pense, increased fel efficiency, #etter safety and environmental compliance, as well as potential revene gainsfrom faster completion of proEects and #etter cost'#id management. (nalysis specifically of the marine dredgesector indicates 1.)*1. #illion S in vale creation is possi#le. %ith smaller assets, the vale per asset willdecrease, however, there is the systematic vale where a small, lower vale asset cold impact the prodctivity of alarger, higher vale asset i.e., having a tg or #arge casalty cold impact the prodctivity of the ctter or hopper dredge/. Aor large, comple0 dredge proEects, rather than assessing the vale of individal assets, often it is theimpact on the overall system i.e., the collection of large and small assets wor$ing together/ that is the greatestimpact on prodction. %hile the potential vale is large, it is not always clear to owners, especially those who arenot technology e0perts, how to captre this vale. 6essel owners need to thin$ careflly a#ot what are the rightinvestments to ma$e, #oth for new*#ilds and retrofits. &wners need to thin$ throgh what their own o#Eectives areas well as their cstomerFs/ today and what they might #e in the ftre, and compare soltions that are availa#letoday with what cold come in the ftre. Ma$ing the right investments today will increase the Retrn onnvestment R&/ #y ensring the investment is minimi-ed and the retrn is ma0imi-ed.

%hile newer vessels will have the greatest potential vale creation de to e0isting sensors and technology

infrastrctre, there are many e0isting dredge vessels which will li$ely have an attractive R&, provided the rightinvestment decisions are made4. Many of these vessels already have sfficient sensors and a ro#st technologyinfrastrctre ma$ing the re"ired investment minimal. The vale gained from the e0isting on#oard data will ena#les#stantial improvement in how the #siness is operated. %hile there is tremendos vale at sta$e, it will also ta$eindividal owners and operators time to flly captre the vale. n the mean*time, #efore performance analytics areflly incorporated into all #siness processes, even partially captring the vale in the short term will #e attractive tomany owners and operators.

This white paper wal$s throgh the vale of sing data and information in the marine dredge space. t is #ased onthe e0perience of 5aterpillar, as well as 5aterpillarFs new Marine (sset ntelligence organi-ation which was formedin (pril 2)1 with the ac"isition of !SR. !SR #rings 1 years of marine data analytics e0pertise, helpingorgani-ations across different sectors in the marine indstry leverage data analytics to predict and avoid failres,increase efficiency, get assets #ac$ online faster with remote tro#leshooting, etc. 5aterpillar #rings over 1)) years

of leadership in marine power systems, with a strong history of prodct applications across the entire range of thedredge indstry. Aor many organi-ations, data analytics and the oT present an opportnity to increase profita#ility, provide greater cstomer vale and create differentiation in the mar$et. &wners are ma$ing investments in e0pensive, comple0e"ipment with the e0pectation that the e"ipment will perform for many years or even the life of the vessel. (s thecomple0ity of the e"ipment increases, sing data analytics is necessary to determine the condition of the e"ipmentand ensring proper operation and maintenance. Aor those who do not proactively #egin to incorporate dataanalytics into their decision ma$ing and operations, there is a ris$ of #ecoming less competitive in an increasinglychallenging mar$et.

OVERVIEW OF THE INDUSTRIAL INTERNET OF THINGS IIOT! CONCE"T

The oT concept is "ic$ly transforming into the ne0t indstrial revoltion. t is #ecoming more widespread acrossa variety of indstries from power generation and healthcare to commercial aviation and manfactring.

Mc@insey ? 5ompany estimated that in 2)2, the indstrial internet will #e creating 2.9*:.2 trillion dollars per year of vale. 5isco has estimated that the Ginternet of thingsH wold connect ) #illion devices and create over 14trillion dollars of additional profits over the ne0t decade in increased prodctivity :. eneral !lectric estimated themar$et for indstrial internet technology and services to grow to I))B #y 2)2)9.

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The #asic concept is connecting machines with each other and with people to get more ot of assets, help people #emore prodctive, ma$e spply chains more efficient, enhance cstomer e0perience and drive innovation. There arethree primary components of the oT. eneral !lectric, in their recent whitepaper Gndstrial nternet7 Jshing theBondaries of Minds and MachinesH defines these as7 ntelligent Machines, (dvanced (nalytics and Jeople at%or$ .

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Kewer ships are often already e"ipped with more sensors, providing performance and condition data that can #esed to operate and maintain e"ipment at a higher performance level and lower cost. This wealth of data, while itcan create vale, does create a challenge as it is overwhelming withot analytical tools. Aor e0ample, a new vesseltoday might have over ))) data points, which wold create 13 #illion pieces of data over a month. %hene0trapolated across a fleet of 1)) assets, this e"als more than 1 trillion data points per year. Software analytics canintegrate a variety of data sorces in a variety of formats and se atomated algorithms to help sers ma$e sense of the data, trning it into actiona#le information. Lastly, new information is consmed #y people as they ma$e moreinformed decisions ranging from planning maintenance to optimi-ing e"ipment configration to prioriti-ingresorces across an enterprise. To transform the data into actiona#le information, domain e0pertise is needed in howthe machines operate, how the #siness wor$s, and how to analy-e data. These people need access to the data andinformation throgh mltiple channels, inclding we#, mo#ile, intelligent reports, and enterprise applications.

VALUE OF IIOT IN THE MARINE DREDGE INDUSTRY

The marine dredge indstry stands to reap significant rewards from applying the oT concept. Li$e other indstries,improvements can #e e0pected in maintenance, fel and energy efficiency, relia#ility and availa#ility of assets,wor$er prodctivity, and vale delivered to the cstomer vale creation, increased environmental compliancetransparency and logistics efficiency. %hile the high level components of the vale proposition are common acrossthe marine indstry, magnitde differences e0ist across mar$et segments. 5aterpillar has recently condcted several

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proEects which have proven ot the vale of sing data analytics across mltiple sectors in the marine indstry. SeeAigre 2 for an overview of the potential vale creation=.

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%ith assets operating all over the world, inclding some of the most remote locations on the planet, the potentialvale created #y improving how maintenance is condcted is higher than in indstries with more remote assets withlower access to high "ality technical resorces. (ny improvements in maintenance planning and moving moremaintenance from nschedled to schedled will help to redce all costs associated with emergent wor$, which aremagnified when assets are greatly dispersed and in remote locations. The dredge sector faces the challenge of

remote operations in environments that also ma$e it challenging to find and retain e0perienced technical resorces tomaintain the vessels.

The dredge indstry operates increasingly comple0 assets. Kewer hopper vessels have 4),))) c#ic meter capacities representing do#ling of capacity in the last 2) years/ and larger ctters now have 2,)))8 $% installed power representing an do#ling of power over the last 4) years/. (s the assets have gotten larger, they have gottenmore comple0 and are of corse a higher concentration of capital, which needs to #e wor$ing. 1) These large assets #ring together e"ipment manfactred #y mltiple &!Ms and re"ire a very diverse s$ill set to operate andmaintain effectively. This comple0ity, com#ined with the contined pressre to find "alified, e0perience technicalcrew while redcing costs/, can create a mismatch #etween the s$ills re"ired to sccessflly operate and maintainall of the e"ipment, and the s$ills and e0perience that the on#oard crew possesses. The level of monitoring thatdata analytics software can provide goes #eyond the typical s$ill*set of an on#oard engineer who is historicallytrained in the mechanical operation and maintenance of the engine.

n addition to geographic remoteness and asset comple0ity, e0ecting in*depth maintenance often involves ptting avessel into dry*doc$. This is a significant e0pense, #oth in terms of cost of dry*doc$ing the vessel as well as thedowntime created. (s sch, there is a significant incentive to ensre that all maintenance to #e completed dring thedry*doc$ period is nderstood and can #e planned prior to the vessel entering dry*doc$ in order to avoid e0pensivedelays and penalties #y e0tending the period in dry*doc$. This increases the incentive to have a thoroghnderstanding of the performance and health of all of the e"ipment on#oard on a continos #asis.

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(pplying the oT concept to marine dredge maintenance will ena#le a shift from the Goperate*#rea$*fi0H paradigmto a Gpredict*optimi-e*preventH paradigm. This shift will help redce maintenance costs preventive maintenanceinstead of high*cost overhals or replacements/ and redce operational downtime. 5ondition Based Maintenance5BM/ is often referred to in the marine indstry as the ne0t shift in maintenance philosophies and the oT conceptis necessary to effectively move to 5BM. n addition to 5BM, this also ena#les 5ondition Based &perations. 5B&is focsed on the optimi-eF step in Gpredict*optimi-e*preventH. By ena#ling operators to se information prodced #y their e"ipment and analytics to ma$e #etter real*time decisions, operators can #etter configre and operate their e"ipment to ma0imi-e relia#ility today and optimi-e total cost of ownership, inclding maintenance costs, in theftre.

n one e0ample where 5aterpillar Marine (sset ntelligence monitored an inland river tg#oat, several maintenanceisses, inclding a failing fel pmp, were a#le to #e identified #efore failre. This early identification of the isseena#led the maintenance to #e planned dring some schedled downtime for the vessel as opposed to having animpact on operations. n some sitations, identifying the isse prior to failre can also ena#le preventivemaintenance to #e condcted instead of Est the corrective'repair maintenance.

%hile most modern vessels have on#oard alarm systems for safety, these systems are dependent pon good datafrom sensors in order to protect the on#oard e"ipment and crew. n another proEect of monitoring and analy-ing adiesel engine, three senor isses were identified prior to the sensor failre having an impact on the e"ipment or vessel operations. Airst, coolant pressre spi$es were identified #y the analysis software, which cold have #een

mas$ing a larger isse and cold have prevented engine control logics from protecting the engine in the case of acooling system failre. The analytics software ena#led the sensor to #e identified as the li$ely isse and resolved #efore there was any greater impact. Second, an air pressre sensor was also identified as falty, which cold haveled to engine stall. Lastly, a falty thermocople was mas$ing e0hast temperatre spi$es, which cold have led toan nprotected catastrophic failre if not addressed. (ll of these were identified sing data analytics software ands#Eect matter e0perts, ena#ling simple sensor repairs as opposed to mch more costly catastrophic e"ipmentrepairs costing tens or hndreds of thosands of dollars and nschedled downtime that impacts revenes and profits.

n addition to predicting potential failres and identifying sensor pro#lems, data analytics can also help gidemaintenance #y identifying assets whose performance has decreased. %hile monitoring a pair of medim speeddiesel engines, the data analytics soltion identified that one engine had significantly higher e0hast temperatrescompared to the other engine for compara#le operations, and #asic performance crves were also degraded from theoriginal engine performance. This ena#led the owner to prioriti-e tning of this engine ahead of others and focsmaintenance spend on where it cold yield the highest retrn.

Lastly, while data analytics can help predict failres, identify falty sensors and identify potential performancedegradation, data analytics can also help move schedled maintenance to a 5ondition Based Maintenance 5BM/

strategy. n another marine e0ample 5at® (sset ntelligence technology was sed to move the oil and filter changes

from an hors #ased maintenance to condition #ased maintenance. !very oil change that cold #e deferred whennnecessary determined #y good "ality of oil and filter/ saved the cstomer I2,3)) in oil cost alone, pls the costof disposal, la#or and engine downtime. Aor the same vessel, the condition of the fel inEectors was also a#le to #emonitored sing on#oard sensors and data analytics. The (sset ntelligence technology was a#le to identify whenthe fel inEectors were li$ely foled even #efore their schedled replacement. By resolving the foled fel inEector,the engine was a#le to reclaim the 1)N fel efficiency that was lost de to the dirty inEector, as well as potentiallyavoid an e0pensive catastrophic engine casalty. The pay#ac$ with the fel savings alone was 3 days for felinEector.

The preceding e0amples are all focsed on individal vessels. The vale of data analytics and its impact onmaintenance and relia#ility increases significantly when sed across a fleet of assets. Aor e0ample, traditionally,each chief engineer wor$s throgh the isses on his vessel individally. Those same pro#lems may #e evident onother vessels, yet he li$ely is not aware and is not a#le to leverage the learning of the other crews especially if thevessels are not geographically concentrated/. Leveraging data analytics across the fleet can help identify commonisses, which can #e elevated a#ove the individal ship level and #e solved at the enterprise level.

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F%e) +.d E.er$y

The marine indstry #rings together the comple0ity of #oth power generation assets main engines, generators/ andenergy consmption assets shaft, thrsters, prodction'dredge e"ipment, compressors, cranes, air conditioning,

water prodction, electronics, etc/. %hile many indstries focs their efforts on Est one piece of this, a ship has tofocs on #oth the energy prodction and consmption sides of the e"ation. This comple0ity nderscores the valethat collecting operational and condition data in real*time and atomatically analy-ing can have a#ove what anon#oard operator can do with simple spreadsheets. riven #y rising fel costs, many owners and operators havealready acted on many of the low hanging fritF opportnities for energy efficiency. The ne0t wave of increased

efficiency will re"ire optimi-ing the entire vessel as a system, instead of Est a single asset.

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Li$e maintenance savings, fel and energy savings will also #e shared #y several sta$eholders. n some segments,the ownership, operation, management and assignment of the vessel are not within a single organi-ation anddifferent costs are #orn #y different entities i.e., in some sectors, the cstomer often pays for fel while theowner'operator pays the maintenance costs/. n some sitations, owners and ship*management companies often are

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not responsi#le for fel cost in the short rn, #t are incentivi-ed to improve their fel efficiency to ma$e their vessels more attractive to cstomers. 5stomers, who are often responsi#le for prchasing fel, are often not in the position to ma$e long term investments in a vessel, as their charter contract may only #e a fraction of the estimated pay#ac$ for a technology investment. &wners will need to ta$e some ris$ in ma$ing technology investments, andthen actively mar$et the #enefits to prospective charterers to achieve either higher effective charter rates or higher tili-ation. This same challenge of aligning incentives is also in place for vessels where the owner'operator does pay for the fel directly in that often capital #dgets which wold pay for technology to save fel/ are set separatelyand independently from the operating #dget the #dget which covers operating fel e0penditre/.

ata analytics can #e sed in several ways to improve fel and energy consmption in the marine dredge indstry.ata analytics can identify isses with e"ipment performance that are casing increased fel or energyconsmption and can help the technical team prioriti-e maintenance to address those isses. n addition toimproving the condition, and therefore efficiency, of e"ipment, the information from data analytics can also helpimprove how the e"ipment is operated. This ranges from the speed that a vessel shold #e operated to howgenerators and a0iliaries are sed dring different operations to how prodction'dredge e"ipment shold #econfigred. Lastly, data analytics can #e sed to loo$ across an entire fleet and identify which vessels are more or less efficient, and therefore which vessels shold #e focsed on + #oth from a perspective of learning what iswor$ing well, as well as where improvement is needed.

n the case of the har#or tg that 5aterpillar Marine (sset ntelligence grop monitored and analy-ed, the optimal

speed was determined #ased on incorporating the actal engine data, fel consmption and vessel performance data.Transiting to and from operations at the optimal speed verss what feels rightF e"ated to p to I11),))) per year infel savings for this particlar tg. n addition to identifying the optimal speed for this specific tg, with its specificengines, proplsion and hll form, the team was also a#le to pin*point that one of the engines was consming morefel than the other, e"ating to I4),))) per year in increased fel consmption. This information, along with other analytics, was sed #y the cstomerFs 5at ealer to tne the engine and improve the fel efficiency. ( monthlyreport was also esta#lished to create transparency into engine performance and fel efficiency for the ftre.

Similar to improving maintenance and relia#ility, data analytics can also #e sed to loo$ across a fleet or anenterprise to improve fel efficiency. sing data analytics can identify the vessels that are most efficient as well asthe vessels that are least efficient. Resorces can #e focsed on the least efficient vessels to improve the fel andenergy consmption, while the organi-ation can learn from the most efficient vessels as to what is ena#ling their redced fel consmption. This enterprise view can also ena#le analysis regarding vessel design and e"ipmentchoicesO #y analy-ing mltiple vessels of mltiple designs and e"ipment profiles, managers can ma$e #etter ftredesign and e"ipment selection decisions.

E.'#ro.0e.*

There is significant vale that can #e created from an environmental perspective in the marine indstry. Thetransportation sector, as a whole, acconts for 13N of the total, glo#al greenhose gas emissions. 11 The marineindstry is a significant driver. This has led to varios international, national and local organi-ations to imposestricter reglations on the types of fel #eing consmed and reslting emissions. n addition to emissions, alldischarges are heavily reglated oily waste, #allast and sewage'grey water/. (ll of these can #e monitored andatomatically analy-ed to ensre compliance and transparency for a wide variety of sta$eholders.

The oT will ena#le vessel owners, managers and operators to have visi#ility into the actal performance andoperation of their e"ipment. Reglatory organi-ations will also li$ely eventally move to electronic reporting,sing actal data from the e"ipment, whether it is an oily water separator or an emissions monitoring sensor, to #etransmitted ashore and atomatically verify compliance withot any hman intervention.

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n contrast to fel and maintenance incentives, almost all sta$eholders are incentivi-ed to ensre environmentalcompliance. %hile vale to an individal ship*owner or charterer is li$ely to #e less than potential fel or maintenance savings, environmental compliance is li$ely to #e an initial driver for many owners, managers andcharters to ma$e investments in the technology on#oard their vessels.

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Marine dredge operations are very comple0, with many moving pieces and e0ternal factors that heavily inflencedecision ma$ing and performance. mproving operations can come in many forms and creates vale along severaldimensions. mproving how vessels and e"ipment is operated will ena#le improved e"ipment condition andlower maintenance costs/, redced fel costs, potentially greater tili-ation and less idle time, as well as increasedsafety. Some of the e0amples of improved operations overlap with the e0amples discssed in the Maintenance andAel'!nergy sections of this white paper.

n a proEect that 5aterpillar condcted, there were several operational conditions identified that impacted #oth feland maintenance. These inclded e0tended periods at idle while stationary alongside the pier and high stressmanevering high rate of trn at high engine power/ not necessary for operations. n the case of the e0tended idle periods, this not only was casing e0cess fel to #e #rned, as well as increased wear on the e"ipment, there mighthave #een a potential operation for ta$ing a vessel ot of service #ecase demand was not as e0pected. This woldhave reslted in decreased la#or costs as well as availa#ility for maintenance withot impacting operations. naddition, having a crew at idle for e0tended periods of time can case complacency, which cold impact safety. nthe case of the high stress manevering, this was not only casing increased fel consmption, #t was also creating

increased wear on the vessel as well as increasing the chances of a safety mishap, either de to e"ipment failre or hman error.

n addition to specific opportnities to create vale in fel savings, improved maintenance, more efficient operationsand #etter environmental compliance, data analytics can also provide sta$eholders at mltiple levels of theorgani-ation with #etter transparency and information into their #siness. This helps them to have a #etter sense of what is actally happening on the front lines, and se that information to ma$e #etter day*to*day decisions and crafta more informed strategic direction.

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WHAT DOES IIOT LOOK LIKE TODAY6

Many owners are already moving in this direction, whether they reali-e it or not. There are many standalone andintegrated oT applications that are #eing sed with greater fre"ency across different sectors in the marineindstry. Aor e0ample, many owners'operators are sing more operations decision spport tools to do things li$e

manage prodction'operations, optimi-e voyage planning, plan a rote optimally sing weather data, remotelytro#leshoot their e"ipment, etc. &thers are sing higher level applications to help manage their fleet position and performance or do remote tro#leshooting. &thers still are sing remote e"ipment monitoring to #etter nderstande"ipment health and #etter plan maintenance. Many of these applications are stand*alone applications, while someare integrated with other on#oard or shore*#ased applications.

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There are several challenges that will need to #e overcome for the #roader oT7 increased sensors and smarter e"ipment, increased #andwidth to share data, open standards to commnicate across different types of e"ipmentand systems, more advanced analytics, and availa#ility of data scientists with the s$ills and domain e0pertise to trnthat data into actiona#le information. These same high level challenges are essentially the same as those facing themarine sector in the adoption of the oT, however, the details of what ena#lers will help overcome these challengesvary from other, traditional land*#ased indstries.

6essel lifecycle is an important factor in how the oT is adopted in the marine space. Kew vessels are often #eing #ilt with significant sensors and a strong technology infrastrctre. This will ma$e it easier for data to #econsmed #y analytics applications to convert it into actiona#le information. &n#oard new #ilds, the re"iredinvestment cold #e very low, ena#ling fast pay#ac$ times and therefore fast adoption of the oT concept. &lder ships, withot electronic engines and fewer sensors, will face a different re"ired investment than newer ships with #ilt in sensors to captre vale. &wners of older, e0isting ships mst weigh the increased investment in sensors,data integration, networ$ing and commnications with the potential retrn. %hile the #enefits will almost alwayssignificantly overcome the re"ired investment for higher vale assets, it will greater analysis for older, lower valevessels with little e0isting technology infrastrctre.

Based on an analysis of the glo#al dredge fleet today, there are 3)*4)) large capital assets hopper dredges andctter dredges/ com#ined with thosands of spport vessels that shold #e investigated for implementing an oTsoltion. The Retrn on nvestment Ro/ of implementing an oT soltion will vary #ased on the vale of thevessel. The R& will almost definitely Estify the investment for large capital vessels as the cost of downtime and prodction losses are high for these types of vessels. This will li$ely even apply with many older vessels where thelevel of investment might #e higher de to costs of additional sensors and electronics moderni-ation. &ther criticalspporting vessels shold #e analy-ed to determine the level of criticality and investment re"ired. Most new*#ildsare #eing #ilt from the $eel p with greater level of sensors and atomation, ena#ling easier captre of oT vale.

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nstead of ma$ing individal investments in individal soltions, owners shold condct a more comprehensive andholistic assessment of their needs and what the optimal soltion is to achieve those needs. Airst, owners shold start #y clearly defining and nderstanding their o#Eectives. &nce o#Eectives are defined, then the owner needs toidentify the data and information that is re"ired to measre performance against those o#Eectives as well as ena#leoperators and managers to ma$e #etter decisions to achieve the o#Eectives. &nce the o#Eectives and data'information

needs are clear, the e"ipment and integration shold #e analy-ed to determine the #est way to o#tain the re"ireddata. n one of the 5aterpillar proEects, one of the proEect otcomes was the identification of speed throgh water asa necessary data point to measre #oth vessel and tow fel efficiency. &nce integrated, one or more analyticssoltions will need to #e sed to provide decision spport to sta$eholders to ma$e #etter decisions, as well as to provide performance transparency to senior leaders to measre against o#Eectives. Lastly, this actiona#leinformation needs to #e commnicated to the right people at the right time. The Gright peopleH incldes #othon#oard and shore #ased managers, re"iring a commnications soltion to #e pt in place. By going throgh this process holistically, owners can #egin to redce investment in individal systems and retrofit efforts and ma0imi-e

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the R& on strategic investments, ena#ling owners to achieve their crrent o#Eectives while also positioning well for n$nown ftre o#Eectives. n one of the 5aterpillar proEects, the vessel was over 3) years old and did not have asignificant technology infrastrctre. n order to captre vale sing data analytics, the cstomer made investmentsin #oth sensors as well as a technology infrastrctre that will provide their re"ired information today, as well asfle0i#ility for ftre applications and integration.

F#$%re 8: "ro-ess *o +.+)y9e ,o*e.*#+) d+*+ +.d +.+)y*#-s #.'es*0e.*s

%hile thin$ing throgh investments, owners shold also consider things sch as data ownership and se rights, datasecrity, commnications #andwidth, secrity and relia#ility/, new sensors, vale of information verss data, potential cstomer specific applications and #ilding in the fle0i#ility for ftre se cases.

CONCLUSIONS

ata analytics and oT present a hge opportnity to the marine dredge indstry, with the potential to createappro0imately 1.)*1. #illion dollars of vale today, with additional growth in the ftre.

The #enefits to marine sta$eholders are significant. S#stantial fel savings, redction in maintenance and repair costs, increased ptime, improved prodctivity'operations, and greater assrance of environmental compliance arethe largest drivers. &rgani-ations need to start thin$ing now a#ot how they are going captre #enefit from the oT.&wners need to #e thoghtfl a#ot the investments they ma$e and loo$ #eyond the immediate pro#lem they are

trying to solve.

Those that donFt start to em#race the vale that technology and data analytics can create ris$ #ecoming lesscompetitive and #eing left #ehind. The marine dredge indstry has the opportnity to learn from other indstrieswhich are frther along the oT Eorney, sch as commercial aviation and power generation. Learning from theseindstry e0amples will help marine organi-ations mitigate challenges and minimi-e costs.

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%hile those who do not start to e0plore the vale that data analytics and oT can #ring to their #siness ris$ falling #ehind, those who do not thin$ throgh their investments also ris$ ma$ing investments that do not achieve thedesired R&, and also pt them at a disadvantage. Many of these investments are not insignificant, and so it isimportant to thin$ throgh o#Eectives, information and data needs as well as e"ipment selection and integration as part of a comprehensive plan, not Est individal investments.

CAT CATER"ILLAR BUILT FOR IT */e#r res,e-*#'e )o$os ;C+*er,#))+r Ye))ow


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END=NOTES REFERENCES > CITATIONS

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1 Chambers, John, “Internet of Everything”, Cisco, February 21, 20132 Braenham, !ob an "en "rooner, “Bringing the Inustria# Internet to the $arine Inustry an%hi&s into the Cou#”, E%!', (ctober 20133 )reging mar*et ana#ysis, “+rot $argins e-&ecte to remain fair#y hea#thy unti# 201.”,!aboban*, %e&tember 2013/ Braenham, !ob an "en "rooner, “Bringing the Inustria# Internet to the $arine Inustry an%hi&s into the Cou#”, E%!', (ctober 2013 $anyi*a, James $ichae# Chui, Jacues Bughin, !ichar )obbs, +eter Bisson, 4#e- $arrs,

“)isru&tive techno#ogies5 4vances that 6i## transform #ife, business, an the g#oba# economy”$c"insey '#oba# Institute, $ay 20137 Chambers, John, “Internet of Everything”, Cisco, February 21, 20138 'enera# E#ectric &ress re#ease, June 1., 2013. 4nnun9iata, $arco an Evans, +eter C, “Inustria# Internet5 +ushing the Bounaries of $ins an$achines”, 'enera# E#ectric, :ovember 27,2012; 4na#ysis of &otentia# im&act on reging inustry one by Cater&i##ar, 6ith ata from com&any6ebsites, >666/e&a/gov>c#imatechange>ghgemissions>g#oba#/htm#?t6o
http://www.epa.gov/climatechange/ghgemissions/global.html#twohttp://www.epa.gov/climatechange/ghgemissions/global.html#two

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