IntroductionData, data everywhere, and not enough time to think. Like the sailors on Coleridge’s mythical sea adventure, many manufacturing executives find themselves surrounded by rising waves of data. But where are the hidden treasures — those golden nuggets of information needed to optimize margins? More than ever, rapidly evolving technology has become the albatross around the manufacturing decision maker’s neck.

Over the last decade, many manufacturers have made significant investments in their information technology infrastructure. With such systems in place, executives are expecting and needing shorter response times. Given the current realities in manufacturing, several tough questions need answering, such as:•Howmuchcanadjustmentsinoneormorekey

performance areas affect us?•Whichcourseofactionisthemostworthwhile?•ArewegettingtheadvantageweexpectedfromourIT

investments?

InarecentDeloitteDbriefswebcastsurvey,20percentof polled participants reported that they viewed their analytic capabilities as strong in certain domains. More encouraging, another 17 percent responded that data analytics is becoming an everyday need across the enterprise.1

Becoming analytics driven requires a fundamental shift inglobalbusinessoperations. Thisstartswithknowingwhich questions to ask — the crunchy questions — that help manufacturers build the foundation for their analytics initiatives. By asking the right questions, you can reveal the most relevant data and the most valuable insights. Deloitte is working with many clients to identify the priority questions — those often unanswered but critical inquiries that — when answered — evoke “ah-ha!” moments.

1 T.Hanley,L.Dittmar,T.Leatherberry(June2011);DeloitteDbrief:Using analytics to gain a competitive edge in manufacturing.

For example, how well can you answer questions, such as:•Whatreallydrivesvalueforourcustomers?•Whichofourcustomersareprofitabletoserve?•Howdoourcustomersviewuscomparedtothe

competition?•Whatinvestmentsdrivethehighestreturns?•Whothehighpotentialperformersareinour

organization?•Wherearewelikelytoexperiencesafetyproblems?•Dowehaveinformationneededtoanswerimportant

questions?

More and more, manufacturing executives are increasing their focus on analytics for the answers. New advancements in analytics can provide not only reports on past and present operations, but also help forecast the future — giving progressive manufacturers the competitive edge.

Analytics drivenUsing analytics to help gain a competitive edge in manufacturing

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Setting the stageIncreasedpressureonmarginsandaglobalsupplychainarejusttwoofthekeychallengesfacingtoday’smanufacturers.SuchbusinessobjectivesaredrivingtheneedtoharnessthepoweroftheinformationstoredinITsystems.

Data analytics provides an opportunity for the manufacturer to look upstream into the global supply chain and downstream into the consumer base to better anticipate what the customer will need in the future. Similar to retail, customer loyalty and establishing long-term relationships are growing in importance for manufacturers.Inthearticle,“KnowWhatYourCustomersWantBeforeTheyDo,”theauthorsexplainhowretailerscan use advanced data analytics to “target customized offers at the right place in the right moment across the right channel” and thus increase the chances of marketing successbynailingthe“nextbestoffer”(NBO).2Asthemanufacturer’s ability to capture and analyze highly granular data improves, targeted offers, or NBOs, become more possible and offer a competitive advantage for early adoptees.

Data analytics can also be applied in the area of manufacturer pricing to improve profitability. With analytics, manufacturers can begin to better understand not only what, but how and why various factors impact thebottomline.Informationaboutdownstreamcustomersthat can be valuable to pricing and product placement can be relatively basic and easily acquired. For example, information, such as age, gender, number of children, residential address, income or assets, and psychographic lifestyle and behavior data can be easily acquired and very telling of what pricing is suited to a particular region or market.

2 TomDavenport,JohnLucker,LeandroDalleMule(2011,December);KnowWhatYourCustomersWantBeforeTheyDo.Harvard Business Review.

Pricingbasedondataanalyticscanbeparticularlybeneficial to the global supply chain — the manufacturer’s worldwide capabilities. More than ever, manufacturers should focus on their suppliers’ reliability both at the local andnationallevels.Thismeansmanagingrelationshipswith many countries, understanding the impact of their regulations, and identifying the cost of basic storage, logistics, and other expenses related to doing business.

Many manufacturers are also struggling with labor shortages in critical workforce areas, such as engineering, science, and technology. Data analytics can help by giving insight in the increasingly important arena of talent management, as well as foresight into employment and wage growth rates, thus helping manufacturers meet their personnel needs.

Thekeytogettingbetteranswersinvolvesleveragingenterprise information management, business intelligence, andadvancedanalyticstogethertogeta360-degreeviewof the organization. Once in balance, the competitive advantage revolves around a better-integrated supply chain, a stronger focus on customer relationships, and new insight into the product life cycle.

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Analytics

Simulation and modeling

Quantitative analysis

Advanced forecasting

Role-based performance metrics

Exception and alerts

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Theconvergenceofexplodingdatavolumes,newdatasources, powerful new technology, and other factors has caused the need for more and better analytic capabilities. Until now, manufacturers have focused mostly on basic reporting, and but more and more are using sophisticated toolsandmethodstogodeeper.Inadditiontobeingable to more effectively and efficiently report on what happened, analytics provides the ability to answer important questions:•Why it happened,•What will happen next,•What will happen if, and•What is the most significant outcome for business

optimization

“Nothing new, totally revolutionary”

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Investmentstodayarepavingthewayformorepredictiveand prescriptive analytics. While experience is still a critical factor in effective decision making, manufacturers can no longer rely on experience, “gut feel” and intuition alone. Theharshbusinessrealityistheneedforanincreasingfocusondataandtheanalysisofthatdata.Thisshiftmarks a significant change and possibly the most important culturetrendtoday.Thecrunchyquestionhereis,“Doyouthink or do you know?”Ifyouknow,thenwhere’sthedatato support your decision?

Overcoming barriersManyorganizationsarestillearlyintheirjourneystodevelop fact-based cultures and to put in place new capabilities that can effectively and efficiently turn data intonew,insightfulinformation.Inourexperience,mostbusiness leaders understand the inherent value of using high-quality information and analytic insights to improve operations and drive smarter decisions. But even when there is clear recognition of the potential, companies sometimes fall short of achieving the capabilities they want and need.

Lacking the level of information management automation and analytic tools they desire, these companies make do with manual processes and fragmented solutions, working outsideoftheexistingenterprisesystems. Theremaybe pockets of analytics innovation, but what we often encounter is a sea of spreadsheets.   Why is this happening when the benefits of improved information management and enhanced analytic capabilities seem clear? Deloitte has identified a variety of barriers that companies face when trying to become more proficient with analytics across the enterprise: •Lackofacompellingbusinesscase•Concernsaboutqualityofdata•Organizationalsilos•Insufficientexecutivesponsorship•Acceptanceofcurrentstate

Thebusinesscasecanbemade—andourexperienceshows that high-quality analytics investments can be actually self-funding. Concerns about data quality, while oftenjustified,aretypicallyaddressableandsometimesmerely serve as excuses for not moving forward. Leadership itself is the key ingredient to moving forward, overcoming the inertia of silos, motivating the team, and setting the vision for the future.  Clarity on both the opportunities and the risks of not acting is essential. Trends in the driver’s seatPowerfultrendsaredrivingtheadoptionofnewapproaches to business analytics and an overall increase inthedemandforanalyticcapabilities.Aconvergenceofforces and factors are making business analytics pervasive acrossmultipleindustriesandsectors.Itisnotanarrowpocket;itisnotinjustsomepartsoftheworld;it’severywhere — and manufacturing by far is no exception.

Keyfactorsdrivingtoday’sbusinessneedformoreeffectivedata analysis include:•Exponentiallyincreasingamountsofdata,including“Big

Data”•Anaggressiveregulatoryenvironment•Increasedpressuresonprofitablegrowth•Thequestfornewsignalsandhiddeninsights

With these business drivers in mind, manufacturers need deeper insights into the risks of not being in compliance with new laws and regulations and the ability to be increasingly responsive to the public and other types of stakeholders.Particularlyinthemanufacturingsector—with the supply chain becoming increasingly complex and the competition being global in nature — it’s a tough game.There’slittleroomforeraorassumptions.

Do you think or do you know?Manufacturers also need to be aware of new signals, which come from other types of data sources like social media and other unstructured data that is not found in traditional systems. With the application of new techniques, new tools, and better capabilities, insights that havebeenhiddenarecomingtolight.Thegoodnewsistoday’s technology can meet challenges of answering the why and substantiating the facts. Linking analytics to high-impact areasAfull-scaleshifttowardanalyticsisunderwaytodayinvirtually every domain of the manufacturing company. From product design to customer relations, finance, risk, supplier and partner management, and sales and marketing — every facet of the manufacturing operation is on a quest for more specific and accessible information.

“Perhaps the most important cultural trend today: The explosion of data about every aspect of our world and the rise of applied math gurus who know how to use it.”— Chris Anderson, editor-in-chief of Wired

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Noareaismorerelevantthananother.Infact,aneffectivedata analytics initiative requires symbiosis amongst divisions.

Theopportunities,needs,anddriversmayvaryinaparticularorganization;however,theoverallgoalremainsthe same — a search for better analytic capabilities across each and every one of these domains or functions. So if youarethinkingthatmaybethereisjustoneplacewherethis happens, that wouldn’t be true.

Themorechallengingissuesfocusonpricingandprofitability, commodity volatility, and endeavoring to provideproductsafetyintheglobalsupplychain.Theexpanding global economy has turned a once siloed view oftheworldintothenecessityfora360-degreeviewbeyond the four walls of the manufacturing entity.

Thehigh-impactareastendtodefinethebasisofthe“crunchy questions,” and thus dictate what kind of data is needed. Questions, such as:•HowdoIdealwithexternaldataintomyarchitectural

framework?•HowcanIbetterunderstandwhatactionshaveoccurred

to improve the overall function of the organization?•HowdoIstackupcomparedwithwhatothercompanies

are doing?

Most manufacturers are still in the experimental phase of data analytics. Not any one company is doing everything well or “has arrived” and is done. Business leaders typically desire to have information quickly, so answers areinstant.Butit’snotjustasimplereportingmatter.Advancedanalyticsusesdataminingcapabilitiestounearthuncommon insights, such as the risks of supplier failure and the future of products.

The rise of analytics in manufacturingGiven the current demands on manufacturing, there is no single “best place” to start improving data analytics. Most CFOs still tend to be the driving catalysts for implementing analytics initiatives. During the Dbriefs webcast, participants reported that supply chain, customer relations, production, and risk management would benefit from improvements in data analytics. “So what are the business issues driving data analytics in manufacturing?”Productdesign,supplychain,pricing,customer, and service are all critical performance areas.

For example, R&D data historically has been isolated from the actual manufacturing process. By paying more attention to this data with their next wave of products and services, companies can significantly reduce time to market, better define their client niche, and improve overall production quality. “Unstructured data” from social networks and blogs can provide a wealth of such information to gain insights on customer wants and needsthattranslateintoadvancementofproducts.Asaresult, many manufactures are trying to build a continuous learning environment around R&D.

Data analytics in supply chain involves an understanding of thevolatilityoccurringacrossthedemandchannel.Today’ssupply chain is “multi-channeled” with a particular focus on looking downstream from the manufacturer to the retailer. Data analytics can help bring insight into the supply chain process by:•Aidingindemandforecastingandsupplychainplanning•Integratingdatafrommultiplesources,including

retailers, customers, R&D, and inventory data •Developingmacroeconomicmodelstomoreeffectively

predict prices

Most manufacturing companies have invested heavily in improvingsupplychainmanagement.However,recentlydeveloped tools are designed to provide a means to better exploit supplier and supply chain information. Manufacturing companies can realize additional improvements around their efficiency by driving analytics into their operations and pulling multiple data sets together.

Combined with design and supply chain, value-based pricing and cost analytics provide an understanding of how productioncostsaffectthebottomline.Effectivepricingcannot be sustained without the supporting analytics. Manufacturers are continuously challenged with delivering products that attract new customers while maintaining theirloyalbase.Thus,loyaltyanalyticsprogramsarenolongerjustaretailissue.

Market/sales management

Enterprise management

Customer management

Supplier/partner management

Product management

Service management

Analytics

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Because of the rapid pace of product development, customer service is becoming a key differentiating factor amongstmanufacturers.Tiedtocustomerfocus,manycompanies are looking at how do they integrate service information into product design, which involves leveraging R&D data to anticipate the ever-changing consumer base. Thus,noareaisisolatedinmanufacturingdataanalytics;the key is pulling it all together.

DELTTA: Elements for effective analyticsDeloittehasworkedwithTomDavenport—aninternationally recognized expert on analytics—to help clientsunderstandhowtobecomeanalyticsdriven. Aspartofthiscollaboration,wehaveextendedTom’swork to include critical elements in an effective analytics program. Thegoalistoeffectivelyembedanalyticsintoan organization’s systems and culture so that value can berealizedatvariouslevelsofthebusiness.ThisnewDELTTA(Data,Enterprise,Leadership,Targets,Technology,andAnalysts)Modelisdesignedtohelpguidechangemanagement plans and activities:

•Data: Solid analytical decision making requires access to usable and actionable data. Collecting, accessing, and drawing insight from such data also necessitates corporate consensus on business metrics and strategy. Deloitte has the experience needed to help reach such consensus, to clean data where needed, and to converge silos of data located in disparate systems so that it is accessible across the enterprise when and where it is needed.

•Enterprise: Whether it is data, tools, or people, Deloitte takesanenterpriseapproachtoanalytics.Thisrequiresexamining all areas of the organization for answers to those crunchy questions that help drive strategic business direction.Thisaffectsdatagovernanceandapplicationarchitecturedecisions.Anditfitspreciselywiththerecognition of the need to integrate data from across multiple functions and groups.

•Leadership:Althoughenterprise-wideinvolvementis

essential to an effective analytics initiative, corporate leadership—orsponsorship—isjustascritical.Visibleand persistent leadership support will be essential to analytics’ initiatives. Deloitte has experience working with leaders at various levels of the organization to get analytical decision-making initiatives underway and to keep them on track.

•Targets: Deloitte can help identify and refine the capabilities it needs.  Focus is important.  We will bring a demonstrated framework for exploring, categorizing, rationalizing, and prioritizing reporting and analytics needs.

•Technology: While technology is certainly not the entire solution, it is important to recognize that the “right” technology — properly designed, deployed and used — isessential. Aneffectiveanalyticsprogramcannotexistwithout leveraging modern tools.

•Analysts: Of course, an effective analytics initiative requireshighlyskilled,experiencedanalysts.Herewewillhelp identify the skills and experience it needs to exploit the power of analytics.

Pulling it togetherDespite cross-enterprise demands, barriers can still exist to implement an effective analytics strategy or program. DuringtheDbriefswebcast,slightlymorethan24percentof the participants expressed concerns about quality of data,whilemorethan20percentalsocitedorganizationalsilosassignificantbarriers.Inadditiontodataqualityandsilos, stumbling blocks also included lack of a solid business case, insufficient executive sponsorship, and management’s satisfactionwiththecurrentstate.Alloftheseissuesare decreasing in importance, indicating a trend toward increasedinterestinanalytics.Inourpoll,thelargestpercentage, 33 percent chose “all of the above” as biggest barrier. Clearly, undertaking improvements in analytics capabilities across an enterprise is not an easy undertaking.

Gettingstartedontheanalyticsjourneyrequiresstartingwhere you are and knowing where you currently are on thejourney.Highlyfragmentedorganizationstypicallystruggle the most in getting started, but it’s a recursive processnomatterwhereyouneedtobegin.Today’sbusinessobjectiveindataanalyticswillultimatelybecometomorrow’s new beginning.

Agoodstartingpointisoftenaplaceofpainwherethebenefits of implementing a data analytics initiative can be obviously seen. Success leads to success, and the focus is on being value driven. Once established, data analytics can be built outward through the organization.

Regardlessofwhereitbegins,theanalyticsjourneyisadynamicandongoingone.Ifyoudecidetotakethisjourney,it’snotbusinessasusual.Afact-drivenmanufacturing company has a pervasive and persistent focus on what the data is revealing. Some long-held beliefs maybedemonstratedtobeuntruebythedata.Agilityandopen-mindednessareessential.Intheend,thehindsight,insightandforesighttheanalyticsjourneybringstoyourorganizationcanbeinvaluable.Soembracethejourney,and let analytics help transform your business!

For more information, please contact:Lee DittmarPrincipalDeloitte Consultingldittmar@deloitte.com +12124463692

Jointheanalyticsconversationat www.realanalyticsinsights.com

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