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Dette er slutten av forhåndsvisningen Registrer deg for å få tilgang til resten av dokumentet. Uformatert tekstforhåndsvisning STORE DATA I LOGISTIKKER Et DHL-perspektiv på hvordan man beveger seg utover sprøytenarkivet desember 2013 Drevet av Løsninger Innovasjon Trendforskning PUBLISHER DHL Kundeløsninger Innovasjon Representert av Martin Wegner Vice President Solutions Innovation 53844 Troisdorf, Tyskland PROJEKTDIREKTOR Dr Markus Kckelhaus Solutions Innovasjon, DHL PROJECT MANAGEMENT OG EDITORIAL OFFICE Katrin Zeiler Solutions Innovasjon, DHL I SAMARBEID MED AUTORISERING MAR tin Jeske, Moritz Grner, Frank Wei Forord FORDEL Big Data og logistikk er laget for hverandre, og i dag posisjonerer logistikkindustrien seg selv for å sette denne rikdommen av informasjon bedre i bruk. Potensialet for Big Data i logistikkindustrien er allerede uthevet i den anerkjente DHL Logistics Trend Radar Denne overordnede studien er et dynamisk, levende dokument utviklet for å hjelpe organisasjoner med å skape nye strategier og utvikle kraftigere prosjekter og innovasjoner. Big Data har mye å tilby verden av logistikk. Sofistikert dataanalyse kan konsolidere denne tradisjonelt fragmenterte sektoren, og disse nye evnene legger logistikkleverandører i poleposisjon som søkemotorer i den fysiske verden. Det har blitt utviklet i fellesskap med T-Systems og eksperter fra Detecon Consulting. Forskningsgruppen har kombinert erfaring fra både verdensledet fra både logistikkdomenet og informasjonsforvaltningen domene H ow kan vi flytte fra en dyp brønn til data til dyp utnyttelse Vi h takket være at Big Data in Logistics gir deg noen kraftige nye perspektiver og ideer. Takk for at du valgte å bli med oss på denne Big Data-reisen sammen, kan vi alle ha nytte av en ny samarbeids - og samarbeidsmodell i logistikkindustrien. Hvordan kan vi bruke informasjon for å forbedre operasjonell effektivitet og kundeopplevelse og skape nyttige nye forretningsmodeller Med vennlig hilsen For å skarpere fokuset, trener den trendrapporten du leser nå, de store Big Data-spørsmålene. Stor data er en relativt uutnyttet ressurs som bedrifter kan utnytte når de vedtar en skifte av tankegang og anvende de riktige boreteknikkene. Det går også langt utover buzz-ordene for å tilby bruk i real-world bruk, avslørende hva som skjer nå, og hva som er sannsynlig å skje i fremtiden. Denne trendrapporten starter med en introduksjon til konseptet og betydningen av Big Data, gir eksempler hentet fra mange forskjellige bransjer, og presenterer deretter logistikk bruk saker Martin Wegner Dr Markus Kckelhaus 1 2 Table of C ontenter Forord 1 1 Forstå store data 3 2 Store data Beste praksis over bransjer 6 2 1 Driftseffektivitet 7 2 2 Kundeopplevelse 10 2 3 Nye forretningsmodeller 13 3 Store data i logistikk 15 3 1 Logistikk som data-drevet virksomhet 15 3 2 Bruk sager Driftseffektivitet 18 3 3 Brukssaker Kundeopplevelser 22 3 4 Bruk sager Nye forretningsmodeller 25 3 5 Suksessfaktorer for implementering av Big Data Analytics 27 Outlook 29 Forstå store data 1 FORSTÅ BIG DATA Den vedvarende suksessen til internettpowerhouses som Amazon, Google , Facebook og eBay gir bevis på en fjerde produksjonsfaktor i dagens hyperforbundne verden. I tillegg til ressurser, arbeidskraft og kapital, er det ingen tvil om at informasjonen har blitt et essensielt i universet1, takket være veksten av sosiale medier, allestedsnærværende nettverkstilgang og det stadig økende antallet smarte tilkoblede enheter Dagens digitale univers ekspanderer med en hastighet som dobler datavolumet hvert to år2 se figur 1 elemen t av konkurransedyktig differensiering Bedrifter i alle sektorer arbeider for å handle med gut-følelsen for nøyaktig data-drevet innsikt for å oppnå effektiv beslutningsprosess. Uansett om det skal avgjøres forventede salgsvolum, kundeproduktpreferanser, optimerte arbeidsplaner, er det data som Nå har makten til å hjelpe bedrifter med å lykkes Som en søken etter olje, med Big Data tar det utdannet boring for å avsløre en brønn med verdifull informasjon. Hvorfor er søket etter meningsfylt informasjon så komplisert? Det er på grunn av den enorme veksten av tilgjengelige data i bedrifter og på det offentlige Internett Tilbake i 2008 har antall tilgjengelige digitale informasjonstykkelser overgått antall stjerner I tillegg til denne eksponensielle volumveksten, har to ytterligere egenskaper av data blitt vesentlig endret. For det første henter dataene i den massive distribusjonen av tilkoblede enheter slik som biler, smarttelefoner, RFID-lesere, webkameraer og sensornettverk legger til et stort antall autonome datakilder Enheter som disse genererer kontinuerlig datastrømmer uten menneskelig innblanding, øker hastigheten på dataaggregering og prosessering For det andre er data ekstremt variert. De aller fleste nyopprettede data stammer fra kamerabilder, video og overvåkingsopptak, blogginnlegg, forumdiskusjoner og e-handelskataloger Alle disse ustrukturerte datakilder bidrar til et mye høyere utvalg datatyper 40 000 30 000 Exabytes 20 000 10 000 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Figur 1 Eksponensiell datavekst mellom 2010 og 2020 Kilde IDC s Digital Universe Study, sponset av EMC, desember 2012 Det mangfoldige og eksploderende digitale universet, IDC, 2008 1 Det digitale universet i 2020 Store data, større digitale skygger og størst vekst i Fjernøsten, IDC, sponset av EMC , Desember 2012 2 3 4 Forstå store data Telefonica måtte svare på reisen til slutt å lansere sin Smart Steps-tjeneste. Hvilken tilleggsverdi gjør det? Den eksisterende massen av data bærer og hvordan kan vi kapitalisere på det. Mens forbrukerne er kjent med å gjøre informasjonsdriven dagliglivsavgjørelser som kjøp, ruteplanlegging, eller å finne et sted å spise, går selskapene bak. For å utnytte sine informasjonsmidler har bedrifter først og fremst endre holdning til hvordan du bruker data Tidligere ble dataanalyser brukt til å bekrefte beslutninger som allerede var tatt. Hva kreves er en kulturell forandring. Bedrifter må overgå til en fremtidsrettet dataanalysestil som gir ny innsikt og bedre svar Dette skiftet i tenkemåte innebærer også en ny kvalitet på eksperimentering, samarbeid og gjennomsiktighet over hele selskapet. Volum, hastighet og variasjon 3V er denne store data I litteratur har 3Vene blitt omtalt mye som egenskapene til Big Data analytics Men det er langt mer å vurdere om bedrifter ønsker å utnytte informasjon som produksjonsfaktor og styrke konkurranseposisjonen W Hue s kreves et skifte i tankegang og anvendelse av de riktige boreteknikkene Bli en informasjonsdrevet bedrift Da global teleleverandør Telefonica begynte å utforske informasjonsdrevne forretningsmodeller, var selskapet allerede i stand til å behandle hundrevis av millioner dataposter fra sin mobilnett hver dag for å rute og fakturere telefonsamtaler og datatjenester. Håndtering av et stort datavolum med høy hastighet var ikke hovedproblemet. I stedet er nøkkelspesifikasjonen Sammen med denne overgangen er en annen forutsetning for å bli en informasjonsdrevet bedrift å etablere et bestemt sett av datavitenskapsfag Dette inkluderer å mastere både et bredt spekter av analytiske prosedyrer og ha en omfattende forståelse for virksomheten Og bedrifter må ta nye teknologiske tilnærminger for å utforske informasjon i en høyere rekkefølge av detalj og hastighet Forstyrrende paradigmer for databehandling for eksempel i-minnet databaser og slutt konsistent databehandling modus Jeg lover å løse storskala dataanalyseproblemer til en økonomisk gjennomførbar kostnad. Hver bedrift eier allerede mye informasjon. Men de fleste av dataene må bare raffineres, da kan de omdannes til forretningsverdi. Med Big Data-analyser kan bedrifter oppnå holdningen , skillset og teknologi som kreves for å bli et data raffinaderi og skape merverdi fra deres informasjonskapasiteter. Forståelse av Big Data Logistics og Big Data er en perfekt match Logistikksektoren er ideelt plassert for å dra nytte av de teknologiske og metodologiske fremskrittene i Big Data. En sterk hint at datastyring alltid har vært nøkkelen til disiplinen er at logistikk i sine gamle greske røtter betyr praktisk aritmetikk. 3 I dag administrerer logistikkleverandører en massiv flyt av varer og samtidig skaper store datasett. For millioner av forsendelser hver dag, opprinnelse og destinasjon, størrelse, vekt, innhold og plassering spores alle over globale leveringsnettverk, men gjør disse dataene sporing fullt ut utnyttelsesverdi Sannsynligvis ikke Sannelig er det stort uutnyttet potensial for å forbedre operasjonell effektivitet og kundeopplevelse og skape nyttige nye forretningsmodeller. Vurder for eksempel fordelene med å integrere leverandørkjeden datastrømmer fra flere logistikkleverandører, dette kan eliminere dagens markedssplittring , som muliggjør kraftig nytt samarbeid og tjenester Mange tilbydere innser at Big Data er en gamechanging trend for logistikkindustrien I en nylig studie om forsyningskjeden trender, sa sixty prosent av respondentene at de planlegger å investere i Big Data analytics innen de neste fem år4 se figur 2 nedenfor. Søket etter konkurransefortrinn starter imidlertid med identifisering av sterke Big Data-brukstilfeller. I dette papiret ser vi først på organisasjoner som med hell har distribuert Big Data analytics i sammenheng med deres egne næringer. Da presenterer vi en Antall brukstilfeller som er spesifikke for logistikksektoren Sosiale nettverk Internt B2B Business Analytics-plattformer som en tjeneste i dag Fem års nettverk Redesign Software Systems Product Lifecycle Management 0 10 20 30 40 50 60 70 Figur 2 Aktuelle og planlagte investeringsområder for Big Data-teknologier Kildeutvikling og strategier innen logistikk og Supply Chain Management, p 51, BVL International, 2013 Definisjon og utvikling, Logistik Baden-Wrttemberg, jfr 3 Trender og strategier innen logistikk og Supply Chain Management, BVL International, 2013 4 5 6 Stor data Best Practice Over Industries 2 STOR DATA BEST PRAKSIS AKTIV INDUSTRIER Kapitalisering på Verdien av informasjonsmidler er et nytt strategisk mål for de fleste bedrifter og organisasjoner Bortsett fra Internett-kraftverk som har opprettet informasjonsdrevne forretningsmodeller, er selskaper i andre sektorer vanligvis i de tidlige stadier av å utforske hvordan de skal dra nytte av deres voksende haug med data, og legg disse dataene til god bruk Ifølge nyere forskning5, er det kun 14 av europeisk samarbeid Mappene adresserer allerede Big Data analytics som en del av deres strategiske planlegging, se figur 3. Likevel forventer nesten halvparten av disse selskapene en årlig datautvikling i organisasjonen over 25. Den første og mest åpenbare er operasjonell effektivitet. I dette tilfellet er data vant til ta bedre beslutninger, optimere ressursforbruk og forbedre prosesskvalitet og ytelse Det er hva automatisert databehandling alltid har gitt, men med et forbedret sett av muligheter. Den andre dimensjonen er kundeopplevelse. Vanlige mål er å øke kundeloyaliteten, utføre presis kunde segmentering og optimalisering av kundeservice Inkludert de store datafildene til det offentlige Internett, driver Big Data CRM-teknikker til neste evolusjonerende stadium. Det gjør det også mulig for nye forretningsmodeller å komplementere inntektsstrømmer fra eksisterende produkter og å skape ekstra inntekter fra helt nye dataprodukter Stor datavare Dimensjoner Når bedrifter vedtar Big Data som en del av sin virksomhet strategi, er det første spørsmålet om overflaten vanligvis hvilken type verdi Big Data vil kjøre. Vil det bidra til topp - eller bunnlinjen, eller vil det være en ikke-økonomisk driver. Fra et verdisynspunkt faller applikasjonen av Big Data analytics inn i en av tre dimensjoner, se figur 4 For hver av disse store dataverdimåttene er det økende antall overbevisende applikasjoner. Disse viser forretningspotensialet ved å tjene penger på informasjon på tvers av et bredt spekter av vertikale markeder. I de følgende avsnittene presenterer vi flere brukssaker til illustrere hvor tidlige mobilister har utnyttet datakilder på innovative måter og dermed skapt betydelig tilleggsverdi Har din bedrift definert en stor datastrategi Har din bedrift definert en stor datastrategi Nei 63 23 Planlagt Ja 14 Figur 3 Store data som et strategisk mål i europeisk selskaper Statistikk fra BARC studie N 273 Kilde Big Data Survey Europa, BARC, februar 2013, s. 17 Big Data Survey Europa, BARC-instituttet, Febr uary 2013 5 Stor data Best Practice Over Industries Operational Operational Efficiency Kundekundeopplevelse Bruk data for å kunne bruke data Utnyttelse av datautnyttelse for å øke kunden Øk kundenes lloyalty oyalty og retention retention Utfør presis Utfør kundesupport segmentering og målretting segmentering og målretting Optimaliser kundeoptimaliser samhandling interaktivitetskunder og service og service Effektivitet Øk nivå Øk nivået på gjennomsiktighetsgjenkjenning Optimaliser ressursoptimaliser Forbrukerressursforbedre prosess Kvalitetsforbruk og ytelse Forbedre prosesskvalitet og ytelse Opplev nye modeller Nye forretningsmodeller Aktivere datadata ved å kapitalisere på Utvide inntektsstrømmer Utvide inntektsstrømmer fra eksisterende fra eksisterende produktprodukter. Opprette nyskapende inntekter. Opprette nye strømmer fra helt strømmer fra helt nye, nye dataprodukter. Dataprodukter Figur 4 Verdi dim ensioner for Big Data-brukstilfeller Kilde DPDHL Detecon 2 1 Driftseffektivitet 2 1 1 Bruke data for å forutsi forbrytelsesproblemer For hovedstadspolitiske avdelinger kan oppgaven med å spore opp kriminelle for å bevare den offentlige sikkerheten noen ganger være kjedelig. gjør manuell tilkobling av mange datapunkter Dette tar tider og dramatisk forsinker saksoppløsningen Videre blir veibeskyttelsesressurser implementert reaktivt, noe som gjør det svært vanskelig å fange kriminelle i loven. I de fleste tilfeller er det ikke mulig å løse disse utfordringene ved å øke politiets bemanning som statsbudsjettene er begrenset En myndighet som utnytter sine ulike datakilder, er New York Police Department NYPD. Ved å fange og forbinde kriminalitetsrelaterte opplysninger håper det å være et skritt foran forbrytelsene 6 Langt før termen Big Data ble laget, NYPD gjorde en innsats for å bryte opp avdelingen av dataene jeg f. eks. data fra 911 samtaler, undersøkelsesrapporter og mer. Med en enkelt oversikt over all informasjon knyttet til en bestemt forbrytelse, oppnår offiserer et mer sammenhengende, sanntidsbilde av deres tilfeller. Dette skiftet har betydelig økt tilbakevirkende analyse og gjør det mulig for NYPD å handle tidligere i sporing av enkeltkriminelle. Den stadig avtagende graden av voldelig kriminalitet i New York7 er ikke bare tilskrevet denne mer effektive strømlinjen av de mange datapostene som kreves for å utføre sakarbeid, men også til en grunnleggende endring i politiet 8 Ved å introdusere statistisk analyse og georafisk kartlegging av kriminalitetsposisjoner har NYPD vært i stand til å skape et større bilde for å veilede ressursutbredelse og patruljepraksis. Nå kan avdelingen gjenkjenne kriminalitetsmønstre ved hjelp av beregningsanalyse, og dette gir innsikt som gjør at hver kommandør kan proaktivt identifisere hotspots av kriminell aktivitet NYPD endrer forbrytelsesreguleringsligningen med wa du bruker informasjon, IBM cf 6 Index Crimes By Region, New York State Divisjon for strafferettslig tjenesteyting, mai 2013, jfr. 7 Compstat og organisasjonsendring i Lowell Police Department, Willis et al. politifond, 2004 cf 8 content compstat - og Organisasjons-Change-Lowell-Police-avdelingen 7 8 Big Data Best Practice Over Industries Dette forutgående perspektivet gir NYPD muligheten til effektivt å målrette utplasseringen av arbeidskraft og ressurser. I kombinasjon med andre tiltak har den systematiske analysen av eksisterende informasjon bidratt til en stadig avtagende grad av voldelig kriminalitet se figur 5 Teknikken til bruk av historiske data for å oppnå mønstergenkjenning og derfor forutsi kriminalitetsposisjoner har over tid blitt vedtatt av en rekke kommuner i USA. Etter hvert som flere og flere politi-avdelinger tilbyr kriminalitet informasjon til offentligheten, tredjeparter har også begynt å gi forutsigelser om kriminalitet, de samler data til nasjonale synspunkter a nd gir også anonyme tipping-funksjonalitet se Figur 6 9 26 000 1 000 24 000 -3 900 22 000 800 20 000 700 18 000 -4 600 Røveri 16 000 500 14 000 400 12 000 300 10 000 2002 Mord 2004 2006 2008 2010 2012 Figur 5 Utvikling av voldelige forbrytelser i New York City-data hentet fra indekskriminalitet Rapportert til politiet etter region New York City, 2003 2012, Kilde New York State-avdelingen for strafferettslig tjenesteyting, jf. Figur 6 En offentlig motordrevne skjermbilde, jf. Eksempel 9 Big Data Best Practice Over Industries 2 1 2 Optimal skiftplanlegging i butikkene For forhandlerforvaltere er planleggingsklyftene for å møte kundenes behov en overkommelig oppgave. Overstaffing butikken skaper unødvendig kostnad og reduserer områdets lønnsomhet. Å kjøre butikken med lavt personalenivå negativt påvirker kunde og Ansattes tilfredshet Begge er dårlige for virksomheten På DM-forhandlere var skiftplanleggingsoppgaven historisk utført av butiksjefen basert på enkle ekstrapoleringer og personlig erfaring. For reg ular virkedager var denne prosessen god nok Men med et økende antall unntak ble det utilfredsstillende Overhead eller mangel på personell begrenset butikkytelse Så DM besluttet å effektivt bistå butikkforvaltere i deres personell forplanlegging ved å finne måter å pålidelig forutse etterspørselen på hver Spesielt salgssted 10 Tilnærmingen var å implementere en langsiktig forutsigelse av daglige butikkinntekter, med tanke på et bredt spekter av individuelle og lokale parametere. Inndata til en ny algoritme inkluderte historiske inntektsdata, åpningstider og ankomsttider for Nye varer fra distribusjonsstedene I tillegg til dette ble andre data inntatt for å oppnå det høyeste nivået av presisjon. Disse dataene inkluderte lokale forhold som markedsdager, helligdager i nabolandene, veiviser og fremtidige værmeldingsdata som værforholdene betydelig påvirke forbrukeradferd DM evaluerte ulike prediktive algoritmer og den valgte løsningen på nå gir slike nøyaktige fremskrivninger at det har vist seg å være en kraftig støtte til skiftplanlegging. Basert på høyoppløselig prediksjon av daglig salg for hver enkelt butikk, kan ansatte nå legge inn sine personlige preferanser i skiftplanen fire til åtte uker i forveien Når de er godkjent, er det ikke sannsynlig at skiftene deres kan endre seg på den langsiktige planen, og en siste liten endring er en eksepsjonell hendelse. Dette viser hvordan bruk av prediktiv analyse ved DM øker operasjonell effektivitet i butikken og samtidig , bidrar til en bedre balanse mellom arbeid og liv for forretningspersonell Business Intelligence Guide 2012 2013, isreport, isi Medien Mnchen eller cf 10 9 2 2006 4. kvartal 2007 10 Big Data Best Practice Over Industries 2 2 Kundeopplevelse 2 2 1 Sosial innflytelsesanalyse for kundeoppbevaring For å få innblikk i kundetilfredshet og fremtidig etterspørsel, bruker selskapene en rekke ulike forretningsmodeller. Den konvensjonelle tilnærmingen er å gjennomføre markedsreservering earch på kundebasis, men dette skaper en generell visning uten fokus på individuelle forbruksbehov og atferd. Et problem som utfordrer telekommunikasjonsleverandører, er at kunden tåler tap av kunder over en tidsperiode. For å bidra til å redusere churn, analyserer organisasjoner vanligvis bruksmønstre av individuelle abonnenter og egen tjenestekvalitet. De tilbyr også spesifikke fordeler11 for å holde noen kunder lojale, basert på parametere som kundeutgifter, bruk og abonnementslengde. Tidligere har disse opprettholdelsesarbeidene basert på individuell kundeverdi oppnådd en viss forbedring i lojalitet12, men kunden er fortsatt et problem for tilbydere, se Figur 7 For å bedre forutsi kundeadferd har T-Mobile USA begynt å inkludere sosiale relasjoner mellom abonnenter i sin churn management model 13. Organisasjonen bruker en multi-graf teknikk, ligner metodene som ble brukt. Dette helt nye perspektivet av sine kunder krevde T-Mobile å berike sin eldre analyse av data som er historisk hentet fra faktureringssystemer og kommunikasjonsnettverkselementer I tillegg innhentes omtrent en petabyte av rå data, inkludert informasjon fra webklikkstrømmer og sosiale nettverk, for å bidra til å spore de sofistikerte mekanismene bak kundekjernen. Denne svært innovative tilnærmingen har allerede betalt off for T-Mobile Etter bare første kvartal med å bruke sin nye styringsmodell, ble organisasjonen sanket med 50 sammenlignet med samme kvartal året før. Etterbetalt Forbetalt Blend Etterbetalt trend Forutbetalt trend Blended trend 6 5 Churn rate i sosialt nettverksanalyse for å identifisere såkalte stamme-ledere Dette er mennesker som har stor innflytelse i større sammenhengende grupper Hvis en stamme leder bytter til en konkurrent s tjeneste, er det sannsynlig at et antall venner og familiemedlemmer også vil bytte det er som en dominoeffekt Med denne forandringen i måten den beregner kundeverdi, har T-Mobile forbedret sin måling rement for å inkludere ikke bare kundens livstidsabonnement på mobiltjenester, men også størrelsen på hans eller hennes sosiale nettverk eller stamme se Figur 8 4 3 2 1 0 2. kvartal 2005 4. kvartal 2005 2. kv. 2006 Etterbetalt Forbetalt 4. kvartal 2006 2. kvartal 2007 Blended Etterbetalt trend Forutbetalt trend Blended trend Q4 2007 Q2 2008 Figur 8 Identifisering av innflytelse innenfor en mobil abonnentbase Etterbehandlet Prepaid Blended Etterbetalt trend Forhåndsbetalt trend Figur 7 Utvikling Blended trend av abonnentkurs, fra Mobile Churn og Loyalty Strategies, Informa, s. 24 Kunde Lojalitetssporing, Informa , 2012 11 Q4 2006 12 2007 Q2 2007 MobileQ4Churn Q2 Loyalty 2008 and Strategies, 2. utgave, Informa, 2009 T-Mobile utfordringer krummer med data, Brett Sheppard, O Reilly Strata, 2011 cf 13 Q2 2008 Stor data Best Practice Over Industries 2 2 2 Unngå utelukkende forhold for kundetilfredshet Dette er en hyppig og skuffende opplevelse for kjøpere når de finner det perfekte klæret, de oppdager at Størrelsen de trenger er ikke på lager. Med økende konkurranse i tekstil - og klær-segmentet, er tilgjengeligheten av populære klær nå vanligvis begrenset. Dette skyldes konsolidering av merkevarer og akselererte produktsykluser. I noen tilfeller er det bare tre uker mellom den første design av et plagg og sin ankomst i butikken 14 Den hyppige lanseringen av nye samlinger drevet av vertikalt organiserte kjeder, innsnevrer innkjøp av artikler til en enkelt sats. Dette medfører en risiko for klærkjeder, noe som gjør det viktigere enn noensinne å nøyaktig forutse forbrukernes etterspørsel etter et bestemt produkt Evnen til å riktig forutsi etterspørselen har blitt en nøkkelfaktor for lønnsom virksomhet. Multikannelforhandleren Otto Group innså at konvensjonelle metoder for å prognose etterspørsel etter elektroniske og postordre katalogkataloger viste seg å være utilstrekkelige i et stadig mer konkurransedyktig miljø. 63 av gjenstander oversteg avviket i forhold til de faktiske salgsmengder ca. 20 15 T hans gruppe verdsatt forretningsrisikoen for både overproduksjon og mangel Overproduksjon ville påvirke lønnsomheten og låse opp for mye kapital Mangel ville irritere kunder For å møte kundenes behov, spesielt de høye forventningene til digitale innfødte når de foretok et online kjøp, tok Otto-gruppen en nyskapende og forstyrrende tilnærming for å forbedre sin evne til å levere se Figur 9 Forutsigelsesavvik 63 Forutsigelsesavvik 20 1000 500 Absolutt frekvens Klassisk prediksjon utvikler merchandiseringsrisiko 100 20 0 20 Klassisk prediksjon Neuro Bayes utvikler backlogrisiko 11 prognoseavvik 20 100 200 Forutsigelse med Neuro Bayes Figur 9 Relativ avvik av prognose fra det faktiske salgsvolumet, fra Big Data Predictive Analytics Det er ikke noe å si om prognoser og oppfølging i Otto-gruppen, Michael Sinn konferansesamtale, Big Data Europe, Zürich, 28. august 2012 Moteindustrien, Patrik Aspers, Årsbok 2007 2008, Max Planc k Institutt for studien av samfunn 14 Otto rechnet mit knstlicher Intelligenz, Lebensmittel Zeitung, 21. august 2009 15 11 12 Stor data best praksis over bransjer 70 63 60 50 40 30 20 11 10 0 Konvensjonell etterspørselsprognose Etterspørselsprognose med prediktiv analyse Figur 10 Prosentandel av katalogartikler med faktiske salgstall som avviger mer enn 20 fra etterspørselsvarsel Kilde Perfektes Bestandsmanagement durch Prediktiv Analytics, Mathias Stben, Otto Group, 29. Tysk Logistikk-kongres, okt 2012 Etter å ha vurdert en rekke løsninger for å generere stabil prognose for salgsvolum, Otto-gruppen lyktes til slutt ved å anvende en metode som stammer fra høyfysisk fysikk. Det brukte et multivariat analyseværktøy som benytter selvlærende evner fra nevrale nettverksteknikker og kombinerer dem med bayesisk statistikk. 16 Med dette analysverktøyet etablerte gruppen en helt ny prognosemotor det trente verktøyet med historiske data fra 1 6 foregående årstider og kontinuerlig innspill til verktøyet med 300 millioner transaksjonsposter per uke fra indeværende sesong. Dette nye systemet genererer mer enn en milliard individuelle prognoser per år og har allerede levert overbevisende resultater. Med kun 11 katalogprodukter mangler salgsforutsigelsen med over 20 se figur 10, er Otto-konsernet nå bedre i stand til å tilfredsstille kundeefterspørselen 17 Samtidig reduserer denne nye prediktive tilnærming lagerbeholdningen, noe som resulterer i bedre lønnsomhet og tilgjengelighet av midler Cf 16 Treffsichere Absatzprognose mit Predictive Analytics, Michael Sinn, Konferansesamtale om Big Data Analytics Kongress, Köln, 19. juni 2012 17 cf Stor data Best Practice Over Industries 2 3 Nye forretningsmodeller 2 3 1 Crowdsanalyser leverer detaljhandel og reklameinnsikt For å gi effektiv mobil tale - og datatjenester, nettverksoperatører må kontinuerlig fange et sett med data på hver abonnent Bortsett fra registrering av bruk av mobiltjenester for accounting and billing purposes , operators must also record each subscriber s location so it can direct calls and data streams to the cell tower to which the subscriber s handset is connected This is how every subscriber creates a digital trail as they move around the provider network And in most countries it is just a small group of network operators that have captured most of the population as customers their combined digital trails of the subscriber base provide a comprehensive reflection of society or, more precisely, of how society moves For example, it s possible to assess the attractiveness of a specific street for opening a new store, based on high-resolution analysis of how people move and rest in this area, and find the opening hours likely to create maximum footfall see Figure 11 In a larger context, it s also possible to see the impact of events such as marketing campaigns and the opening of a competitor store by analyzing any change in movement patterns When gender and a ge group splits are included in the data, and geo-localized data sets and social network activity are included, this segmentation adds even greater value for retailers and advertisers In the past, organizations could only make internal use of location and usage data from mobile networks This is because of privacy laws that limit the exploitation of individual subscriber information But once subscriber identity has been split from the movement data, substantial business value remains in this anonymous crowd data, as Telefonica has discovered With the launch of the Telefonica Digital global business division, the network operator is now driving business innovation outside its core business units and brands As part of Telefonica Digital, the Dynamic Insights initiative has commercialized the analysis of movement data, creating incremental revenue from retail, property, leisure, and media customers 18 Other carriers have developed similar offerings, such as Verizon s Precision Market Insig hts service 19 In urban areas, the density of digital trails is sufficiently high to correlate the collective behavior of the subscriber crowd with characteristics of a particular location or area Figure 11 Analysis of customer footfall in a particular location based on mobile subscriber data, from Cf 18 Cf 19 13 14 Big Data Best Practice Across Industries 2 3 2 Creating new insurance products from geo-localized data Climate sensitivity is a characteristic of the agriculture industry, as local temperatures, sunshine hours, and precipitation levels directly impact crop yield With the increasing occurrence of extreme weather conditions due to global warming, climate variation has become a substantial risk for farmers 20 To mitigate the impact of crop shortfalls, farmers take out insurance policies to cover their potential financial losses Insurance companies in turn are challenged with increasingly unpredictable local weather extremes On the one hand, the conventional risk models based o n historical data are no longer suitable to anticipate future insured loss 21 On the other hand, claims have to be controlled more accurately as damages may vary across an affected region For farmers, the combination of these two aspects results in higher insurance rates and slower payout of damage claims In the United States, most private insurance companies viewed crop production as too risky to insure without federal subsidies 22 In 2006, The Climate Corporation started out to create a new weather simulation model based on 2 5 million temperature and precipitation data points, combined with 150 million soil observations The high resolution of its simulation grid allows the company to dynamically calculate the risk and pricing for weather insurance coverage on a per-field basis across the entire country see Figure 12 As the tracking of local growing conditions and the calculation of crop shortfall are performed in real time, payouts to policy holders are executed automatically when b ad weather conditions occur This eliminates the need for sophisticated and time-consuming claims processes Based on 10 trillion simulation data points23, The Climate Corporation s new insurance business model is now successfully established After only six years, the organization s insurance services have been approved across all 50 states in the U S Figure 12 Real-time tracking of weather conditions and yield impact per field screenshot taken from Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation , Chapter 4 3 4, Intergovernmental Panel on Climate 20 Change IPCC , 2012 cf Warming of the Oceans and Implications for the Re - Insurance Industry , The Geneva Association, June 2013 21 Weather Insurance Reinvented , Linda H Smith, DTN The Progressive Farmer, November 2011 cf 22 About us , The Climate Corporation, cf 23 Big Data in Logistics 3 BIG DATA IN LOGISTICS Companies are learning to turn large-scale quantities of data into competitive advantage The ir precise forecasting of market demand, radical customization of services, and entirely new business models demonstrate exploitation of their previously untapped data As today s best practices touch many vertical markets, it is reasonable to predict that Big Data will also become a disruptive trend in the logistics industry However, the application of Big Data analytics is not immediately obvious in this sector The particularities of the logistics business must be thoroughly examined first in order to discover valuable use cases 3 1 Logistics as a Data-driven Business A kick-start for discussion of how to apply Big Data is to look at creating and consuming information In the logistics industry, Big Data analytics can provide competitive advantage because of five distinct properties These five properties highlight where Big Data can be most effectively applied in the logistics industry They provide a roadmap to the well of unique information assets owned by every logistics provider In the following sections, we identify specific use cases that exploit the value of this information, and contribute to operational efficiency, a better customer experience, or the development of new business models Optimization of service properties like delivery time, resource utilization, and geographical coverage is an inherent challenge of logistics 1 Optimization to the core Large-scale logistics operations require data to run efficiently The earlier this information is available and the more precise the information is, the better the optimization results will become Advanced predictive techniques and real-time processing promise to provide a new quality in capacity forecast and resource control The delivery of tangible goods requires a direct customer interaction at pickup and delivery 2 Tangible goods, tangible customers 3 In sync with customer business On a global scale, millions of customer touch points a day create an opportunity for market intelligence, product feedback or eve n demographics Big Data concepts provide versatile analytic means in order to generate valuable insight on consumer sentiment and product quality Modern logistics solutions seamlessly integrate into production and distribution processes in various industries The tight level of integration with customer operations let logistics providers feel the heartbeat of individual businesses, vertical markets, or regions The application of analytic methodology to this comprehensive knowledge reveals supply chain risks and provides resilience against disruptions The transport and delivery network is a high-resolution data source 4 A network of information Apart from using data for optimizing the network itself, network data may provide valuable insight on the global flow of goods The power and diversity of Big Data analytics moves the level of observation to a microeconomic viewpoint Local presence and decentralized operations is a necessity for logistics services 5 Global coverage, local presence A fleet of vehicles moving across the country to automatically collect local information along the transport routes Processing this huge stream of data originating from a large delivery fleet creates a valuable zoom display for demographic, environmental, and traffic statistics 15 16 Big Data in Logistics Big Data in Logistics 17 New Customer Base Big Data in Logistics Shop The Data-driven Logistics Provider 5 Existing Customer Base Customer Loyalty Management Financial Industry Market and customer intelligence External Online Sources Manufacturing FMCG SME Marketing and Sales Product Management New Business Address Verification Market Intelligence Supply Chain Monitoring Environmental Statistics 11 Environmental Intelligence CO2 Sensors attached to delivery vehicles produce fine-meshed statistics on pollution, traffic density, noise, parking spot utilization, etc Continuous sensor data Service Improvement and Product Innovation Retail Operations Order volume, received service quality 6 Market Research Commercial Data Services Public customer information is mapped against business parameters in order to predict churn and initiate countermeasures High-tech Pharma Public Authorities Customer sentiment and feedback A comprehensive view on customer requirements and service quality is used to enhance the product portfolio 3 8 Supply chain monitoring data is used to create market intelligence reports for small and medium-sized companies Strategic Network Planning Long-term demand forecasts for transport capacity are generated in order to support strategic investments into the network Commerce Sector 9 Households SME Network flow data Core Market Intelligence for SME Location, traffic density, directions, delivery sequence Tr a n s p o r t N e t w ork Financial Demand and Supply Chain Analytics 1 Real-time Route Optimization Delivery routes are dynamically calculated based on delivery sequence, traffic conditions and recipient status Real-time incidents A micro-economic vi ew is created on global supply chain data that helps financial institutions improve their rating and investment decisions Network flow data 10 2 Location, destination, availability Crowd-based Pickup and Delivery A large crowd of occasionally available carriers pick up or deliver shipments along routes they would take anyway Address Verification Fleet personnel verifies recipient addresses which are transmitted to a central address verification service provided to retailers and marketing agencies 4 Operational Capacity Planning Short - and mid-term capacity planning allows optimal utilization and scaling of manpower and resources 7 Risk Evaluation and Resilience Planning By tracking and predicting events that lead to supply chain disruptions, the resilience level of transport services is increased Flow of data Flow of physical goods 2013 Detecon International 18 Big Data in Logistics 3 2 Use Cases Operational Efficiency A straightforward way to apply Big Data analytics in a business env ironment is to increase the level of efficiency in operations This is simply what IT has always been doing accelerating business processes but Big Data analytics effectively opens the throttle 3 2 1 Last-mile optimization A constraint in achieving high operational efficiency in a distribution network occurs at the last mile 24 This final hop in a supply chain is often the most expensive one The optimization of last-mile delivery to drive down product cost is therefore a promising application for Big Data techniques Two fundamental approaches make data analysis a powerful tool for increasing last-mile efficiency In a first and somewhat evolutionary step, a massive stream of information is processed to further maximize the performance of a conventional delivery fleet This is mainly achieved by real-time optimization of delivery routes The second, more disruptive approach utilizes data processing to control an entirely new last-mile delivery model With this, the raw capacity of a huge cro wd of randomly moving people replaces the effectiveness of a highly optimized workforce 1 Real-time route optimization The traveling salesmen problem was formulated around eighty years ago, but still defines the core challenge for last-mile delivery Route optimization on the last mile aims at saving time in the delivery process Rapid processing of real-time information supports this goal in multiple ways When the delivery vehicle is loaded and unloaded, a dynamic calculation of the optimal delivery sequence based on sensor-based detection of shipment items frees the staff from manual sequencing On the road, telematics databases are tapped to automatically change delivery routes according to current traffic conditions And routing intelligence considers the availability and location information posted by recipients in order to avoid unsuccessful delivery attempts In summary, every delivery vehicle receives a continuous adaptation of the delivery sequence that takes into account geographi cal factors, environmental factors, and recipient status What makes this a Big Data problem It requires the execution of combinatorial optimization procedures fed from correlated streams of real-time events to dynamically re-route vehicles on the go As a result, each driver receives instant driving direction updates from the onboard navigation system, guiding them to the next best point of delivery DHL SmartTruck Daily optimized initial tour planning based on incoming shipment data Dynamic routing system, which recalculates the routes depending on the current order and traffic situation Cuts costs and improves CO2 efficiency, for example by reducing mileage The term last mile has its origin in telecommunications and describes the last segment in a communication network that actually reaches the 24 customer In the logistics sector, the last mile is a metaphor for the final section of a supply chain, in which goods are handed over to the recipient Source The definition of the first and l ast miles , DHL Discover Logistics, cf Big Data in Logistics 2 Crowd-based pick-up and delivery The wisdom and capacity of a crowd of people has become a strong lever for effectively solving business problems Sourcing a workforce, funding a startup, or performing networked research are just a few examples of requisitioning resources from a crowd Applied to a distribution network, a crowd-based approach may create substantial efficiency enhancements on the last mile The idea is simple Commuters, taxi drivers, or students can be paid to take over lastmile delivery on the routes that they are traveling anyway Scaling up the number of these affiliates to a large crowd of occasional carriers effectively takes load off the delivery fleet Despite the fact that crowd-based delivery has to be incentivized, it has potential to cut last-mile delivery costs, especially in rural and sparsely populated areas On the downside, a crowd-based approach also issues a vital challenge The automated control of a huge number of randomly moving delivery resources This requires extensive data processing capabilities, answered by Big Data techniques such as complex event processing and geocorrelation A real-time data stream is traced in DHL MyWays order to assign shipments to available carriers, based on their respective location and destination Interfaced through a mobile application, crowd affiliates publish their current position and accept pre-selected delivery assignments The above two use cases illustrate approaches to optimizing last-mile delivery, yet they are diametrically opposed In both cases, massive real-time information originating from sensors, external databases, and mobile devices is combined to operate delivery resources at maximum levels of efficiency And both of these Big Data applications are enabled by the pervasiveness of mobile technologies Unique crowd-based delivery for B2C parcels Flexible delivery in time and location Using existing movement of city residents 19 20 Big Data in Logistics 3 2 2 Predictive network and capacity planning Optimal utilization of resources is a key competitive advantage for logistics providers Excess capacities lower profitability which is critical for low-margin forwarding services , while capacity shortages impact service quality and put customer satisfaction at risk Logistics providers must therefore perform thorough resource planning, both at strategic and operational levels Strategic-level planning considers the long-term configuration of the distribution network, and operational-level planning scales capacities up or down on a daily or monthly basis For both perspectives, Big Data techniques improve the reliability of planning and the level of detail achieved, enabling logistics providers to perfectly match demand and available resources 3 Strategic network planning At a strategic level, the topology and capacity of the distribution network are adapted according to anticipated future demand The results from this s tage of planning usually drive investments with long requisition and amortization cycles such as investments in warehouses, distribution centers, and custom-built vehicles More precise capacity demand forecasts therefore increase efficiency and lower the risks of investing in storage and fleet capacity Big Data techniques support network planning and optimization by analyzing comprehensive historical capacity and utilization data of transit points and transportation routes In addition, these techniques consider seasonal factors and emerging freight flow trends by learning algorithms that are fed with extensive statistical series External economic information such as industry-specific and regional growth forecasts is included for more accurate prediction of specific transportation capacity demand In summary, to substantially increase predictive value, a much higher volume and variety of information is exploited by advanced regression and scenario modeling techniques The result is a new quality of planning with expanded forecast periods this effectively reduces the risk of long-term infrastructure investments and contracted external capacities It can also expose any impending over-capacity and provide this as automated feedback to accelerate sales volume This is achieved by dynamic pricing mechanisms, or by transfer of overhead capacities to spot-market trading Big Data in Logistics 4 Operational capacity planning At operational level, transit points and transportation routes must be managed efficiently on a day-to-day basis This involves capacity planning for trucks, trains, and aircraft as well as shift planning for personnel in distribution centers and warehouses Often operational planning tasks are based on historical averages or even on personal experience, which typically results in resource inefficiency Instead, using the capabilities of advanced analytics, the dynamics within and outside the distribution network are modeled and the impact on capacity requireme nts calculated in advance Real-time information about shipments items that are entering the distribution network, are in transit, and are stored is aggregated to predict the allocation of resources for the next 48 hours This data is automatically sourced from warehouse management systems and sensor data along the transportation chain In addition detection of ad-hoc changes in demand is derived from externally available customer information e g data on product releases, factory openings, or unexpected bankruptcy Additionally, local incidents are detected e g regional disease outbreaks or natural disasters as these can skew demand figures for a particular region or product This prediction of resource requirements helps Both of the above Big Data scenarios increase resource efficiency in the distribution network, but the style of data processing is different The strategic optimization combines a high data volume from a variety of sources in order to support investment and contracting deci sions, while the operational optimization continuously forecasts network flows based on real-time streams of data DHL Parcel Volume Prediction operating personnel to scale capacity up or down in each particular location But there s more to it than that A precise forecast also reveals upcoming congestions on routes or at transit points that cannot be addressed by local scaling For example, a freight aircraft that is working to capacity must leave behind any further expedited shipments at the airport of origin Simulation results give early warning of this type of congestion, enabling shipments to be reassigned to uncongested routes, mitigating the local shortfall This is an excellent example of how Big Data analytics can turn the distribution network into a self-optimizing infrastructure Analytic tool to measure influences of external factors on the expected volume of parcels Correlates external data with internal network data Results in a Big Data Prediction Model that significantly inc reases operational capacity planning Ongoing research project by DHL Solutions Innovation 21 22 Big Data in Logistics 3 3 Use Cases Customer Experience The aspect of Big Data analytics that currently attracts the most attention is acquisition of customer insight For every business, it is vitally important to learn about customer demand and satisfaction But as organizations experience increased business success, the individual customer can blur into a large and anonymous customer base Big Data analytics help to win back individual customer insight and to create targeted customer value 3 3 1 Customer value management Clearly, data from the distribution network carries significant value for the analysis and management of customer relations With the application of Big Data techniques, and enriched by public Internet mining, this data can be used to minimize customer attrition and understand customer demand 5 Customer loyalty management For most business models, the cost of winning a new cu stomer is far higher than the cost of retaining an existing customer But it is increasingly difficult to trace and analyze individual customer satisfaction because there are more and more indirect customer touch points e g portals, apps, and indirect sales channels Because of this, many businesses are failing to establish effective customer retention programs Smart use of data enables the identification of valuable customers who are on the point of leaving to join the competition Big Data analytics allow a comprehensive assessment of customer satisfaction by merging multiple extensive data sources For logistics providers, this materializes in a combined evaluation of records from customer touch points, operational data on logistics service quality, and external data How do these pieces fit together Imagine the scenario of a logistics provider noticing a customer who lowers shipment volumes despite concurrently publishing steady sales records through newswire The provider then checks de livery records, and realizes that this customer recently experienced delayed shipments Looking at the bigger picture, this information suggests an urgent need for customer retention activity To achieve this insight not just with one customer but across the entire customer base, the logistics provider must tap multiple data sources and use Big Data analytics Customer touch points include responses to sales and marketing activities, customer service inquiries, and complaint management details This digital customer trail is correlated with data from the distribution network comprising statistical series on shipping volume and received service quality levels In addition, the Internet provides useful customer insight Publicly available information from news agencies, annual reports, stock trackers, or even sentiments from social media sites enrich the logistics provider s internal perspective of each customer From this comprehensive information pool, the logistics provider can extract the a ttrition potential of every single customer by applying techniques such as semantic text analytics, natural-language processing, and pattern recognition On automatically generated triggers, the provider then initiates proactive counter-measures and customer loyalty programs Although business relationships in logistics usually relate to the sender side, loyalty management must also target the recipient side Recipients are even more affected by poor service quality, and their feedback influences sender selection for future shipments A good example of this is Internet or catalog shopping Recurring customer complaints result in the vendor considering a switch of logistics provider But to include recipients into loyalty management requires yet more data to be processed, especially in B2C markets Big Data analytics are essential, helping to produce an integrated view of customer interactions and operational performance, and ensure sender and recipient satisfaction Big Data in Logistics 6 Con tinuous service improvement and product innovation Logistics providers collect customer feedback as this provides valuable insight into service quality and customer expectations and demands This feedback is a major source of information for continuous improvement in service quality It is also important input for the ideation of new service innovations To get solid results from customer feedback evaluation, it is necessary to aggregate information from as many touch points as possible In the past, the single source of data has been ingests from CRM systems and customer surveys But today, Big Data solutions provide access to gargantuan volumes of useful data stored on public Internet sites In social networks and on 3 3 2 Suppy chain risk management discussion forums, people openly and anonymously share their service experiences But extracting by hand relevant customer feedback from the natural-language content created by billions of Internet users is like looking for that proverbial need le in a haystack The uninterrupted direct supply of materials is essential to businesses operating global production chains Lost, delayed, or damaged goods have an immediate negative impact on revenue streams Whereas logistics providers are prepared to control their own operational risk in supply chain services, an increasing number of disruptions result from major events such as civil unrest, natural disasters, or sudden economic developments 25 To anticipate supply chain disruptions and mitigate the effect of unforeseen incidents, global enterprises seek to deploy business continuity management BCM measures 26 Sophisticated Big Data techniques such as text mining and semantic analytics allow the automated retrieval of customer sentiment from huge text and audio repositories In addition, this unsolicited feedback on quality and demand can be broken down by region and time This enables identification of correlation with one-time incidents and tracking the effect of any initiated action In summary, meticulous review of the entire public Internet brings unbiased customer feedback to the logistics provider This empowers product and operational managers to design services capable of meeting customer demand This demand for improved business continuity creates an opportunity for logistics providers to expand their customer value in outsourced supply chain operations Rapid analysis of various information streams can be used to forecast events with a potentially significant or disastrous impact on customer business In response to arising critical conditions, counter-measures can be initiated early to tackle arising business risks Are you ready for anything , DHL Supply Chain Matters, 2011, cf 25 for-anything Making the right risk decisions to strengthen operations performance , PriceWaterhouseCoopers and MIT Forum for Supply Chain Innovation, 2013 26 23 24 Big Data in Logistics 7 Risk evaluation and resilience planning Contract logistics providers know their customers suppl y chains in great detail To cater for the customer need for predictive risk assessment, two things must be linked and continuously checked against each other A model describing all elements of the supply chain topology, and monitoring of the forces that affect the performance of this supply chain Data on local developments in politics, economy, nature, health, and more must be drawn from a plethora of sources e g social media, blogs, weather forecasts, news sites, stock trackers, and many other publically available sites , and then aggregated and analyzed Most of this information stream is unstructured and continuously updated, so Big Data analytics power the retrieval of input that is meaningful in the detection of supply chain risks Both semantic analytics and complex event processing techniques are required to detect patterns in this stream of interrelated information pieces 27 The customer is notified when a pattern points to a critical condition arising for one of the supply chain elements e g a tornado warning in the region where a transshipment point is located This notification includes a report on the probability and impact of this risk, and provides suitable counter-measures to mitigate potential disruption Equipped with this information, the customer can re-plan transport routes or ramp up supplies from other geographies Robust supply chains that are able to cope with unforeseen events are a vital business capability in today s rapidly changing world In addition to a resilient and flexible supply chain infrastructure, businesses need highly accurate risk detection to keep running when disaster strikes With Big Data tools and techniques, logistics providers can secure customer operations by performing predictive analytics on a global scale Coming Soon A New Supply Chain Risk Management Solution by DHL A unique consultancy and software solution that improves the resilience of your entire supply chain Designed to reduce emergency costs, maintain service leve ls, protect sales, and enable fast post-disruption recovery Protects your brand and market share, informs your inventory decisions, and creates competitive advantage The Power of Events An Introduction to Complex Event Processing in Distributed Enterprise Systems , David C Luckham, Addison-Wesley Long - 27 man, 2001 Big Data in Logistics 3 4 Use Cases New Business Models 3 4 1 B2B demand and supply chain forecast The logistics sector has long been a macroeconomic indicator, and the global transportation of goods often acts as a benchmark for future economic development The type of goods and shipped volumes indicate regional demand and supply levels The predictive value of logistics data for the global economy is constituted by existing financial indices measuring the macroeconomic impact of the logistics sector Examples are the Baltic Dry Index28, a price index for raw materials shipped, and the Dow Jones Transportation Average29, showing the economic stability of the 20 largest U S log istics providers By applying the power of Big Data analytics, logistics providers have a unique opportunity to extract detailed microeconomic insights from the flow of goods through their distribution networks They can exploit the huge digital asset that is piled up from the millions of daily shipments by capturing demand and supply figures in various geographical and industry segments 8 The result has high predictive value and this compound market intelligence is therefore a compelling service that can be offered by third parties To serve a broad range of potential customers, the generated forecasts are segmented by industry, region, and product category The primary target groups for advanced data services such as these are small and medium-sized enterprises that lack capacity to conduct their own customized market research Market intelligence for small and medium-sized enterprises The aggregation of shipment records comprising origin, destination, type of goods, quantity, and value i s an extensive source of valuable market intelligence As long as postal privacy is retained, logistics providers can refine this data in order to substantiate existing external market research With regression analysis, DHL Geovista the fine-grained information in a shipment database can significantly enhance the precision of conventional demand and supply forecasts Online geo marketing tool for SMEs to analyze business potential Provides realistic sales forecast and local competitor analysis based on a scientific model A desired location can be evaluated by using high-quality geodata Baltic Dry Index , Financial Times Lexicon, cf 28 Dow Jones Transportation Average , S P Dow Jones Indices, cf 29 25 26 Big Data in Logistics 9 Financial demand and supply chain analytics Financial analysts depend on data to generate their growth perspectives and stock ratings Sometimes analysts even perform manual checks on supply chains as the only available source to forecast sales figures or market vol umes So for ratings agencies and advisory firms in the banking and insurance sector, access to the detailed information collected from a global distribution network is particularly valuable An option for logistics providers is to create a commercial analytics platform allowing a broad range of users to slice and dice raw data according to their field of research effectively creating new revenue streams from the huge amount of information that controls logistics operations 10 In the above use cases, analytics techniques are applied to vast amounts of shipment data This illustrates how logistics providers can implement new informationdriven business models In addition, the monetization of data that already exists adds the potential of highly profitable revenue to the logistics provider s top line 3 4 2 Real-time local intelligence Information-driven business models are frequently built upon existing amounts of data, but this is not a prerequisite An established product or service can als o be extended in order to generate new information assets For logistics providers, the pickup and delivery of shipments provides a particular opportunity for a complementary new business model No other industry can provide the equivalent blanket-coverage local presence of a fleet of vehicles that is constantly on the move and geographically distributed Logistics providers can equip these vehicles with new devices with camera, sensor, and mobile connectivity miniaturization powered by the Internet of Things to collect rich sets of information on the go This unique capability enables logistics providers to offer existing and new customers a completely new set of value-added data services Address verification The verification of a customer s delivery address is a fundamental requirement for online commerce Whereas address verification is broadly available in industrialized nations, for developing countries and in remote areas the quality of address data is typically poor This is also part ly due to the lack of structured naming schemes for streets and buildings in some locations Logistics providers can use daily freight, express, and parcel delivery data to automatically verify address data to achieve, for example, optimized route planning with correct geocoding for retail, banking, and public sector entities DHL Address Management Direct match of input data with reference data Return incomplete or incorrect incoming data with validated data from database Significant increase of data quality for planning purposes route planning Big Data in Logistics 11 Environmental intelligence The accelerated growth of urban areas30 increases the importance of city planning activities and environmental monitoring By using a variety of sensors attached to delivery vehicles, logistics providers can produce rich environmental statistics Data sets may include measurements of ozone and fine dust pollution, temperature and humidity, as well as traffic density, noise, and parking spot utiliz ation along urban roads As all of this data can be collected en passant in passing , it is relatively easy for logistics providers to offer a valuable data service to authorities, environment agencies, and real-estate developers while achieving complementary revenues to subsidize, for example, the maintenance of a large delivery fleet There are numerous other local intelligence use cases exploiting the ubiquity of a large delivery fleet From road condition reports that steer plowing or road maintenance squads, to surveys on the thermal insulation of public households, logistics providers are in pole position as search engines in the physical world Innovative services that provide all kinds of data in microscopic geographical detail are equally attractive to advertising agencies, construction companies, and public bodies such as police and fire departments Big Data techniques that extract structured information from real-time footage and sensor data are now building a technical backbone for the deployment of new data-driven business models 3 5 Succcess Factors for Implementing Big Data Analytics Our discussion of Big Data analytics has been focused on the value of information assets and the way in which logistics providers can leverage data for better business performance This is a good start, as solid use cases are a fundamental requirement for adopting new information-driven business models But there needs to be more than a positive assessment of business value The following five success factors must also be in place 3 5 1 Business and IT alignment In the past, advancements in information management clearly targeted either a business problem or a technology problem While trends such as CRM strongly affected the way sales and service people work, other trends such as cloud computing have caused headaches for IT teams attempting to operate dynamic IT resources across the Internet Consequently, business units and the IT department may have different perspectives on wh ich changes are worth adopting and managing But for an organization to transform itself into an information-driven company one that uses Big Data analytics for competitive advantage both the business units and the IT department must accept and support substantial change It is therefore essential to demonstrate and align both a business case and an IT case for using Big Data including objectives, benefits, and risks To complete a Big Data implementation, there must be a mutual understanding of the challenges as well as a joint commitment of knowledge and talent According to the United Nations, by 2050 85 9 of the population in developed countries will live in urban areas Taken from Open-air computers , 30 The Economist, Oct 27, 2012 cf 27 28 Big Data in Logistics 3 5 2 Data transparency and governance Big Data use cases often build upon a smart combination of individual data sources which jointly provide new perspectives and insights But in many companies the reality is that three major challenges must be addressed to ensure successful implementation First, to locate data that is already available in the company, there must be full transparency of information assets and ownership Secondly, to prevent ambiguous data mapping, data attributes must be clearly structured and explicitly defined across multiple databases And thirdly, strong governance on data quality must be maintained The validity of mass query results is likely to be compromised unless there are effective cleansing procedures to remove incomplete, obsolete, or duplicate data records And it is of utmost importance to assure high overall data quality of individual data sources because with the boosted volume, variety, and velocity of Big Data it is more difficult to implement efficient validation and adjustment procedures 3 5 3 Data privacy In the conceptual phase of every Big Data project, it is essential to consider data protection and privacy issues Personal data is often revealed when exploiting informa tion assets, especially when attempting to gain customer insight Use cases are typically elusive in countries with strict data protection laws, yet legislation is not the only constraint Even when a use case complies with prevailing laws, the large-scale collection and exploitation of data often stirs public debate and this can subsequently damage corporate reputation and brand value or breaks reliable and meaningful insights In most industries, the required mathematical and statistical skillset is scarce In fact, a talent war is underway, as more and more companies recognize they must source missing data science skills externally Very specialized knowledge is required to deploy the right techniques for each particular data processing problem, so organizations must invest in new HR approaches in support of Big Data initiatives 3 5 5 Appropriate technology usage Many data processing problems currently hyped as Big Data challenges could, in fact, have been technically solved five years a go But back then, the required technology investment would have shattered every business case Now at a fraction of the cost, raw computing power has exponentially increased, and advanced data processing concepts are available, enabling a new dimension of performance The most prominent approaches are in-memory data storage and distributed computing frameworks However, these new concepts require adoption of entirely new technologies 3 5 4 Data science skills For IT departments to implement Big Data projects therefore requires a thorough evaluation of established and new technology components It needs to be established whether these components can support a particular use case, and whether existing investments can be scaled up for higher performance For example, in-memory databases such as the SAP HANA system are very fast but have a limited volume of data storage, while distributed computing frameworks such as the Apache Hadoop framework are able to scale out to a huge number of nodes bu t at the cost of delayed data consistency across multiple nodes A key to successful Big Data implementation is mastery of the many data analysis and manipulation techniques that turn vast raw data into valuable information The skillful application of computational mathematics makes In summary, these are the five success factors that must be in place for organizations to leverage data for better business performance Big Data is ready to be used Outlook OUTLOOK Looking ahead, there are admittedly numerous obstacles to overcome data quality, privacy, and technical feasibility, to name just a few before Big Data has pervasive influence in the logistics industry But in the long run, these obstacles are of secondary importance because, first and foremost, Big Data is driven by entrepreneurial spirit Several organizations have led the way for us Google, Amazon, Facebook, and eBay, for example, have already succeeded in turning extensive information into business Now we are beginning to see fi rst movers in the logistics sector These are the entrepreneurial logistics providers that refuse to be left behind the opportunity-oriented organizations prepared to exploit data assets in pursuit of the applications described in this trend report But apart from the leading logistics providers that implement specific Big Data opportunities, how will the entire logistics sector transform into a data-driven industry What evolution can we anticipate in a world where virtually every single shipped item is connected to the Internet We may not know all of the answers right now But this trend report has shown there is plenty of headroom for valuable Big Data innovation Joining resources, labor, and capital, it is clear that information has become the fourth production factor and essential to competitive differentiation It is time to tap the potential of Big Data to improve operational efficiency and customer experience, and create useful new business models It is time for a shift of mindset, a clear strategy and application of the right drilling techniques Over the next decade, as data assumes its rightful place as a key driver in the logistics sector, every activity within DHL is bound to get smarter, faster, and more efficient 29 FOR MORE INFORMATION About Big Data in Logistics , contact RECOMMENDED READING LOGISTICS TREND RADAR Dr Markus Kckelhaus DHL Customer Solutions Innovation Junkersring 57 53844 Troisdorf, Germany Phone 49 2241 1203 230 Mobile 49 152 5797 0580 e-mail Katrin Zeiler DHL Customer Solutions Innovation Junkersring 57 53844 Troisdorf, Germany Phone 49 2241 1203 235 Mobile 49 173 239 0335 e-mail KEY LOGISTICS TRENDS IN LIFE SCIENCES 2020 View Full Document. 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