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[{"key": "dc.contributor.advisor", "value": "Palonen, Teija", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Tarvainen, Jenna", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2019-07-02T07:34:31Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2019-07-02T07:34:31Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2019", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/64948", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Suuria m\u00e4\u00e4ri\u00e4 dataa muodostuu jatkuvasti monista eri l\u00e4hteist\u00e4 niin yrityksien sis\u00e4lt\u00e4 kuin ulkopuolelta. T\u00e4llaista nopeasti muodostuvaa ja monissa eri muodoissa olevaa valtavaa m\u00e4\u00e4r\u00e4\u00e4 dataa kutsutaan massadataksi. Yritykset voivat hy\u00f6dynt\u00e4\u00e4 massadataa analytiikan kautta tehokkaan datal\u00e4ht\u00f6isen p\u00e4\u00e4t\u00f6ksenteon mahdollistamiseksi. Yksi massadatan sovelluskohteista on toimitusketju, joka muodostuu monista yrityksist\u00e4, joiden teht\u00e4v\u00e4n\u00e4 on tuotteiden valmistaminen materiaaleista ja niiden v\u00e4litt\u00e4minen asiakkaille asti. Massadataa muodostuu monista eri l\u00e4hteist\u00e4 pitkin toimitusketjua. T\u00e4m\u00e4n kirjallisuuskatsauksena toteutetun kandidaatintutkielman tarkoituksena oli tutkia, miten massadataa voidaan hy\u00f6dynt\u00e4\u00e4 toimitusketjussa. Tutkielmassa k\u00e4siteltiin massadatan hy\u00f6dynt\u00e4misen mahdollisuuksia ja haasteita sek\u00e4 esiteltiin massadatan onnistuneen hy\u00f6dynt\u00e4misen elementtej\u00e4. Tutkielman pohjalta selvisi, ett\u00e4 massadataa voidaan hy\u00f6dynt\u00e4\u00e4 l\u00e4pi toimitusketjun tehokkaamman p\u00e4\u00e4t\u00f6ksenteon mahdollistamiseksi. T\u00e4m\u00e4 voidaan saavuttaa massadatasta analysoitujen reaaliaikaisten n\u00e4kemyksien kautta. Mahdollisuuksia massadatan hy\u00f6dynt\u00e4miselle havaittiin niin strategisella kuin operatiivisella tasolla. Tutkielman mukaan massadataa voidaan k\u00e4ytt\u00e4\u00e4 esimerkiksi tuotekehityksess\u00e4, tuotannossa, logistiikassa ja markkinoinnissa sek\u00e4 asiakasl\u00e4ht\u00f6isen toimitusketjun kehitt\u00e4misess\u00e4. Massadatan hy\u00f6dynt\u00e4misen haasteiksi puolestaan nousivat esimerkiksi johdon tuen puute, ep\u00e4selv\u00e4 tuottoprosentti ja vastahakoisuus tietyn datan jakamiseen muiden toimitusketjun j\u00e4senten v\u00e4lill\u00e4. Tutkielmasta selvisi, ett\u00e4 onnistuneen massadatan hy\u00f6dynt\u00e4misen elementtej\u00e4 ovat muun muassa massadatan hy\u00f6dynt\u00e4minen yrityksen strategiaa tukien, massadatan integrointi kaikille toimitusketjun funktioille ja massadatan hy\u00f6dynt\u00e4misen arvioiminen suorituskykymittareilla. Kaiken kaikkiaan tutkielmassa havaittiin massadatan mahdollistavan dynaamisemman informaation prosessoinnin, mik\u00e4 puolestaan tekee toimitusketjun toiminnasta ketter\u00e4mp\u00e4\u00e4, joustavampaa ja reagoivampaa.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Large amounts of data are constantly created from many different sources both inside and outside companies. This kind of rapidly generated huge data that is in many different forms is called big data. Companies can utilize big data through big data analytics to enable effective data-driven decision making. One of the applications of big data is the supply chain which consists of multiple companies that are to manufacture products from materials and deliver them to customers. Big data is generated from many different sources across the supply chain. The purpose of this bachelor\u2019s thesis, conducted as a literature review, was to study how big data can be utilized in the supply chain. In the thesis, the possibilities and challenges of utilizing big data were discussed, and the elements of successful utilization of big data were presented. On the basis of the thesis, it was found that big data can be utilized along the supply chain to enable more efficient decision making. This can be achieved through real time insights analyzed from big data. Possibilities for utilizing big data were found at both strategic and operational levels. According to the thesis, big data can be used for example in product development, production, logistics and marketing as well as to develop a customer-oriented supply chain. The challenges of utilizing big data were found to be for example the lack of management\u2019s support, unclear return on investment and unwillingness to share specific data between other supply chain members. It was found that the elements for successful utilization of big data include using big data to support the company\u2019s strategy, integrating big data to every supply chain function and evaluating the utilization of big data using key performance indicators. All in all, it was found in the thesis that big data enables more dynamic information processing that in turn makes supply chain operations more agile, flexible and responsive.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2019-07-02T07:34:31Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2019-07-02T07:34:31Z (GMT). No. of bitstreams: 0\n Previous issue date: 2019", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "35", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "massadata", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "massadata-analytiikka", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Massadatan hy\u00f6dynt\u00e4minen toimitusketjussa", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201907023534", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "601", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "big data", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "toimitusketjut", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "p\u00e4\u00e4t\u00f6ksenteko", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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