| fullrecord |
[{"key": "dc.contributor.advisor", "value": "Karvanen, Juha", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Luomala, Oskari", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2018-08-02T06:24:41Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2018-08-02T06:24:41Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2018", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/59075", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Kysynn\u00e4n ennustaminen on tyypillinen ongelma teollisuusyrityksill\u00e4. Ennusteiden laatimisessa usein hy\u00f6dynnet\u00e4\u00e4n tilaushistoriaa tai jotain muuta aineistoa. Laadituilla ennusteilla on vaikutusta koko yrityksen toimintaan ja p\u00e4\u00e4t\u00f6ksentekoon. Aina ei kuitenkaan ole riitt\u00e4v\u00e4\u00e4 aineistoa saatavilla, joten tarvitaan muita keinoja ennusteiden tekemiseen. Yrityksen henkil\u00f6st\u00f6\u00e4 voidaan t\u00e4m\u00e4nkaltaisessa tilanteessa hy\u00f6dynt\u00e4\u00e4, sill\u00e4 heill\u00e4 on kokemusta ja tietoa tilauksiin liittyvist\u00e4 tekij\u00f6ist\u00e4. Ennusteisiin tarvittava tieto voidaan hankkia yrityksen henkil\u00f6st\u00f6lt\u00e4 asiantuntijahaastatteluin. Tiedon hankkiminen edellytt\u00e4\u00e4 tilastotieteen, todenn\u00e4k\u00f6isyysjakaumien ja haastattelutekniikoiden yhdist\u00e4mist\u00e4. Ennustejakauman mallintamiseksi tarvitaan joitain tunnuslukuja jakaumasta. Kvartiilien m\u00e4\u00e4ritt\u00e4minen puolitusmenetelm\u00e4ll\u00e4 on luotettavaksi todettu menetelm\u00e4 tunnuslukujen m\u00e4\u00e4ritt\u00e4miseen. Ennustejakauman sovittaminen haastattelun aikana edesauttaa kommunikointia ja helpottaa jakauman sopivuuden m\u00e4\u00e4ritt\u00e4mist\u00e4. T\u00e4ss\u00e4 tutkielmassa esitell\u00e4\u00e4n interaktiivinen haastatteluty\u00f6kalu, joka sovittaa jakauman eksaktisti asiantuntijan m\u00e4\u00e4ritt\u00e4miin kvantiileihin. Jakauman sovittamisessa ja hienos\u00e4\u00e4d\u00f6ss\u00e4 hy\u00f6dynnet\u00e4\u00e4n polynomisia kvantiilisekoituksia ja L-momentteja. Ty\u00f6kalu mahdollistaa jakauman tarkastelun numeerisesti ja visuaalisesti. Tulokset tallennetaan lokitietoina, joista haastatteluiden tulokset voidaan johtaa. Ty\u00f6kalun avulla mallinnettiin jyv\u00e4skyl\u00e4l\u00e4isen teollisuusyrityksen Black Bruin Oy:n tuotteille tilausten ennustejakaumat vuoden 2018 ensimm\u00e4isess\u00e4 kvartaalissa. Yrityksest\u00e4 valikoitiin asiantuntijoita, joille asiantuntijahaastattelu tehtiin. He saivat ennakkotietoina aineistoa aikaisemmista tilauksista ja t\u00e4yden ohjeistuksen haastattelun kulusta ja huomioitavista seikoista. Kohteena oli muutama esimerkkituote, jotka kattavat suuren osan yrityksen liikevaihdosta. Asiantuntijoiden suoriutumista tutkittiin kollektiivisesti ja vertaillen tuotteita ja asiantuntijoita kesken\u00e4\u00e4n. Ennustejakaumia verrataan toteutuneisiin tilausm\u00e4\u00e4riin. Tulokset osoittivat, etteiv\u00e4t asiantuntijat suoriudu teht\u00e4v\u00e4st\u00e4 kovin hyvin yksin\u00e4\u00e4n. Yhdistetyt ennustejakaumat osoittivat, ett\u00e4 kollektiivisesti ennusteet osuivat melko hyvin kohdalleen. Asiantuntijahaastattelu osoittautui toimivaksi tavaksi tuottaa ennusteita tilauksille aineiston hy\u00f6dynnett\u00e4vyyden ollessa v\u00e4h\u00e4ist\u00e4. Menetelm\u00e4n kehitt\u00e4minen yleisk\u00e4ytt\u00f6isemm\u00e4ksi edellytt\u00e4\u00e4 pieni\u00e4 parannuksia haastattelumenetelm\u00e4\u00e4n ja esiteltyyn ty\u00f6kaluun. Tuloksien avulla Black Bruin pystyy kehitt\u00e4m\u00e4\u00e4n tilausten ja kysynn\u00e4n ennustamista. Yritys sai lis\u00e4\u00e4 tietoa tilaushistorian hy\u00f6dynnett\u00e4vyydest\u00e4 ja henkil\u00f6st\u00f6n tiet\u00e4myksest\u00e4 tilausten ennustamisessa.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Forecasting incoming orders is a typical problem in the industry. The estimates of future orders affect the operations and the precision has an impact on finance. When relevant data for predictions cannot be found other approaches are needed. Employees hold relevant information regarding orders. Summarizing this information as a distribution requires statistical methods combined with elicitation techniques. Common and reliable method is to elicitate the quantiles of a distribution using the bisection method. Fitting the distribution during the interview helps in giving feedback and deciding whether the distribution is acceptable. In this thesis, I present an interactive visualization tool which fits a distribution exactly to the given quantiles. The fitting method uses polynomial quantile mixtures and L-skewness and L-kurtosis as fine tuning parameters. The tool also makes it possible to inspect the fitted distribution numerically and visually. The results are saved as a log file which consists of all the operations made by the user. The objective of this study was to predict the number of ordered products from an industrial company during the first quarter of 2018. Multiple experts from the company were elicitated separately. The experts were given the data on the previous orders and full guidance on things to consider in the interview. The elicitation was performed for a few example products which cover a large portion of the total orders. The performance was compared between the experts and between the products. Predicted orders were compared with the actual orders. The results show that none of the experts excelled in the order prediction. Collectively the experts were able to predict the orders fairly well. The methods and the visualization tool used were found suitable for elicitation and for forecasting orders. Improvements would be possible in the briefing of the experts and also in the elicitation tool. The company can improve prediction of the incoming orders by utilizing its data and the knowledge of its employees.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2018-08-02T06:24:41Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2018-08-02T06:24:41Z (GMT). No. of bitstreams: 0\n Previous issue date: 2018", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "59", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "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": "kysynn\u00e4n ennustaminen", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "asiantuntijahaastattelu", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "haastatteluty\u00f6kalu", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "L-momentit", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "kvantiilisekoitukset", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "todenn\u00e4k\u00f6isyysjakaumien yhdist\u00e4minen", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Teollisuusyrityksen tuotteiden kysynn\u00e4n ennustaminen asiantuntijahaastatteluiden avulla", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201808023711", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Matemaattis-luonnontieteellinen tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Sciences", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Matematiikan ja tilastotieteen laitos", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Department of Mathematics and Statistics", "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": "Tilastotiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Statistics", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.collaborator", "value": "business", "language": "", "element": "contractresearch", "qualifier": "collaborator", "schema": "yvv"}, {"key": "yvv.contractresearch.funding", "value": "1200", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "yvv.contractresearch.initiative", "value": "student", "language": "", "element": "contractresearch", "qualifier": "initiative", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "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": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "4043", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "todenn\u00e4k\u00f6isyyslaskenta", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "haastattelut", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "kysynt\u00e4", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "ennusteet", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "jakaumat", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.type.okm", "value": "G2", "language": null, "element": "type", "qualifier": "okm", "schema": "dc"}]
|