[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117867-en":3,"doc-seo-117867-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117867,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Exploring Machine Learning to Improve Procurement and Purchasing Processes","Machine learning enables systems to improve performance by learning patterns and correlations from data without task-specific manual programming. The study reviews practical opportunities and potential challenges of applying machine learning to procurement and purchasing, motivated by growing commercial adoption and expanding research. It combines a theoretical framework on machine learning and procurement/purchasing sub-areas with interviews at one company involving eight participants. Results highlight value in pricing support, cost analysis, and material requirement forecasting, while key challenges stem from data quality, volume, and traceability, as well as limited automation and analysis-related difficulties.","Matti Vahermo  \nExploring Machine Learning to Improve Procurement and Purchasing Processes  \nSchool of Technology and Innovation Master of Science in Economics and Business Administration Information systems  \n\n| Vaasan Yliopisto\u003Cbr>School of Technology and Innovation\u003Cbr>Tekijä: Matti Vahermo\u003Cbr>Tutkielman nimi: Exploring Machine Learning to Improve Procurement and Purchas\u003Cbr>ing Processes\u003Cbr>Tutkinto: Kauppatieteiden maisteri\u003Cbr>Oppiaine: Information systems\u003Cbr>Työn ohjaaja: Tomi Pasanen\u003Cbr>Valmistumisvuosi: 2023 Sivumäärä: 85 |\n| --- |\n| TIIVISTELMÄ:\u003Cbr>Koneoppimisella tarkoitetaan tekoälyn osa-aluetta, joka mahdollistaa järjestelmien suorituskyvyn parantamisen oppimalla datasta ilman, että sitä on tarkoituksenmukaisestiohjelmoitu kyseisestä tehtävää varten. Oppiminen tapahtuu kouluttamalla algoritmeja tunnistamaan suurista tietomääristä korrelaatioita ja malleja, joiden perusteella pystytäänluoman ennusteita sekä tekemään johtopäätöksiä . Koneoppiminen on kasvattanut viime vuosina suosiotaan erilaisten kaupallisten sovelluskohteiden muodossa, eikä myöskään hankinta- ja ostoprosessit ole tässä asissa poikkeus. Jatkuvasti parantuva tietokoneiden laskentakyky ja tiedonhallinta ovat mahdollistaneet entistä kehittyneempiä koneoppimista hyödyntäviä sovelluksia, mikä on myös laajentanut koneoppimisen ympärillä tapahtuvaatutkimustyötä . Kuitenkin kun tarkastellaan tutkimuksia, joissa käsitellään hankinta - jaostoprosessien kehittämistä koneoppimisen avulla on julkaisumäärä rajallista, erityisestisellaisten tutkimusten osalta, joissa on hyödynnetty kokemusperäistä, haastatteluista saatua tietoa.\u003Cbr>Täten tämän työn tavoitteena oli tarjota ajankohtainen katsaus koneoppimista hyödyntäviensovellusten tarjoamista mahdollisuuksista ja potentiaalisista haasteista niitä hankinta ja ostoprosseihin käyttöönotettaessa. Lisäksi suoritettiin haastatteluita, joiden tavoitteena oli saadaselville syyt, jotka ovat esteinä tehokkaan hankinnan-ja ostoprosessien tapahtumiselle sekä mihin työtehtäviin haastateltavat toivoisivat erityisesti apua tietojärjestelmien kautta ja voisikokoneoppiminen tarjota apua koettuihin ongelmiin. Työn tutkimusmetodit olivat teoreettisia sekä empiirisiä . Teoreettinen osio koostui koneoppimisen sekä hankinnta-ja ostoprosessien eri osa-aluiden esittelystä, joiden lähdemateriaalina hyödynnettin saatavilla olevia akateemisia sekä koneoppimisen ja hankinnan ja oston alojen julkaisuja. Lähdemateriaali pyrittiin pitämäänmahdollisimman ajankohtaisena. Haastattelut suoritettiin yhden yrityksen kanssa, johon ottiosaa kahdeksan henkilöä .\u003Cbr>Tutkimuksen tulosten perusteella koneoppimisen sovellukset, jotka auttavat hinnoittelussa, kustannusten analysoinnissa sekä materiaalitarpeiden ennustamisessa nähtiin erityisen hyödyllisinä. Haasteina koettin ongelmat, jotka johtuivat erityisesti käytetyn datan heikostalaadusta, datan suuresta määrästä sekä datan jäljitettävyydestä . Haastatteluiden perusteella haasteina koettiin prosessien vähäinen automatisointi, datan luotettavuussongelmat, materiaalitarpeiden ennustaminen sekä materiaalien hinnoitteluun ja analysointiin liittyväthaasteet. Tuloksista voitiin tulkita, että hankinnan ja oston prosesseja voidaan kehittää koneoppimisen avulla, ja empiirinen tutkimusosio myös tukee tätä johtopäätöstä . |\n\nAvainsanat: Koneoppiminen; hankinta; ostaminen; algoritmi; koneoppimismallit.  \n\n| UNIVERSITY OF VAASA\u003Cbr>School of Technology and Innovation\u003Cbr>Author: Matti Vahermo\u003Cbr>Title of the Thesis: Exploring Machine Learning to Improve Procurement and Purchas\u003Cbr>ing Processes\u003Cbr>Degree: Master of Science in Economics and Business Administration\u003Cbr>Discipline: Information systems\u003Cbr>Supervisor: Tomi Pasanen\u003Cbr>Year: 2023 Pages: 85 |\n| --- |\n| ABSTRACT:\u003Cbr>Machine learning is an area of artificial intelligence that enables systems to improve their performance by learning from data without being purposefully programmed for the task. Learning occurs by training algorithms to identify correlations and patte","cbCaifYnBnsi7vcQ","https://ap.wps.com/l/cbCaifYnBnsi7vcQ","pdf",1470432,1,85,"English","en",105,"# Abstract\n## Research aim and scope\n## Methods: theoretical and interviews\n## Key findings and challenges","[{\"question\":\"What was the main aim of the research?\",\"answer\":\"To provide an up-to-date overview of opportunities and challenges in implementing machine learning in procurement and purchasing processes, supported by interview insights on perceived barriers and desired assistance from information systems.\"},{\"question\":\"What research methods were used?\",\"answer\":\"The thesis used both theoretical and empirical methods: a theory-based section covering machine learning and procurement/purchasing sub-areas, plus interviews conducted with one company involving eight participants.\"},{\"question\":\"Which machine learning applications were considered most useful?\",\"answer\":\"Applications supporting pricing, cost analysis, and forecasting material requirements were viewed as especially beneficial.\"},{\"question\":\"What challenges emerged from the results?\",\"answer\":\"Challenges were linked to poor data quality, large data volume, and data traceability, alongside perceived limitations such as low automation and difficulties in materials pricing and analysis.\"}]","Exploring Machine Learning to Improve Procurement and Purchasing Processes | 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