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Studie av artificiell intelligens för ökad resurseffektivitet inom produktionsplanering: En studie med fokus på hur nuvarande samt potentiella implementeringar av artificiell intelligens inom produktionsplanering kan öka resurseffektiviteten hos ett tillverkande företag
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
2021 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
A study on artificial intelligence for increased resource efficiency in production planning : A study focusing on how current and potential implementations of artificial intelligence in production planning can increase the resource efficiency of a manufacturing company (English)
Abstract [sv]

Industri 4.0 har medfört stora förändringar och med denna våg av förändringar har artificiell intelligens tillkommit. AI är inget nytt och har forskats på utvecklats sedan den första datorn uppfanns. Tanken var då enligt Alan Turing fadern av datalogi att om en maskin inte kan särskiljas från en människa då är det en AI. Sedan dess har vi sett flera AI modeller slå människan i olika fält och sett AI teknologiers förmåga. Att AI ska implementeras inom den mest innovativa branschen var inte långtsökt. Industriell AI är till skillnad från vanliga AI modeller en kontrollerad process som hittills tillämpats inom begränsade områden. Eftersom standardisering och systematisk tillvägagångsätt kan likställas som synonymer till industriella verksamheter. Är det ingen skillnad på processer inom fabriker, och AI teknologier måste anpassas efter dessa processer. Det har under det senaste decenniet globalt investerats i innovation inom industrier. Länder världen över vill att deras industrier med Industri 4.0 hamnar i framkanten. Där Tyskland introducerade Industri 4.0, USA Smart Manufacturing Leadership Coalition, Kina deras plan kallad China 2025 och EU tillkännagett Factories for the future. Som en konsekvens av dessa enorma satsningar har denna studie som mål att se hur AI kan hjälpa tillverkande företag öka resurseffektiviteten inom produktionsplanering. Eftersom forskningsområdet är relativt nytt kommer studien basera resultaten på fallstudier där ABB och Scania intervjuas. Dock behöver detta område mer forskning.

Abstract [en]

The global introduction of Industry 4.0 has brought with it changes within industry. The indirect consequence of Industry 4.0 being artificial intelligence. The idea of AI is as old as the invention of computers with Alan Turing the father of computer science stating the first description of AI. His thought was that if a machine could be mistaken for a human then the machine was intelligent. The thought being that machine never could outperform humans back then. Now in modern times we have witnessed great feats made by intelligent algorithms where they outperform humans in various fields. For AI to be implemented in industry the most innovative buisness it has to adapt to the workings of indutrial processes. Systematic approach and standardization being two values that strongly represents industries. During the last decade global initiative and investment in innovation of industry. Has led to global competitors such as Germany creating Industry 4.0, The United States creating Smart Manufacturing Leadership Coalition, China introducing their plan called China 2025 and EU with Factories for the future. This paper is a reaction of these enormous investments made into Industry 4.0. The objective of this paper is to examine how AI can help manufacturing enterprises increase their resource efficiency within production planning. Since this field of science stillbeing in its infancy this paper will base its result on interviews made with companies as ABB and Scania. However this field needs more work.

Place, publisher, year, edition, pages
2021. , p. 30
Series
TRITA-ITM-EX ; 2021:198
Keywords [en]
Production planning, Machine learning, Industrial artificial intelligence, Shop floor scheduling
Keywords [sv]
Produktionsplanering, Maskininlärning, Fabriksplanering, Industriell artificiell intelligens, smarta fabriker
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-299735OAI: oai:DiVA.org:kth-299735DiVA, id: diva2:1585113
Subject / course
Production Engineering
Educational program
Master of Science in Engineering
Presentation
2021-06-14, 00:00
Supervisors
Examiners
Available from: 2021-08-16 Created: 2021-08-16 Last updated: 2022-06-25Bibliographically approved

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