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  <doi_batch_id>aspg-1-1696-1791417743</doi_batch_id>
  <timestamp>20261008000223</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>American Journal of Business and Operations Research</full_title>
    <abbrev_title>AJBOR</abbrev_title>
    <issn media_type="print">2770-0216</issn>
    <issn media_type="electronic">2692-2967</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2021</year>
    </publication_date>
    <journal_volume>
     <volume>5</volume>
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    <issue>1</issue>
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   <journal_article publication_type="full_text">
    <titles>
     <title>Optimizing Business Intelligence and Operations Research for Sustainable Growth: A Comparative Study of Manufacturing and Service Industries</title>
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    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mahmoud M.</given_name>
      <surname>Ibrahim</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mahmoud M.</given_name>
      <surname>Ismail</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Shereen</given_name>
      <surname>Zaki</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of computers and Informatics, Zagazig University, Zagazig, 44519, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This paper presents a comparative study of two optimization techniques, business intelligence (BI) and operations research (OR), for achieving sustainable growth in manufacturing and service industries. The study explores the strengths and weaknesses of both techniques and examines their suitability for addressing sustainability challenges in these industries. The paper also discusses various factors that influence the choice of optimization technique and presents a framework for selecting the most appropriate technique based on the problem domain, data availability, and organizational requirements. The study concludes that both BI and OR have significant potential for improving sustainability in manufacturing and service industries, and their effectiveness depends on the problem domain and organizational context. The paper provides valuable insights for researchers and practitioners interested in leveraging optimization techniques for sustainable growth.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2021</year>
    </publication_date>
    <pages>
     <first_page>61</first_page>
     <last_page>71</last_page>
    </pages>
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     <item_number item_number_type="article-number">1696</item_number>
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     <doi>10.54216/AJBOR.050104</doi>
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