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  <doi_batch_id>aspg-1-4525-1792176548</doi_batch_id>
  <timestamp>20261016184908</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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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>2027</year>
    </publication_date>
    <journal_volume>
     <volume>15</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>From Demand Signals to Retail Decisions in Business Intelligence Research</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mustafa</given_name>
      <surname>Musa</surname>
      <affiliations>
       <institution>
        <institution_name>Center of Research and Innovation Management, Universiti Teknikal Malaysia Melaka, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Raden</given_name>
      <surname>Nur Rachman Dzakiyullah</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Information System, Universitas Alma Ata, Yogyakarta, Indonesia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Retail analytics has moved beyond improving point forecasts, yet evidence about the value of a forecast for pricing, replenishment, fulfillment, and managerial adoption remains dispersed. This structured review compares 36 journal studies published from 2023 through 2025, identified within a search window ending in January 2026, across four connected areas: demand forecasting (12), pricing and commercial levers (7), inventory and fulfillment (8), and decision systems (9). Each study is examined for its information setting, analytic approach, decision connection, and evidence boundary. The synthesis identifies four recurring discontinuities: sales data can conceal unmet demand; forecast scores can misrank policies; a policy that works in a model may be infeasible in a distributed retail organization; and staff may override recommendations whose value has not been made legible. A decision-centered evaluation structure links data availability, uncertainty estimation, feasible action, adoption, and realized economic outcomes. The paper develops a research agenda around observable-demand corrections, decision-calibrated uncertainty, joint pricing and fulfillment, transportable evaluations, and human-in-the-loop deployment. The review is deliberately bounded rather than a claim of exhaustive coverage or pooled effect sizes. It offers business intelligence researchers a common basis for studying whether analytical improvements survive the operational decisions they are meant to inform.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2027</year>
    </publication_date>
    <pages>
     <first_page>27</first_page>
     <last_page>37</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4525</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/AJBOR.150105</doi>
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