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ANALYSIS OF PT SEMEN INDONESIA'S SUPPLY CHAIN USING PRODUCTION ORDER QUANTITY AND FORECASTING MODELS FOR ORDER OPTIMIZATION.

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Title: ANALYSIS OF PT SEMEN INDONESIA'S SUPPLY CHAIN USING PRODUCTION ORDER QUANTITY AND FORECASTING MODELS FOR ORDER OPTIMIZATION.
Authors: MAULANA, Yudi1,2, MANSHOOR, Bukhari1 bukhari@uthm.edu.my, ZAINUDDIN, Mairizal2, NURMUTIA, Syahreen2
Source: Academic Journal of Manufacturing Engineering. 2026, Vol. 24 Issue 1, p32-39. 8p.
Subjects: Production quantity, Forecasting, Cement industries, Industrial efficiency, Supply chain management, Cost functions, Inventory control, Inventories
Geographic Terms: Indonesia
Abstract: This study investigates the integration of quantitative inventory management techniques--Mean Absolute Deviation (MAD), Economic Order Quantity (EOQ), and Production Order Quantity (POQ)--within the production operations of PT Semen Merah Putih Indonesia, a major cement manufacturer. Analysis of clinker production data from 2021 to 2023 identified a substantial decline in output in 2023, attributed to an increase in kiln stoppage days (82 days), which adversely affected production continuity and efficiency. The MAD method quantified significant deviations between forecasted and actual demand, indicating inadequate forecasting accuracy. EOQ modelling yielded an optimal order quantity of 740,548.45 tons, effectively minimizing total inventory costs through a balance of ordering and holding costs. The POQ model recommended a 24-day production cycle, providing a more consistent and cost-effective scheduling approach aligned with production capacity and demand variability. The results highlight the operational benefits of integrating statistical forecasting with inventory control models in large-scale manufacturing environments. The combined application of EOQ and POQ supports both cost reduction and production stability, enhancing the responsiveness of the supply chain. Future research should focus on dynamic EOQ adaptations under fluctuating demand, integration with Just-In-Time (JIT) methodologies, and the inclusion of sustainability parameters--such as energy consumption and waste reduction--into inventory optimization frameworks. [ABSTRACT FROM AUTHOR]
Copyright of Academic Journal of Manufacturing Engineering is the property of Academic Association for Manufacturing Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: This study investigates the integration of quantitative inventory management techniques--Mean Absolute Deviation (MAD), Economic Order Quantity (EOQ), and Production Order Quantity (POQ)--within the production operations of PT Semen Merah Putih Indonesia, a major cement manufacturer. Analysis of clinker production data from 2021 to 2023 identified a substantial decline in output in 2023, attributed to an increase in kiln stoppage days (82 days), which adversely affected production continuity and efficiency. The MAD method quantified significant deviations between forecasted and actual demand, indicating inadequate forecasting accuracy. EOQ modelling yielded an optimal order quantity of 740,548.45 tons, effectively minimizing total inventory costs through a balance of ordering and holding costs. The POQ model recommended a 24-day production cycle, providing a more consistent and cost-effective scheduling approach aligned with production capacity and demand variability. The results highlight the operational benefits of integrating statistical forecasting with inventory control models in large-scale manufacturing environments. The combined application of EOQ and POQ supports both cost reduction and production stability, enhancing the responsiveness of the supply chain. Future research should focus on dynamic EOQ adaptations under fluctuating demand, integration with Just-In-Time (JIT) methodologies, and the inclusion of sustainability parameters--such as energy consumption and waste reduction--into inventory optimization frameworks. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Academic Journal of Manufacturing Engineering is the property of Academic Association for Manufacturing Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.5281/zenodo.19217310
    Languages:
      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 32
    Subjects:
      – SubjectFull: Production quantity
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Cement industries
        Type: general
      – SubjectFull: Industrial efficiency
        Type: general
      – SubjectFull: Supply chain management
        Type: general
      – SubjectFull: Cost functions
        Type: general
      – SubjectFull: Inventory control
        Type: general
      – SubjectFull: Inventories
        Type: general
      – SubjectFull: Indonesia
        Type: general
    Titles:
      – TitleFull: ANALYSIS OF PT SEMEN INDONESIA'S SUPPLY CHAIN USING PRODUCTION ORDER QUANTITY AND FORECASTING MODELS FOR ORDER OPTIMIZATION.
        Type: main
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          Name:
            NameFull: MAULANA, Yudi
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            NameFull: MANSHOOR, Bukhari
      – PersonEntity:
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            NameFull: ZAINUDDIN, Mairizal
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            NameFull: NURMUTIA, Syahreen
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            – D: 01
              M: 01
              Text: 2026
              Type: published
              Y: 2026
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              Value: 24
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            – TitleFull: Academic Journal of Manufacturing Engineering
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