A METHODOLOGY FOR A MULTIDIMENSIONAL INTEGRAL INDEX OF DEMAND–PRODUCTION ALIGNMENT IN THE BUILDING MATERIALS INDUSTRY
DOI:
https://doi.org/10.5281/zenodo.22875295Abstract
This article develops a formalized methodology for multidimensional assessment of demand–production alignment in the building materials industry. The study specifies the indicators, normalization rules, and aggregation procedure needed to assess quantitative, assortment, spatial-logistics, temporal, and resource-capacity alignment jointly. The methodological design synthesizes established approaches to composite indicators, industrial statistics, forecast evaluation, and missing-data treatment. Five component scores—BQ, BA, BH, BT, and BR—are defined alongside asymmetric normalization of shortages and surpluses, geometric aggregation, a coverage measure (COV), data-quality statuses, and a mandatory constraint triggered by breaches of quality, safety, or environmental requirements. The resulting calculation protocol permits the Integral Matching Index (IMI) to be reported only with its component profile, normalization parameters, uncertainty interval, and data provenance record. A complete industry-level IMI is not calculated because a harmonized panel of orders, inventories, logistics, effective capacity, and resource availability is not currently available. The methodology is intended for subsequent reproducible validation using enterprise data and may be incorporated into sales and operations planning (S&OP) and strategic monitoring. Its practical interpretation focuses on identifying the component that constrains overall alignment rather than merely comparing aggregate scores.Keywords
Integral Matching Index, demand, production, building materials, alignment, composite indicator, normalization, geometric aggregation, data qualityReferences
1. OECD, European Union, Joint Research Centre. Handbook on Constructing Composite Indicators: Methodology and User Guide. Paris: OECD Publishing, 2008. DOI: https://doi.org/10.1787/9789264043466-en.
2. United Nations Statistics Division. International Recommendations for the Index of Industrial Production 2010. Statistical Papers, Series F, No. 107. New York: United Nations, 2010. URL: https://unstats.un.org/unsd/industry/Standards/.
3. Republic of Uzbekistan. Law No. ZRU-707 of 11 August 2021 “On Official Statistics.” URL: https://lex.uz/docs/- 5569619.
4. Saisana, M., Saltelli, A., & Tarantola, S. (2005). Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society: Series A, 168(2), 307–323. https://doi.org/10.1111/j.1467-985X.2005.00350.x
5. Becker, W., Saisana, M., Paruolo, P., & Vandecasteele, I. (2017). Weights and importance in composite indicators: closing the gap. Ecological Indicators, 80, 12–22. https://doi.org/10.1016/j.ecolind.2017.03.056
6. Saltelli, A., et al. (2008). Global Sensitivity Analysis: The Primer. Chichester: Wiley, 2008. DOI: https://doi. org/10.1002/9780470725184.
7. Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data. 3rd ed. Wiley, 2019. DOI: https://doi. org/10.1002/9781119482260.
8. van Buuren, S. (2018). Flexible Imputation of Missing Data. 2nd ed. CRC Press, 2018. DOI: https://doi. org/10.1201/9780429492259.
9. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
10. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice. 3rd ed. OTexts, 2021. URL: https://otexts. com/fpp3/.
11. Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70–83. https://doi.org/10.1016/j
12. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
13. National Statistics Committee of the Republic of Uzbekistan, SIAT. “Production of Portland Cement,” code 1.02.01.0062. URL: https://siat.stat.uz/data/3902/.
14. Republic of Uzbekistan. Resolution of the President No. PP-4335 of 23 May 2019. URL: https://lex.uz/docs/- 4351738.
15. Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 6th ed. Thousand Oaks: SAGE, 2023.
16. National Statistics Committee of the Republic of Uzbekistan. “Construction Work Worth UZS 313.8 Trillion Was Completed in Uzbekistan in 2025.” 22 January 2026. URL: https://stat.uz/uz/matbuot-markazi/qo-mita-yangiliklar/66447- zbekistonda-2025-jilda-313-8-trln-s-mlik-urilish-ishlari-bazharilgan-3.
17. Official Website of the President of the Republic of Uzbekistan. “Ongoing Work and Key Plans in the Building Materials Industry Reviewed.” 20 November 2025. URL: https://president.uz/oz/lists/view/8696.
18. Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141, 61–94. https://doi.org/10.1007/s11205-017-1832-9
Downloads
Published
How to Cite
Issue
Section
