TEXT-DERIVED MINING SUPPLY-CHAIN RISK FOR RISK-AWARE DEMAND FORECASTING IN ZIMBABWE’S COAL AND MANUFACTURING VALUE CHAINS: A RESEARCH PROTOCOL
Department of Industrial and Manufacturing Engineering, Faculty of Engineering, National University of Science and Technology (NUST), Bulawayo, Zimbabwe.
* Corresponding Author
ORCID Details
Antony G. Foya: https://orcid.org/0009-0003-5215-8504
Destine Mashava: https://orcid.org/0009-0002-9487-9038
Takudzwa M. Muhla: https://orcid.org/0000-0002-6212-8099
Innocent Mapindu: https://orcid.org/0009-0007-2024-1919
Research Article
Open Access Research Journal of Science and Technology, 2026, 18(02), 001–016.
Article DOI: 10.53022/oarjst.2026.18.2.0102
Publication history:
Received on 27 September 2026; revised on 03 November 2026; accepted on 05 November 2026
Abstract:
Industrial value chains built around coal in Zimbabwe, spanning mining, power, metallurgical processing and manufacturing, face interacting disruptions: electricity interruptions, transport constraints, foreign-exchange shortages, regulatory change, climate stress, equipment failure and commodity-price volatility. These disruptions usually appear first as text, in news reports, utility notices, statutory instruments and corporate announcements, and only later in production or sales records. This paper specifies a reproducible research protocol that converts Zimbabwe-focused text into supply-chain risk indices and embeds them in demand and volatility forecasts. Latent Dirichlet Allocation (LDA) identifies persistent structural themes and BERTopic identifies emerging, context-dependent ones. Expert-validated topic probabilities are aggregated into lagged weekly or monthly risk indices that enter the mean and variance equations of ARIMAX-GARCH-X models. The protocol sets out an eight-family risk taxonomy; a multi-case portfolio described by process, product and sector, never by name; information-availability and leakage controls; a nested model ladder; six hypotheses stated in advance with matched statistical tests; human-in-the-loop decision rules; and minimum reporting standards. No empirical forecast improvement is claimed, because the firm-level and text-corpus data have not yet been assembled. The contribution is a testable and auditable design for data-constrained emerging-market settings, intended to support procurement, inventory, production scheduling, logistics, maintenance and industrial-policy planning.
Keywords:
Supply-Chain Risk; Demand Forecasting; Topic Modelling; LDA; BERTopic; ARIMAX-GARCH-X; Coal Value Chain; Manufacturing; Zimbabwe; Industrial Resilience
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