Forecasting sustainable steel supply chains: a case study

Abstract

To maintain and enhance competitiveness, organizations must monitor market dynamics, conduct comprehensive analyses, and proactively make informed decisions while continually adapting their business strategies to evolving conditions. In the modern business environment, the implementation of business intelligence plays a key role in improving the decision-making processes of management within organizations. This implementation implies the utilization of processes, technologies, and tools that facilitate the transformation of data into actionable information, information into knowledge, and knowledge into strategic plans, ultimately enabling effective decision-making and management. An essential objective of business intelligence is the identification of trends, patterns, and intentions, which advance the formulation of predictive scenarios for future outcomes. Consequently, the aim of this paper is to present a comprehensive approach to quantitative time series prediction and qualitative Delphi prediction techniques. The specific focus of the study centres on forecasting supply chain dynamics, particularly the estimation of raw material consumption within organizations observing circular economy principles. Supply chain management within the circular economy framework involves detailed planning and management of all system activities with a primary goal of reducing resource utilization during production, reducing the amount of material stocks in the plant, and minimising the amount of generated waste. For this research, a specific production organization X was selected, specializing in the manufacture of bodies for motor vehicles, trailers, and semi-trailers, with steel as the predominant raw material. The choice of this organization is justified by the potential for a substantial impact on climate change mitigation through steel flow planning in the production process. Steel industry is recognized as a significant contributor to CO2 emissions, accounting for approximately 4-5 % of total emissions. By minimizing the use of the analysed raw material, circular economy objectives can be effectively realized. The study highlights substantial disparities between the actual and predicted figures for steel consumption, specifically hot-rolled steel sheets. These variations are attributed to the exceptional circumstances during the SARS-CoV-2 pandemic. Consequently, the research highlights that the historical data from 2019-2020 utilized as the foundation for this study may not be fully representative due to the fact that operations during that period were carried out under an emergency regime prompted by the SARS-CoV-2 pandemic.