From Prediction to Strategy: Transforming Demand Management in Emerging Markets

Mauro Rodríguez-Marín (), Miguel Ángel Montoya Bayardo () and Evodio Kaltenecker ()
Additional contact information
Mauro Rodríguez-Marín: Tecnologico de Monterrey
Miguel Ángel Montoya Bayardo: Tecnologico de Monterrey
Evodio Kaltenecker: Northeastern University

Chapter 15 in The Expanding Horizons of Business and Management, Volume I, 2026, pp 345-367 from Palgrave Macmillan

Abstract: Abstract This chapter examines the strategic evolution of demand management in emerging markets across Europe and Latin America. Historically perceived as a basic forecasting tool, demand management has evolved into a critical strategic function that drives competitiveness, resilience, and operational excellence (Basavaraju & Valilai, 2025). As economies matured, integrated approaches became essential to improving inventory performance, forecast accuracy, and customer service. Strategic Demand Management (SDM) enables firms to optimize resources, enhance efficiency, and sustain long-term growth, particularly in Europe’s emerging economies (Shvidanenko et al., 2017). Countries such as Poland, Hungary, Romania, Serbia, and Bulgaria face challenges arising from rapid transformation, institutional change, and shifting consumer patterns. The specific constraints of emerging markets—limited infrastructure, regulatory volatility, and scarce resources—demand a sophisticated SDM framework incorporating predictive analytics, artificial intelligence, and localized inventory strategies to strengthen supply chain resilience (Lawal & Isiyaku, 2025). Persistent adoption gaps across Latin America highlight the need for deeper research and broader implementation to promote sustainable economic growth. The study contributes to the theoretical development of SDM by reframing it as a bridge between forecasting and strategy, emphasizing data-driven decision-making, digital transformation, and contextual adaptation as essential pillars for achieving competitiveness and sustainable performance in emerging economies.

Keywords: artificial intelligence; complex supply chains; critical process; data-driven; efficiency; emerging economies; emerging markets; forecast accuracy; forecasting; inventory levels; Latin America; Strategic Demand Management (SDM); strategic indicators (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:pscchp:978-3-032-26496-1_15

Ordering information: This item can be ordered from
http://www.palgrave.com/9783032264961

DOI: 10.1007/978-3-032-26496-1_15

Access Statistics for this chapter

More chapters in Palgrave Studies in Cross-disciplinary Business Research, In Association with EuroMed Academy of Business from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().