H. Niles Perera

This research addresses the inefficiencies in traditional forecasting methods for the intermittent and erratic demand for aircraft components, which typically results to either high inventory costs or expensive aircraft-on-ground situations from stockouts. The main objective is to build an enhanced forecasting model that uses installed base information to improve demand forecasting accuracy for aircraft components. An extensive literature review followed by a Delphi method study is used to identify the significant contextual factors impacting the demand and integrated into an Artificial Neural Network forecasting model. The model's performance is evaluated using Mean Squared Error and benchmarked against the Croston method. This research provides efficient forecasting practices to the airline industry, aiming to reduce operating expenses and improve maintenance as well as service quality.

Recent global disruptions, including the COVID-19 pandemic, geopolitical conflicts, and volatile trade policies, have intensified scholarly and managerial discussions on promoting supply chain resilience. Existing literature has been dominated by two perspectives: the Stability-Based View (SBV), which emphasizes rapid recovery from disruptions, and the Adaptation-Based View (ABV), which focuses on adaptive continuity under uncertainty. While these views provide valuable operational insights, they underplay resilience’s potential as a source of strategic value creation. This conceptual paper introduces the Value-Based View (VBV) as a third and complementary perspective. VBV reframes resilience as a strategic capability that generates long-term benefits such as competitive advantage, stakeholder trust, ESG performance, and resilience dividends. Drawing on the Dynamic Capabilities Theory, and Stakeholder Theory, the paper develops a triadic framework linking SBV, ABV, and VBV, illustrating their interdependencies and progression from reactive to proactive to strategic resilience. The study proposes theoretical propositions to guide future empirical research and offers managerial implications for reframing resilience from a cost-driven necessity to a value-generating strategic investment. By integrating SBV, ABV, and VBV, the framework advances resilience theory and provides a more holistic foundation for managing supply chains in environments characterized by volatility-as-normal.

Sales promotions pose challenges to retail operations by causing sudden fluctuations in demand, not only during the promotional period but also across the entire sales promotional life cycle. Previous research has predominantly focused on promotional and nonpromotional periods, often overlooking the postpromotional phase, where demand decreases due to consumer stockpiling during promotions. To address this research gap, we investigate both traditional statistical forecasting methods and contemporary approaches, such as global models, implemented using gradient boosting and deep learning techniques. We assess their performance throughout the entire demand life cycle. We employ the base‐lift approach as our benchmark model, commonly used in the retail sector. Our study results confirm that machine learning methods effectively manage demand volatility induced by retail promotions while enhancing forecast accuracy across the demand life cycle. The base‐lift model performs comparably to alternative machine learning methods, albeit with the additional effort required for data cleansing. Our proposed forecasting framework possesses the capability to automate the retail forecasting process in the presence of sales promotions, facilitating efficient retail planning. Thus, this research introduces a novel demand forecasting framework that considers the complete demand life cycle for generating forecasts, and we rigorously evaluate it using real‐world data.