Machine learning’s (ML’s) unique power to approximate functions and identify non-obvious regularities in data has attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains...

Despite numerous optimization techniques proposed to address inventory record inaccuracy (IRI), there continues to be a lack of empirical appreciation of IRI, nor do existing item-level inspection policies accommodate managerial needs for allocating inspection effort to groups of items. We...

Despite its importance to OM, demand forecasting has been perceived as a “problem-solving” exercise; most empirical work in the field has focused on explanatory models but neglected prediction problems that are part of empirical science. The present study, involving one of the leading...

Lean retailing philosophy appears to be manifested in retail industry patterns showing decreasing and historically low retailer inventories. Whether these lean inventory developments translate to better retailer operational performance is still an open question. We empirically examine...

Addressing endogeneity can be a challenging task given the different sources of endogeneity and their impacts on empirical results. While premier business journals typically expect authors to rigorously address endogeneity, this expectation is relatively new to many Operations Management (OM)...

Inventory record inaccuracy (IRI) is a pervasive problem in retailing and causes non-trivial profit loss. In response to retailers' interest in identifying antecedents and consequences of IRI, we present a study that comprises multiple modeling initiatives. We first develop a dynamic simulation...