Retailers of short life cycle products, such as [life and style goods are required to commit an order with their suppliers far ahead of their selling seasons inclusive scant demand information. Most of the time, they practice preseason two-stage ordering (instants) that provide an opportunity to modify an initial order based on updated demand forecast obtained at a later stage. The present paper utilizes expert judgment to assess potential impact(s) of contextual information acquired between two instants, in order to revise demand forecast. Additionally, the scant demand information available may not reveal the underlying demand distribution. In this context, we develop inventory models under distribution free newsvendor framework to determine optimal order quantity and weight factor considering also the revised demand forecast. The models consider bidirectional changes in demand and three cases of demand variability: a constant variance case (“CVC”), a constant coefficient of variation case (“CCVC”), and General Case (“GC”). The models developed in the first instance without constraints are subsequently extended by enforcing constraints for practical consideration such as limited storage space or maintenance of pre-defined service level. Moreover, these single-item models are extended to multi-items case to improve their practical utility. The closed form expressions are obtained for decision variables and lower bound of expected profit and their results are discussed using numerical examples. Results show economic benefits in revising the demand forecast using expert judgment and/or negative impact of constraints and/or negative role of demand variability. In addition, a case study is presented to illustrate the potential demand impact assessment and the application of the proposed models within real life circumstances.