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Abstract
This study addresses the problem of stochasticity in forecasting diffusion of a new product with scarce historical data. Demand uncertainties are calibrated using a geometric Brownian motion (GBM) ...
Highlights
Nowadays, the success of innovative industries relies on how they encounter certain challenges and arrange activities in order to meet customer requirements
In the majority of studies conducted, the uncertainty in demand is calibrated by generating random data as representative of the uncertain demand in the corresponding time period. The disadvantage of this approach is in ignoring the dynamism and pattern of demand growth through the Product Life Cycle (PLC). To consider both dynamic growth and possible stochasticity in future demand, this study suggests a methodology for generating sufficient demand paths as representatives of possible demand trajectories through the PLC
The proposed method aims to address the problem stemming from unavailability of historical data in predicting future demand trends, by utilizing the spline interpolation (SI) approach
Summary
The success of innovative industries relies on how they encounter certain challenges and arrange activities in order to meet customer requirements. In such situations, accurate forecasting of product demand and its diffusion into the market are very crucial in providing sufficient amounts of required resources at the right time and the right place. Demand forecasting acts as a foundation for all supply chain planning activities (Chopra & Meindl, 2007). Knowing the product diffusion characteristics and PLC features helps firms develop appropriate contingency plans in order to respond to possible future changes in market demand. The dynamic and uncertain nature of demand through the PLC, for newly innovated products, deserves more attention
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