The happening disasters of both natural and man-made have increased manifold during last few decades causing incalculable casualties and thus effecting hundreds of millions people around the world every year. The effects of disaster on logistics chain management of road projects during reconstruction phase have drawn an attention to the effective and efficient response to mitigate these impacts. The most significant factor of logistic management in disaster areas is uncertainties in demand and supply which affect road projects. Inadequate warehouses due to deficient data, irrational product storage, lengthy lead times, un-utilized material at site, variance between raw product, work in progress and final product and lastly imbalance inventory, economies of scale and lead time contribute towards uncertain demand and supply. This study aims at formulating an Applied Multivariate Statistical Model (multi-variant regression model) showing the relationship between supply chain performance of road project in disaster areas with the variables adding to major factor of supply chain management which is uncertain demand and supply. If the effects of above six factors are minimized, there is improvement in certainty of demand and supply. Thus use of geospatial techniques and improved demand and supply will enhance the road project performance by upgrading the supply chain management.

Keywords: Supply Chain Performance, Demand and Supply, Disasters, Lead Time, Economies of Scale, Multivariate Analysis