Optimization of Supply Chain Efficiency in Multi Criteria Decision Environment Using AHP Model

Inventory optimization is a key element in the global supply chain. Optimization can be achieved only through trail and error method from time to time in a dynamic environment. Inventory Optimization is can focus on the inventory positions based on customized requirements of the clients for independent products. It helps to gain a substantial market share by way of consistently satisfying the requirements of the clients.” Powerful optimization algorithms determine service levels and inventory targets for each product location leveraging demand forecast data, sales history, manufacturing and distribution assets, and transportation networks to consider the total landed cost of inventory – including transportation expenses, handling charges and holding costs – which can change dramatically and swiftly in today’s volatile environment. Time-phased execution accounts for demand trends and seasonality effects. Additionally, the solution also considers existing multi-environment network complexity, lead times, costs and constraints, as well as demand and supply variability. This Paper presents the different models with the empirical data to make decisions about supply chain organization using AHP model and highlights key factors to optimize the supply chains. The centralized organization model identifies process control , decentralized organization model indicates the need for time saving, centre led organization model and organization model fits with corporate strategy suggests the need for cost benefits, and finally, governance structure elevates the supply chain function emphasizes the need for time saving as primary factors for the optimization of supply chains in the market. The factors are identified through administration of AHP model on the real time data observed in the supply chain firm. Supply chain optimization is a regular function in a dynamic market and the success depends on the suitability of the model selected and degree of optimization administered in the supply chain function.