Adaptive Stochastic Approach for Evaluating Service Level Using a (SIM-ESL) Module Developed for Optimizing Product Availability and Order Accuracy

Abstract Inventory decisions are both high risk and high benefit throughout supply chain. Inventory management plays a crucial role since it assists manufacturing firms enhance their efficiency and competitiveness. Service level is a basic performance indicator of shortage inventory when an order is placed. Precise forecasts and optimum level of inventory can decrease the stock-outs and enhance the inventory service level. Finished goods are most common form of inventories have direct related with customer. Thus, the importance of specifying the suitable level of finished goods inventory assets for right planning and control of factory operations and optimization of the overall process to optimize production cost and time and consequently to improve service level. So, inventory management system supported by real time is requisite to ensure consistently both product availability and order accuracy. In this study, adaptive stochastic inventory management approach for evaluation service level was modelled which addressed inventory policies in order to optimize service level as a realistic value. This approach developed a Stochastic Inventory Management for Evaluating Service Level (SIMESL) module basically formulated Computational Intelligent (CI) in such a way that evaluated Availability Of Product (AOP) and Accuracy Of Order (AOO). It is structured with two models are: Evaluation Dynamic Maximum Economic Order Quantity (EDEOQ) model developed Artificial Neural Network (ANN) to evaluate the optimum inventory level of finished goods as a function of product varied demand and lead time. The second one, Variability Between-Among Dynamic Economic Order Quantity (VB-ADEOQ) is modeled a fuzzy logic to estimate the performance measures (AOP and AOO) that make sure the required order is being processed and delivered on time. Finally, the effectively of the adaptive approach is evaluated through a tested theoretical data. The obtained results were proof it’s efficiently in exactly evaluating of an unavailable the required demand about 46.937% and delivered to their final destination without errors about 56.655%. While, Taguchi method used to optimize and determine relative magnitude of the effect of various factors and their interactive. The obtained results were proved and reliability in determining the customer satisfaction score in real-world. Finally, the developed module was proved their reliability and accurately in addressing the vague data, uncertainty, no formulas, and varied inputs in determining the customer satisfaction score in real-world. Consequently, it clearly makes the developed module a better choice for inventory level forecasting, and then it will assist manufacturing industries improve their business process procedures without incurring significant capital expenditure.