Stock management and control is a critical element to the success and overall financial well-being of an organization. Through the application of innovative practices and technology, businesses are now able to effectively monitor their operations and manage their inventory by evaluating sales patterns and customer preferences.
Optimal Inventory Control and Management Techniques explores emergent research in stock management and product control within organizations. Featuring diverse perspectives on the implementation of various optimization techniques, genetic algorithms, and datamining concepts, as well as research on big data applications for inventory management, this publication is a comprehensive reference source for practitioners, educators, and researchers in the fields of logistics, operations management, and retail management.
The many academic areas covered in this publication include, but are not limited to:
Mathematicians and computer scientists offer diverse perspectives on implementing various optimization techniques, genetic algorithms, and data mining concepts for controlling and managing the inventory of a business. Their topics include a lot size model for reverse logistics with quadratic demand, a deteriorating inventory model under permissible delay in payments and fuzzy environment, modeling an inventory system with variable demands and lead times using a fuzzy approach, optimal inventory classification using data mining techniques, and a gentle introduction to the Bayesian paradigm for some inventory models.