Description
Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. It explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex probabilistic multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models (cost optimization, fill rate and expected loss sales, service level optimization, gamma distribution, using forecast error instead of demand variability) as well as an explanation on how one can easily optimize a multi-echelon supply chain based on the guaranteed service model. Part 4 discusses the optimization of inventory optimization under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions to the problems (models) for industry will benefit from the “do-it-yourself” Python programs and examples included in each chapter. Nicolas Vandeput, Founder, SupChains; Co-founder SKU Science, Belgium Deterministic supply chains Inventory policies How much should I order? When should I order? Stochastic supply chains Safety stocks Inventory policies Stochastics lead times Advanced stochastic models Fill rate Cost and service level optimization Beyond normality Forecast Multi echelon inventory optimization Discrete inventory optimization Newsvendor Simple simulations Multi echelon inventory optimization simulations
