We analyze stochastic dynamic multi-product pricing models for durable goods and consider a single advertising channel to promote all types of products. We include general adoption effects, unit costs as well as inventory holding costs. In case of isoelastic, exponential and linear demand, we derive solution formulas for the expected profit, the optimal feedback prices for all types of products, and the optimal advertising rate. In order to evaluate optimally controlled sales processes over time, we use efficient simulation techniques. Moreover, for the case of exponential demand, we demonstrate how to include risk aversion in the model.

Many firms are selling different types of products. Typically sales applications are characterized by competitive settings, limited information and substitution effects. The demand intensities of single types of products are affected by the own products as well as the products of competitors. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic multi-product pricing models under competition for the sale of durable goods. In a first step, a data-driven approach is used to measure substitution effects and to estimate sales probabilities in competitive markets. In a second step, we use a dynamic model to compute powerful heuristic feedback pricing strategies, which are even applicable if the number of competitors' offers is large and their pricing strategies are unknown. Moreover, our approach allows taking additional features, such as customer ratings or shipping times into account. Adaptive estimations are used to update the estimation of sales probabilities and to further improve the strategy.

This book aims to contribute to the discussion about the implications of fuzzy logic, neural networks, digital era, and other intelligent techniques on organizations. This book will be very useful for academic researchers and postgraduate students aiming to introduce themselves to the field of quantitative techniques for overcoming uncertain environments and developing models to make decisions. Developments in other theories and socioeconomic and computational changes have shed light on the importance of fuzzy applications in social sciences. The treatment of uncertainty in the economic and business analysis is fundamental and requires instruments compatible with the uncertain environment of economics and business, because most of the traditional models have been overtaken by this reality when trying to make decisions with uncertain information. In the face of information technology, digitization, and uncertainty, organizations confront new opportunities and challenges. In order to take advantage of these opportunities and overcome current and future challenges, it is needed to understand the evolution of these phenomenon.

This paper examines the sale of perishable and durable goods in a general class of time-dependent dynamic pricing and advertising models. We derive general qualitative insights in the complex interplay of optimal pricing and advertising decisions. Using a path-wise argumentation we prove structural properties of the optimal expected profits with respect to time, inventory level, price impact, advertising impact and different model parameters, such as discount rate, marginal unit costs, and the holding cost rate. Moreover, we identify sufficient conditions which imply structural properties of optimal joint pricing and advertising strategies. Our analysis also implies general results for other related models, such as pure pricing and pure advertising models.

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. The Handbook of Research on Promoting Business Process Improvement Through Inventory Control Techniques is a critical scholarly resource that examines optimization techniques, data mining concepts, and genetic algorithms to manage inventory control. Featuring coverage on a broad range of topics such as logistics and supply chain management, stochastic inventory modelling, and inventory management in healthcare, this book is geared towards academicians, practitioners, and researchers seeking various research methods to get optimal ordering policy.

This paper analyzes a dynamic pricing and advertising model for the sale of perishable products under constant absolute risk aversion. We consider a time-dependent version of Gallego and van Ryzin's (1994) model with advertising effects, accounting for marginal unit costs. We derive closed-form expressions of the optimal risk-averse pricing and advertising policies of the value function and of the certainty equivalent. The formulas provide insight into the (complex) interplay between risk-sensitive pricing and advertising decisions. Moreover, to evaluate the optimally controlled sales process over time we propose efficient simulation techniques. These are used to analyze the characteristics of different degrees of risk aversion, particularly the concentration of the profit distribution and the impact on the expected evolution of price and advertising rates.