Inventory and pricing management in probabilistic selling

Context: Probabilistic selling is the strategy that the seller creates an additional probabilistic product using existing products. The exact information is unknown to customers until they receive the probabilistic products. This strategy is still a relatively new area for both researchers and practitioners. Many of the corresponding operations problems need to be solved to take full advantage of the opportunity of this innovative marketing strategy. However, limited attention has been paid to examining the inventory management of probabilistic selling from the perspective of Operations Management, which cannot meet the needs of decision-making in reality. Objectives: Considering different characteristics of the probabilistic product, the buyer, and the seller involved in probabilistic selling, i.e., the probabilistic product form, the buyers’ behaviours of demand switch and barter exchange, and the seller's product allocation behaviour, we establish models and solve the decision problems of pricing, inventory, joint decision of pricing-inventory, and product allocation, etc. Based on the analysis of optimal decisions and strategy comparison results, we shed some lights on the effectiveness of probabilistic selling on managing uncertainty, and its profitability. Method: First, we analyze the practice scenarios of probabilistic selling. Next we mainly use newsvendor inventory model, hotelling model, and optimization theory to model, solve, and analyze the operational problems. Then we give some analytical results. Next we conduct the numerical analysis using softwares of Matlab and Mathematica. Finally, we provide insightful managerial implications for the practice of probabilistic selling. Results: The thesis derives the optimal operational decisions of inventory order, pricing, inventory allocation, and product line design in probabilistic selling. Overall, the analysis of the results show that probabilistic selling can benefit the seller with higher expected profit by reducing demand/supply uncertainty and improving inventory efficiency. The performance of probabilistic selling is closely dependent on customers' price sensitivity, product similarity, and uncertainty level, etc. Main results considering different research scenarios are as follows: 1) When the price for the probabilistic product is independent on demand reshape, a proper cannibalization can benefit the retailer in terms of yielding a higher expected profit. Probabilistic selling is more profitable with relatively lower product similarity and higher price-sensitive customers, while inventory substitution strategy outperforms probabilistic selling with higher product similarity. 2) When the price for the probabilistic product is dependent on demand reshape, probabilistic selling can benefit the seller with higher expected profit and lower inventory. Probabilistic selling is more profitable with lower product differentiation, higher customers' price sensitivity, and higher demand uncertainty. Improper pricing would undermine the seller's profit. 3) When the seller offers physical probabilistic product, he can benefit from two effects, namely the risk pooling effect due to demand reshape and the risk diversification effect due to inventory flexibility. 4) When the seller offers barter choice in probabilistic selling, he may benefit from the marketing effect in the barter process. Offering barter choice can broaden the application range of probabilistic selling, which will increase with successful barter probability. Conclusions/Implications: First, the thesis helps sellers understand how to manage their inventory, pricing and related implementation issues to take full advantage of probabilistic selling. Second, this thesis explores the mechanism of this innovative marketing strategy as an inventory management tool to combat uncertainty which also riches the literature on Operations Management, especially inventory management.Antecedentes: Los productos probabilísticos son productos adicionales creados por un proveedor que combina productos existentes y oculta parte de la información del producto. Es decir, cierta información de atributos de los productos probabilísticos es opaca para el cliente. El cliente que compra el producto probabilístico obtiene una de las combinaciones de productos con una cierta probabilidad. Las ventas probabilísticas son una estrategia de ventas que permite la venta de productos probabilísticos. Todavía es un modelo de ventas relativamente nuevo para empresas e investigadores. La implementación de ventas probabilísticas es diversa y aún no se ha verificado la rentabilidad de las diferentes formas de ventas probabilísticas. Se deben abordar las situaciones de inventario y fijación de precios que tengan en cuenta las diferentes realidades. Por el momento, desde la perspectiva de la gestión operativa, existen pocos estudios sobre la toma de decisiones de inventario y fijación de precios bajo el modelo de ventas probabilísticas, que no puede satisfacer las necesidades de las empresas para tomar decisiones científicas en el proceso de implementación. Objetivo: Este documento se centra en los tres actores principales en el proceso de venta probabilística: los productos probabilísticos, compradores y vendedores. Considere el afecto de las diferentes realidades y circunstancias (en concreto, la forma de productos probabilísticos, la demanda de transferencia y el comportamiento de intercambio del comprador, y si el vendedor reemplaza el producto en el proceso de distribución de los productos) sobre la fijación de precios y las decisiones de inventario. Al establecer un modelo que considera los factores realistas antes mencionados, se resuelve el problema de fijación de precios, la decisión conjunta de inventario- precios y la asignación de productos bajo el modelo probabilístico de ventas. Finalmente, a través del análisis de las decisiones y la comparación de estrategias, se obtendrá sugerencias de gestión para la implementación de ventas probabilísticas. Método: En primer lugar, este documento analiza los escenarios de diferentes ventas de probabilidad. En segundo lugar, utilizando el modelo de vendedor de periódicos, el modelo de Hotelling y la teoría de optimización, se intenta resolver y analizar la fijación de precios, el inventario, la toma de decisiones conjunta de inventario-precios y los problemas de decisión de asignación de productos. Luego, da el teorema y analízalo. Finalmente, proporcione asesoramiento de gestión de inventario- precios para los comerciantes que implementan ventas probabilísticas. Conclusión: Este documento ha encontrado las decisiones operativas óptimas para el inventario, fijación de precios, asignación de inventario y diseño de línea de producto en ventas probabilísticas. Los resultados generales muestran que las ventas probabilísticas pueden aumentar la eficiencia del inventario al reducir la incertidumbre de la demanda / oferta, lo que permite a los vendedores obtener mayores ganancias esperadas. El rendimiento de las ventas probabilísticas está estrechamente relacionado con factores tales como la sensibilidad del precio del cliente, la similitud y la incertidumbre del producto. Significado: Primero, permita que los vendedores hagan un buen uso de las ventas probabilísticas. Este artículo los ayuda a comprender cómo resolver problemas de inventario, precios y decisiones operativas relacionadas en modelos de ventas probabilísticas. Segundo, consideramos esta estrategia de marketing innovadora como una herramienta de gestión de inventario, por lo que este documento enriquece la investigación de gestión operativa, especialmente la teoría de gestión de inventarioPostprint (published version

Managing demand uncertainty: probabilistic selling versus inventory substitution

Demand variability is prevailing in the current rapidly changing business environment, which makes it difficult for a retailer that sells multiple substitutable products to determine the optimal inventory. To combat demand uncertainty, both strategies of inventory substitution and probabilistic selling can be used. Although the two strategies differ in operation, we believe that they share a common feature in combating demand uncertainty by encouraging some customers to give up some specific demand for the product to enable demand substitution. It is interesting to explore which strategy is more advantageous to the retailer. We endogenize the inventory decision and demonstrate the efficiency of probabilistic selling through demand substitution. Then we analyze some special cases without cannibalization, and computationally evaluate the profitability and inventory decisions of the two strategies in a more general case to generate managerial insights. The results show that the retailer should adjust inventory decisions depending on products' substitution possibility. The interesting computational result is that probabilistic selling is more profitable with relatively lower product similarity and higher price-sensitive customers, while inventory substitution outperforms probabilistic selling with higher product similarity. Higher demand uncertainty will increase the profitability advantage of probabilistic selling over inventory substitution.Peer ReviewedPostprint (author's final draft

Inventory and pricing management in probabilistic selling

Context: Probabilistic selling is the strategy that the seller creates an additional probabilistic product using existing products. The exact information is unknown to customers until they receive the probabilistic products. This strategy is still a relatively new area for both researchers and practitioners. Many of the corresponding operations problems need to be solved to take full advantage of the opportunity of this innovative marketing strategy. However, limited attention has been paid to examining the inventory management of probabilistic selling from the perspective of Operations Management, which cannot meet the needs of decision-making in reality. Objectives: Considering different characteristics of the probabilistic product, the buyer, and the seller involved in probabilistic selling, i.e., the probabilistic product form, the buyers’ behaviours of demand switch and barter exchange, and the seller's product allocation behaviour, we establish models and solve the decision problems of pricing, inventory, joint decision of pricing-inventory, and product allocation, etc. Based on the analysis of optimal decisions and strategy comparison results, we shed some lights on the effectiveness of probabilistic selling on managing uncertainty, and its profitability. Method: First, we analyze the practice scenarios of probabilistic selling. Next we mainly use newsvendor inventory model, hotelling model, and optimization theory to model, solve, and analyze the operational problems. Then we give some analytical results. Next we conduct the numerical analysis using softwares of Matlab and Mathematica. Finally, we provide insightful managerial implications for the practice of probabilistic selling. Results: The thesis derives the optimal operational decisions of inventory order, pricing, inventory allocation, and product line design in probabilistic selling. Overall, the analysis of the results show that probabilistic selling can benefit the seller with higher expected profit by reducing demand/supply uncertainty and improving inventory efficiency. The performance of probabilistic selling is closely dependent on customers' price sensitivity, product similarity, and uncertainty level, etc. Main results considering different research scenarios are as follows: 1) When the price for the probabilistic product is independent on demand reshape, a proper cannibalization can benefit the retailer in terms of yielding a higher expected profit. Probabilistic selling is more profitable with relatively lower product similarity and higher price-sensitive customers, while inventory substitution strategy outperforms probabilistic selling with higher product similarity. 2) When the price for the probabilistic product is dependent on demand reshape, probabilistic selling can benefit the seller with higher expected profit and lower inventory. Probabilistic selling is more profitable with lower product differentiation, higher customers' price sensitivity, and higher demand uncertainty. Improper pricing would undermine the seller's profit. 3) When the seller offers physical probabilistic product, he can benefit from two effects, namely the risk pooling effect due to demand reshape and the risk diversification effect due to inventory flexibility. 4) When the seller offers barter choice in probabilistic selling, he may benefit from the marketing effect in the barter process. Offering barter choice can broaden the application range of probabilistic selling, which will increase with successful barter probability. Conclusions/Implications: First, the thesis helps sellers understand how to manage their inventory, pricing and related implementation issues to take full advantage of probabilistic selling. Second, this thesis explores the mechanism of this innovative marketing strategy as an inventory management tool to combat uncertainty which also riches the literature on Operations Management, especially inventory management.Antecedentes: Los productos probabilísticos son productos adicionales creados por un proveedor que combina productos existentes y oculta parte de la información del producto. Es decir, cierta información de atributos de los productos probabilísticos es opaca para el cliente. El cliente que compra el producto probabilístico obtiene una de las combinaciones de productos con una cierta probabilidad. Las ventas probabilísticas son una estrategia de ventas que permite la venta de productos probabilísticos. Todavía es un modelo de ventas relativamente nuevo para empresas e investigadores. La implementación de ventas probabilísticas es diversa y aún no se ha verificado la rentabilidad de las diferentes formas de ventas probabilísticas. Se deben abordar las situaciones de inventario y fijación de precios que tengan en cuenta las diferentes realidades. Por el momento, desde la perspectiva de la gestión operativa, existen pocos estudios sobre la toma de decisiones de inventario y fijación de precios bajo el modelo de ventas probabilísticas, que no puede satisfacer las necesidades de las empresas para tomar decisiones científicas en el proceso de implementación. Objetivo: Este documento se centra en los tres actores principales en el proceso de venta probabilística: los productos probabilísticos, compradores y vendedores. Considere el afecto de las diferentes realidades y circunstancias (en concreto, la forma de productos probabilísticos, la demanda de transferencia y el comportamiento de intercambio del comprador, y si el vendedor reemplaza el producto en el proceso de distribución de los productos) sobre la fijación de precios y las decisiones de inventario. Al establecer un modelo que considera los factores realistas antes mencionados, se resuelve el problema de fijación de precios, la decisión conjunta de inventario- precios y la asignación de productos bajo el modelo probabilístico de ventas. Finalmente, a través del análisis de las decisiones y la comparación de estrategias, se obtendrá sugerencias de gestión para la implementación de ventas probabilísticas. Método: En primer lugar, este documento analiza los escenarios de diferentes ventas de probabilidad. En segundo lugar, utilizando el modelo de vendedor de periódicos, el modelo de Hotelling y la teoría de optimización, se intenta resolver y analizar la fijación de precios, el inventario, la toma de decisiones conjunta de inventario-precios y los problemas de decisión de asignación de productos. Luego, da el teorema y analízalo. Finalmente, proporcione asesoramiento de gestión de inventario- precios para los comerciantes que implementan ventas probabilísticas. Conclusión: Este documento ha encontrado las decisiones operativas óptimas para el inventario, fijación de precios, asignación de inventario y diseño de línea de producto en ventas probabilísticas. Los resultados generales muestran que las ventas probabilísticas pueden aumentar la eficiencia del inventario al reducir la incertidumbre de la demanda / oferta, lo que permite a los vendedores obtener mayores ganancias esperadas. El rendimiento de las ventas probabilísticas está estrechamente relacionado con factores tales como la sensibilidad del precio del cliente, la similitud y la incertidumbre del producto. Significado: Primero, permita que los vendedores hagan un buen uso de las ventas probabilísticas. Este artículo los ayuda a comprender cómo resolver problemas de inventario, precios y decisiones operativas relacionadas en modelos de ventas probabilísticas. Segundo, consideramos esta estrategia de marketing innovadora como una herramienta de gestión de inventario, por lo que este documento enriquece la investigación de gestión operativa, especialmente la teoría de gestión de inventari

Allocation Planning for Demand Fulfillment in Make-to-Stock Industries - A Stochastic Linear Programming Approach

Demand fulfillment is a planning process which is concerned with the processing of customer orders. Its main objectives are providing a high customer service, especially in terms of providing real-time order confirmations and promising reliable delivery dates, as well as maximizing profits. In make-to-stock industries, such as the consumer goods industry, production quantities are usually determined mid-term, based on forecasts and not on actual customer requests. As a consequence, bottleneck situations can occur in the short-run. Furthermore, customers are usually heterogeneous regarding their profitability and their strategic importance. Consequently, the firm has to decide carefully on which orders to accept and whether to fulfill an accepted order from stock or from future production quantities, which entails either inventory holding or customer-specific backlogging costs. The setting in make-to-stock demand fulfillment is comparable to the situation in service industries: capacity is scarce in the short-run, customers are heterogeneous, and demand is uncertain. Therefore, quantity-based revenue management ideas have been transferred to the context of make-to-stock industries. However, typical revenue management assumptions like the perishability of products do not hold in the make-to-stock context. As a consequence, the allocation planning problem’s complexity increases. Existing allocation planning models for make-to-stock industries show two main drawbacks: they either do not consider information about demand uncertainty appropriately or they are not scalable and, thus, not applicable to problems of practical sizes. Commercial advanced planning systems also provide the opportunity of determining allocations by means of simple rules. However, they also do not consider information about demand uncertainty (nor about customer heterogeneity) appropriately. The dissertation shows how allocation planning for demand fulfillment in make-to-stock industries can be improved by means of two-stage stochastic linear programming (SLP) with recourse. SLP formulations for both the single-period and the multi-period case are given. Moreover, the dissertation illustrates that allocation planning can be further improved if information about the consumption process, which follows the allocation planning process, is integrated into the allocation planning SLP model. In particular, information about the order arrival sequence as well as about consumption policies such as nesting or time-based policies is integrated by means of the second stage. The benefit of both performing allocation planning and considering information about demand uncertainty by using two-stage SLP depends on the input data like, e.g. the degree of customer heterogeneity, of capacity shortage as well as of demand uncertainty (or the forecast accuracy, respectively). Within a numerical study, we evaluate when allocation planning is likely to be beneficial and, in case it is, when SLP models are likely to outperform the allocation planning rules of commercial advanced planning systems or other existing allocation planning models

Poor choices: the limits of competitive markets in the provision of essential services to low-income consumers

A major study of the problems faced by the poor in the market for seven essential services in the UK - energy, food, housing, water, telecoms, transport, and financial services. Together these represent 60% of spending by the poorest 30% of households