Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

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 inventarioPostprint (published version

Optimal postponement in supply chain network design under uncertainty: an application for additive manufacturing

This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterized by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology for meeting each market’s demand, each operation’s optimal production quantity, and each selected technology’s optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies.This work was developed under an Accenture Open Innovation University [grant number I-01326] and was also partially supported by grant RTI2018-097580-B-I00 of the Ministry of Economy and Competitiveness of Spain.Peer ReviewedPostprint (published version

Structuring postponement strategies in the supply chain by analytical modeling

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Optimal Supply Chain Strategy through Stochastic Programming

In this project, a new two-stage stochastic programming decision model has been developed to assess: (a) the convenience of introducing 3D printing into any generic manufacturing process, both single and multi-product; and (b) the optimal degree of postponement known as the customer order decoupling point (CODP) while also assuming uncertainty in demand for multiple markets. To this end, we propose the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies. These are defined through a set of operations for manufacturing, assembly and distribution, each of which is characterized by a lead time and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology to meet the demand of each market, the optimal production quantity for each operation, and the optimal CODP for each technology. The results obtained from several case studies in real manufacturing companies are presented and analyzed. The work presented in this master s thesis is part of an ongoing research project between UPC and Accenture

Smart Pricing: Linking Pricing Decisions with Operational Insights

The past decade has seen a virtual explosion of information about customers and their preferences. This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost. At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners. These two developments are narrowly intertwined. Pricing decisions have a direct effect on operations and visa versa. Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice. This article reviews a number of key linkages between pricing and operations. In particular, it highlights different drivers for dynamic pricing strategies. Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field.supply chain management;inventory;capacity;dynamic pricing;operations-marketing interface

Capacity, flexibility and pricing decisions for substitutable products under demand and supply uncertainties

We study the resource investment and pricing decisions for a profit-maximizing firm producing two substitutable products with partially flexible resources facing three types of uncertainties separately: demand uncertainties, capacity uncertainties and supply disruptions. The resources are partially flexible indicating efficiency losses when a resource designed for one type of product is used to produce another type (i.e., cross-production): The Shrinking Capacity model explicitly captures the fact that fewer units of products will be produced under cross-production. If the degree of flexibility is zero, the firm cannot cross-produce. The Additional Cost model captures the unit increase in production cost due to cross-production. Cross-production is possible even when degree of flexibility is zero but incurs a higher production cost.We find that product substitutability, type and severity of uncertainties as well as type of efficiency loss play a key role in deciding the optimal investment strategies. When facing low or moderate demand and capacity uncertainties, flexibility is not required under both models. However, if demand or capacity uncertainties is high, a moderate degree of flexibility maybe beneficial under shrinking capacity if the products are highly differentiated and demands are negatively correlated. Shrinking capacity also suggests a moderate degree of flexibility that decreases with product substitutability under high capacity uncertainties. As the degree of supply disruptions increases flexibility is extremely valuable under any demand intercept correlation even for highly substitutable products. The additional cost model suggests investing in full flexibility only if the unit cost of dedicated capacities were higher, demands are negatively correlated and firm is forced to clear capacities. While literature has shown flexibility to be less beneficial as resource investments become less reliable, our research shows that this is not always true. It also explains why completely flexible resources are still rare in industrial practice, although it has been highly advocated in academia

Benefits of Collaboration in Capacity Investment and Allocation

This paper studies capacity collaboration between two (potentially competing) firms. We explore the ways that the firms can collaborate by either building capacity together or sharing the existing capacity for production. We consider cases where the two firms' products are potential substitutes and also where the firms' products are independent. We find that a firm can benefit from collaboration even with its competitor. Moreover, the firms do not have to jointly make the production decisions to realize the benefits of collaboration. We consider a model where firms build capacity before demand is realized and make production decisions after they receive a demand signal. They can potentially collaborate in jointly building capacity and/or in exchanging capacity once they receive their demand signals. Interestingly, we find that having firms compete at the production stage can result in firms deciding to build less overall capacity than if they coordinated capacity investment and production. Also, we find that though collaboration in capacity investment is bene cial, collaboration in production using existing capacity is often more beneficial. The benefits of collaboration is largest when competition is more intense, demand is more variable and cost of investment is higher.http://deepblue.lib.umich.edu/bitstream/2027.42/94207/1/1179_HAhn.pd