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

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing

Dampening variability by using smoothing replenishment rules.

A major cause of supply chain deficiencies is the bullwhip effect which can be substantial even over a single echelon. This effect refers to the tendency of the variance of the replenishment orders to increase as it moves up a supply chain. Supply chain managers experience this variance amplification in both inventory levels and replenishment orders. As a result, companies face shortages or bloated inventories, run-away transportation and warehousing costs and major production adjustment costs. In this article we analyse a major cause of the bullwhip effect and suggest a remedy. We focus on a smoothing replenishment rule that is able to reduce the bullwhip effect across a single echelon. In general, dampening variability in orders may have a negative impact on customer service due to inventory variance increases. We therefore quantify the variance of the net stock and compute the required safety stock as a function of the smoothing required. Our analysis shows that bullwhip can be satisfactorily managed without unduly increasing stock levels to maintain target fill rates.Bullwhip effect; Companies; Cost; Costs; Impact; Inventory; Managers; Order; Replenishment rule; Rules; Safety stock; Supply chain; Supply chain management; Variability; Variance; Variance reduction;

Analysis of dual sourcing strategies under supply disruptions

We study a dual-sourcing problem of a firm in the face of supply disruptions from two suppliers: local and overseas. Under four different scenarios of disruption source and information availability, we characterize the optimal dynamic policy that simultaneously determines sourcing decisions to minimize the expected total discounted cost. Different from the previous dual-sourcing models without information availability in the literature, we develop a two-dimensional stochastic dynamic programming model to explicitly address this issue. Further, we analyze the impact of disruption source and information availability on cost performance. We find that (i) a supply disruption at the local source may cause a more remarkable deterioration of cost efficiency than a supply disruption at the overseas source; (ii) the information about the local source is more valuable than that about the overseas source; (iii) when a firm orders from both sources, the disruption information can achieve a significant cost saving. These findings contribute to the theory of strategic sourcing by demonstrating the value of information available at different sources. Moreover, they can also be used as a valuable guideline for managers to select an appropriate sourcing strategy in business practices. (C) 2015 Elsevier B.V. All rights reserved

Dynamic Pricing and Inventory Management with Dual Suppliers of Different Lead Times and Disruption Risks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109985/1/poms12221-sup-0001-OnlineSupplement.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109985/2/poms12221.pd