1,721,286 research outputs found
Reengineering Management Science for a Sharper Focus and Broader Appeal
No full textManagement Science is a scholarly journal that publishes scientific research on the practice of management. Therefore, papers published in Management
Science should deal with issues and problems important to managers and executives; they must be interesting to a wide range of people in the management science community; and they should have the potential
to impact management practice
Handbook of quantitative supply chain analysis : modeling in the e-business era/ Edit.: David Simchi-Levi
No full textxiii, 817 hal.: ill, tab.; 23 cm
Handbook of quantitative supply chain analysis : modeling in the e-business era/ Edit.: David Simchi-Levi
No full textxiii, 817 hal.: ill, tab.; 23 cm
The logic of logistics: theory, algorithms and applications for logistics management
No full text<p>Nesse texto o autor apresenta uma resenha acerca do livro "The logic of logistics: theory, algorithms and applications for logistics management", de autoria de Julien Bramel e David Simchi-Levi, publicado pela Springer-Verlag, em 1997.</p>
Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation
No full textIn a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice, sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst-case regret—i.e., the expected total revenue loss compared with a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log(m)T), or m iterations of the logarithm. Furthermore, we describe an implementation of this pricing policy at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings
Powering retailers’ digitization through analytics and automation
No full textRetailers face significant pressure to improve revenue, margins and market share by applying price optimisation models. These are mathematical models that calculate how demand varies at different price levels, then combine that data with information on costs and inventory levels to recommend prices that will improve revenue and profits. These models have been around for a while-so what is different now? We have identified three important changes: (1) Data: availability of internal and external real-time data such as traffic to a website, consumers making buy/no buy decisions and competitor pricing strategies; (2) Analytics: advances in machine learning and ease of access (R, Python) have enabled the development of systems that learn on the fly about consumer behaviour and preferences and generate effective estimates of demand-price relationships; and (3) Automation: increase in computing speed enables real-time optimisation of prices of hundreds of competing products sold by the same retailer. We take advantage of these new opportunities by showing how they were applied at Boston-based flash sales retailer Rue La La, online market maker Groupon, and the largest online retailer in Latin America, B2W Digital (B2W). While all these examples are of on-line businesses which have readily available data and can change prices dynamically, we have also implemented similar methods for brick-and-mortar retailed in applications such as promotional pricing, new product introduction, and assortment optimisation with similar business impacts. Thus, beyond applications to price optimisations, these new trends enable companies to revolutionise their business from procurement to supply chain all the way to revenue management. Keywords: analytics, machine learning, price theory, online retail, forecastin
IoT and anomaly detection for the iron ore mining industry
No full textThesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 176-182).In the context of a world flooded with data, the Internet of Things (IoT) is exploding. This thesis considers the problem of applying IoT technology to the reduction of costs in the iron ore mining industry, to compensate for the iron ore price slumping observed over the past years. More specifically, we focused on improving the quality of the output in a data-driven iron ore concentration factory. In this plant, mined iron ore goes through a series of complex physical and chemical transformations so as to increase the concentration in iron and reduce the concentration in impurities such as silica. In this thesis, we developed an IoT infrastructure comprising of machines, a network of sensors, a database, a random forest prediction model, an algorithm for adjusting its cutoff parameter dynamically, and a predictive maintenance algorithm. It can preventively detect and maybe fix poor quality events in the iron ore concentration factory, improving the overall quality and decreasing costs. The random forest model was selected among other anomaly detection techniques. It is able, on an independent test data set, with an AUC of about 0.92, to detect 90% of the poor quality events, with a false positive rate of 23.02%, lowered by the dynamic cutoff algorithm. These methods can be applied to any factory in any industry, as long as it has a good infrastructure of sensors, providing sufficiently precise and frequent data.by Carl Elie Saroufim.S.M
Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation
No full textThesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 53-55).We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. First, we build a price-sensitive random forest model to predict the number of daily bookings for each customer market segment. We feed these predictions into a mixed integer linear program (MILP) to optimize prices and capacity allocations at the same time. We prove that the MILP can be equivalently solved as a linear program, and then show that it produces upper and lower bounds for the expected revenue maximization Dynamic Program (DP), and that the gap between the bounds depends on the probabilistic distribution of the demand. Thus, for high prediction accuracies, the optimal value of the DP can be closely approximated by the MILP solution. Finally, numerical results show that the optimized decisions are able to generate an increase in revenue compared to the historical policies, and that the fast running time achieved permits real time policy updates.by Eduardo Candela Garza.S.M
Emission regulations in the electricity market : an analysis from consumers, producers and central planner perspectives
Full text linkThesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 119-122).In the first part of this thesis, the objective is to identify optimal bidding strategies in the wholesale electricity market. We consider asymmetric producers submitting bids to a system operator. The system operator allocates demand via a single clearing price auction. The highest accepted bid sets the per unit market price payed by consumers. We find a pure Nash equilibrium to the bidding strategies of asymmetric producers unattainable in a symmetric model. Our results show that producers with relatively large capacities are able to exercise market power. However, the market may seem competitive due to the large number of producers serving demand. The objective of the second part of the thesis, is to compare two regulation policies: a fixed transfer price, such as tax regulation, and a permit system, such as cap-and-trade. For this purpose, we analyze an economy where risk neutral manufacturers satisfy price sensitive demand. The objective of the regulation established by the central planner is to achieve an external objective, e.g. reduce pollution or limit consumption of scarce resource. When demand is uncertain, designing these regulations to achieve the same expected level of the external objective results in the same expected consumer price but very different manufacturers' expected profit and central planner revenue. For instance, our results show that when the firms are price takers, the manufacturers with the worst technology always prefer a tax policy. Interestingly, we identify conditions under which the manufacturers with the cleanest technology benefit from higher expected profit as tax rate increases. In the third part of the thesis, we investigate the impact labeling decisions have on the supply chain. We consider a two stage supply chain consisting of a supplier and a retailer. Demand is considered stochastic, decreasing in price and increasing in a quality parameter, e.g. carbon emissions. The unit production cost for the supplier is increasing in the quality level chosen. We identify two different contracts that maximize the efficiency of the supply chain while allowing the different parties to achieve their objectives individually.by Cristian Ricardo Figueroa Rodriguez.Ph.D
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