The Experts below are selected from a list of 37179 Experts worldwide ranked by ideXlab platform
Xiaohang Yue - One of the best experts on this subject based on the ideXlab platform.
-
Demand Forecast sharing in supply chains
Production and Operations Management, 2009Co-Authors: Birendra K Mishra, Srinivasan Raghunathan, Xiaohang YueAbstract:This paper examines the incentives of a manufacturer and a retailer to share their Demand Forecasts. The Demand at the retailer is a linearly decreasing function of price. The manufacturer sets the wholesale price first, and the retailer sets the retail price after observing the wholesale price. Both players set their prices based on their Forecasts of Demand. In the make-to-order scenario, the manufacturer sets the production quantity after observing the actual Demand; in the make-to-stock scenario, the manufacturer sets the production quantity before the Demand is realized. In the make-to-order scenario, we show that sharing the Forecast unconditionally by the retailer with the manufacturer benefits the manufacturer but hurts the retailer. We also demonstrate that a side payment contract cannot induce Pareto-optimal information sharing equilibrium, but a discount based wholesale price contract can. The social welfare as well as consumer surplus is higher under the discount contract, compared with under no information sharing. In the make-to-stock scenario, the manufacturer realizes additional benefits in the form of savings in inventory holding and shortage costs when Forecasts are shared. If the savings from inventory holding and shortage costs because of information sharing are sufficiently high, then a side payment contract that induces Pareto-optimal information sharing is feasible in the make-to-stock scenario. We also provide additional managerial insights with the help of a computational study.
-
Demand Forecast sharing in a dual channel supply chain
European Journal of Operational Research, 2006Co-Authors: Xiaohang Yue, John J LiuAbstract:Abstract We assess the benefits of sharing Demand Forecast information in a manufacturer–retailer supply chain, consisting of a traditional retail channel and a direct channel. The Demand is a linear function of price with a Gaussian primary Demand (i.e., zero-price market potential). Both the manufacturer and the retailer set their price based on their Forecast of the primary Demand. In this setting, we investigate the value of sharing Demand Forecasts. We analyze the ‘make-to-order’ scenario, in which prices are set before and production takes place after the primary Demand is known, and the ‘make-to-stock’ scenario, in which production takes place and prices are set before the primary Demand is known. We also compare the supply chain performance with and without the direct channel under some assumptions (production cost is zero, and each Demand function has the same slope of price). We find that the direct channel has a negative impact on the retailer’s performance, and, under some conditions, the manufacturer and the whole supply chain are better off. Our research extends and complements prior research that has investigated only the inventory and replenishment-related benefits of information sharing.
Danko Turcic - One of the best experts on this subject based on the ideXlab platform.
-
temporary contract adjustment to a retailer with a private Demand Forecast
Management Science, 2019Co-Authors: Sherif Nasser, Danko TurcicAbstract:This paper analyzes a setting in which a manufacturer (he) and a retailer (she) face uncertain Demand, but the retailer has an information advantage in the form of a private Demand Forecast. Such information asymmetry causes the manufacturer to incur a hidden information cost. The results show that a manufacturer can leverage his timing advantage to strategically implement a temporary contract adjustment (TCA) mechanism, which allows him to counter his informational disadvantage and either eliminate or reduce the hidden information cost. The manufacturer implements the TCA mechanism by designing the supply contract with two sets of terms: presignal and postsignal. Presignal terms engage the retailer before observing the Forecast signal and induce her to decide under high uncertainty. As a result, she overinvests when the Forecast signal is low. Postsignal terms engage her after the signal, allowing her to make use of the information and purchase additional stock efficiently based on the Forecast signal. T...
-
temporary contract adjustment to a retailer with a private Demand Forecast
2017Co-Authors: Sherif Nasser, Danko TurcicAbstract:This paper analyzes a setting in which a manufacturer and a retailer face uncertain Demand, but the retailer has an information advantage in the form of a private Demand Forecast. We first analyze the case when the Demand Forecast is common knowledge, which yields the manufacturer's first-best equilibrium profits. Next, we analyze the case where the Forecast is privately observed by the retailer and establish that the manufacturer incurs a hidden information cost as his equilibrium profits drop below the first-best case. Finally, we analyze the case where the manufacturer uses a temporary price discount to incentivize the retailer to purchase some quantity before she observes the Forecast signal; after observing the signal, she can order more at the full (un-discounted) price. We show that by offering the retailer a temporary price discount, the manufacturer can achieve first-best profits without actually observing the Demand Forecast. In other words, by offering the retailer a temporary price discount, the manufacturer can achieve the same expected profits as if he observes the retailer's private information, without this information being directly revealed.
Sherif Nasser - One of the best experts on this subject based on the ideXlab platform.
-
temporary contract adjustment to a retailer with a private Demand Forecast
Management Science, 2019Co-Authors: Sherif Nasser, Danko TurcicAbstract:This paper analyzes a setting in which a manufacturer (he) and a retailer (she) face uncertain Demand, but the retailer has an information advantage in the form of a private Demand Forecast. Such information asymmetry causes the manufacturer to incur a hidden information cost. The results show that a manufacturer can leverage his timing advantage to strategically implement a temporary contract adjustment (TCA) mechanism, which allows him to counter his informational disadvantage and either eliminate or reduce the hidden information cost. The manufacturer implements the TCA mechanism by designing the supply contract with two sets of terms: presignal and postsignal. Presignal terms engage the retailer before observing the Forecast signal and induce her to decide under high uncertainty. As a result, she overinvests when the Forecast signal is low. Postsignal terms engage her after the signal, allowing her to make use of the information and purchase additional stock efficiently based on the Forecast signal. T...
-
temporary contract adjustment to a retailer with a private Demand Forecast
2017Co-Authors: Sherif Nasser, Danko TurcicAbstract:This paper analyzes a setting in which a manufacturer and a retailer face uncertain Demand, but the retailer has an information advantage in the form of a private Demand Forecast. We first analyze the case when the Demand Forecast is common knowledge, which yields the manufacturer's first-best equilibrium profits. Next, we analyze the case where the Forecast is privately observed by the retailer and establish that the manufacturer incurs a hidden information cost as his equilibrium profits drop below the first-best case. Finally, we analyze the case where the manufacturer uses a temporary price discount to incentivize the retailer to purchase some quantity before she observes the Forecast signal; after observing the signal, she can order more at the full (un-discounted) price. We show that by offering the retailer a temporary price discount, the manufacturer can achieve first-best profits without actually observing the Demand Forecast. In other words, by offering the retailer a temporary price discount, the manufacturer can achieve the same expected profits as if he observes the retailer's private information, without this information being directly revealed.
Juan J Flores - One of the best experts on this subject based on the ideXlab platform.
-
multi model prediction for Demand Forecast in water distribution networks
Energies, 2018Co-Authors: Rodrigo Lopez Farias, Vicenc Puig, Hector Rodriguez Rangel, Juan J FloresAbstract:This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for Demand Forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is Forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water Demand Forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real Demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the Forecasting accuracy.
-
short term Demand Forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks
Journal of Hydroinformatics, 2017Co-Authors: Hector Rodriguez Rangel, Rodrigo Lopez Farias, Vicenc Puig, Juan J FloresAbstract:Efficient management of a drinking water network reduces the economical costs related to the water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economical costs. Therefore, short-term Forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon Demand Forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of Forecasting water Demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for Forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using Demand data from the Barcelona water network.
Marco Slikker - One of the best experts on this subject based on the ideXlab platform.
-
a collaborative decentralized distribution system with Demand Forecast updates
European Journal of Operational Research, 2012Co-Authors: Ulas Ozen, Greys Sosic, Marco SlikkerAbstract:Abstract In this paper, we study inventory pooling coalitions within a decentralized distribution system consisting of a manufacturer, a warehouse (or an integration center), and n retailers. At the time their orders are placed, the retailers know their Demand distribution but do not know the exact value of the Demand. After certain production and transportation lead time elapses, the orders arrive at the warehouse. During this time, the retailers can update their Demand Forecasts. We first focus on cooperation among the retailers – the retailers coordinate their initial orders and can reallocate their orders in the warehouse after they receive more information about their Demand and update their Demand Forecasts. We study two types of cooperation: Forecast sharing and joint Forecasting. By using an example, we illustrate how Forecast sharing collaboration might worsen performance, and asymmetric Forecasting capabilities of the retailers might harm the cooperation. However, this does not happen if the retailers possess symmetric Forecasting capabilities or they cooperate by joint Forecasting, and the associated cooperative games have non-empty cores. Finally, we analyze the impact that cooperation and non-cooperation of the retailers has on the manufacturer’s profit. We focus on coordination of the entire supply chain through a three-parameter buyback contract. We show that our three-parameter contract can coordinate the system if the retailers have symmetric margins. Moreover, under such a contract the manufacturer benefits from retailers’ cooperation since he can get a share of improved performance.
-
a collaborative decentralized distribution system with Demand Forecast updates
2010Co-Authors: Ulas Ozen, Greys Sosic, Marco SlikkerAbstract:In this paper, we study inventory pooling coalitions within a decentralized distribution system consisting of a manufacturer, a warehouse (or an integration center), and n retailers. At the time their orders are placed, the retailers know their Demand distribution but do not know the exact value of the Demand. After certain production and transportation lead time elapses, the orders arrive at the warehouse. During this time, the retailers can update their Demand Forecasts.We first focus on cooperation among the retailers - the retailers coordinate their initial orders and can reallocate their orders in the warehouse after they receive more information about their Demand and update their Demand Forecasts. We study two types of cooperation: Forecast sharing and joint Forecasting. We show that the cooperative games associated with both situation have non-empty cores. However, by using an example we illustrate how Forecast sharing collaboration might lead to bad performance, and asymmetric Forecasting capabilities of the retailers might harm the cooperation. On the other hand, joint Forecasting always results in higher total expected profit.Finally, we analyze the impact that cooperation and non-cooperation of the retailers has on the manufacturer's profit. We focus on coordination of the entire supply chain through a three- parameter buyback contract. We show that our three-parameter contract can coordinate the system if the retailers have symmetric margins. Moreover, under such a contract the manufacturer prefers retailers' cooperation since he can get a share of improved performance.