When ordering under uncertainty of demand distribution, a newsvendor may make wrong judgments of demand distribution, which raises expected inventory costs. How to identify the demand distribution and make right inventory decisions is the main concern of this paper. Assume that the demand follows two different types of normal distribution, which have the same variance but different means. By introducing signal detection theory, an optimal ordering strategy is proposed based on historical demand information and nontraditional demand information to minimize inventory costs for the newsvendor, and it is compared with the intuition rule merely based on the probability of demand distribution. The results show that, excluding the two extreme cases where the probability of demand distribution is either too large or too small, the proposed optimal strategy is better than the intuition rule in controlling inventory costs. It means that with few historical demand information, the newsvendor can effectively adjust the result of demand distribution detection and thus improve his order decisions. Also, the managerial implications of this study in relation to the integration of historical demand information and nontraditional demand information and the exchange of demand information are discussed.
[1] Tokar T, Aloysius J, Williams B, et al. Bracing for demand shocks:An experimental investigation[J]. Journal of Operations Management, 2014, 32(4):205-216.
[2] O'Neil S, Zhao Xuying, Sun D, et al. Newsvendor problems with demand shocks and unknown demand distributions[J]. Decision Sciences, 2016, 47(1):125-156.
[3] Levi R, Roundy RO, Shmoys DB. Provably near-optimal sampling-based policies for stochastic inventory control models[J]. Mathematics of Operations Research,2007, 32(4):821-839.
[4] 宋华明, 杨慧, 罗建强, 等. 需求预测更新情形下的供应链Stackelberg博弈与协调研究[J]. 中国管理科学, 2010, 18(4):86-92.
[5] 陈敬贤,孟庆峰. 应对突发事件的库存共享策略[J]. 中国管理科学,2015, 23(4):65-72.
[6] 张欢,汪贤裕. 虚拟第三方控制下供应链对突发事件的协调研究[J]. 中国管理科学,2010,18(1):66-71.
[7] 徐贤浩, 邓晨, 彭红霞. 基于供应链金融的随机需求条件下的订货策略[J]. 中国管理科学, 2011, 19(2):63-70.
[8] Massey C, Wu G. Detecting regime shifts:The causes of under-and overreaction[J]. Management Science, 2005, 51(6):932-947.
[9] Kremer M, Moritz B, Siemsen E. Demand forecasting behavior:system neglect and change detection[J]. Management Science, 2011, 57(10):1827-1843.
[10] 冯芷艳,郭迅华,曾大军,等. 大数据背景下商务管理研究若干前沿课题[J]. 管理科学学报,2013, 16(1):1-9.
[11] Xie K L, Zhang Z Lili, Zhang Ziqiong. The business value of online consumer reviews and management response to hotel performance[J]. International Journal of Hospitality Management, 2014, 43(1):1-12.
[12] Nassirtoussi A K, Aghabozorgi S, Wah T H, et al. Text minings of news-headlines for FOREX market prediction:A multi-layer dimension reducing algorithm with semantics and sentiment[J]. Expert Systems with Applications, 2015, 42(1):306-324.
[13] McAfee A, Brynjolfsson E. Big data:The management revolution[J]. Harvard Business Review, 2012, 90(10):60-68.
[14] Murphy A H. The early history of probability forecasts:Some extensions and clarification[J]. Weather and Forecasting, 1998, 13(1):5-15.
[15] Arrow K J, Harris T, Marschak J. Optimal inventory policy[J]. Econometrica, 1951,19(3):250-272.
[16] Azoury K S. Bayes solution to dynamic inventory models under unknown demand distribution[J]. Management Science, 1985, 31(9):1150-1160.
[17] Scarf H. Bayes solutions of the statistical inventory problem[J]. The Annals Mathematical Statistics, 1959, 30(2):490-508.
[18] Godfrey G A, Powell W B. An adaptive, distribution-free algorithm for the Newsvendor problem with censored demands, with applications to inventory and distribution[J]. Management Science, 2001, 47(8):1101-1112.
[19] Fisher M, Raman A. Reducing the cost of demand uncertainty through accurate response to early sales[J]. Operations Research, 1996, 44(1):87-99.
[20] Goodwin P. Intergrating management judgment and statistical forecast to improve short-term forecast[J]. Omega:The International Journal of Management Science, 2002, 30(2):127-135.
[21] Arora S, Taylor JW. Short-term forecasting of anomalous load using rule-based triple seasonal methods[J]. IEEE Transactions on Power Systems, 2013, 28(3):3235-3242.
[22] 高山晟. 经济学中的分析方法(第一版)[M].北京:中国人民大学出版社,2013.
[23] Dvoretzky A, Kiefer J, Wolfowitz J. The inventory problem:I. Case of known distributions of demand[J]. Econometrica, 1952, 20(2):187-222.
[24] Dvoretzky A, Kiefer J, Wolfowitz J. The inventory problem:Ⅱ. Case of unknown distributions of demand[J]. Econometrica, 1952, 20(3):450-466.
[25] Karlin S, Scarf H. Inventory models of the arrow-harris-marschak type with time lag[M]//Arrow K J, Karlin S, Scarf H. Studies in the mathematical theory of inventory and production, Palo Alto, CA:Standford University Press, 1958.
[26] Hayes R H. Statistical estimation problems in inventory control[J]. Management Science, 1969, 15(11):686-701.
[27] Liyanage L H, Shanthikumar J G. A practical inventory control policy using operational statistics[J]. Operations Research Letters, 2005, 33(4):341-348.
[28] Wu Jianghua. Quantity flexibility contracts under Bayesian updating[J]. Computers & Operations Research, 2005, 32(5):1267-1288.
[29] Janssen E, Strijbosch L, Brekelmans R. Assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function[J]. International Journal of Production Economics, 2009, 118(1):34-42.
[30] Yue Jinfeng, Wang M C, Chen Bintong. Mean-range based distribution-free procedures to minimize "overage" and "underage" costs[J]. Europe Journal of Operations Research, 2007, 176(2):1103-1116.
[31] Gülplnar N, Pachamanova D, Çanako?lu E. Robust strategies for facility location under uncertainty[J]. European Journal of Operational Research, 2013, 225(1):21-35.
[32] Kunnumkal S, Topaloglu H. Using stochastic approximation methods to compute optimal base-stock levels in inventory control problems[J]. Operations Research, 2008, 56(3):646-664.
[33] Huh W T, Rusmevichientong P. A nonparametric asymptotic analysis of inventory planning with censored demand. Math[J]. Operations Research, 2009, 34(1):103-123.
[34] 隽志如,王海燕. 基于需求分布突变检测的两阶段库存管理方法[J]. 系统工程学报. 2013. 28(5):651-659.
[35] Lynn S K, Barrett L E. "Utilizing" signal detection theory[J]. Psychological Science, 2014, 25:1663-1673.
