统计过程控制在网络销售中的应用
The Application of Statistical Process Control in Network Sales
DOI: 10.12677/AAM.2023.122064, PDF,  被引量    国家自然科学基金支持
作者: 廉惠然, 齐德全*:长春理工大学数学与统计学院,吉林 长春
关键词: 统计过程控制网络销售控制图订货周期Statistical Process Control Network Sales Control Chart Ordering Cycle
摘要: 现有网络销售的研究中,人们非常关心订货量,订货周期,销售价格和利润等变量。从统计过程控制的角度,给出了监控网络销售变量参数变化的一般框架。鉴于网络销售变量分布的多样性,建立了多元的Max-EWMA控制图用来监控网络销售变量的均值向量的漂移。通过蒙特卡洛模拟比较了Max-EWMA控制图与MEWMA控制图和T2控制图的性能。以马氏距离度量漂移的大小,以AR(1)模型刻画变量间的相关性。模拟结果表明,Max-EWMA 控制图对监控中小漂移比较有效,具有较理想的性能。
Abstract: In the existing research of online sales, people are very concerned about the variables such as order quantity, order cycle, sales price and profit. From the perspective of statistical process control, a general framework for monitoring the change of network sales variable parameters is given. In view of the diversity of the distribution of online sales variables, a multivariate Max-EWMA control chart is established to monitor the drift of the mean vector of online sales variables. The performances of Max-EWMA control chart, MEWMA control chart and T2 control chart are compared by Monte Carlo simulation. Mahalanobis distance is used to measure the drift, and AR(1) model is used to describe the correlation between variables. The simulation results show that the Max-EWMA control chart is effective in monitoring small and medium drift, and has better performance.
文章引用:廉惠然, 齐德全. 统计过程控制在网络销售中的应用[J]. 应用数学进展, 2023, 12(2): 609-614. https://doi.org/10.12677/AAM.2023.122064

参考文献

[1] 陈金平. 我国电子商务发展的特点和趋势[J]. 上海商业, 2022(12): 28-30.
[2] 戴先红. 基于Logistic分析的安徽特色农产品网络营销发展影响因素[J]. 安徽科技学院学报, 2022, 36(5): 102-108.
[3] 张函弛, 张幸福. 后疫情时代下餐饮业的网络销售研究[J]. 商业经济, 2023(1): 64-66+70.
[4] 王斌, 杨抒, 贾清, 等. ARIMA 模型在电商平台新疆灰枣订单预测中的应用研究[J]. 统计与决策, 2020, 36(6): 35-38.
[5] 王长琼, 田仁久, 邵明霞. 考虑转运的新零售供应链订货模型及策略[J]. 物流技术, 2021, 40(5): 91-97.
[6] 吴宇平, 李磊. 易逝品在线销售数据的稀疏性问题及处理[J]. 统计理论与实践, 2022(12): 62-68.
[7] 朱建平, 冯冲, 梁振杰. 交叉学科促进统计学的发展[J]. 统计研究, 2023, 40(1): 134-144.
[8] Sanusi, R.A., Yin, T.S. and Khoo, M. (2020) Simultaneous Moni-toring of Magnitude and Time-between-Events Data with a Max-EWMA Control Chart. Computers & Industrial Engi-neering, 142, Article ID: 106378. [Google Scholar] [CrossRef
[9] 王兆军, 邹长亮, 李忠华. 统计质量控制图理论与方法[M]. 北京: 科学出版社, 2013.
[10] 齐德全, 陈实. 一种线性轮廓数据的在线监控方法[J]. 长春理工大学学报(自然科学版), 2022, 45(4): 129-134.
[11] Dai, Y., Luo, Y., Li, Z. and Wang, Z. (2011) A New Adaptive CUSUM Control Chart for Detecting the Multivariate Process Mean. Quality & Reliability Engineering International, 27, 877-884. [Google Scholar] [CrossRef
[12] Lowry, C.A., Woodall, W.H., Champ, C.W. and Rigdon, S.E. (1992) A Multivariate Exponentially Weighted Moving Average Control Chart. Technometrics, 34, 46-53. [Google Scholar] [CrossRef
[13] Ghute, V.B. and Shirke, D.T. (2013) A Multivariate Moving Average Con-trol Chart for Mean Vector. Journal of Academia and Industrial Research, 1, 795-800.
[14] Zou, C., Wang, Z. and Tsung, F. (2012) A Spatial Rank-Based Multivariate EWMA Control Chart. Naval Research Logistics (NRL), 59, 91-110. [Google Scholar] [CrossRef
[15] Qi, D., Li, Z. and Wang, Z. (2016) On-Line Monitoring Data Qual-ity of High-Dimensional Data Streams. Journal of Statistical Computation and Simulation, 86, 2204-2216. [Google Scholar] [CrossRef
[16] Zou, C., Jiang, W. and Tsung, F. (2011) A LASSO-Based Diagnostic Framework for Multivariate Statistical Process Control. Technometrics, 53, 297-309. [Google Scholar] [CrossRef