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大数据预测:需求驱动与供应链变革

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要转变为需求导向的发展思路,企业需要正确识别市场信号,建立需求感应能力,围绕需求塑造相关流程,从而有效转换需求信号,打造高效响应机制。可行吗?可行,在《大数据预测:需求驱动与供应链变革》一书的帮助下。
因关注需求预测实践的挑战而与众不同,《大数据预测:需求驱动与供应链变革》在提升预测流程以更好满足客户需求的细节方面更有全面升级。本书作者——需求预测先行者Charles Chase不仅对统计方法进行了全面讲述,也对如何使用真实数据和案例将方法应用于需求驱动预测流程实践进行了全面探讨。
《大数据预测:需求驱动与供应链变革》以更新的研究和案例作为其主要特点,包括新理论发展,展示了新的实证发现和技术发展。新版增加了需求位移、非季节性和季节性的ARIMA模型、传递函数和互相关函数图等。
《大数据预测:需求驱动与供应链变革》适用于每一个专业为预测和需求规划的有志人士,本书为你提供已验证的流程、方法论以及可立即应用于预测准确性显著提升的评估指标。
Content Description

《大数据预测:需求驱动与供应链变革》带领读者历经了从50年前预测先锋Bob Brown所信奉的基本方法到今天所用的一些很具创新性的预测方法的整个过程。全书共有11章,从对需求驱动的预测进行界定开始,带领读者回顾基本的预测方法后,进入高级的时间序列方法,然后再进入今天所使用的很具创新性的技术,比如利用供需关系来支持多层次预测和对下游需求信号的整合。
《大数据预测:需求驱动与供应链变革》文字朴实平白,条理清晰,实证结合方法说明,极具说服力和操作意义。本书意义重大,加快了需求驱动预测专业的发展。对于希望通过利用更科学、更精准、更符合客户导向原则的需求驱动预测方法,来推动并提升企业运营管理水平的预测分析人员及业务规划人员来说,本书极具参考价值。
Author Description

Charles W. Chase Jr.是SAS制造业和供应链行业全球实践部首席咨询专家,也是首席架构师和战略师,为SAS客户提供需求规划及预测解决方案,以提高客户的供应链效率。他在快速消费品(CPG,consumer packaged goods)行业的从业经验超过26年,是销售预测、市场响应建模、计量经济学以及供应链管理等方面的专家。在进入SAS工作之前,Chase领导了支持SAS预测服务器上市的战略营销活动,该服务器被《知识管理世界》杂志誉为“2005年年度受欢迎产品”,即SAS需求驱动的预测。他还参与了3个预测和营销智能过程/系统的再造、设计和实施。Chase也在Mennen Company、Johnson Johnson、Consumer Products Inc.、Reckitt Benckiser、the Polaroid Corporation、Coca-Cola、Wyeth-Ayerst Pharmaceuticals和Heineken USA等公司工作过。
Chase是《商业预测期刊》的前副主编,现在是《预测:全球应用预测期刊》从业者咨询委员会的活跃成员。他写过几篇有关销售预测和市场响应建模的文章,被《供需链管理杂志》2004年2/3月刊评为“2004年度知名教授”,同时,他也是《Bricks Matter:The Role of Supply Chains in Building Market- Driven Differentiation》(Wiley,2012)一书的合著者。
Comments

“Charles Chase是商业预测协会很有想法的领导者之一,这使得他极具完成这本我认为对于需求驱动预测具有决定性意义一书的资格。新版带领读者对需求导向预测的基本理论、高级时间序列方法以及其他已于今天应用的一些创新方法进行了回顾。”
——Lawrence“Larry”Lapide博士,
麻省理工学院(MIT)交通与物流中心研究人员,马萨诸塞大学讲师,
商业预测和规划研究会会员申请资格审查者,商业预测和规划终身成就奖获得者

“商业预测领域很有敏锐头脑的Charles Chase写的《大数据预测:需求驱动与供应链变革》,是我喜爱的书之一,能向大家推荐是我极大的荣幸。Charles Chase创造了一本真正完整意义上的书。很罕见地,他将关注聚焦于关键方法论以及与CPG行业高度相关的商业挑战之上。本书是新版,它在新产品预测领域拓展了一门新的关键专业学科,在越来越短的产品生命周期世界里,这门学科显得尤为重要,对于预测成功,它要么成就它,要么破坏它。总而言之,如果你想寻找一本包罗万象的预测书,那么这本就是。《大数据预测:需求驱动与供应链变革》是本必读书。”
——Lauge Valentin,乐高(LEGO)集团公司预测总监

“Charles Chase所呈上的这本书,为预测人员提供了几乎覆盖全部层面的实践知识和定量工具,预测人员利用它们,可以在需求建模和预测方面再上一个大台阶。利用平实精准的语言,作者将现实世界的案例与对高级统计方法的阐述相结合,以证明如何通过分析能力的应用推动预测解决方案的发展。预测行内人士将从本书所分享的知识中受益,若将此书随手携带,必会有颇具价值的参考之用。”
——Glenn Keltner,博士,雀巢(Nestle)公司需求规划发展部经理
Catalogue

1 第1章 揭秘预测:神话与现实
1.1 数据采集、存储和处理的现状
1.2 预测艺术的神话
1.3 特惠区的困扰
1.4 判断超控的现状
1.5 由烤箱清洁剂引发的关联关系
1.6 更多并不一定就是更好
1.7 不受约束的预测、受约束的预测和规划的现状
1.8 东北地区销售综合预测
1.9 层层递进法则
1.10 欠佳的计划
1.11 按订单包装和按订单生产
1.12 “你需要配上炸薯条吗?”
1.13 总结
1.14 注释
25 第2章 什么是需求驱动的预测?
2.1 传统需求预测的转变
2.2 需求生成存在什么问题?
2.3 传统需求生成的根本缺陷
2.4 仅仅依靠供应驱动策略并非解决之道
2.5 什么是需求驱动的预测?
2.6 什么是需求感知和需求塑造?
2.7 需求管理流程的改变是关键
2.8 沟通是关键
2.9 成功需求管理的评估
2.10 需求驱动预测流程的好处
2.11 需求管理流程推进的关键步骤
2.12 为什么企业不接受需求驱动的概念?
2.13 总结
2.14 注释
61 第3章 预测方法概述
3.1 基础方法论
3.2 不同类别的方法
3.3 未来的可预见程度如何?
3.4 导致预测误差的一些原因
3.5 细分产品以选择合适的预测方法
3.6 总结
3.7 注释
83 第4章 预测性能测算
4.1 “我们超预测完成任务,让我们开个Party庆祝吧!”
4.2 预测性能测算的目的
4.3 标准统计误差术语
4.4 预测误差的具体测算
4.5 样本外测算
4.6 预测价值增加
4.7 总结
4.8 注释
103 第5章 使用时间序列数据的定量预测法
5.1 模型拟合过程的理解
5.2 定量时间序列方法简介
5.3 定量时间序列法
5.4 移动平均
5.5 指数平滑法
5.6 一次指数平滑法
5.7 Holt双参数法
5.8 Holt-Winters法
5.9 Winters加法季节性模型
5.10 总结
5.11 注释
133 第6章 回归分析
6.1 回归方法
6.2简单回归
6.3 相关系数
6.4 判定系数
6.5 多元回归
6.6 基于散点图和线图的数据可视化
6.7 相关矩阵
6.8 多重共线性
6.9 方差分析
6.10 F检验
6.11 调整后的R2
6.12 参数系数
6.13 t检验
6.14 P值
6.15 差异膨胀因子
6.16 德宾—瓦特逊统计
6.17 干预变量(或哑变量)
6.18 回归模型的结果
6.19 建立多元回归模型的关键行动
6.20 有关回归模型的忠告
6.21 总结
6.22 注释
171 第7章 ARIMA模型
7.1 步骤1:确定初始试验性模型
7.2 步骤2:对模型参数进行评估和诊断
7.3 步骤3:生成预测结果
7.4 季节性ARIMA模型
7.5 Box-Jenkins总结
7.6 ARIMA模型拓展:涵盖解释变量
7.7 传递函数
7.8 分子和分母
7.9 理性传递函数
7.10 ARIMA模型结果
7.11 总结
7.12 注释
201 第8章 加权综合预测法
8.1 加权综合预测是什么?
8.2 建立方差加权综合预测
8.3 加权综合预测使用指南
8.4 总结
8.5 注释
211 第9章 感知、塑造和关联需求以指导供应:MTCA应用案例
9.1 利用多层次因果分析(MTCA)将需求与供应进行关联
9.2 案例研究:碳酸软饮料的故事
9.3 总结
9.4 附录9A:消费者包装货品专用术语
9.5 附录9B:广告GRP/TRP的广告遗留指数转化
9.6 注释
237 第10章 新产品预测:结构判断法应用
10.1 改良型新产品与革命性新产品之间的区别
10.2 新产品预测的总体感觉
10.3 新产品预测概述
10.4 候选产品界定
10.5 新产品预测流程
10.6 结构化判定分析
10.7 结构化流程步骤
10.8 统计过滤步骤
10.9 建模步骤
10.10 预测步骤
10.11 总结
10.12 注释
263 第11章 战略价值评估:评估需求预测过程的预备性
11.1 战略价值评估体系
11.2 战略价值评估流程
11.3 SVA案例研究:XYZ公司
11.4 总结
11.5 建议阅读
11.6 注释
293 译者后记

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Jingdong book