☆酒店收益管理培训(Revenue_Management)酒店英文培训资料
星级酒店收益管理
V5i = Forecasts 预报
– Group and transient demand 团队和散客的需求 – Calculates the value of each type of demand 演算各类型需求的价值 – Self Learning / Auto correcting Forecasts 自动认知／自动更正预报
– At House & Room Type 在店 ＆ 房型
• Cluster Decisions 群组的决策
– To optimize multiple inventories 优化多种库存房量
(Please see the IDeaS brochures for more details about these decision outputs)
Demand Forecast
需求预测
Observed Demand
150 Rooms
Total Demand
MON
TUE
WED
THU
FRI
Demand Mix - Hotel Capacity – 150 需求组合-150间客房的酒店
Total Demand 1120 Rooms
100
100 100
Results of Optimization
优化的成果
• This is one example of how a Revenue System can assist your hotel to make more money, by having more detailed accurate forecasts and better managing demand. • 这是一个例子，收益管理系统是怎样通过详细精 确的预报及更好的决策来帮助酒店增加收入。
酒店收益管理
3、收益管理绩效指标 平均可供出租客房收入（RevPar) 平均可供出租客房收入指数（RevPar MPI) 毛利润（GOP,Gross Operating Profit) 满房率（Sellout Successful Rate) 预测准确率（Forecast Accuracy)
3、收益管理绩效指标 平均可供出租客房收入（RevPar) 平均可供出租客房收入指数（RevPar MPI) 毛利润（GOP,Gross Operating Profit) 满房率（Sellout Successful Rate) 预测准确率（Forecast Accuracy)
3、收益管理绩效指标 平均可供出租客房收入（RevPar) 平均可供出租客房收入指数（RevPar MPI) 毛利润（GOP,Gross Operating Profit) 满房率（Sellout Successful Rate) 预测准确率（Forecast Accuracy)
成绩斐然
共励并进 任重道远
平均房价、客房出租率与平均可出租客房收入的关系
情形 1 平均客房价格 ￥550 出租客房总数 10 客房出租率 10% 平均可出租客房收入 ￥55 客房总收入 5500
2
3 4 5 6
￥450
￥350 ￥288 ￥200 ￥110
40
70 85 92 110
40%
70% 85% 92% 100%
￥180
谢
谢
时间 时机 顾客 市场 销售租客房收入（RevPar) 平均可供出租客房收入指数（RevPar MPI) 毛利润（GOP,Gross Operating Profit) 满房率（Sellout Successful Rate) 预测准确率（Forecast Accuracy)
收益管理英语
收益管理英语收益管理revenue management /yield management市场占有率market share应有市场占有率market fair share动态定价法dynamic pricing 产品product价格price 销售渠道place 促销promotion 无限制需求unconstrained demdnd 分析analysis 预订进度booking pace 被拒绝的需求denials 后悔的需求regrets超订转房客walks预订进度分析booking pace analysis细分市场预订模式分析booking pattern analysis订房模式booking pattern 细分市场入住模式staying pattern analysis低谷valley斜坡slope肩膀shoulder 高峰peak定量预测quantitative forecasting methodeuda差异化differntiation 优化optimation距离入住日期天数lead time最低入住天数minimum length of stay最高入住天数maximum length of stay 提前关闭天数cut off day 预订规律booking pattrnty入住规律staying pattern 酒店管理平台amadeus platform酒店管理系统amadeuspms收益管理系统amadeusrms酒店分销系统amadeus distribution收支平衡点breakeven point预订类型denial types 忠诚顾客计划loyalty programs淡季low season平季shoulder season旺季high season容量控制capacity contro团队替换分析displacement旅行社联盟报价RFP（requoest for proposal）。
RevenueManagement酒店收益管理
RevenueManagement酒店收益管理Introduction to Revenue Management收益管理简介What is Revenue Management?什么是收益管理？Components ofRevenue Management收益管理的组成部分Necessary Conditions必要条件Relatively fixed Capacity相对固定的库存量Time-perishable Inventory非耐久性产品Time-variable Demand季节性需求量Appropriate Cost Structure合理的成本结构Segmented Market市场细分化AdvanceBooking预订What is Revenue Management?什么是收益管理？Selling the right PRODUCT for the right PRICE to the right PEOPLE at the right TIME through the right DISTRIBUTION CHANNEL in order to MAXIMIZE REVENUE for the hotel在适当的时间、通过适当的分销渠道、以适当的价格、向适当的客户销售适当的产品，以此最大化酒店的收益。
PRODCUT: RoomTypes, Room Nights, F&B, Meeting Space 产品：房型，房晚, 餐饮, 会议设施PRICE:Group,Transient, Wholesale, etc价格：团队价、散客价、批发价及其他PEOPLE:Corporate, Leisure, etc客户：商务客、休闲客及其他TIME:Seasonality, Weekday vs. Weekend, LOS时间: 季节性，周中 vs. 周末，住客时间长短DISTRIBUTION CHANNEL分销渠道Call Center电话预订中心On property酒店直接定房GDS (Global Distribution System)全球分销系统 品牌网站3rd Party Channels (Ctrip, Elong, Expedia)第三方渠道（携程、易龙、Hotwire）Components of Revenue Management收益管理的组成1. CompetitiveAnalysis 竞争分析2. Forecasting 预测3. Pricing 定价4. InventoryControls 客房控制5. PerformanceMeasurement 考核指数RM Component—Competitive Analysis竞争分析Helps Determine the“Competitive Set”帮助确定“竞争对手组”A hotel’s closest competitors in terms of product, geography,and/or type of business在产品、地理位置以及/或客户方面最接近的竞争对手Primary and secondary competitive sets主要竞争对手组以及次要竞争对手组Macro-Level: Determine the value of the hotel in the overallmarketplace based on the product positioning 宏观层面：根据产品确定酒店在市场中的定位及价值Micro-Level: Benchmark performances against competitors 微观层面：衡量与竞争对手表现差异RMComponent—Forecasting预测Why do we forecast?我们为什么要预测？Determine pricing确定价格Based on UNCONSTRAINE DDEMAND基于”无限制的需求”Unconstrained demand = the number of people who would have stayed at the hotel if it had an infinite number of rooms.无限制需求 = 假设酒店无房间数限制情况下的住店客人数Must measure unconstrained demand to determine price sensitivity of the customer.All customers have different price sensitivities based on product, market, and individual needs.必须通过衡量无限制的需求来了解客人的价格敏感度。
现代酒店收益管理培训教材
高峰期的动态定价
低谷期的动态定价
在市场竞争激烈、客房出租率处于低谷时，为减少亏损、保住市 场份额，争取扭转局势的时机，可在短期内把边际贡献作为客房定价 的下限。
门来承担实现酒店目标收益率的责任。由计算可看出，如其他部门盈利高， 则房价可低一些；如其他部门盈利低，则房价就需上升。
事实上，如其他部门收益低，不应由高昂的、缺乏竞争力的房价来弥补。 同样，其他部门的高收益，也不应成为制定低房价的理由。
赫伯特定价法
酒店现行竞争导向定价方法的评析
如果酒店行业的竞争异常激烈，许多酒店在定价时就会把竞 争因素放在首位，这样就形成了以竞争为中心的随行就市定价法。
创造利润最大化的理念、方法与步骤
现代酒店收益管理
Content
一.收益管理起源与要素 二.收益管理的定价决策 三.收益管理的实战技巧 四.收益管理的预测方法
一.收益管理的起源与要素
一1.收. 收益益益管管理理在的航起起空源业的成功运用
收益管理的理论起源于20世纪80年代中期的美国航空业。1978年， 美国废除了航空管制，使得航空公司能够自由地增减飞行线路并自由地 浮动票价。同时，新的航空公司大量进入市场，尤其是以美国西南航空 公司为代表的低成本航空公司进入市场，导致了航空公司之间前所未有 的激烈竞争。
采用竞争为中心定价法的酒店，紧盯竞争对手酒店的价格， 使自己的价格随着竞争对手价格的变化而变化。在这种思想指导 下，酒店价格管理的目标，就是使自己的价格与竞争对手的价格 保持一致。所以，当竞争对手涨价时，跟着涨价；当竞争对手降 价时，跟着降价。
收益管理入门培训课件
响或者也许产生影响？市场上是否有供给增加（例如：新的 竞争者加入）需求强度如何？
32
定价 Pricing
竞争 查看你的市场份额报告并对过去酒店相对于竞争对手的 绩效进行评估。相比去年，你的平均价格收益（RevPAR），是 增加还是下降了？ 客户满意度 同竞争者相比，你的客人满意如何？征询客人意 见？在那些方面有改进，那些方面退步了？
33
评判性地衡量散客价格定位
价格的有效性 – 衡量我们的门市价格定位，例如：散客平均 房价/散客的门市价格. 酒店需制定出全年的日常市场目标并且
每个季度评估一次。即使价格评估体现出一个运作健全酒店的 行为，但是，评估结果对于某些酒店会有些其它的判断，例如 ，较多的公司合约客户的价格可能会是这种评估降低。
4
收益管理的原理
收益管理的基本原理就是通过对酒店客源市场的细分， 对不同客源在不同时刻的需求进行定量预测，然后通过优化 方法来确定动态价格和预定渠道控制，最终使得酒店总收益 达到最大化，并保持公司的持续增长。当然对不同的酒店， 由于各自的市场定位，顾客来源，管理理念，控制机制的不 同，其价格和收益管理的方法及其作用也不尽相同。
20
5.超售或酒店即将满房
• 限制第三方网络订房 • 设置最大或最小居住天数 • 关闭员工价格和业主特价 • 严格预订担保和取消预订政策 • 房间销售应筛选高价位和重要的协议客户
21
5. 超售或酒店即将满房
• 前台、销售人员在接收新预订时，随时查阅电脑和价格 • 报价应由高到低 • 每天需密切留意并检查：
减少价格，并使其趋 于合理性
分析市场结构并进行 价格与客源的优化
酒店客房收益最大化
酒店收益管理
酒店收益管理——如何有效预测客房出租率第一章收益管理第一节收益管理及概念酒店的收益管理对客房预测具有很大的意义。
收益管理又称收入管理，英文Revenue 第一章收益管理第二节酒店收益管理的解说一、酒店收益的特殊性酒店的一个最主要的特征：酒店在短期内或在某一个时期内无法调整自己的产品，以客房的供给量为例，因淡旺季等原因所引起的客房供给量的高低都会影响到酒店的收益。
u 客房需求量＜客房供给量：待售客房的价值消失u 客房需求量＞客房供给量：无法满足顾客需求，潜在的收益消失平衡供给和需求之间的矛盾一直是酒店业研究的重要课题，因此，准确估计需求和合理分配资源的迫切引发了收益管理的出现。
二、收益管理的重要性1、对于管理的价值只有了解了收益目标，才能根据收益目标采取措施来提高管理的水平和服务的水准。
2、对于企业的效益应用收益管理的企业，在没有重大支出的情况下，收益增加了3%-7%，利润增加了50%-l00%。
收益管理是个大概念，酒店的收益管理包括需求预测、超量预订、客房分配和定价等内容；需求预测对酒店收益管理来说尤其重要。
【举例说明】客房部安排员工做清洁工作、餐饮部为客人提供早餐时都会按照客房出租率，假如客房出租率的预测为70% ，而实际可到达率为90% ，这种情况下，将会有部分房间无人打扫，也会有部分客人没地方吃早餐或者服务的人员不够。
因此，在日常运营中，每天都应该对第二天及以后的时间进行预测，从而可以合理地安排工作计划，最大化酒店的收益，提高酒店的服务水平。
第二章客房出租率的预测第一节什么是预测有关酒店的预测内容很多，本课程主要讲解客房出租率的预测，收入及平均房价的预测在后面的预测表中将有简单的介绍。
一、预测（forecast）二、预测统计分析的原理通过对过去和现在的数据资料进行分析，揭示出历史数据背后的必然规律性，明确未来的发展趋势。
这也就意味着做好一个预测，必须掌握某些因素的规律。
而掌握规律所需要的时间因酒店的不同而不同，有些酒店可能需要3—5个月，有些酒店可能需要把去年的数字联系起来，才能做好一个预测。
酒店收益管理培训PPT课件(精)
制定竞争策略
根据竞争对手的评估结果，制定相 应的竞争策略，如差异化竞争、成 本领先等。
03
房型与价格策略
房型设置与优化
房型分类
根据酒店定位、市场需求和竞争情况 ，合理划分房型，如标准间、豪华间 、套房等。
房型特点
房型优化
根据入住率、客人反馈和市场需求， 不断调整和优化房型配置，提高酒店 整体收益。
协调措施
通过制定合理的价格策略、促销策略和服务标准，减少渠道冲突的发 生。同时，加强与合作伙伴的沟通和协作，共同推动业务发展。
05
收益最大化策略
超额预订策略
含义
超额预订是指在酒店订房已满的情况下，再适当增加订房数量， 以弥补因客人取消预订或临时取消入住而造成的损失。
优点
降低空房率，提高酒店收益。
收益管理在酒店业的应用
预测市场需求
通过分析历史数据、市场调查 等手段，预测未来一段时间内
的市场需求情况。
制定价格策略
根据市场需求预测结果，制定 相应的价格策略，包括基础价 格、折扣价格、促销价格等。
优化资源配置
根据市场需求和价格策略，合 理调整酒店房源、人力资源等 资源配置，提高资源利用效率 。
数据分析与决策支持
市场需求预测方法
时间序列分析
利用历史数据预测未来市 场需求的趋势和周期变化 。
回归分析
通过建立数学模型，分析 自变量和因变量之间的关 系，预测未来市场需求。
机器学习算法
运用机器学习算法对历史 数据进行训练和学习，预 测未来市场需求。
竞争对手分析
识别竞争对手
通过市场调研和数据分析，确定 酒店的主要竞争对手。
等。
酒店收益管理培训课件PPT
酒店A的问题在于：预订部、前台、销售部都没有在意第二天的客房供给。酒店A内上至经理下至员工几乎没有人意识到客房出租率预测 的重要性，进而导致预订部与前台在不知晓客房供给的情况下，持续不断地接纳订单，以致于多订了一个团，使其自己的酒店进入一种 尴尬的困境。酒店B则处理得很好，在保证自己持续稳定运营的基础上，不但帮助了别人，得到了别人的感激，同时自己也获得了收益 由此案例可以看出：如果预测意识不强或者对房间的控制不是很好会给酒店带来很大的损失。任何一家酒店都应该以预测为基础，将预 测作为一个非常重要的数据
17
WHOLESALE
旅行社 旅游公司散客
旅行社、旅游公司散客是 通过旅行社、旅游公司订 房的散客，这样做的好处 是没有任何后顾之忧
LONG STAY
长住客
长住客在酒店的定义是至 少住满14天，任何一个酒 店从总经理到底下的员工 对长住客的关注程度要超 过其他任何一个客源市场 的客人，因为这部分收入 非常稳定
目前一些国际酒店集团，像喜来登饭店、希尔顿饭店、 凯悦饭店、洲际酒店、玛丽奥特国际饭店、香格里拉饭 店等全球顶级管理集团已经将收益管理作为本企业在市 场竞争中的制胜法宝。但是反观过来，我们国内又有多 少酒店会注重收益管理这个概念？
收集信息
预测是有效收益管理的核心环节，预测必须以天为基础， 必须提前30到60天(甚至于90天）进行预测，为了做出 准确的预测就需要收集各种各样的信息
预测统计分析的原理
揭示规律
通过对过去和现在的数据资料进行分析，揭示出历史数 据背后的必然规律性，明确未来的发展趋势。这也就意 味着做好一个预测，必须掌握某些因素的规律。而掌握 规律所需要的时间因酒店的不同而不同，有些酒店可能 需要3—5个月，有些酒店可能需要把去年的数字联系起 来，才能做好一个预测
第一章 酒店收益管理内涵 2- 衡量收益的指标
_________________________ =饭店整体收入最大化
客房收入最大化是饭店收益管理的重点
通常饭店70%以上收入来源客房
RevPAR和TrePAR
平均每间可出租的客房创造的客房收入： Revenue Per Available Room
平均每间可出租的客房创造的总收入： 每房总收益则是包含了所有收入中心的总收入。 Total Revenue Per Available Room
或 = 实际平均房价 × 实际出租率 = 160 元× 50% = 80 (元)
实例2:哪个饭店表现最佳?
饭店
A B C
平均房价
$107 $149 $189
出租率
100% 75% 50%
RevPAR
$107 $111.7 $94.5
两家饭店的年收益差比较
饭店 A B
平均房价 $107 $149
出租率 100% 75%
2. 低出租率但是略高的每日平均房价来出售房间的饭店需要打扫的房间 少，在同样的客房收入的情况下，变动成本最低。倾向于以较低的每日平均 房价出售房间获得更高出租率的饭店则盼望着更多客人来到饭店会带来饭店 其他收入的增加（停车，餐饮，房间内电影点播服务等等）。
3.收益经理会视这两个场景为完全不同的战略所导致的完全不同的结果。
客房出租率的区间
0—100%
？
价格与需求关系的一般市场规律
售价
$100
$50
$1 1间
50间
100间
销量
价格与需求的平衡点
$100
售价
房价 $ 50
$50
销量 50%
酒店收益管理外文翻译中英文2019
酒店收益管理中动态客房分配的解决方法中英文2019原文Decomposition methods for dynamic room allocation in hotel revenue managementN.Aydin，S.I.BirbilAbstractLong-term stays are quite common in the hotel business. Consequently, it is crucial for the hotel managements to consider the allocation of available rooms to a stream of customers requesting to stay multiple days. This requirement leads to the solving of dynamic network revenue management problems that are computationally challenging. A remedy is to apply decomposition approaches so that an approximate solution can be obtained by solving many simpler problems. In this study, we investigate several room allocation policies in hotel revenue management. We work on various decomposition methods to find reservation policies for advance bookings and stay-over customers. We also devise solution algorithms to solve the resulting problems efficiently.Keywords：Revenue management，Hotel，Capacity control，Decomposition methodsIntroductionHistorically, the airline industry played the steering role in revenue management (RM). Today, however, there is a wide range of applications in different industries with volatile demand, requesting fixed and perishable capacity (Kimes, 1989). Although the hotel industry is one of the typical application areas of revenue management, the research in this particular area lags behind the work produced for other service industries. In their recent work, Ivanov and Zhechev (2012) and Ivanov (2014) present a review of the methods proposed in the hotel RM literature and point out the gaps.In general, well-known airline RM techniques, such as booking control and pricing, can be applied to hotel RM problems. However, it is important to consider several constraints that are unique to hotel reservation systems. First, multi-day staysin hotels are quite common. While a flight itinerary includes, on average fewer than three legs, the number of nights a typical customer spends in a hotel can be a week or even more (Zhang & Weatherford, 2017). Second, the demand process is different. Hotel customers may decide to stay longer and extend their reservation while they are staying in the hotel (Kimes, 1989). Third, airline customers generally make advance bookings but a number of hotel customers consist of walk-ins. Moreover, the early reservations in the booking interval are even allowed to cancel their bookings at no extra cost.In this paper, we focus on the room allocation decisions for a hotel. The optimal policy to accept or reject an arriving customer can be obtained by analyzing the stochastic nature of the customer arrival process. In hotel reservation systems, the customers are classified as the advance bookings, the stay-overs and the walk-ins. While the advance bookings make room reservations before they arrive at the hotel, the walk-ins show up without any reservation. The stay-overs are the customers who ask for an extension for their reservations during their stay in the hotel. Recently, hotel reservation systems have started offering extended stay as an option due to high customer demand (Tepper, 2015). For instance, Priceline (2017) and Hotwire (2017)present “add-a-night” and “add to your stay” options to their existing customers. The arrival process of the advance bookings and walk-ins are similar. The only difference is that the walk-in customers arrive after the reservation period ends. However, the stay-over requests depend on the accepted advance bookings. To simplify our notation, we ignore the walk-in customers and formulate our problem by considering the advance bookings and the stay-overs. Then, we explain how one can easily incorporate the walk-in customers to our proposed models. To the best of our knowledge, the dynamic model of stay-over customers in a network setting has not been previously studied in the literature.The research contributions in this paper come from the application and the analysis of two decomposition approaches. These are the day-based and the period-based decompositions. Our day-based decomposition is similar to the one proposed by Kunnumkal and Topaloglu (2010). We simplify their decompositionmethod and show that our proposed model provides a lower bound to their model. We set forth a dynamic model for the advance bookings and formulate a linear program for the problem. The resulting model is then solved with the constraint generation method. We also propose alternate approximate models, which provide upper and lower bounds on the optimal expected revenue of the original model. To manage the stay-over requests, one needs to keep track of the number of reservations in each booking type. A day-based method, however, decomposes the network problem into independent days, and this decomposition approach causes loss of information on the number of customers in each booking type. Our solution to this hindrance is a period-based decomposition method, which is an extension of another approach recently proposed by Birbil, Frenk, Gromicho, and Zhang (2014). First, we focus on the single-day stay-over problem, as the request for an additional night is the most frequently realized stay-over case in real-life (Talya, 2016). Though our model is different than the one set forth by Birbil et al. (2014), we successfully build on their decomposition idea. Second, we consider the multi-day stay-over problem and present a two-period approximation, which combines the pair-based decomposition with the deterministic linear programming approach. In period one, we observe the reservation activity of the advance booking customers. In period two, we take into account the stay-over requests of the customers whose bookings have been accepted. To test the performances of the proposed decomposition approaches, we conduct simulation experiments and compare our results with those obtained by several well-known models from the literature. Our computational study indicates that the proposed decomposition approaches are apt to effective room allocation in hotel RM.Review of related literatureWe begin by reviewing the related work on hotel RM. Then, we summarize the decomposition approaches frequently applied to the network RM problems.Ladany (1976) works on a single-day stay model for a hotel with two types of resources. The aim of the model is to find an allocation policy to maximize the daily expected revenue. He develops a dynamic programming formulation and obtains the decision policy for each resource. Williams (1977) works on the single-day staymodel during the peak demand period. In this model, he assumes that demand arrives from three different sources: the stay-overs, the reservations and the walk-ins. He computes the reservation policy for each customer type by comparing the costs of underbooking and overbooking. Bitran and Leong (1989) focus on the multi-day problem by considering the walk-in and stay-over requests. They model the multi-day reservations as a series of independent, single-day reservations. Bitran and Mondschein (1995) develop a dynamic programming model for a single-day stay problem with multiple products. Since the resulting model is computationally intractable for the real size problems, they utilize several heuristics when searching for the optimal allocation policy. Weatherford (1995) focuses on the effect of the length of stay. He proposes a heuristic method based on a static model and compares this method with the other booking policies developed for the single-day stay problems. Bitran and Gilbert (1996) work on a single-day stay and single-room problem. They assume that during the service day, three types of customers show-up: the customers with guaranteed reservations, the customers with reservations and the walk-ins. They develop a dynamic model and propose a heuristic method to obtain the room allocation policy. Baker and Collier (1999) extend the study of Weatherford (1995) as well as the work of Bitran and Mondschein (1995) by allowing cancellations, overbooking and stay-overs. They develop two heuristics that integrate overbooking with the capacity allocation decisions. They compare the performances of these heuristics against the other booking control policies in the literature. Through this comparison, Baker and Collier (1999) discuss the advantages of each policy under different operating environments.Later studies focus on multi-product and multi-day stay problems. Chen (1998)presents a general formulation for a deterministic problem and discusses that it can be transferred to a network flow problem. Moreover, he shows that the optimal solution of the linear program is always integral. Goldman, Freling, Pak, and Piersma (2002) propose deterministic and stochastic linear programming models to find the nested booking limits and the bid prices for the multi-day stay problem. They follow the work of Weatherford (1995) to develop the deterministic model. For the stochasticmodel, they extend the work of De Boer, Freling, and Piersma (2002)on the airline revenue management problem. However, unlike the models proposed by Weatherford (1995) and De Boer et al. (2002), they use the booking control policies over a rolling horizon of decision periods. Lai and Ng (2005) work on a stochastic programming formulation for a multi-day stay problem. They apply robust optimization techniques to solve the problem on a scenario basis. They also consider the risk aversion of the decision maker and use the mean absolute value to measure the revenue deviation risk. Koide and Ishii (2005) work on the optimal room allocation policies for a single-day stay by considering the early discounts, the cancellations and the overbookings. They examine the properties of the expected revenue function and show that it is unimodal on the number of allocated rooms for early discount and overbooking. As with Lai and Ng (2005), Liu, Lai, and Wang (2008) present revenue optimization models for a multi-day stay problem by considering the revenue risk. They propose a stochastic programming model with semi-absolute deviations to measure the risk. Guadix, Cortes, Onieva, and Munuzuri (2010) present a decision support system for forecasting and room allocation decisions. They work on the deterministic and stochastic programming models by considering group arrivals. The proposed decision support system integrates these models for room allocation and pricing decisions. Nadarajah, Lim, and Ding (2015) study dynamic pricing policy for a single type of room by considering the multiple day stays. Since the resulting model is computationally intractable, they propose pricing heuristics based on fluid approximation and approximate linear programming. They analyze the properties of the pricing policy under the peak demand.The solution approaches considered in this study build on the literature on decomposition methods in network revenue management. The output of a decomposition method is used to construct various capacity controls, such as bid-prices and nested booking limits. Adelman (2007) develops an approximation method to compute the dynamic bid prices. He first formulates the network problem as a dynamic model, which suffers from the curse of dimensionality. Thus, he derives a standard linear program by approximating the dynamic programming valuefunctions. This approach provides an upper bound on the optimal expected revenue. Zhang (2011) proposes a nonlinear, non-separable approximation to the dynamic programming model that leads to a tighter upper bound. Topaloglu (2009)focuses on a Lagrangian relaxation method to decompose the network problem into many single capacity problems. Erdelyi and Topaloglu (2009) work on the overbooking problem in an airline network and develop separable approximations to decompose the problem by individual flights (legs). This approach constructs capacity dependent bid prices. However, it becomes quite difficult to compute the value functions for each leg as the size of the problem increases. To reduce the computational burden, Kunnumkal and Topaloglu (2011) develop a stochastic approximation algorithm that provides a set of capacity independent bid prices. In this approach, they formulate the total expected profit as a function of the bid prices and use stochastic gradients to obtain a good bid price policy. Recently, Kunnumkal and Topaloglu (2010) propose a new leg-based decomposition method for the airline revenue management problems that involve the customer choices. In this method, they first allocate the revenue of each itinerary among the legs covered by the itinerary. Then, they define a penalty term to incorporate the network effect. They view the revenue allocations and the penalty terms as decision variables, and use subgradient search to find the optimal solution. Although this solution approach is manageable in small size networks, it can be impractical for the problems of substantial size networks. Hotel network revenue management problems are also tackled with the decomposition methods. Zhang and Weatherford (2017) work on a dynamic pricing problem. They generalize the approximation method of Zhang (2011) and decompose the problem into independent single-day problems by approximating the value functions with nonlinear non-separable functions. They test the proposed approach by using the data from a hotel. Aslani et al. (2013) also propose a decomposition method for a pricing problem in hotel revenue management. They develop an approach to estimate the effective arrival rate for each day by considering the stock-outs and the customer losses due to high price levels. They decompose the network problem into single-day subproblems by using thesedaily arrival rates. Our study has several distinguishing features compared with the earlier work. To begin with, we focus on the multiple day problem and propose several decomposition methods to attack the problem. In particular, our aim is to find a dynamic capacity allocation policy that takes into account the advance bookings and the stay-over customers. We first study advance bookings and propose day-based decomposition methods. We work on a fare-allocation strategy where the reservation fares are allocated on day basis depending on the time of the booking. Our method is based on dynamic programming formulations for the single-day revenue management problems, hence it can capture the temporal dynamics of the reservation requests more accurately compared with the static models. We also present alternate solution methods to improve the computational time for the large-scale problems. Later, we study stay-over requests in hotel RM and propose a pair-based dynamic programming method. To the best of our knowledge, the dynamic model of stay-over customers in a network setting has not previously been studied in the literature. We also discuss the applicability of the proposed models to several cases, such as late checkout and overbooking. Finally, our computational experiments demonstrate that the proposed methods can generate significantly higher profits than the well-known benchmarks in the literature. The performance gaps are especially significant when the daily hotel capacity is tight and the stay-over probability is high. In addition, day-based decomposition methods perform significantly better when the hotel controls the fares on a per-day basis and does not offer discount for long-term stays.ConclusionIn this study, we work on the dynamic room allocation problem in hotel revenue management. Due to the complexity of this problem, we concentrate on several approximation methods. We analyze the structural properties of the problem and present day- and pair-based decomposition approaches that can handle the walk-in and the stay-over customers. First, we work on the day-based decomposition methods. Day-based decomposition generates independent subproblems for each day and, hence, it cannot store the number of reserved rooms for each product. Therefore, incorporating the stay-over customers becomes a challenge. In the second part, wework on the stay-over extension. To the best of our knowledge, the dynamic programming model that includes the stay-over customers has not been proposed in the literature before. We first focus on the single-day stay-over problem. By extending the work of Birbil et al. (2014), we propose a solution method. Second, we consider the multi-day stay-over problem and present a two-period approximation, which combines the pair-based decomposition with the deterministic linear programming. We conduct a thorough computational study and investigate the performances of our proposed models along with some well-known approaches used in the literature. Our computational experiments indicate that the proposed policies perform well. The performance gaps are especially significant when the hotel’s daily capacity is t ight and the stay-over probability is high.As we mentioned in Section 5.2, our stay-over models can be extended to several other applications in hotel RM. Recently, hotel reservation systems have started to offer late checkout option to their customers. Late checkout requests can be considered as a special case of stay-over problem where the customers can extend their stay until the allowed time specified by the hotel. Following the same construction as for the stay-over model, we can obtain the reservation policies for late checkouts. Another important issue in hotel revenue management is overbooking. Similarly, the overbooking option can be incorporated in the multi-day stay-over model and it can also be solved in two stages. However, it is important to note that preallocating the hotel capacity to even more pairs and determining the individual overbooking limit for each pair may poorly affect the control of hotel capacity network-wide. Incorporation of the overbooking option is a potential topic for future research.译文酒店收益管理中动态客房分配的解决方法摘要长期住宿在酒店行业中很常见。
酒店的收益管理
收益管理目录收益管理概述原理收益管理前提和工作内容收益管理在实践中需注意的问题展开收益管理概述收益管理(Revenue Management 或Yield Management)是一种谋求收入最大化的新经营管理技术。
它诞生于上世纪八十年代，最早由民航开发。
收益管理,又称产出管理、价格弹性管理；亦称“效益管理”或“实时定价”,它主要通过建立实时预测模型和对以市场细分为基础的需求行为分析，确定最佳的销售或服务价格。
其核心是价格细分亦称价格歧视(price discrimination)，就是根据客户不同的需求特征和价格弹性向客户执行不同的价格标准。
这种价格细分采用了一种客户划分标准，这些标准是一些合理的原则和限制性条件。
这种划分标准的重要作用在于:通过价格藩篱将那些愿意并且能够消费得起的客户和为了使价格低一点而愿意改变自己消费方式的客户区分开，最大限度地开发市场潜在需求，提高效益。
收益管理是一种用来增加收入的方法，即根据市场供需和竞争程度的动态变化去制定价格，从而达到增加收入的目的。
从根本上讲，当需求强劲并趋向大于供给时，则应提高价格。
反之，当需求趋于下降，从而供给超过需求时，则应降低价格。
其目的是在给定供需状况下，力争实现最大收益。
收益测定方法将实际实现的收入同理论上的潜在总收入进行比较。
对于潜在总收入的解释及其计算方法，各饭店或各饭店连锁公司往往多有不同。
我们可以根据提前一小时、一天、一周乃至数月或数年确定的需求动向，去应用收益管理的原理。
例如，对于未来需求预计会变得强劲的时期，我们可以调高价格。
在需求疲弱时期，我们则可以调低价格，以便通过占有市场份额去争取客房收入。
收益管理所关注的不是测定出租率或房价，而是以每间客房的收入为焦点，如同在汽车租赁业中是以每租出一辆车的所得收入为焦点或者在航空运输业中是以每一乘客所带来的收入为关注焦点一样。
虽然“收益管理”这一术语源于航空运输业，但饭店和度假地对收益管理原理的应用远远早于航空公司。
酒店收益管理培训
．美国酒店和汽车旅馆 协 会 (AHMA)对收益 管理 给 出了如下的定义：
收益管理是用 来决 定客房价格升降和订房 请求接收或拒绝 的一 系列的需求预测技术 ， 以使客房的收益最大
收益管理其实是一种指导饭店业 如何在合适的时间,以合适的价格, 组成合适的产品,卖给合适的顾客 的科学管理方法.
营销中的“七个合适”
核对预订 增加保证类预订 超额预定
预定误区：
将一特定时期的客房预订一空？
超额预定超过的房间总数？
未预订客人的房价要高于提前预订顾客，晚 预订的顾客的房价高于早预订的顾客。
超额预订超过的不是饭店的客房总数，而是 饭店决定通过预订进行销售的客房总数。
举例：
停留时间控制 duration control
合适的产品 合适的价格 合适的时间 合适的地点 合适的渠道 合适的顾客 合适的方式
收益管理的核心:
通过制定一套灵活的且符合市场竞争规律的 价格体系,再结合现代化的微观市场预测及 价格优化手段对公司资源进行动态调控。
收益管理的理论基础
收益管理的焦点是产生收入，实现收入的最 大化，而不是削减成本。
或：=出租率×平均房价
（二）收入ቤተ መጻሕፍቲ ባይዱ生系数
收入产生系数=本饭店Pevpar/ 本市同星级饭店平均 Pevpar
收益管理的应用工具
需求预测 overcast demand
预测期
短期预测 中期预测 长期预测
预测根据
顾客细分及其行为 季节性及节假日 酒店市场区域的活动
接待量控制 capacity control
成功实施收益管理的关键是在于监测需求和 提高可靠预测的能力
收益管理以供求关系为基础 重视预订，从大量预订向高利润预订转移
洲际集团收益管理培训The Revenue Room PPT
“Target and action”
“Target and action”
“Target and action”
“Target and action”
“Target and action”
Conference & Banqueting
Food & Beverage
Front Desk
Sales & Marketing
• A weekly meeting to review and action revenue • The venue where …...
Measurement
• Improve Revenue Yield • Increase RGI • Increase in CAPS ‘Team’ Score
“Where are we strong, where are we weak?”
“Who do we have. Who do we want. What is their potential?”
Food & Beverage
GDS
“What is missing, that I can monitor & target?”
Sales & Marketing
“What can we learn, target and gain?”
Conference & Banqueting
Rooms, Reservations (CRO)
Market Competitors
Guidelines Actions
Indiv Business Corporate Groups Leisure Individual Wholesale
第三章 收益管理的预测
收益管理Revenue Management第三章收益管理的预测授课内容•预测的重要性•什么是预测•预测的主要内容•预测的方法•预测的准确性教学目的•学习完本课以后（含自学），你会：◦解释预测的重要性◦定义什么是预测◦解释限制的需求预测和无限制的需求预测◦运用置换分析的方法分析团队市场◦运用简单的定量方法进行预测◦根据预订进度进行预测◦衡量预测的准确性◦将无限制的需求预测的理念运用于解决问题课程导入•两种处理预订的方式产生的差异◦先到先得◦价高者先得预测Forecasting•预测是指推算和预料未来可能会发生的情况•历史、现状→未来•已知→未知预算VS预测•预算Budget◦一定周期内，通过合理的计划和预期，制定的关于各项明细分类的收入和支出文件◦记录了酒店的财务目标◦需要审批，批准后轻易不能修改•预测Forecast◦根据所掌握的数据（信息）对未来做出的推测◦预测不稳定，需要定期更新◦预测周期越短，精度要求越高预测的重要性•预测是有效、有益的运营酒店的一种基本工具◦预测是追求更好的收入，更高的利润的第一步◦正确的预测引领后续的收益管理决策◦预测能够满足酒店在业务、财务、运营等方面的需求——《礼记·中庸》预测是实施收益管理策略的基础•收益管理工作的核心任务：◦供＞求：最大限度减少现有存量资源的浪费◦供＜求：有效的资源分配，运用价格杠杆调节市场•只有准确预测出未来的市场情况，才能制定下一步的行动策略预测可以满足酒店多部门的需求财务导向•预算•利润•长期绩效•……运营导向•人员安排•费用计划•部门费用计划•……预测运营业主/投资者总部财务营销销售收益管理业务导向•制定销售指标•定价策略•渠道管理•限制条件•房量控制•……资料来源：IDeaS Revenue Solutions预测的必要性——需求•需求变化的影响•需求预测Demand Forecasting 预测的必要性——供给•供给变化的影响•供给预测（Supply Forecasting ）•维修房（OOO ）•免费房（Complimentary ）•内部用房（House-Use ）•延住（Extended Stay ）•提前取消预订（Cancellation ）•预订未到达（No-Show ）•提前退房（Early Departure ）预测的必要性——事件•事件的影响•事件的预测（Events Forecasting ）需求预测•Demand Forecasting is the act of estimating, calculating and predicting consumers’ demand in the future for products and services资料来源：HKPU RM PPT需求预测•需求是不受供给限制的限制的需求预测Constrained Demand •A demand that is held back or confined by rules, restrictions, and availability无限制的需求预测Unconstrained Demand •Naturally occurring demand that occurs in the absence of the restraints and restrictions限制的需求从供给的角度来考虑需求就是限制的需求无限制的需求没有任何限制条件︐市场对产品的总需求量是多少根据细分市场进行无限制的需求预测•站在酒店（供给）的角度，很难统计出高于客房数量的需求•按照细分市场的购买行为来分析才有可能找到市场的所有需求量无限制的需求预测示例按照细分市场进行无限制的需求预测•低价需求出现的相对较早（团队、包价等）•高价需求出现的相对较晚（无限制条件的需求）需求有规律•细分市场分类•分析每个细分市场的价值•勾勒酒店的业务结构无限制的需求预测•了解不同业务出现的规律及趋势•了解酒店业务的构成及变化•选择最佳业务结构无限制的需求预测的好处无限制的需求量的确定•已确认的需求量•潜在的需求量◦供给方面的原因导致的-产品限制、价格限制、渠道限制……◦顾客自身的原因导致的-Cancellation 、No-Show 、Regret……无限制的需求量的计算公式•无限制的需求量=◦＋Rooms Sold ＋Regrets ＋Denials ＋Walks ◦－No Shows －Cancellations•Walks 是因超额预订而被送到其它酒店的客人◦为何要把Walks 纳入计算公式中？当需求低时•开放所有客房类型•开放所有订房渠道•调整价格◦降低价格◦退出促销价◦提供优惠条件所有细分市场的顾客都能订房尽量提高Occ当需求高时•设置限制条件◦如：停留天数要求•关闭一部分低价房间•调整价格◦提高房价◦停止促销◦要求预付选择“含金量”高的细分市场无限制的需求预测的难度•酒店缺乏根据细分市场进行的数据记载和收集•酒店不注重对潜在需求进行的数据记载和收集•需求改变的动因具有复杂性，酒店难以进行准确记载和数据收集供给预测•一定时期内可以投放到市场中的产品资源•不仅是酒店自身的供给情况•还有整个市场的供给情况酒店的客房供给Available Rooms=Total Rooms－OOO－Complimentary－House-Use•减去◦Rooms Sold◦OTB◦Extended Stay•加上◦Cancellation◦No-Show◦Early Departure 事件预测•事件对酒店的经营能够产生重要的影响•正面影响◦需求增加，收益增加，利润增加•负面影响◦需求减少，收益减少，利润减少置换分析Displacement Analysis•选择会产生机会成本•通过确定选择不同细分市场的量化收益，帮助酒店确定应该接待或放弃的细分市场•主要依据是利润◦=客房利润差额◦+餐饮利润差额◦+宴会会议利润差额◦+其他经营项目利润差额全面收益管理时间序列预测方法•朴素法•简单平均预测法◦加法预测模式、乘法预测模式◦加权平均预测模式•移动平均预测法•指数平滑法朴素法•利用最近时期中发生的情况进行预测忽略季节性或周期性变化：上个月这个月考虑季节性或周期性变化：去年这个月今年这个月•基于简单推算的一种预测方法•适用于预测短期之内的情况变化（如一年以内）•不需要引入复杂的模型，成本很小•适用于小规模的酒店•具有一定的准确性加法、乘法预测模式•都是以过去各个时期的数据平均数作为预测值•加法预测模式◦在已有的数据基础上加上对应的平均数值•乘法预测模式◦用已有的数据与对应的平均比值进行计算加法、乘法预测模式•计算简单•要注意选取具有相同季节属性的观测值，避免预测结果出现严重偏差•因为赋予每个观测值一样的权重，所以适用于没有明显波动或较大增减变化的事件的预测加权平均模式•每个观测值都会影响预测结果•随着时间推移，观测值的影响会发生改变◦距离预测期越近，对预测值的影响越大→较大权重◦距离预测期越远，对观测值的影响越小→较小权重加权平均模式预测公式•Y n+1：预测值•X i：观察值（观察期内的实际数据）•F i：与X i对应的加权平均数加权平均模式•计算简单•考虑了不同时期的影响：长期和短期•纳入了更多的数据进行参考，准确率会提高•没有考虑市场波动，预测存在一定误差•关键：如何确定权重移动平均法•根据时间序列，逐项推移，依次计算包含相同项数的平均数，进行预测的方法•公式：•久远的观察值的影响考虑的比较少•消除观察值的随机性•问题：“N”（跨越时段）应该选几Y t+1：预测值X t：第t期的观察值n：跨越的时段数指数平滑法•解决移动平均法需要搜集、储存大量数据的弊端•保留最近两期的预测值和实际值就可以进行预测指数平滑法的缺点•用来计算的时期不一样，平滑系数不同，需要酒店进行平滑系数的选择、调整和确认•只适用于短期的预测•只适用于对于精确度要求不高的预测根据预订进度进行预测案例3-8︓现有一家酒店14天之内的客房预订统计表︒其中︐“到达日”那天的数据为实际销售的客房数资料来源：IDeaS ．提升酒店收益的绝佳策略：无限制的需求预测[EB/OL]．环球旅行，/article/91531，2015-04-27．•去年和今年4月第三个星期三的预订进度对比如图所示。
国际酒店收入管理词汇
ACD 自动通话分派台–一套自动追踪通话量旳系统，供订房销售办公室在提供顾客优质服务时检查接听旳效率，更合理地安排人员，并增长创收旳机会ADRM 收入管理地区经理–负责区域内某一特定地区在营业收入方面进行酒店管理和培训旳人。
分别定价(A La Carte Pricing)一种把商品个别单独而不是混在一起定价旳价格构造。
一般用于食品和饮料，如咖啡休息期间旳茶点（即咖啡和茶每人$2.95，面包圈和甜甜圈每人$4.50）接待订房规定(Accommodation Booking Rules)MARSHA 中作为价格表定义一部分旳参数，以便执行与尤其价格计划有关旳规定和限制。
这些登记规定影响销售栏并构成订房规定旳限制。
这些限制被“硬性”规定为价格计划旳一部分而不受需求旳影响。
例如，周末价格只能用于星期五和星期六。
地区订房销售办公室(Area Reservation Sales Office (ARSO))订房销售中心代表一定旳地理区域或酒店集团。
订房被转到地区销售中心给每一家参与网络旳酒店。
假设销售(Assumed Sale)向顾客报出一种非企业资格散客价格，假设成交，因而建立一种起始记录。
例如，在需求疲软季节旳假日房价。
短缺(Attrition)实际占用局限性原订旳客房和/或活动场所数量旳短缺或下降。
例如，客人本来订了100个房/晚，实际上只用了90个房/晚。
下降或短缺10个房/晚或10%。
经同意授权旳(Authorized)酒店分派给多种客房组合供销售旳房间库存。
空房(Availability)尚未订出给顾客而仍在待售旳库存（客房和活动场所）。
平均每日房价(Average Daily Rate)每晚房间销售价格旳平均率。
用总收入除以总旳房/晚数。
一般也称为ADR。
基准房价(Benchmark Rate)一种市场定位旳季节性房价，以此向酒店旳大多数顾客报价并为其所接受。
基准房价为周日和周末旳散客市场制定。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
IDeaS V5 Forecasting
What We Need to Know • Last Room Value
– What is it?
• True Demand
– What is it?
IDeaS V5 Forecasting
The IDeaS V5 System • Is a tool that is equipped to make decisions on your behalf
$$ Revenues $$ Monitor Control
IDeaS V5 Forecasting
Forecasting
To produce optimal revenue Controls, a Forecast of the volumes and values of each type of demand is required for Optimization
IDeaS V5 Forecasting
IDeaS V5 Forecasting
Agenda • Revenue Management Cycle • Forecasting
– Volume
• Transient (True Demand) • Group
– Patterns – No-Show & Wash – Rate
IDeaS V5 Forecasting
Revenue Optimization Overview • Successful Revenue Optimization has several components:
– Revenue Optimization Culture
• Alignment between the departments in the hotel setting strategies (sales and marketing) and the hotel operations (reservations and front desk)
Forecasting • Let’s look at forecasting, the first step on the road to maximizing revenue
IDeaS V5 Forecasting
Why Forecast Demand? • IDeaS V5 Controls require the calculation of the Last Room Value in order to ensure that optimal decisions can be applied • The forecasting on which they are based must include reliable estimates of demand at all points along the booking curve by arrival date, segment, rate and length of stay
IDeaS V5 Forecasting
Demand Forecasting
Volume
IDeaS V5 Forecasting
Demand Forecasting Demand forecasting must: • Be derived from accurately measured historical demand • Perform at a sufficiently detailed level to allow for revenue management decisions and controls • Involve separate analysis of any periods of abnormal demand • Involve separate analysis of the risk of cancellations and “no-shows”
IDeaS V5 Forecasting
The Revenue Optimization Cycle
Volumes Values Optimize Forecast No Show & Wash
$$ Profits $$
$$ Revenues $$ Monitor Control
IDeaS V5 Forecasting
User Interaction
Forecast
Optimize
Hale Waihona Puke Data$$ Profits $$
$$ Revenues $$ Monitor Control
IDeaS V5 Forecasting
The Revenue Optimization Cycle • When working with Revenue Optimization, the Forecast Optimize four main components of the Revenue Optimization Cycle must be accounted for $$ Profits $$ • The Cycle provides a framework to structure your work around
– It uses….
• Data from your Reservation System • Inputs from the Revenue Management Team
Share information about your business with the system -e.g. Special Events, Rate Configuration -- so that the decisions IDeaS makes are in line with your business and business objectives
– Revenue Optimization Tools
• IDeaS V5
– Suitable Controls in the reservations systems and channels that are used to sell rooms
IDeaS V5 Forecasting
The Revenue Optimization Cycle