電腦銷售外文翻譯--- 模擬空房情況展望電子商務中的個人電腦銷售

1、<p><b>　　杭州電子科技大學</b></p><p>　　畢業(yè)設計外文文獻翻譯</p><p>　　模擬空房情況展望電子商務中的個人電腦銷售</p><p>　　楊李 IBM公司T.J.沃森研究中心 </p><p><b>　　摘要</b></p><p>

2、;　　對于新設計或改造業(yè)務流程，準確地預測業(yè)務進行，如成本和客戶服務的實際之前部署，是非常很重要的。我們已經(jīng)成功地開發(fā)和利用仿真模型，為IBM的個人電腦業(yè)務通過建模多個，離散事件，如客戶為了抵達，補充規(guī)劃和可用性，數(shù)據(jù)刷新，和不確定性的需求預測，訂單規(guī)模和消費者心目中的產(chǎn)品功能。使用模型，我們能夠預測動態(tài)的可用性，發(fā)貨日期，并確定了其他改進機會。我們還研究如何不同的庫存政策，規(guī)劃政策和供應采購政策影響企業(yè)業(yè)績等指標庫存和客戶服務。<

3、;/p><p><b>　　1引言</b></p><p>　　對于電子商務企業(yè)，如基于Web的銷售電腦，為客戶提供所需的準備時間來裝運（若干天的時間將產(chǎn)品發(fā)運之后，為了放置）。在當今競爭激烈的市場，實際運送產(chǎn)品時間是一個成功的關鍵因素。確定籌備時間，以確定和裝運向客戶提供在多次在客戶的網(wǎng)上購物過程。但如當新的業(yè)務計劃，業(yè)務變革計劃或商業(yè)環(huán)境預期的變化，是不容易準確地估計

4、的概況預期船日期，如范圍，標準差，斜及其隨時間變化等自發(fā)貨日期這些直接關系到客戶服務，這一點在發(fā)貨日期項目之前是非常重要準確，一個新的配置文件是執(zhí)行業(yè)務流程或其變化是執(zhí)行。</p><p>　　離散事件仿真已經(jīng)有幾個幾十年來模擬隨機行為的材料，服務和信息流等在分析過程制造業(yè)，服務業(yè)和各種業(yè)務。 尤其是，供應鏈管理（ SCM ）的地區(qū)模擬方法已被用于評價其有效性。大部分的這種用法一直是根據(jù)不同的生產(chǎn)和銷售情況調查的

5、庫存水平和客戶服務的業(yè)績和政策，庫存，制造，補充和運輸。麥克萊倫（ 1992 ）使用模擬研究的影響，變異需求/供應商的客戶服務的反應，以便周期和庫存管理。Hieta （ 1998年）的效果分析替代產(chǎn)品結構，替代清單和生產(chǎn)控制方法對庫存和客戶服務管理。 Bagchi等（ 1998年）評價設計和操作的供應鏈，用模擬和優(yōu)化，分析供應鏈管理的問題，如地點， 增資政策，生產(chǎn)政策，交通運輸政策，畜水平，交貨時間和客戶服務。 議（ 2002 ）分析了

6、影響汽車混合模型和選擇的主要供應鏈績效如客戶等待時間，條件不匹配。</p><p>　　業(yè)務流程建模的另一個領域是模擬方法積極用來識別業(yè)務改善的機會的評估業(yè)務進程的政策，程序辦法，并充分估計資源的各項工作在一個業(yè)務流程。李等人 （ 2003年）模擬業(yè)務流程的計算機制造商，并確定了大量的工藝改進機會在企業(yè)管理中的循環(huán)時間變化的處理步驟和適當?shù)姆峙滟Y源應用。</p><p>　　在這項工作中，

7、我們描述了仿真模型的估計供應前景，例如，預計日期和船舶其準確性電子商務業(yè)務在終端產(chǎn)品從配置的不同組成部分的客戶。 該模型模擬的效果是隨機客戶購物交通;秩序的不確定性大小，客戶的喜好產(chǎn)品特點和需求預測，庫存政策，采購政策和供應規(guī)劃政策; 生產(chǎn)準備時間等的概況船舶日期。 模擬模型提供了重要的統(tǒng)計資料供應前景和客戶的服務企業(yè)投產(chǎn)，使智能業(yè)務作出決定之前投資。那個模型還估計的準確性船舶日期確定頻率所產(chǎn)生的數(shù)據(jù)通信之間的計算機系統(tǒng)支持的網(wǎng)上業(yè)務。

8、對于多個數(shù)量訂單，仿真模型還計算船舶裝運日期部分，如果它是可選的計算出貨量。</p><p><b>　　2模擬分量空房情況</b></p><p>　　提供大量的組件中使用計算日期的船舶客戶的要求和命令。 供應量的變化是由于四個分立事件模擬。它改變客戶訂單發(fā)行后，作為補充措施，是作為數(shù)據(jù)刷新這樣做，并向前進行。有兩個實例供應陣列的組成部分;一個代表提供實時（動態(tài)鑒于

9、情況而定） ， 另一個則代表根據(jù)已知情況內容在可用性數(shù)據(jù)庫（靜態(tài)鑒于情況而定）當時的情況。后者可用性刷新了批處理的時間表由于拖延履行的進程。例如，提供數(shù)據(jù)可以刷新每隔幾分鐘或小時之間的差異，并認為這些動態(tài)靜態(tài)鑒于提供的數(shù)據(jù)引起的準確性船舶日期計算。</p><p><b>　　3.1訂單生成事件</b></p><p>　　客戶訂單中產(chǎn)生的發(fā)明在某些隨機區(qū)間之間，因為

10、它們的某些分布模型職能。在這個時候，為了下一代，每一個命令被指定的一個或多個項目，并在每一行項目的分配與一個或一個以上的數(shù)量。這項任務每個屬性秩序的概率模型分布函數(shù)為基礎的歷史銷售數(shù)據(jù)或預期在今后的業(yè)務。訂單穿過業(yè)務流程中所界定的仿真模型，并當訂單達到了一定量時，模擬客戶提交秩序，指定供應數(shù)量組成部分是保留給秩序，并正在遞減從可用性。分配的具體組成部分在決定采購政策，分配部分功能優(yōu)先，客戶類等。</p><p>

11、<b>　　3.2補貨事件</b></p><p>　　作為積木組件消費產(chǎn)品被出售給顧客，獲得額外的元件通過規(guī)劃的供應。這項活動，通常稱為供應規(guī)劃，提前發(fā)生，例如，月，周或天前元件實際需要，以適應供應的準備時間。供應的頻率規(guī)劃還可以月，周，日。由于供應規(guī)劃，供應的部分補充在某些頻率和數(shù)量。增資頻率可以是固定的間隔，如每天，每周等，或者它可以模擬使用分布函數(shù)。增資數(shù)量是基于預測的客戶需求，它的

12、不確定性。補充量模型采用分布函數(shù)，通常是正態(tài)分布與某些平均和標準差，代表的不確定性需求預測。在這項工作中，我們使用的歷史性需求分布數(shù)據(jù)到達分布函數(shù)。在這里，各種replenishmentpolicies可以模仿指定頻率和規(guī)模的補充。</p><p><b>　　3.3推展活動</b></p><p>　　作為模擬時鐘從一天到另一天， 部分已被消耗結轉到前一天。例如，供

13、應量第2天將獲得數(shù)量的第1天，和的第3天將是第2天等也，可沒有消費的數(shù)量第1天呆在同一天，假設這是不易變質的。滾動著活動中可以產(chǎn)生一個固定的時間間隔，如每日，或不同的前滾翻事件還可以模仿的基礎上營商環(huán)境。</p><p><b>　　3.4數(shù)據(jù)刷新事件</b></p><p>　　在理想的電子商務環(huán)境，當客戶訂購特定產(chǎn)品的被接受，部分組成該產(chǎn)品應立即予以保留，并沒有提

14、供為未來的訂單。然而，在現(xiàn)實中的可用性數(shù)據(jù)無法實時更新。其中一個原因是，一些計算機系統(tǒng)都參與了處理和履行客戶訂單，他們的數(shù)據(jù)沒有更新，實時同步，因為它是昂貴的把它架構，以確保這樣的數(shù)據(jù)通信和同步。另一個原因是，在訂單履行任務，其中可能包括調度，生產(chǎn)，銷售和會計等，需要一些時間或一般進行了作為一個批處理過程。批處理進程執(zhí)行在一定的時間間隔，并提供數(shù)據(jù)更新只有在履行任務的完成。之間的差異實際可用性（動態(tài)可用性）和已知的可用性（靜態(tài)情況而定）

15、船舶造成的準確日期的計算方法。在這工作中，該船日期計算動態(tài)和靜態(tài)鑒于可用性，和不準確的發(fā)貨日期計算估計。不準確的發(fā)貨日期計算的一個重要標志客戶服務水平。數(shù)據(jù)刷新事件可以模仿的固定時間間隔發(fā)生的事件或隨機生成事件描述的分布函數(shù)。樣品模擬結果：船舶日期簡介及其精度。示例船日期的形象作為一個隨著時間的推移由于模擬運行。在這里，船日期之間波動3天及10天，平均船日期四點四天和標準偏差一點七六天。</p><p><

16、b>　　6結論</b></p><p>　　客戶服務是最重要的成功因素和生存的企業(yè)在今天的動態(tài)的商業(yè)環(huán)境。 能夠估算供應前景和預期客戶服務的投資是前運行企業(yè)是相當有利的任何企業(yè)。我們已開發(fā)了一個仿真模型，估計該船日期，其中一個最重要的客戶服務因素在網(wǎng)上銷售業(yè)務，以及如何送貨的準確日期。通過模擬各種業(yè)務情況，分析預計發(fā)貨日期統(tǒng)計和比較，運行費用的情景，我們能夠作出明智的商業(yè)決定，以促進更高的利潤和

17、更好的客戶服務。仿真建模工作是使用IBM的全身建模（ IBM公司）。</p><p>　　SIMULATING AVAILABILITY OUTLOOK FOR</p><p>　　E-COMMERCE BUSINESS OF PERSONAL COMPUTER SALES</p><p>　　Young M. Lee</p><p>　　I

18、BM T.J. Watson Research Center</p><p>　　1101 Kitchawan Road</p><p>　　Yorktown Heights, NY 10598, U.S.A.</p><p><b>　　ABSTRACT</b></p><p>　　For newly designed

19、 or transformed business processes, accuratelypredicting business performances such as costsand customer services before actual deployment is veryimportant. We have successfully developed and used asimulation model for t

20、he IBM’s Personal Computer Divisionby modeling multiple, discrete events such as customerorder arrival, replenishment planning and availabilitydata refresh, and uncertainty of demand forecast, ordersize and customer pref

21、erence of product feature. Using themodel </p><p>　　1 INTRODUCTION</p><p>　　For e-commerce businesses, such as Web-based sales ofcomputers, providing customers the desired lead time toshipment (

22、a number of days for the product to be shippedafter the order is placed) and actually shipping the producton time is a critical factor for success in today’s competitivemarket. The lead time to shipment is determined and

23、provided to customer in multiple times during the customers’e-shopping process. For businesses that are already inoperation, when customers inquire the ship date,</p><p>　　evaluate its effectiveness. Most of

24、 such usage has been to investigate inventory levels and customer service performance</p><p>　　based on various manufacturing and distribution scenarios, and policies in inventory, manufacturing, replenishme

25、nt and transportation. McClellan (1992) used simulation to study the effect of MPS method, variability of demand/supplier response on customer services, order cycle and inventory. Hieta (1998) analyzed the effect of alte

26、rnative product structures, alternative inventory and production control methods on inventory and customer service performance. Bagchi et al. (1998) evaluated the desig</p><p>　　In this work, we describe a s

27、imulation model that estimates availability outlook, e.g., expected ship dates and their accuracy of an e-commerce business where end products are configured from different components by customers. This model simulates t

28、he effect of stochastic customer shopping traffic; uncertainty of order size, customer preferences of product features and demand forecast; inventory</p><p>　　policies, sourcing policies and supply planning

29、policies; manufacturing lead time etc. on the profiles of ship dates. The simulation model provides important statistical information of availability outlook and customer services before the business is put into operatio

30、n so that intelligent business decisions are made before investment is made. The model also estimates the accuracy of the ship dates determination</p><p>　　arising from frequency of data communications betwe

31、en the computer systems supporting the on-line business. For multiple quantity orders, the simulation model also computes ship dates for partial shipments, if it is optional, and the total shipment. </p><p>

32、　　2 MODELING OF COMPONENT AVAILABILITY</p><p>　　The availability quantities of components are used in computing the ship date of customer requests and orders. The availability quantity changes as a result of

33、 four discrete events in the simulation. It changes as customer order is released, as replenishment is done, as data refresh is done, and as roll forward is carried. There are two instances of component availability arra

34、ys; one representing the availability at real time (dynamic view of availability), and another representing known availab</p><p>　　3.1 Order Generation Event</p><p>　　Customer orders are generat

35、ed in the invention in certain stochastic interval as they are modeled with certain distribution functions. At this time of the order generation, each order is assigned with one or more line items, and each line item is

36、assigned with one or more quantities. This assignment of attributes to each order is modeled with probability distribution functions based on historic sales data or expected business in the future. The orders travel thro

37、ugh the business process as defin</p><p>　　4.2 Replenishment Event </p><p>　　As building block components are consumed as products are sold to customers, additional components are acquired throu

38、gh planning of supply. This activity, typically known as supply planning, occurs in advance, e.g., months, weeks or days before the components are actually needed, to accommodate the supply lead time. The frequency of su

39、pply planning can also be months, weeks or days. As a result of the supply planning, the component availability is replenished</p><p>　　in certain frequency and quantity. The replenishment frequency can be a

40、 fixed interval such as daily, weekly etc, or it can be modeled using a distribution function. The replenishment quantity is based on the forecast of customer demand, which has uncertainty. The replenishment quantity is

41、modeled using a distribution function, typically a normal distribution with certain mean and standard deviation, which represent the uncertainty of demand forecast. In this work, we use the historic demand dis</p>

42、<p>　　3.2 Replenishment Event</p><p>　　As building block components are consumed as products are sold to customers, additional components are acquired through planning of supply. This activity, typical

43、ly known as supply planning, occurs in advance, e.g., months, weeks or days before the components are actually needed, to accommodate the supply lead time. The frequency of supply planning can also be months, weeks or da

44、ys. As a result of the supply planning, the component availability is replenished in certain frequency and quantity. T</p><p>　　3.3 Roll Forward Event</p><p>　　As simulation clock moves from a d

45、ay to another day, the component that has not been consumed are carried forward to a day earlier. For example, the availability quantity for the 2nd day will be the availability quantity of 1st day, and that of 3rd day w

46、ill be that of 2nd day etc. Also, the availability quantity not consumed on the 1st day stayed on the same day, assuming it is non-perishable. The roll forward event can be generated in a fixed interval, e.g., daily, or

47、different roll forward eve</p><p>　　3.4 Data Refresh Event</p><p>　　In ideal e-business environment, when a customer order for a specific product is accepted, the components that constitute the

48、product should immediately be reserved and not available for future orders. However, in reality the availability data are not updated in real time. One of the reasons is that</p><p>　　several computer system

49、s are involved in processing and fulfilling customer orders, aand their data are not updated and synchronized in real time because it is expensive to have IT architecture that ensures such data communication and synchron

50、ization. Another reason is that the order fulfillment tasks, which may include scheduling, production, distribution and accounting etc., takes some time or are typically carried out as a batch process. The batch process

51、is executed in certain time intervals</p><p><b>　　6 SUMMARY</b></p><p>　　Customer service is one of most important factors of success and survival of enterprises in today’s dynamic b

52、usiness environment. Being able to estimate availability outlook and expected customer services before investment is made to run the business is quite beneficial to any enterprises. We have developed a simulation model t