公司供應(yīng)鏈管理流程參考模型

1、SECTION 1 SCM TEMPLATE WORKFLOW SCM Template Workflow Release 421 Copyright 2000 i2 Tech nologies, I nc. This no tice is inten ded as a precauti on aga in st in adverte nt publicati on and does not imply any waiver of con fide ntiality .In formati on in this docume nt is subject to cha nge without n

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4、ed by the federal gover nment, the followi ng stateme nt is applicable:In accorda nee with FAR 52.227-19 Commercial Computer Software Restricted Rights, the followi ng applies: This software is Un publishedrights reserved un der the copyright laws of the Un ited States. The text and draw ings set fo

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6、roduct n ames used in this man ual are the trademarks, registered trademarks, service marks or trade n ames of their respective own ers. i2 Tech no logies, In c. is not associated with any product or ven dor men ti oned in this publicati on unl ess otherwise no ted. The followi ng trademarks and ser

7、vice marks are the property of i2 Tech no logies, In c.: EDGE OF INSTABILITY; i2 TECHNOLOGIES; ORB NETWORK; PLANET; and RESULTS DRIVEN METHODOLOGY. The followi ng registered trademarks are the property of i2 Tech no logies, In c.: GLOBAL SUPPLY CHAIN MANAGEMENT; i2; i2 TECHNOLOGIES and desig n; TRAD

8、EMATRIX; TRADEMATRIX and desig n; and RhythmL ink. February, 2000 Docume nt ID: HiTech 4.2 SCM Template Workflow Docume nt Vers ion: V 1.0 Docume nt Title: HiTech 4.2 SCM Template Workflow Docume nt Revisi on: Draft 1 Revisi on Date: 3 February, 2000 Docume nt Refere nee: ,Primary Author(sy SCM Team

9、 -Krish nan Subrama nia n, Jatin Bin dal, Abhay Sin ghal Comme nts: Contents SCM PROCESSES OVERVIEW SCMPROCESSES DEMAND PLANNING Demandforecasting Top-Dow n Forecasti ng Bottom-Up Forecast ing Life Cycle Planning- New Product Introductions and Phase-In/Phase-Out Eve nt Pla nning Consen sus Forecast

10、Attach-Rate Forecasti ng/Depe ndent Dema nd Forecast ing in Con figure-to-Order en viro nments DEMANDCOLLABORATION Flex Limit Pla nning forecastnetting Forecast Extracti on MASTER PLANNING SUPPLY PLANNING En terprise Pla nning: Inven tory Pla nning En terprise pla nning: Long term capacity pla nning

11、 Enterprise planning: Long term material planning Facility Pla nning: Supply pla n for en terprise man aged comp onents Collaborati on Pla nning for En terprise and Factory Man aged Comp onents Procureme nt Collaborati on Collaboration Planning with Transportation Providers - Transportation Collabor

12、ati on Allocationplanning DEMAND FULFILLMENT CRDERPROMISING Promisi ng new orders Con figure to Order (CTO) Orders Build to Order (BTO) Orders CRDERPLANNING Factory Pla nning Transportation Planning SCM Processes Overview The follow ing figure briefly describes the soluti on architecture for the cor

13、e processes that con stitute the SCM solutio n. SCM Fu nctio nal Workflow SCM Processes The SCM template as a whole performs the following functions: 1. Dema nd Pla nnina : Forecasti ng and dema nd collaborati on. Sales forecasts are gen erated using various statistical models and customer collabora

14、tion. 2. Master Planning : Long term and medium term master planning for material as well as capacity. Master planning can be done at both the enterprise level (for critical shared components) and the factory level. Inaddition, decisions relating to material procurement and capacity outsourcing can

15、be made. 3. Allocation Planning : Reserving product supply for channel partners or customers based on prespecified rules. Also, managing the supply so that orders that have already bee n promised can be fulfilled in the best possible manner (on the promised dates and in the promised quantities). 4.

16、Order Promising : Promising a date and quantity to customer orders. These promises are made looking at the projected supply. In addition, sourcing decisions are also made here after considering such variables as lead-time, product cost, shipping cost, etc. 5. Order Planning : Detailed order planning

17、 encompassing multiple factories. In addition detailed transportation planning is also done which can handle such complex requirements as merging two shipme nts from differe nt locati ons duri ng tran sit. In formatio n flows seamlessly betwee n all these fun cti ons. The in puts to the system are t

18、he static data (supply cha in structure, supplier relati on ships, seller and product hierarchies, supplier relati on ships, etc), some forecast data and actual orders. The output is a comprehensive and intelligent supply chain plan which takes all the supply chain delivery processes into considerat

19、ion in order to maximize customer satisfaction, at the same time reducing order fulfillment lead times and costs. The scope of this document is to describe the scenarios modeled as a part of the current release of the template (Hitech2). For any pla nning system, the place to beg in pla nning is dem

20、a nd forecasti ng. We look at this in more detail in the n ext secti on. Dema nd Pla nning The objective of the Dema nd Pla nning process is to develop an accurate, reliable view of market dema nd, which is called the dema nd pla n. The Dema nd Pla nning process un dersta nds how products are organi

21、zed and how they are sold. These structures are the foundation of the process and determ ine how forecast aggregati on and disaggregati on is con ducted. A baseli ne statistical forecast is gen erated as a starti ng point. It is improved with in formatio n directly from large customers and cha nnel

22、partners through collaborati on. The forecast is refined with the pla nned eve nt schedule, so the dema nd pla n is syn chro ni zed with internal and exter nal activities. Each product is evaluated based on its lifecycle, and contin ually mon itored to detect deviati on. New product in troducti ons

23、are coord in ated with older products, pipeli ne inven tories, and comp onent supply to maximize their effectiveness. Attach rates are used to determine component forecasts given the proliferation of products. The result is a dema nd pla n that sig nifica ntly reduces forecast error and calculates d

24、ema nd variability, both of which are used to determine the size of the response buffers. The specific response buffers and their placement are different based on the manufacturing model employed, therefore the Dema nd Pla nning process must represe nt those differe nces. The follow ing figure ide n

25、tifies the key processes that con stitute dema nd pla nning and the sce narios that are modeled in the template. Dema nd Forecast ing Top-Dow n Forecasti ng Defin iti on Top dow n forecasti ng is the process of tak ing an aggregate en terprise reve nue target and converting this revenue target into

26、a revenue forecast by sales unit/product line. This allocation process of revenue targets can be done using historical performanee measures or using rule based allocation techniques. The revenue targets can further be broken down into unit volume forecasts by using Average Selli ng Price in formati

27、on for product lin es. Historical in formatio n is typically more accurate at aggregate levels of customer/product hierarchies. Therefore, statistical forecast ing tech ni ques are typically applied at these aggregate levels. At levels where historical in formati on might not be very releva nt or is

28、 not perceived to be accurate, this allocati on can be done with a rule-based approach. Freque ncy: This process is typically performed at a mon thly/quarterly freque ncy, with the forecast being gen erated for the n ext several mon ths/quarters. Seen ario Descripti on Based upon historical bookings

29、 at an aggregate level across the entire company (for all products and geography she system will automatically gen erate multiple forecasts using differe nt statistical tech ni ques.The statistical tech ni ques will accou nt for such thi ngs as seas on ality, tre nds, and quarterly spikes. Each stat

30、istical forecast will be compared with actuals to calculate a sta ndard error. This will automatically occur at every bra nch (in tersecti on) in the product and geographic hierarchies. The aggregate statistical forecast gen erated for the en tire compa ny will be automatically disaggregated at ever

31、y intersection using the statistical technique with the smallest standard error. The outcome of this process will be a“ Pickbest ” statistically gen erated forecast at every level in the product and geography hierarchies. This forecast is the n used as a baseli ne or start ing point. In puts Histori

32、cal Book ings by un its Historical Statistically based Book ings Forecast Outputs Multiple Statistical forecasts Statistical “ Pickbest ” forecast Forecast committed to top-dow n forecast database row. Ben efits Easy disaggregati on of data means faster, more accurate forecast ing Simple alig nment

33、of reve nue targets Uses top dow n statistical adva ntages to easily tie lower level forecasts to reve nue targets i2 Products Used TRADEMATRIX Dema nd Pla nner Bottom-Up Forecasting Definition This process enables the different sales organizations/sales reps/operations planners to enter the best es

34、timate of the forecast for different products. This process consolidates the knowledge of sales representatives, local markets, and operational constraints into the forecasting process. This forecast can be aggregated from bottom up and compared to the targets established by the top-down forecasting

35、 process at the enterprise level. This will enable easy comparison between sales forecasts and financial targets. Frequency : This is a weekly process. However, there is continuous refinement of the forecast at an interval determined by the forecasting cycle time and/or nature of the change required

36、. Scenario Description In parallel with the top-down forecast, the sales force/operational planners will enter forecasts for independent demand for a particular SKU or product series by customer or region as is pertinent to a particular Product / Geography combination. This data will automatically b

37、e aggregated and compared to the targets established by the top-down forecasting process. Using the Average Selling Price for a unit, the unit based forecasts can be converted to revenue dollars and automatically aggregated. The bottom-up forecast can also be generated using collaborative demand pla

38、nning with a customer. In this case, the consensus forecast for a product/product series for a customer is aggregated and compared to the top-down target. Input Sales force input Operations Planning Input Average Selling Price (ASP) Customer forecast (from the Demand Collaboration process) Outputs A

39、ggregated Sales forecast by unit Aggregated Sales Forecast by Dollars Aggregated Operations Plan by unit Benefits Automatic aggregation of data means faster, more accurate forecasting Simple alignment of lower level Sales plans to higher level revenue targets i2 Products Used TRADEMATRIX Demand Plan

40、ner, TRADEMATRIX Collaboration Planner Defin iti on Forecast ing product tra nsiti ons plays a critical role in the successful phas ing out and lau nch of new products. New Product In troducti on (NPI) and phase In/phase out forecast ing allows the en terprise to forecast ramp downs and ramp ups mor

41、e accurately. Ramping can be defined in terms of either a percentage or as units. Typically new products are difficult to forecast because no historical information for that product exists. NPI planning must allow for new product to inherit historical information from other product when it is expect

42、ed that a new product will behave like the older product. In situations where a new product will not behave like any other older product, NPI pla nning allows a user to predict a life cycle curve for a product, and the n overlay lifetime volume forecasts across that curve. Seen ario Descripti on Giv

43、en a forecast for two complimentary products, the user can change the ramping percentage of both to reflect the ramping up of one product and the ramping down of another. Given a New Product In troducti on that is predicted to behave like an older product, the user can utilize historical data from t

44、he older product to be used in predict ing the forecast for the new product. The sce narios for this process are executed in TradeMatrix Dema nd Pla nner. Future releases of the template will use TradeMatrix Tran siti onal Pla nner to do product life cycle pla nning. In puts Historical book ings New

45、 product and associati on with the older part Product ramp ing in formati on for a new product Outputs Adjusted Forecast ramp ing broke n out by % New product forecast based on a similar products history New product forecast based on life cycle in put Ben efits The ability to forecast a new product

46、using history from an ano ther product The ability to forecast using product life cycle curves Clea ner product tra nsiti ons allowi ng for decreased inven tory obsolesce nee i2 Products Used TRADEMATRIX Dema nd Pla nner, TRADEMATRIX Tran sition Pla nner Event Planning Definition This process determ

47、ines the effect of future planned events on the forecast. The marketing forecast is adjusted based on events related factors. A promotional campaign or price change by the company or the competition is an example of an event related factor that may influence demand. The marketing forecast is adjuste

48、d up or down by a certain factor. The factor can be increased or decreased across periods to simulate a ramp-up or a ramp-down in sales depending upon the nature of the event. Frequency : Event Based Scenario Description An event row will model the influence of the event that will change the marketi

49、ng forecast. A promotional campaign or price change by the company or the competition is an example of a factor that may influence demand. The user will populate the Event row with scalar values which when multiplied by the Marketing statistical forecast will adjust the Marketing forecast up or down

50、 by a factor (0.90 for a 10% decline or 1.05 for a 5% increase etc.). Event row can be increased or decreased across periods to simulate a ramp-up or a ramp-down in sales depending upon the nature of the event. Inputs Eve nt - con sta nt factor typically Historical Bookings Marketing forecast Output

51、s Adjusted Marketing Forecast Benefits The ability to allow events to dynamically influence forecast I2 Products Used TRADEMATRIX Demand Planner Consensus Forecast Definition The consensus process is one in which the multiple forecasting processes thus far used are brought together to arrive at one

52、single forecast. All information critical to reaching consensus on the forecast will be brought together for analysis and facilitation of the consensus process. The level at which the consensus process is performed is typically at an intermediate level, where the forecast is most meaningful for the

53、different stakeholder organizations. Thus, top-down forecast, bottom-up forecast, marketing forecast and collaborative forecast will be used to arrive at a consensus forecast. Scenario Description The different forecasts including the top-down, bottom-up, marketing, operations and sales are compared

54、 and contrasted by the various forecast owners and based on considerations such as revenue targets, life-cycle considerations and capacity a consensus forecast is determined. This is the final forecast that is used by the supply planning process. Inputs Top down forecasts, bottom up forecasts, etc.

55、at a specific node (intersection of product and geography) in the hierarchy. Outputs Consensus forecast Benefits Communication between different organizations is achieved Multiple data points can be displayed, allowing for analysis, comparisons and metrics Emphasizes data analysis and reduced data g

56、athering I2 Products Used TRADEMATRIX Demand Planner Attach-Rate Forecasting/Dependent Demand Forecasting in Configure-to-Order environments Definition In a Configure To Order (CTO) manufacturing environment, a particular product model can be sold with several options. The customer chooses the exact

57、 configuration at the time of placing an order. However, for the purpose of procuring these parts, the enterprise will need to forecast the mix of options that will potentially be sold. The forecast percentage mix of options is called can be varying The consensus process essentially determines the f

58、orecast at the product model level. This process performs the option mix analysis to forecast attach rates. The attach rates and/or geography. Product or Product-series level forecasts will be broken down into the components or options that comprise them by using attach rates. Attach rates can be ma

59、nually input or forecasted based upon history. Scenario Description Inputs Model to options mapping Relationship to determine dependent forecast Outputs Attach Rates Dependent Forecast Benefits Easy way to determine dependent forecasts in a CTO environment Attach Rates can be forecast across time an

60、d geography I2 Products Used TRADEMATRIX Demand Planner, RHYTHM PRO Demand Collaboration Definition In situations where the customers of the enterprise have their own forecasting processes, demand collaboration will enable more accurate forecasting by ensuring rapid transmission of any downstream de