工作标准流程参考模型

1、SECTION 1SCM TEMPLATE WORKFLOWSCM Template WorkflowRelease 4.2.1Copyright i2 Technologies, Inc.This notice is intended as a precaution against inadvertent publication and does not imply any waiver of confidentiality. Information in this document is subject to change without notice. No part of this d

2、ocument may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or information storage or retrieval systems, for any purpose without the express written permission of i2 Technologies, Inc.The software and/or database described in thi

3、s document are furnished under a license agreement or nondisclosure agreement. It is against the law to copy the software on any medium except as specifically allowed in the license or nondisclosure agreement. If software or documentation is tobe used by the federal government, the following stateme

4、nt is applicable: In accordance with FAR 52.227-19 Commercial Computer Software Restricted Rights, the following applies: This software is Unpublishedrights reserved under the copyright laws of the United States.The text and drawings set forth in this document are the exclusive property of i2 Techno

5、logies, Inc. Unless otherwise noted, all names of companies, products, street addresses, and persons contained in the scenarios are designed solely to document the use of i2 Technologies, Inc. products.The brand names and product names used in this manual are the trademarks, registered trademarks, s

6、ervice marks or trade names of their respective owners. i2 Technologies, Inc. is not associatedwith any product or vendor mentioned in this publication unless otherwise noted.The following trademarks and service marks are the property of i2 Technologies, Inc.: EDGE OF INSTABILITY; i2 TECHNOLOGIES; O

7、RB NETWORK; PLANET; and RESULTS DRIVEN METHODOLOGY. The following registered trademarks are the property of i2 Technologies, Inc.: GLOBAL SUPPLY CHAIN MANAGEMENT; i2; i2 TECHNOLOGIES and design; TRADEMATRIX; TRADEMATRIX and design; and RhythmLink.February, Document ID: HiTech 4.2 SCM Template Workfl

8、owDocument Version:V 1.0Document Title:HiTech 4.2 SCM Template WorkflowDocument Revision:Draft 1Revision Date:3 February, Document Reference:.Primary Author(s):SCM Team Krishnan Subramanian, Jatin Bindal, Abhay SinghalComments:Contents TOC o 1-3 SCM Processes OverviewSCM ProcessesDemand PlanningDema

9、nd ForecastingTop-Down ForecastingBottom-Up ForecastingLife Cycle Planning New Product Introductions and Phase-In/Phase-OutEvent PlanningConsensus ForecastAttach-Rate Forecasting/Dependent Demand Forecasting in Configure-to-Order environmentsDemand CollaborationFlex Limit PlanningForecast NettingFor

10、ecast ExtractionMaster PlanningSupply PlanningEnterprise Planning: Inventory PlanningEnterprise planning: Long term capacity planningEnterprise planning: Long term material planningFacility Planning: Supply plan for enterprise managed componentsCollaboration Planning for Enterprise and Factory Manag

11、ed Components Procurement CollaborationCollaboration Planning with Transportation Providers - Transportation CollaborationAllocation PlanningDemand FulfillmentOrder PromisingPromising new ordersConfigure to Order (CTO) OrdersBuild to Order (BTO) OrdersOrder PlanningFactory PlanningTransportation Pla

12、nningSCM Processes OverviewThe following figure briefly describes the solution architecture for the core processes that constitute the SCM solution. SCM ProcessesThe SCM template as a whole performs the following functions:Demand Planning: Forecasting and demand collaboration. Sales forecasts are ge

13、nerated using various statistical models and customer collaboration.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. In addition, decisions rel

14、ating to material procurement and capacity outsourcingof materials from suppliers (or capacity outsourcing decisions) can be made.Allocation Planning: Reserving product supply for channel partners or customers based on pre-specified rules. Also, managing the supply so that orders that have already b

15、een promised can be fulfilled in the best possible manner (on the promised dates and in the promised quantities).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 consid

16、ering such variables as lead-time, product cost, shipping cost, etc.Order Planning: Detailed order planning encompassing multiple factories. In addition detailed transportation planning is also done which can handle such complex requirements as merging two shipments from different locations during t

17、ransit.Information flows seamlessly between all these functions. The inputs to the system are the static data (supply chain structure, supplier relationships, seller and product hierarchies, supplier relationships, etc), some forecast data and actual orders. The output is a comprehensive and intelli

18、gent supply chain plan which takes all the supply chain delivery processes into consideration 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

19、of the template (Hitech2). For any planning system, the place to begin planning is demand forecasting. We look at this in more detail in the next section.Demand PlanningThe objective of the Demand Planning process is to develop an accurate, reliable view of market demand, which is called the demand

20、plan. The Demand Planning process understands how products are organized and how they are sold. These structures are the foundation of the process and determine how forecast aggregation and disaggregation is conducted. A baseline statistical forecast is generated as a starting point. It is improved

21、with information directly from large customers and channel partners through collaboration. The forecast is refined with the planned event schedule, so the demand plan is synchronized with internal and external activities. Each product is evaluated based on its lifecycle, and continually monitored to

22、 detect deviation. New product introductions are coordinated with older products, pipeline inventories, and component supply to maximize their effectiveness. Attach rates are used to determine component forecasts given the proliferation of products. The result is a demand plan that significantly red

23、uces forecast error and calculates demand 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 Demand Planning process must represent those differenc

24、es.Order PlanningDemand PlanningOrder PlanningDemand PlanningOrder PromisingAllocationPlanningDemand ForecastingTop down forecastingBottom up forecastingLife cycle planningOption forecast Consensus forecastingForecast extractionDemand CollaborationDemandPlanningCustomersOrder Creation& CaptureForeca

25、st NettingMaster PlanningDemand ForecastingTop-Down ForecastingDefinitionTop down forecasting is the process of taking an aggregate enterprise revenue target and converting this revenue target into a revenue forecast by sales unit/product line. This allocation process of revenue targets can be done

26、using historical performance measures or using rule based allocation techniques. The revenue targets can further be broken down into unit volume forecasts by using Average Selling Price information for product lines.Historical information is typically more accurate at aggregate levels of customer/pr

27、oduct hierarchies. Therefore, statistical forecasting techniques are typically applied at these aggregate levels. At levels where historical information might not be very relevant or is not perceived to be accurate, this allocation can be done with a rule-based approach. Frequency: This process is t

28、ypically performed at a monthly/quarterly frequency, with the forecast being generated for the next several months/quarters. Scenario DescriptionBased upon historical bookings at an aggregate level across the entire company (for all products and geographys), the system will automatically generate mu

29、ltiple forecasts using different statistical techniques. The statistical techniques will account for such things as seasonality, trends, and quarterly spikes. Each statistical forecast will be compared with actuals to calculate a standard error. This will automatically occur at every branch (interse

30、ction) in the product and geographic hierarchies. The aggregate statistical forecast generated for the entire company will be automatically disaggregated at every intersection using the statistical technique with the smallest standard error. The outcome of this process will be a “Pickbest” statistic

31、ally generated forecast at every level in the product and geography hierarchies. This forecast is then used as a baseline or starting point.Inputs Historical Bookings by unitsHistorical Statistically based Bookings ForecastOutputsMultiple Statistical forecastsStatistical “Pickbest” forecastForecast

32、committed to top-down forecast database row.BenefitsEasy disaggregation of data means faster, more accurate forecastingSimple alignment of revenue targetsUses top down statistical advantages to easily tie lower level forecasts to revenue targetsi2 Products Used TRADEMATRIX Demand PlannerBottom-Up Fo

33、recastingDefinitionThis process enables the different sales organizations/sales reps/operations planners to enter the best estimate of the forecast for different products. This process consolidates the knowledge of sales representatives, local markets, and operational constraints into the forecastin

34、g process. This forecast can be aggregated from bottom up and compared to the targets established by the top-down forecasting 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 contin

35、uous refinement of the forecast at an interval determined by the forecasting cycle time and/or nature of the change required.Scenario DescriptionIn parallel with the top-down forecast, the sales force/operational planners will enter forecasts for independent demand for a particular SKU or product se

36、ries by customer or region as is pertinent to a particular Product / Geography combination. This data will automatically be 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

37、 revenue dollars and automatically aggregated.The bottom-up forecast can also be generated using collaborative demand planning 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 inputO

38、perations Planning Input Average Selling Price (ASP)Customer forecast (from the Demand Collaboration process)Outputs Aggregated Sales forecast by unitAggregated Sales Forecast by DollarsAggregated Operations Plan by unitBenefitsAutomatic aggregation of data means faster, more accurate forecastingSim

39、ple alignment of lower level Sales plans to higher level revenue targetsi2 Products UsedTRADEMATRIX Demand Planner, TRADEMATRIX Collaboration PlannerLife Cycle Planning New Product Introductions and Phase-In/Phase-OutDefinition Forecasting product transitions plays a critical role in the successful

40、phasing out and launch of new products. New Product Introduction (NPI) and phase In/phase out forecasting allows the enterprise to forecast ramp downs and ramp ups more accurately. Ramping can be defined in terms of either a percentage or as units. Typically new products are difficult to forecast be

41、cause no historical information for that product exists. NPI planning must allow for new product to inherit historical information from other product when it is expected that a new product will behave like the older product. In situations where a new product will not behave like any other older prod

42、uct, NPI planning allows a user to predict a life cycle curve for a product, and then overlay lifetime volume forecasts across that curve.Scenario Description Given a forecast for two complimentary products, the user can change the ramping percentage of both to reflect the ramping up of one product

43、and the ramping down of another. Given a New Product Introduction that is predicted to behave like an older product, the user can utilize historical data from the older product to be used in predicting the forecast for the new product. The scenarios for this process are executed in TradeMatrix Deman

44、d Planner. Future releases of the template will use TradeMatrix Transitional Planner to do product life cycle planning.InputsHistorical bookingsNew product and association with the older partProduct ramping information for a new productOutputsAdjusted Forecast ramping broken out by % New product for

45、ecast based on a similar products historyNew product forecast based on life cycle inputBenefits The ability to forecast a new product using history from an another productThe ability to forecast using product life cycle curvesCleaner product transitions allowing for decreased inventory obsolescencei

46、2 Products UsedTRADEMATRIX Demand Planner, TRADEMATRIX Transition PlannerEvent PlanningDefinition This process determines 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

47、the competition is an example of an event related factor that may influence demand. The marketing forecast is adjusted 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. Frequen

48、cy: Event BasedScenario Description An event row will model the influence of the event that will change the marketing 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 scal

49、ar values which when multiplied by the Marketing statistical forecast will adjust the Marketing forecast up or down 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

50、 the nature of the event. InputsEvent constant factor typicallyHistorical BookingsMarketing forecastOutputsAdjusted Marketing Forecast Benefits The ability to allow events to dynamically influence forecastI2 Products UsedTRADEMATRIX Demand PlannerConsensus ForecastDefinitionThe consensus process is

51、one in which the multiple forecasting processes thus far used are brought together to arrive at one 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 proces

52、s is performed is typically at an intermediate level, where the forecast is most meaningful for the 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 DescriptionThe

53、different forecasts including the top-down, bottom-up, marketing, operations and sales are compared 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

54、 that is used by the supply planning process.InputsTop down forecasts, bottom up forecasts, etc. at a specific node (intersection of product and geography) in the hierarchy.OutputsConsensus forecastBenefitsCommunication between different organizations is achievedMultiple data points can be displayed

55、, allowing for analysis, comparisons and metricsEmphasizes data analysis and reduced data gatheringI2 Products UsedTRADEMATRIX Demand PlannerAttach-Rate Forecasting/Dependent Demand Forecasting in Configure-to-Order environmentsDefinition In a Configure To Order (CTO) manufacturing environment, a pa

56、rticular product model can be sold with several options. The customer chooses the exact 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

57、 of options is called “attach rates”. The consensus process essentially determines the forecast at the product model level. This process performs the option mix analysis to forecast attach rates. The attach rates can be varying by time and/or geography. Product or Product-series level forecasts will

58、 be broken down into the components or options that comprise them by using attach rates. Attach rates can be manually input or forecasted based upon history.Scenario DescriptionInputsModel to options mappingRelationship to determine dependent forecastOutputsAttach RatesDependent ForecastBenefitsEasy

59、 way to determine dependent forecasts in a CTO environmentAttach Rates can be forecast across time and geographyI2 Products UsedTRADEMATRIX Demand Planner, RHYTHM PRODemand CollaborationDefinitionIn situations where the customers of the enterprise have their own forecasting processes, demand collabo

60、ration will enable more accurate forecasting by ensuring rapid transmission of any downstream demand pattern changes to the enterprise. Furthermore, in the absence of such a workflow, every node in the supply chain invariably tends to put in “sandbagging” inventory to compensate for the lack of fast