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1、SECTIONSECTION 1 1SCMSCM TEMPLATETEMPLATE WORKFLOWWORKFLOWSCMSCM TemplateTemplate WorkflowWorkflowReleaseRelease 4.2.14.2.1Copyright 2000 i2 Technologies, Inc.This notice is intended as a precaution against inadvertent publication and does not imply any waiver of confidentiality. Information in this
2、 document is subject to change without notice. No part of this document 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
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6、ve property of i2 Technologies, 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,
7、registered trademarks, service 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 INSTABI
8、LITY; i2 TECHNOLOGIES; ORB 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, 2000DocumentDocumen
9、t ID:ID: HiTech 4.2 SCM Template WorkflowDocumentDocument Version:Version:V 1.0DocumentDocument Title:Title:HiTech 4.2 SCM Template WorkflowDocumentDocument Revision:Revision:Draft 1RevisionRevision Date:Date:3 February, 2000DocumentDocument Reference:Reference:.PrimaryPrimary Author(s):Author(s):SC
10、M Team Krishnan Subramanian, Jatin Bindal, Abhay SinghalComments:Comments:ContentsContentsSCMSCM PROCESSESPROCESSES OVERVIEWOVERVIEWSCMSCM P PROCESSESROCESSESDEMANDDEMAND PLANNINGPLANNINGD DEMANDEMAND F FORECASTINGORECASTINGTop-Down ForecastingBottom-Up ForecastingLife Cycle Planning New Product Int
11、roductions and Phase-In/Phase-OutEvent PlanningConsensus ForecastAttach-Rate Forecasting/Dependent Demand Forecasting in Configure-to-Order environmentsD DEMANDEMAND C COLLABORATIONOLLABORATIONFlex Limit PlanningF FORECASTORECAST N NETTINGETTINGForecast ExtractionMASTERMASTER PLANNINGPLANNINGS SUPPL
12、YUPPLY P PLANNINGLANNINGEnterprise 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 Managed Components Procurem
13、ent CollaborationCollaboration Planning with Transportation Providers - Transportation CollaborationA ALLOCATIONLLOCATION P PLANNINGLANNINGDEMANDDEMAND FULFILLMENTFULFILLMENTO ORDERRDER P PROMISINGROMISINGPromising new ordersConfigure to Order (CTO) OrdersBuild to Order (BTO) OrdersO ORDERRDER P PLA
14、NNINGLANNINGFactory PlanningTransportation PlanningSCMSCM ProcessesProcesses OverviewOverviewThe following figure briefly describes the solution architecture for the core processes that constitute the SCM solution. ForecastSCM Functional WorkflowDemandPlanningForecastNettingOrderPromisingMasterPlann
15、ingOrder PlanningSupplyAllocationNetted ForecastAllocationsNew Orders, Promise InformationDemand PlanningSupply PlanningDemand FulfillmentAllocation PlanningcopyLatest Available To PromiseSupply PlanOrderCreationDemandCollaborationProcurement CollaborationBacklog ordersSCMSCM ProcessesProcessesThe S
16、CM template as a whole performs the following functions:1.DemandDemand PlanningPlanning: Forecasting and collaboration. Sales forecasts are generated using various statistical models and customer collaboration.2.MasterMaster PlanningPlanning: Long term and medium term master planning for material as
17、 well as capacity. Master planning can be done at both the enterprise level (for critical shared components) and the factory level. In addition, decisions relating to procurement of materials from suppliers (or capacityoutsourcing decisions) can be made.3.AllocationAllocation PlanningPlanning: Reser
18、ving product supply for channel partners or customers based on pre-specified rules. Also, managing the supply so that orders that have already been promised can be fulfilled in the best possible manner (on the promised dates and in the promised quantities).4.OrderOrder PromisingPromising: Promising
19、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.OrderOrder PlanningPlanning: Detailed order planning encompassing mult
20、iple factories. In addition detailed transportation planning is also done which can handle such complex requirements as merging two shipments from different locations during transit.Information flows seamlessly between all these functions. The inputs to the system are the static data (supply chain s
21、tructure, supplier relationships, seller and product hierarchies, supplier relationships, 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 consideration in order to maximize customer s
22、atisfaction, 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 planning system, the place to begin planning is demand forecasting. We look at this in mor
23、e detail in the next section.DemandDemand PlanningPlanningThe objective of the Demand Planning process is to develop an accurate, reliable view of market demand, which is called the demand plan. The Demand Planning process understands how products are organized and how they are sold. These structure
24、s 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 with information directly from large customers and channel partners through collaboration. The forecast is refi
25、ned 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 detect deviation. New product introductions are coordinated with older products, pipeline inventories, and com
26、ponent 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 reduces forecast error and calculates demand variability, both of which are used to determine the size of the resp
27、onse buffers. The specific response buffers and their placement are different based on the manufacturing model employed, therefore the Demand Planning process must represent those differences.The following figure identifies the key processes that constitute demand planning and the scenarios that are
28、 modeled in the template.Order PlanningDemand PlanningOrder PromisingAllocationPlanningDemand ForecastingTop down forecastingBottom up forecastingLife cycle planningOption forecast Consensus forecastingForecast extractionDemand CollaborationDemandPlanningCustomersOrder Creation& CaptureForecast Nett
29、ingMaster PlanningDemandDemand ForecastingForecastingTop-Down ForecastingDefinitionDefinitionTop 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
30、targets can be done 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 l
31、evels of customer/product 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. Frequenc
32、y: This process is typically performed at a monthly/quarterly frequency, with the forecast being generated for the next several months/quarters. ScenarioScenario DescriptionDescriptionBased upon historical bookings at an aggregate level across the entire company (for all products and geographys), th
33、e system will automatically generate multiple 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 autom
34、atically occur at every branch (intersection) 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
35、 process will be a “Pickbest” statistically generated forecast at every level in the product and geography hierarchies. This forecast is then used as a baseline or starting point.InputsInputsHistorical Bookings by unitsHistorical Statistically based Bookings ForecastOutputsOutputsMultiple Statistica
36、l forecastsStatistical “Pickbest” forecastForecast committed to top-down forecast database row.BenefitsBenefitsEasy disaggregation of data means faster, more accurate forecastingSimple alignment of revenue targetsUses top down statistical advantages to easily tie lower level forecasts to revenue tar
37、getsi2i2 ProductsProducts UsedUsed TRADEMATRIX Demand PlannerBottom-Up ForecastingDefinitionDefinitionThis 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 sa
38、les 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 process at the enterprise level. This will enable easy comparison between sales forecasts a
39、nd 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.ScenarioScenario DescriptionDescriptionIn parallel with the top-down forecast, the sales for
40、ce/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 be aggregated and compared to the targets established by the top-down forecas
41、ting 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 planning with a customer. In this case, the consensus forecast for a product/pro
42、duct series for a customer is aggregated and compared to the top-down target. InputInput Sales force inputOperations Planning Input Average Selling Price (ASP)Customer forecast (from the Demand Collaboration process)OutputsOutputs Aggregated Sales forecast by unitAggregated Sales Forecast by Dollars
43、Aggregated Operations Plan by unitBenefitsBenefitsAutomatic aggregation of data means faster, more accurate forecastingSimple alignment of lower level Sales plans to higher level revenue targetsi2i2 ProductsProducts UsedUsedTRADEMATRIX Demand Planner, TRADEMATRIX Collaboration PlannerLife Cycle Plan
44、ning New Product Introductions and Phase-In/Phase-OutDefinitionDefinition Forecasting product transitions plays a critical role in the successful 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
45、 ramp ups more 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
46、 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 product, NPI planning allows a user to predict a life cycle curve for a product, and then overlay lifetime volume forecasts across that curve.ScenarioScenario
47、 DescriptionDescription Given 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 Introduction that is predicted to behave like an older product, the user can utili
48、ze 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 Demand Planner. Future releases of the template will use TradeMatrix Transitional Planner to do product life cycle planning.InputsInputsHist
49、orical bookingsNew product and association with the older partProduct ramping information for a new productOutputsOutputsAdjusted Forecast ramping broken out by % New product forecast based on a similar products historyNew product forecast based on life cycle inputBenefitsBenefits The ability to for
50、ecast a new product using history from an another productThe ability to forecast using product life cycle curvesCleaner product transitions allowing for decreased inventory obsolescencei2i2 ProductsProducts UsedUsedTRADEMATRIX Demand Planner, TRADEMATRIX Transition PlannerEvent PlanningDefinitionDef
