SECTION 1SCM TEMPLATE WORKFLOW©2000 i2 Technologies, Inc. -2-SCM Template WorkflowRelease 4.2.1Copyright 2000 i2 Technologies, Inc.This notice is intended as a precaution against inadvertent publication and does not imply any waiverof confidentiality. Information in this document is subject to change without notice. No part of thisdocument 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 purposewithout the express written permission of i2 Technologies, Inc.The software and/or database described in this document are furnished under a license agreement ornondisclosure agreement. It is against the law to copy the software on any medium except asspecifically allowed in the license or nondisclosure agreement. If software or documentation is tobe used by the federal government, the following statement is applicable:In accordance withFAR 52.227-19 Commercial Computer Software —Restricted Rights, the following applies:This software is Unpublished—rights reserved under the copyright laws of the United States.The text and drawings set forth in this document are the exclusive property of i2 Technologies, Inc.Unless otherwise noted, all names of companies, products, street addresses, and persons contained inthe 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,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 OFINSTABILITY; i2 TECHNOLOGIES; ORB NETWORK; PLANET; and RESULTS DRIVENMETHODOLOGY.The following registered trademarks are the property of i2 Technologies, Inc.: GLOBAL SUPPLYCHAIN MANAGEMENT; i2; i2 TECHNOLOGIES and design; TRADEMATRIX;TRADEMATRIX and design; and RhythmLink.February, 2000©2000 i2 Technologies, Inc. -3-SCP Master Planning Technical Implementer Reference Manual ContentsSCM PROCESSES OVERVIEWSCM P ROCESSESDEMAND PLANNINGD EMAND F ORECASTINGTop-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 environmentsD EMAND C OLLABORATIONFlex Limit PlanningF ORECAST N ETTINGForecast ExtractionMASTER PLANNINGS UPPLY P LANNINGEnterprise 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 – ProcurementCollaborationCollaboration Planning with Transportation Providers - Transportation CollaborationA LLOCATION P LANNINGDEMAND FULFILLMENTO RDER P ROMISINGPromising new ordersConfigure to Order (CTO) OrdersBuild to Order (BTO) OrdersO RDER P LANNINGFactory PlanningTransportation PlanningSCM Processes OverviewThe following figure briefly describes the solution architecture for the core processes that constitute©2000 i2 Technologies, Inc. -4-the SCM solution.SCM ProcessesThe SCM template as a whole performs the following functions:1.Demand Planning: Forecasting and demand collaboration. Sales forecasts are generated usingvarious statistical models and customer collaboration.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 thefactory level. In addition, decisions relating to material procurement and capacity outsourcingcan be made.3.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 been promised can befulfilled in the best possible manner (on the promised dates and in the promised quantities).4.Order Promising: Promising a date and quantity to customer orders. These promises are madelooking at the projected supply. In addition, sourcing decisions are also made here afterconsidering such variables as lead-time, product cost, shipping cost, etc.5.Order Planning: Detailed order planning encompassing multiple factories. In addition detailedtransportation planning is also done which can handle such complex requirements as mergingtwo shipments from different locations during transit.©2000 i2 Technologies, Inc. -5-SCP Master Planning Technical Implementer Reference Manual 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 intelligent supplychain plan which takes all the supply chain delivery processes into consideration in order tomaximize 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 thetemplate (Hitech2). For any planning system, the place to begin planning is demand forecasting. Welook 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 marketdemand, which is called the demand plan. The Demand Planning process understands how productsare organized and how they are sold. These structures are the foundation of the process anddetermine how forecast aggregation and disaggregation is conducted. A baseline statistical forecastis generated as a starting point. It is improved with information directly from large customers andchannel partners through collaboration. The forecast is refined with the planned event schedule, sothe demand plan is synchronized with internal and external activities. Each product is evaluatedbased on its lifecycle, and continually monitored to detect deviation. New product introductions arecoordinated with older products, pipeline inventories, and component supply to maximize theireffectiveness. Attach rates are used to determine component forecasts given the proliferation ofproducts. The result is a demand plan that significantly reduces forecast error and calculates demandvariability, both of which are used to determine the size of the response buffers. The specificresponse 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 scenariosthat are modeled in the template.Demand ForecastingTop-Down ForecastingDefinitionTop down forecasting is the process of taking an aggregate enterprise revenue target and convertingthis revenue target into a revenue forecast by sales unit/product line. This allocation process ofrevenue targets can be done using historical performance measures or using rule based allocationtechniques. The revenue targets can further be broken down into unit volume forecasts by usingAverage Selling Price information for product lines.Historical information is typically more accurate at aggregate levels of customer/product hierarchies.Therefore, statistical forecasting techniques are typically applied at these aggregate levels. At levelswhere historical information might not be very relevant or is not perceived to be accurate, thisallocation can be done with a rule-based approach.Frequency: This process is typically performed at a monthly/quarterly frequency, with the forecastbeing generated for the next several months/quarters.Scenario DescriptionBased upon historical bookings at an aggregate level across the entire company (for all products andgeography’s), the system will automatically generate multiple forecasts using different statisticaltechniques. The statistical techniques will account for such things as seasonality, trends, andquarterly spikes. Each statistical forecast will be compared with actuals to calculate a standard error.This will automatically occur at every branch (intersection) in the product and geographic hierarchies.The aggregate statistical forecast generated for the entire company will be automaticallydisaggregated at every intersection using the statistical technique with the smallest standard error.The outcome of this process will be a “Pickbest” statistically generated forecast at every level in the ©2000 i2 Technologies, Inc. -7-SCP Master Planning Technical Implementer Reference Manual product and geography hierarchies. This forecast is then used as a baseline or starting point.InputsHistorical Bookings by unitsHistorical Statistically based Bookings ForecastOutputsMultiple Statistical forecastsStatistical “Pickbest” forecastForecast 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 UsedTRADEMATRIX Demand Planner©2000 i2 Technologies, Inc. -8-Bottom-Up ForecastingDefinitionThis process enables the different sales organizations/sales reps/operations planners to enter the bestestimate of the forecast for different products. This process consolidates the knowledge of salesrepresentatives, local markets, and operational constraints into the forecasting process. This forecastcan be aggregated from bottom up and compared to the targets established by the top-downforecasting process at the enterprise level. This will enable easy comparison between sales forecastsand financial targets.Frequency: This is a weekly process. However, there is continuous refinement of the forecast at aninterval 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 forindependent demand for a particular SKU or product series by customer or region as is pertinent to aparticular Product / Geography combination. This data will automatically be aggregated andcompared to the targets established by the top-down forecasting process. Using the Average SellingPrice for a unit, the unit based forecasts can be converted to revenue dollars and automaticallyaggregated.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 andcompared to the top-down target.Input☐Sales force input☐Operations Planning Input☐Average Selling Price (ASP)☐Customer forecast (from the Demand Collaboration process)Outputs☐Aggregated Sales forecast by unit☐Aggregated Sales Forecast by Dollars☐Aggregated Operations Plan by unitBenefits☐Automatic aggregation of data means faster, more accurate forecasting☐Simple alignment of lower level Sales plans to higher level revenue targetsi2 Products UsedTRADEMATRIX Demand Planner, TRADEMATRIX Collaboration Planner©2000 i2 Technologies, Inc. -9-SCP Master Planning Technical Implementer Reference Manual Life Cycle Planning – New Product Introductions and Phase-In/Phase-OutDefinitionForecasting product transitions plays a critical role in the successful phasing out and launch of newproducts. New Product Introduction (NPI) and phase In/phase out forecasting allows the enterprise toforecast ramp downs and ramp ups more accurately. Ramping can be defined in terms of either apercentage or as units. Typically new products are difficult to forecast because no historicalinformation for that product exists. NPI planning must allow for new product to inherit historicalinformation from other product when it is expected that a new product will behave like the olderproduct. In situations where a new product will not behave like any other older product, NPIplanning allows a user to predict a life cycle curve for a product, and then overlay lifetime volumeforecasts across that curve.Scenario DescriptionGiven a forecast for two complimentary products, the user can change the ramping percentage ofboth to reflect the ramping up of one product and the ramping down of another. Given a NewProduct Introduction that is predicted to behave like an older product, the user can utilize historicaldata from the older product to be used in predicting the forecast for the new product. The scenariosfor this process are executed in TradeMatrix Demand Planner. Future releases of the template willuse TradeMatrix Transitional Planner to do product life cycle planning.Inputs☐Historical bookings☐New product and association with the older part☐Product ramping information for a new productOutputsAdjusted Forecast ramping broken out by %New product forecast based on a similar products historyNew product forecast based on life cycle inputBenefitsThe 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 obsolescencei2 Products UsedTRADEMATRIX Demand Planner, TRADEMATRIX Transition Planner©2000 i2 Technologies, Inc. -10-Event PlanningDefinitionThis process determines the effect of future planned events on the forecast. The marketing forecastis adjusted based on events related factors. A promotional campaign or price change by the companyor the competition is an example of an event related factor that may influence demand. Themarketing forecast is adjusted up or down by a certain factor. The factor can be increased ordecreased across periods to simulate a ramp-up or a ramp-down in sales depending upon the natureof the event.Frequency: Event BasedScenario DescriptionAn event row will model the influence of the event that will change the marketing forecast. Apromotional campaign or price change by the company or the competition is an example of a factorthat may influence demand. The user will populate the Event row with scalar values which whenmultiplied by the Marketing statistical forecast will adjust the Marketing forecast up or down bya factor (0.90 for a 10% decline or 1.05 for a 5% increase etc.). Event row can be increased ordecreased across periods to simulate a ramp-up or a ramp-down in sales depending upon the natureof the event.Inputs☐Event – constant factor typically☐Historical Bookings☐Marketing forecastOutputs☐Adjusted Marketing ForecastBenefits☐The ability to allow events to dynamically influence forecastI2 Products UsedTRADEMATRIX Demand Planner©2000 i2 Technologies, Inc. -11-SCP Master Planning Technical Implementer Reference Manual Consensus ForecastDefinitionThe consensus process is one in which the multiple forecasting processes thus far used are broughttogether to arrive at one single forecast. All information critical to reaching consensus on theforecast will be brought together for analysis and facilitation of the consensus process. The level atwhich the consensus process is performed is typically at an intermediate level, where the forecast ismost meaningful for the different stakeholder organizations. Thus, top-down forecast, bottom-upforecast, marketing forecast and collaborative forecast will be used to arrive at a consensus forecast.Scenario DescriptionThe different forecasts including the top-down, bottom-up, marketing, operations and sales arecompared and contrasted by the various forecast owners and based on considerations such as revenuetargets, life-cycle considerations and capacity a consensus forecast is determined. This is the finalforecast that is used by the supply planning process.Inputs☐Top down forecasts, bottom up forecasts, etc. at a specific node (intersection of product and geography) in the hierarchy.Outputs☐Consensus forecastBenefits☐Communication between different organizations is achieved☐Multiple data points can be displayed, allowing for analysis, comparisons and metrics☐Emphasizes data analysis and reduced data gatheringI2 Products UsedTRADEMATRIX Demand PlannerAttach-Rate Forecasting/Dependent Demand Forecasting in Configure-to-Order environmentsDefinitionIn a Configure To Order (CTO) manufacturing environment, a particular 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 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 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 DescriptionInputs☐Model to options mapping☐Relationship to determine dependent forecastOutputs☐Attach Rates☐Dependent ForecastBenefits☐Easy way to determine dependent forecasts in a CTO environment☐Attach Rates can be forecast across time and geographyI2 Products UsedTRADEMATRIX Demand Planner, RHYTHM PROSCP Master Planning Technical Implementer Reference Manual Demand CollaborationDefinitionIn situations where the customers of the enterprise have their own forecasting processes, demandcollaboration will enable more accurate forecasting by ensuring rapid transmission of anydownstream demand pattern changes to the enterprise. Furthermore, in the absence of such aworkflow, every node in the supply chain invariably tends to put in “sandbagging” inventory tocompensate for the lack of fast information flow.Scenario DescriptionThe Internet enables the rapid collaborative demand forecasting process. A workflow can originateat either the enterprise or the customer, i.e., the enterprise could initiate a baseline forecast to submitto the customers for feedback, or a baseline forecast could be initiated by a customer and submittedto the enterprise for review and collaboration. The workflow used can differ depending on either thecustomer or product. The collaborative communication will be over the World Wide Web.Customers will only be able to see “their” forecasts, not those of other customers. In addition toforecast, information regarding sell through rates, inventory levels etc. can also be communicatedbetween enterprise and customers.Inputs☐Enterprise initiated baseline forecast or customer initiated baseline forecast☐Revisions to the forecast by customer and enterpriseOutputs☐ A consensus forecast agreed upon between customer and enterprise for different product lines.BenefitsCollaborative forecasting over the Internet reduces cycle time between forecast informationpropagation. Hence enterprise gets more real time updates of changes in downstream demandpatterns.☐Collaborative forecasting processes will enable improving honest information exchange between enterprise and customers thereby reducing the “sandbagging” inventory in the supply chain.I2 Products UsedTRADEMATRIX Collaboration PlannerFlex Limit PlanningDefinitionContracts between the enterprise and their customers place restrictions on how much flexibility is provided to the customers in terms of varying forecast numbers from one time period to another. Based on the collaboration process with channel partners / customers, flex limits on the forecast values are established. These flex limits will then drive the amount of inventory that the enterprise needs to position to cover for the anticipated variation in demand.Scenario DescriptionThis process is currently not a part of the template. Future releases will incorporate this process as a standard workflow in the template.InputsOutputsBenefitsI2 Products UsedTRADEMATRIX Collaboration PlannerSCP Master Planning Technical Implementer Reference Manual Forecast NettingForecast netting as a process can be done outside of Demand Planning or within demand planning.The decision as to where to perform this process would vary by industry. The template supports bothtypes of workflow.DefinitionThe consensus forecast is used as input for supply planning for the enterprise. As customer orders /confirmed orders (order backlog) are realized in a short term (few weeks to few months), the ordersare netted against the forecast for the supply planning purpose. The supply planning process, thus,plans for the netted forecast and the order backlog. It is important to distinguish between forecast andorders in supply planning because orders are firm demand that the enterprise has committed to thecustomers. Therefore, it translates directly into revenue for the enterprise. By providing the ordersand netted forecast as inputs to the supply planning process, we can allocate constrained material andsupply first to the actual orders and then to the forecast, thereby ensuring that the orders are plannedfirst..Scenario Description1.Forecast Netting for BTS and BTO productsForecast netting for a BTS product is done at a seller product level. Consider a particular seller-product combination. We know the forecast for the bucket. From the actual orders, we can determinethe actual orders for the seller-product combination that fall in each bucket. These orders can then benetted against the forecast using pre-specified business rules.2.Forecast Netting for CTO productsForecast for CTO products is done at a model level. However, unlike for BTS and BTO, actualorders for CTO come in at component level. The customer will specify a set of components thatwould be assembled into a model. Because of this discrepancy between the level at which forecastingis done (model level) and the level at which actual demand comes in (component level), forecastnetting for CTO is not so straightforward. So for CTO, we send—not a netted forecast but —anadjusted forecast to Master Planning. To arrive at an adjusted forecast, the gross forecast can beadjusted at two levels: a) The total forecast for the bucket at a seller-product combination node canbe changed, and/or b) The forecasted attach rates (between the CTO model and the components) canbe changed by looking at the way demand actually materialized. For instance, if most CTO orderscame in with the requirement for a 6GB hard disk whereas it had been forecasted that they wouldusually be for a 8GB hard disk, then the attach rates would now have to be changed to reflect the wayactual demand materialized and the way actual demand is expected to materialize in future.A simplistic case: Demand materialized exactly in the same way as had been forecasted for a CTOproduct. In this case, we would not adjust the CTO gross forecast at all, and send the entire forecastto Master Planning.It may be noted here that Master Planning never reads the actual orders for CTO products (unlikefor BTO and CTO). Actual orders for CTO are only read by Order Planning.Inputs☐Consensus Forecast☐Order backlogOutputs☐Netted forecast (BTS/BTO)☐Adjusted Forecast for CTOBenefitsSupply reservation for actual orders can take place during supply planning. This is essential as actual orders (which have already been promised) MUST be met. Explain using the definition paragraphi2 Products UsedTRADEMATRIX Demand Planner, TradeMatrix Demand Fulfillment.SCP Master Planning Technical Implementer Reference Manual Forecast ExtractionOnce forecast netting has been done, we need to extract the forecast so it can be sent to MasterPlanning for supply planning and to Allocation Planning to aid in allocations. Master Planningrequires netted or adjusted forecast while Allocation Planning requires gross forecast.Forecast Extraction for Supply PlanningDefinitionThis is the process of extracting forecast at the appropriate Product/Geography intersections andcommunicating it to the supply planning process so that a supply plan can be determined.ScenariosDepending on whether the manufacturing environment is Build-to-Stock (BTS), Build-to-Order(BTO) or Configure-to-Order (CTO), the forecast extraction for supply planning is done at differentlevels in the product hierarchy. At this point, we must recall that there are two dimensions to aforecast in a given bucket—seller and product. We just mentioned that the level in the producthierarchy at which the forecast has to be extracted will vary depending on product type. Potentially,the seller hierarchy level at which forecast is extracted can also vary depending on product typethough we do not demonstrate this in the template data set. For more details, please see the scenariosbelow.1.BTS productsIn a BTS environment, the forecast is extracted for the supply planning process typically at a modellevel (in the product hierarchy). Also, depending on the level in the seller hierarchy at which theforecast is to be planned, the forecast is extracted and netting is performed at the appropriate level inthe seller hierarchy. Please note that since allocation planning for BTS is done entirely in theallocation planning engine, so there is no additional information that will be generated by extractingthe forecast for BTS at customer-dc level. All we need to know is the aggregated forecast for a BTSproduct in the regions which are served from one supply point.2.BTO productsIn a BTO environment, forecast is typically extracted at the Product Family or at the model level (forassembly coordination). The Supply planning process uses a bill of material to determine thecomponent requirements based on these forecasts to generate a Supply Plan. Depending on the levelin the Seller hierarchy at which this supply is to be planned, forecast is extracted at the appropriatelevel in the seller hierarchy.3. CTO productsIn a CTO environment, the forecast can be extracted in two ways. They are❑Component level: Forecast is extracted at the component level using the attach rates and by breaking down the model level netted forecast into component forecasts.❑Model level: The adjusted forecast is extracted at a model level and is sent to supply planning along with the attach rates.In the SCM template 4.2.1, we follow the latter approach because it helps us to do assemblycoordination. What follows is a brief explanation of how assembly coordination is aided by。
