SECTION 1SCM TEMPLATE WORKFLOW3 / 132SCM Template WorkflowRelease 4.2.1Copyright 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 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 Technologies, Inc. The software and/or database described in this document are furnished under a license agreement or5 / 132nondisclosure agreement. It is against the law to copy the software on any medium except as specifically allowed in the license or nondisclosure agreement. 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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 associated with 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; ORB NETWORK; PLANET; and RESULTS DRIVEN METHODOLOGY.The following registered trademarks are the property of i2 Technologies, Inc.: GLOBAL SUPPLY CHAIN7 / 132MANAGEMENT; i2; i2 TECHNOLOGIES and design; TRADEMATRIX; TRADEMATRIX and design; and RhythmLink. February, 2000ContentsSCM PROCESSES OVERVIEWSCM P ROCESSESDEMAND PLANNINGD EMAND F ORECASTINGTop-Down ForecastingBottom-Up ForecastingLife Cycle Planning – New Product Introductionsand Phase-In/Phase-OutEvent PlanningConsensus ForecastAttach-Rate Forecasting/Dependent DemandForecasting in Configure-to-Order environmentsD EMAND C OLLABORATION9 / 132Flex Limit PlanningF ORECAST N ETTINGForecast ExtractionMASTER PLANNINGS UPPLY P LANNINGEnterprise Planning: Inventory Planning Enterprise planning: Long term capacity planning Enterprise planning: Long term material planning Facility Planning: Supply plan for enterprise managed componentsCollaboration Planning for Enterprise and Factory Managed Components – Procurement Collaboration Collaboration 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 the SCM solution.11 / 132SCM ProcessesThe SCM template as a whole performs the following functions:1.Demand Planning: Forecasting and demandcollaboration. Sales forecasts are generated using various statistical models and customer collaboration.2.Master Planning: Long term and medium term masterplanning 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 relating to material procurement and capacity outsourcing can be made. 3.Allocation Planning: Reserving product supply forchannel 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.Order Promising: Promising a date and quantity tocustomer orders. These promises are made looking at the projected supply. In addition, sourcing13 / 132decisions are also made here after considering such variables as lead-time, product cost, shipping cost, etc.5.Order Planning: Detailed order planningencompassing multiple 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 structure, 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 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 planning system, the place to begin planning is demand forecasting. We15 / 132look 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 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 with information directly from large customers and channel partners through collaboration. The forecast is refined with the planned event schedule, so the demand plan issynchronized 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 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 reduces 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 represent17 / 132those differences.The following figure identifies the key processesthat constitute demand planning and the scenarios that are modeled in the template.Demand 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 using historical19 / 132performance 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/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. Frequency: This process is typically 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 geography’s), the sy stem 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 automatically 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 intersection21 / 132using 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 product and geography hierarchies. This forecast is then used as a baseline or starting point.InputsHistorical Bookings by unitsHistorical Statistically based Bookings Forecast OutputsMultiple 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 Planner23 / 132Bottom-Up ForecastingDefinitionThis 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 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 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.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 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 forecasting process. Using the Average Selling Price for a unit,25 / 132the 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/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☐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 Planner27 / 132Life Cycle Planning – New Product Introductions and Phase-In/Phase-OutDefinitionForecasting 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 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 it is expectedthat 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.Scenario DescriptionGiven 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 utilize historical data from the older product to be used in predicting the forecast for the29 / 132new 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.Inputs☐Historical bookings☐New product and association with the older part☐Product ramping information for a new product OutputsAdjusted 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 Planner31 / 132Event PlanningDefinitionThis 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 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.Frequency: Event BasedScenario DescriptionAn 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 scalar 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 the nature of the event.33 / 132Inputs☐Event – constant factor typically☐Historical Bookings☐Marketing forecastOutputs☐Adjusted Marketing ForecastBenefits☐The ability to allow events to dynamically influence forecastI2 Products UsedTRADEMATRIX Demand PlannerConsensus ForecastDefinitionThe consensus process is 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 process 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 consensus35 / 132forecast.Scenario DescriptionThe 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 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.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 gathering I2 Products UsedTRADEMATRIX Demand Planner37 / 132Attach-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 attachrates. 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 forecast Outputs☐Attach Rates☐Dependent Forecast39 / 132Benefits☐Easy way to determine dependent forecasts in a CTO environment☐Attach 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 collaboration 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 information flow.Scenario DescriptionThe Internet enables the rapid collaborative demand forecasting process. A workflow can originate at41 / 132either the enterprise or the customer, i.e., the enterprise could initiate a baseline forecast to submit to the customers for feedback, or a baseline forecast could be initiated by a customer and submitted to the enterprise for review and collaboration. The workflow used can differ depending on either the customer 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 to forecast, information regarding sell through rates, inventory levels etc. can also be communicated between 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 information propagation. Hence enterprise gets more real time updates of changes in downstream demand patterns.43 / 132Collaborative 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.45 / 132Scenario 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 PlannerForecast 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 both types 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 orders are netted against the forecast for the supply planning purpose. The supply planning process, thus, plans for the netted forecast and the order backlog. It is47 / 132important to distinguish between forecast and orders in supply planning because orders are firm demand that the enterprise has committed to the customers. Therefore, it translates directly into revenue for the enterprise. By providing the orders and netted forecast as inputs to the supply planning process, we can allocate constrained material and supply first to the actual orders and then to the forecast, thereby ensuring that the orders are planned first..Scenario Description1.Forecast Netting for BTS and BTO productsForecast netting for a BTS product is done at aseller product level. Consider a particular seller-product combination. We know the forecast for thebucket. From the actual orders, we can determine the actual orders for the seller-product combination that fall in each bucket. These orders can then be netted 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, actual orders for CTO come in at component level. The customer will specify a set of components that would be assembled into a model. Because of this discrepancy between the level at which forecasting is done (model level) and the level at which actual demand comes in (component level), forecast netting for CTO is not so49 / 132straightforward. So for CTO, we send—not a netted forecast but —an adjusted forecast to Master Planning. To arrive at an adjusted forecast, the gross forecast can be adjusted at two levels: a) The total forecast for the bucket at a seller-product combination node can be changed, and/or b) The forecasted attach rates (between the CTO model and the components) can be changed by looking at the way demand actually materialized. For instance, if most CTO orders came in with the requirement for a 6GB hard disk whereas it had been forecasted that they would usually 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 CTO product. In this case, we would not adjust the CTO gross forecast at all, and send the entire forecast to Master Planning.It may be noted here that Master Planning never reads the actual orders for CTO products (unlike for BTO and CTO). Actual orders for CTO are only read by Order Planning.51 / 132。