某公司供应链管理流程模型(英文版)(doc 37页).doc

SECTION 1 SCM TEMPLATE WORKFLOW 2000 i2 Technologies Inc 3 SCM Template Workflow Release 4 2 1 Copyright 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 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 to be used by the federal government the following statement is applicable In accordance with FAR 52 227 19 Commercial Computer Software Restricted Rights the following applies This software is Unpublished rights reserved under the copyright laws of the United States The text and drawings set forth in this document are the exclusive property of i2 Technologies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 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 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 CHAIN MANAGEMENT i2 i2 TECHNOLOGIES and design TRADEMATRIX TRADEMATRIX and design and RhythmLink February 2000 Document ID HiTech 4 2 SCM Template Workflow Document Version V 1 0 Document Title HiTech 4 2 SCM Template Workflow Document Revision Draft 1 Revision Date 3 February 2000 Document Reference Primary Author s SCM Team Krishnan Subramanian Jatin Bindal Abhay Singhal Comments Contents SCM PROCESSES OVERVIEW SCM PROCESSES DEMAND PLANNING DEMAND FORECASTING Top Down Forecasting Bottom Up Forecasting Life Cycle Planning New Product Introductions and Phase In Phase Out Event Planning Consensus Forecast Attach Rate Forecasting Dependent Demand Forecasting in Configure to Order environments DEMAND COLLABORATION Flex Limit Planning FORECAST NETTING Forecast Extraction MASTER PLANNING SUPPLY PLANNING Enterprise Planning Inventory Planning Enterprise planning Long term capacity planning Enterprise planning Long term material planning Facility Planning Supply plan for enterprise managed components Collaboration Planning for Enterprise and Factory Managed Components Procurement Collaboration Collaboration Planning with Transportation Providers Transportation Collaboration ALLOCATION PLANNING DEMAND FULFILLMENT ORDER PROMISING Promising new orders Configure to Order CTO Orders Build to Order BTO Orders ORDER PLANNING Factory Planning Transportation Planning SCM Processes Overview The following figure briefly describes the solution architecture for the core processes that constitute 2000 i2 Technologies Inc 5 the SCM solution Forecast SCM Functional Workflow Demand Planning Forecast Netting Order Promising Master Planning Order Planning Supply Allocation Netted Forecast Allocations New Orders Promise Information Demand Planning Supply Planning Demand Fulfillment Allocation Planning copy Latest Available To Promise Supply Plan Order Creation Demand Collaboration Procurement Collaboration Backlog orders SCM Processes The SCM template as a whole performs the following functions 1 Demand Planning Forecasting and demand collaboration Sales forecasts are generated using various 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 the factory level In addition decisions relating to material procurement and capacity outsourcingof materials from suppliers or capacity outsourcing decisions can 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 be fulfilled 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 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 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 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 We look at this in more detail in the next section Demand Planning The 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 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 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 represent those differences The following figure identifies the key processes that constitute demand planning and the scenarios that are modeled in the template Order Planning Demand Planning Order Promising Allocation Planning Demand Forecasting Top down forecasting Bottom up forecasting Life cycle planning Option forecast Consensus forecasting Forecast extraction Demand Collaboration Demand Planning Customers Order Creation Capture Forecast Netting Master Planning 2000 i2 Technologies Inc 7 Demand Forecasting Top Down Forecasting Definition Top 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 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 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 Description Based upon historical bookings at an aggregate level across the entire company for all products and geography s the 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 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 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 product and geography hierarchies This forecast is then used as a baseline or starting point Inputs Historical Bookings by units Historical Statistically based Bookings Forecast Outputs Multiple Statistical forecasts Statistical Pickbest forecast Forecast committed to top down forecast database row Benefits Easy disaggregation of data means faster more accurate forecasting Simple alignment of revenue targets Uses top down statistical advantages to easily tie lower level forecasts to revenue targets i2 Products Used TRADEMATRIX Demand Planner 2000 i2 Technologies Inc 9 Bottom Up Forecasting Definition This 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 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 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 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 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 Planner TRADEMATRIX Collaboration Planner Life Cycle Planning New Product Introductions and Phase In Phase Out Definition 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 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 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 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 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 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 Outputs Adjusted Forecast ramping broken out by New product forecast based on a similar products history New product forecast based on life cycle input Benefits The ability to forecast a new product using history from an another product The ability to forecast using product life cycle curves Cleaner product transitions allowing for decreased inventory obsolescence i2 Products Used TRADEMATRIX Demand Planner TRADEMATRIX Transition Planner 2000 i2 Technologies Inc 11 Event Planning Definition 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 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 Based Scenario 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 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 Inputs Event constant factor typically Historical Bookings Marketing forecast Outputs 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 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 consensus forecast Scenario Description The 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 Inputs Top down forecasts bottom up forecasts etc 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 gathering I2 Products Used TRADEMATRIX Demand Planner 2000 i2 Technologies Inc 13 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 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 Description Inputs Model to options mapping Relationship to determine dependent forecast Outputs Attach Rates Dependent Forecast Benefits Easy way to determine dependent forecas