公司供应链管理流程参考模型模板.doc

公司供应链管理流程参考模型

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SECTION 1

SCM TEMPLATE WORKFLOW

SCM Template Workflow
Release 4.2.1
Copyright i2 Technologies, Inc.
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February,
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,
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 the SCM solution.

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.
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
The following figure identifies the key processes that constitute demand planning and the scenarios that are modeled in the template.

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
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
o Sales force input
o Operations Planning Input
o Average Selling Price (ASP)
o Customer forecast (from the Demand Collaboration process)
Outputs
o Aggregated Sales forecast by unit
o Aggregated Sales Forecast by Dollars
o Aggregated Operations Plan by unit
Benefits
o Automatic aggregation of data means faster, more accurate forecasting
o 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
o Historical bookings
o New product and association with the older part
o 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
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