公司供应链管理流程参考模型

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 2000 i2 Technologies, Inc.

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February, 2000

Contents

SCM PROCESSES OVERVIEW

SCM P ROCESSES

DEMAND PLANNING

D EMAND F ORECASTING

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

D EMAND C OLLABORATION

Flex Limit Planning

F ORECAST N ETTING

Forecast Extraction

MASTER PLANNING

S UPPLY P LANNING

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

A LLOCATION P LANNING

DEMAND FULFILLMENT

O RDER P ROMISING

Promising new orders

Configure to Order (CTO) Orders

Build to Order (BTO) Orders

O RDER P LANNING

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 outsourcing 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.

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

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

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 for a 10% decline or 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

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 forecasts in a CTO environment

Attach Rates can be forecast across time and geography

I2 Products Used

TRADEMATRIX Demand Planner, RHYTHM PRO

Demand Collaboration

Definition

In 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 c hain invariably tends to put in “sandbagging” inventory to compensate for the lack of fast information flow.

Scenario Description

The Internet enables the rapid collaborative demand forecasting process. A workflow can originate at either the enterprise or the customer, ., 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 enterprise

Outputs

A consensus forecast agreed upon between customer and enterprise for

different product lines.

Benefits

Collaborative forecasting over the Internet reduces cycle time between forecast information propagation. Hence enterprise gets more real time updates of changes in downstream demand patterns.

Collaborative forecasting processes will enable improving honest information exchange between enterprise and customers thereby reducing the “sandbagging” inventory in the supply chain.

I2 Products Used

TRADEMATRIX Collaboration Planner

Flex Limit Planning

Definition

Contracts 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 Description

This process is currently not a part of the template. Future releases will incorporate this process as a standard workflow in the template.

Inputs

Outputs

Benefits

I2 Products Used

TRADEMATRIX Collaboration Planner