Tài liệu tiếng Anh thương mại Chap006 Integrated operations planning

Tài liệu tiếng Anh thương mại Chap006 Integrated operations planning

Copyright © 2013 by The McGraw-Hill Companies, Inc All rights reserved.

CHAPTER 6: Integrated Operations

Planning

CHAPTER 6: Integrated Operations

Planning

• Supply chain planning

• Supply chain planning applications

• Sales and operations planning

• APS system overview

• Collaborative planning, forecasting and

replenishment

• Forecasting

Overview of integrated operations planning

• Demand planning responsiveness

• Customer relationship collaboration

• Order fulfillment/service delivery

• Supply chain visibility is the ability to track

inventory and resources

– Information about available resources is

effectively evaluated and managed

– Requires exception management of potential

problems as they are identified

Factors that drive effective planning

• Simultaneous resource consideration is the

ability to include demand, capacity, material

requirements, and constraints in defining

alternatives

– Enables identification of trade-offs that can

increase functional costs, but lower total system

costs

Factors that drive effective planning

• Resource utilization is a coordinated

approach to making functional resource

trade-offs

– Considers service requirements while

minimizing combined supply chain resources

– Critical capability when firms emphasize overall

asset utilization

Factors that drive effective planning

• Common software applications for most planning environments include

– Demand planning

– Production planning

– Logistics planning

• These applications can be sourced from the following options

– Custom developed for the organization

– Packaged solutions contained in a larger supply chain management system

– Modules within an ERP system

Supply chain planning applications overview

• Demand management system is the information technology component of the sales and operations planning (S&OP)

process

• Demand management develops the forecasts used by other supply chain processes to anticipate sales levels

– Demand management processes must integrate

• Historical forecasts

• Promotional plans

• Pricing changes

• New product introductions

• Forecasts are then used to determine production and inventory requirements

• Must maintain forecast data consistency across multiple products and warehouse facilities

Demand planning

• Production planning uses requirements from demand management to develop a realistic manufacturing plan

– Must integrate with manufacturing resources and constraints

• Requirements plan defines what items are needed and when

• Production planning systems match the requirements plan with the production constraints

– Limitations include facility, equipment and labor availability

• Effective planning creates a time-sequenced plan to manufacture the correct items in a timely manner while operating

within constraints

• Logistics planning integrates overall movement demand, vehicle availability, and relevant movement

cost into a decision support system that seeks to minimize overall freight expense

– Analysis suggests ways freight can be shifted among carriers or consolidated to lower expenses

• Overcomes these problems resulting from individual perspectives

– Limited economies of scale

– Limited information sharing

– Excessive transportation expense

Logistics planning

+ Forecasts (sales, marketing input, histories, accounts)

+ Customer orders (current orders, future committed orders, contracts)

+ Promotions (promotion, advertising plans)

• Sales & operations planning is an integrated combination of

– Information systems (financial, marketing and supply chain planning)

– Organizational processes

– Personal responsibility and accountability

Sales & operations planning (S&OP)

• Using this S&OP combination, the operations and

sales groups must overcome conflicts to develop

consensus and then execute their collaborative plans

Traditional conflicts between sales and operations groups must be resolved to reach

An overview of the S&OP process illustrating 5 major plans to be integrated

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• Functional leadership from all key operating areas must be committed to the S&OP

process and be responsible for achieving success

– Tie manager’s compensation to successful S&OP performance

– Include regular involvement and accountability at the general management level

Making S&OP work in an organization requires senior leadership

involvement

• Executing the process every month

• Process ownership and clarity of roles and responsibilities

• Organizational commitment to achieving high forecast accuracy

• Focus should be on the next 3 to 12 months

• One integrated plan that integrates the actions of the entire organization

• Senior management decision making

• Measuring end-to-end supply chain performance

• S&OP forecast versus operating plan or budget

8 keys to successful S&OP implementation from Table 6.1

Figure 6.4 APS framework

Table 6.2 Sample APS planning situation

Figures 6.5 APS system components

Requirements Optimization

Demand Management

ERP/Legacy System

Resource Allocation Resource Management

• Facilitates more effective planning with shorter cycle times.

achieve optimal performance.

existing sourcing, production, storage, and transportation capacity.

Supply chain planning benefits

• Greater integration with enterprise financial plans.

Benefits of integrated business planning

• CPFR coordinates the requirements planning process between supply chain partners for

demand creation and demand fulfillment activities

– Process initiated by the consumer products industry

product throughout the supply chain

Collaborative planning, forecasting and replenishment (CPFR)

• Develop a joint business plan

• Create a joint calendar to determine product flow

• Create a common sales forecast based on shared knowledge of each trading partner’s plan

– Share common forecast between retailer and suppliers

– Use an iterative process to share the forecast and requirements plan

• Use the common sales forecast to develop

– Production plan

– Replenishment plan

– Shipment plan

CPFR process steps

Basic relationships for CPFR illustrated in a retail situation

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• Forecast is the specific definition of what is projected

to be sold, when and where

• Forecasting is a critical capability

– Many logistics and supply chain activities must be

completed in anticipation of a sale

• Forecasting approaches to achieve enhanced service

or reduced inventory

– Improve forecast accuracy

– Forecast at a higher level of aggregation

Forecasting

Forecasting is influenced by replenishment time and economies of scale

Table 6.3 How Product Characteristics Influence The Need To Forecast

• Forecasts match the product requirements of customers with capacity of the enterprise

and supply chain

– Customer demands for higher service levels and more product variations with

– A management focus to reduce supply chain assets

Forecasting requirements

• Support collaborative planning

– Collaborative forecasts help avoid inventory excesses and out-of-stock situations

– Common goals are needed to develop effective operating plans

• Drive requirements planning to determine

– Inventory projections

– Replenishment requirements

– Production requirements

• Improve resource management through cost trade-offs of strategies such as

– Extra production capacity

– Extra storage capacity

– Speculative production or product movement

– Outsourcing

Logistics forecasts are necessary to

Forecasting model components for time period t

Cyclic Trend

Seasonal

Forecast

= Ft

Description of model components

other components are removed

downward movement in demand

– E.g Toy demand before Christmas

– Positive, negative or neutral

more than a year

– E.g Housing demand follows business economic cycle

by a firm’s marketing activities

– Advertising, deals, or promotions

unpredictable quantities that do not fit other components

Components of an effective forecast management process

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• Forecast database must include timely historical and planning information

– Must facilitate data manipulation, summarization, analysis and reporting

– E.g., Open orders, demand history, marketing tactics, economy, competitor actions

– E.g., time-series or correlation modeling

Description of forecast management process components

• Faulty communications are costly for supply chains

– Seek to reduce forecast inconsistency across multiple members of the supply chain

• Efforts to perfect a single component do not overcome need for other components

• Process design should consider strengths and weaknesses of each individual component

– Design for optimal performance of integrated system

Meaningful forecast process requires integrated and consistent combination of

components

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• Evaluate technique both quantitatively and qualitatively for

– Accuracy

– Forecast time horizon

– Value of forecasting to business strategy

– Data availability

– Type of data pattern

– Experience of the forecaster

Criteria for evaluating applicability of forecasting techniques

• Qualitative relies on expert opinion and special information

– Costly and time-consuming

– Ideal for situations with little historical data or when much managerial judgment are required

– Developed using surveys, panels and consensus meetings

– “The past is a good predictor of the future”

– E.g., moving averages, exponential smoothing, extended smoothing, and adaptive smoothing

– E.g., simple or multiple regression

Categories of forecast techniques

Forecast techniques from Table 6.4

An unweighted average of the previous periods of sales

An exponentially weighted moving average using smoothing constants to place greater weights on more recent demands

Uses time period as the independent variable to predict future demand patterns

Uses other independent variables, such as price, promotion plans, or related product volumes, to predict sales

Uses more complex statistical techniques to identify more complex demand history relationships;

techniques include spectral analysis, Fourier analysis, transfer functions, and neural networks

• Improving accuracy of forecasts requires error measurement

followed by analysis

• Choice of method for error measurement

– Simple average error can hide problems as positive errors are offset by

negative ones

– Mean absolute deviation (MAD) evaluates absolute error by ignoring the

sign of the error

– Mean absolute percentage error (MAPE) is mean MAD divided by mean

demand

Forecasting accuracy refers to the difference between forecasts and actual

sales

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Second level

Third level

Fourth level

Fifth level

Illustration of alternative measures of forecast error

Table 6.5 Monthly Personal Computer Demand and Forecast

Illustration of how relative forecast error will vary based on the level of

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