CHAPTER 6: Integrated
Operations Planning
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
Overview of integrated operations planning
• Supply chain planning
• Supply chain planning
applications
• Sales and operations
planning
• APS system overview
• Collaborative planning,
forecasting and
replenishment
• Forecasting
6-2
Supply chain planning requires coordination
of key processes
• Demand planning
responsiveness
• Customer relationship
collaboration
• Order fulfillment/service delivery
• Manufacturing customization
• Supplier relationship
collaboration
• Life-cycle support
• Reverse logistics
6-3
Factors that drive effective planning
• 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
6-4
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
6-5
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
6-6
Supply chain planning applications overview
• 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
6-7
Demand planning
•
•
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
6-8
Production 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
6-9
Logistics planning
• 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
6-10
Figure 6.1
Logistics requirements
+ Forecasts (sales, marketing input, histories, accounts)
+ Customer orders (current orders, future committed orders, contracts)
+ Promotions (promotion, advertising plans)
= Period demand
- Inventory-on-hand
- Planned receipts
= Period logistics requirements
6-11
Sales & operations planning (S&OP)
• Sales & operations planning is an integrated combination of
– Information systems (financial, marketing and supply chain planning)
– Organizational processes
– Personal responsibility and accountability
• Using this S&OP combination,
the operations and sales
groups must overcome
conflicts to develop consensus
and then execute their
collaborative plans
6-12
Traditional conflicts between sales and operations
groups must be resolved to reach consensus
Figure 6.2 Planning Process Conflicts
6-13
An overview of the S&OP process
illustrating 5 major plans to be integrated
1
2
5
3
4
Figure 6.2 S&OP Process
6-14
Making S&OP work in an organization requires
senior leadership involvement
• 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
6-15
8 keys to successful S&OP
implementation from Table 6.1
• 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
6-16
Figure 6.4
APS framework
Period 1
Period 2
Period 3
6-17
Table 6.2
Sample APS planning situation
Time Period
1
2
3
4
5
Requirement
200
200
200
600
200
Production Capacity
300
300
300
300
300
Alternative 1 (overtime):
Production
Inventory Carryover
200
-
200
-
200
-
600*
-
200
-
Alternative 2 (build ahead):
Production
Inventory Carryover
300
100
300
200
300
300
300
-
200
-
6-18
Figures 6.5
APS system components
Resource Management
Requirements
Optimization
Demand Management
Resource Allocation
ERP/Legacy System
6-19
Supply chain planning benefits
• Facilitates more effective planning with shorter
cycle times.
• Offer capability to consider the extended supply
chain and make appropriate trade-offs to achieve
optimal performance.
• More effective and responsive planning allows a
more level assignment of resources for existing
sourcing, production, storage, and transportation
capacity.
6-20
Benefits of integrated business planning
• Greater integration with enterprise financial plans.
• Increased inclusion of strategic initiatives and
activities.
• Improved simulation and modeling of alternatives.
• Easier translation between aggregate and detailed
planning levels.
6-21
Collaborative planning, forecasting and
replenishment (CPFR)
• CPFR coordinates the requirements planning
process between supply chain partners for demand
creation and demand fulfillment activities
– Process initiated by the consumer products industry
• Developed to reduce unplanned and uncoordinated
events that distort the smooth flow of product
throughout the supply chain
6-22
CPFR process steps
• 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
6-23
Basic relationships for CPFR
illustrated in a retail situation
Figure 6.6 CPFR in the Retail Information Technology Environment
6-24
Forecasting
• 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
6-25
Forecasting is influenced by replenishment
time and economies of scale
Table 6.3 How Product Characteristics Influence The Need To Forecast
6-26
Forecasting requirements
• Forecasts match the product requirements of
customers with capacity of the enterprise and
supply chain
• Forecasts must be more timely and accurate to
align
– Customer demands for higher service levels and more
product variations with
– A management focus to reduce supply chain assets
6-27
Logistics forecasts are necessary to
• 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
6-28
Forecasting model components for time
period t
Forecast
=
Ft
Base
demand
Bt
×
Seasonal
St
×
Trend
T
×
Cyclic
Ct
×
Promotion
Pt
+
Irregular
I
6-29
Description of model components
• Base demand is long-term
average demand after other
components are removed
• Seasonal component is annual
recurring upward or downward
movement in demand
– E.g. Toy demand before
Christmas
• Trend component is longrange shift in periodic sales
– Positive, negative or neutral
• Cyclic component is periodic
shifts in demand lasting more
than a year
– E.g. Housing demand follows
business economic cycle
• Promotional component is
demand swings initiated by a
firm’s marketing activities
– Advertising, deals, or promotions
• Irregular component includes
random or unpredictable
quantities that do not fit other
components
6-30
Components of an effective forecast
management process
Figure 6.7 Forecast Management Process
6-31
Description of forecast management process
components
• 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
• Technique is the computational method used to combine model
components into a forecast quantity
– E.g., time-series or correlation modeling
• Support system must facilitate the maintenance, updating and
manipulation of the database and the forecast
• Administration includes organizational, procedural, motivational,
cross-functional and personnel aspects of forecasting
6-32
Meaningful forecast process requires integrated
and consistent combination of 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
6-33
Bullwhip effect showing requirements error
amplification between supply chain partners
Figure 6.8 Response of a Simulated Production / Distribution System to a Sudden 10 Percent
Increase in Sales at the Retail Level
6-34
Criteria for evaluating applicability of
forecasting techniques
• 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
6-35
Categories of forecast 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
• Time series focuses entirely on historical patterns and pattern
changes to generate forecasts
– “The past is a good predictor of the future”
– E.g., moving averages, exponential smoothing, extended smoothing, and
adaptive smoothing
• Causal uses specific information to develop relationships between
lead events and forecasted activity
– E.g., simple or multiple regression
6-36
Forecast techniques from Table 6.4
Moving average
Exponential smoothing
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
Time series
Uses time period as the independent variable to predict
future demand patterns
Regression
Uses other independent variables, such as price, promotion
plans, or related product volumes, to predict sales
Multivariate
Uses more complex statistical techniques to identify more
complex demand history relationships; techniques include
spectral analysis, Fourier analysis, transfer functions, and
neural networks
6-37
Forecasting accuracy refers to the difference
between forecasts and actual sales
• 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
6-38
Illustration of alternative measures of
forecast error
Table 6.5 Monthly Personal Computer Demand and Forecast
6-39
Illustration of how relative forecast error
will vary based on the level of measurement
Figure 6.9 Comparative Forecast Errors
6-40