1. CHAPTER 6: Integrated Operations Planning
McGraw-Hill/Irwin

2. 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

3. 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

4. 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

5. 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. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. Figure 6.2 Planning Process Conflicts
Traditional conflicts between sales and operations groups must be resolved to reach consensus
Figure 6.2 Planning Process Conflicts

14. An overview of the S&OP process illustrating 5 major plans to be integrated1 2 5 3 4
Figure 6.2 S&OP Process

15. 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

16. 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

17. Figure 6.4 APS framework
Period 3
Period 1
Period 2

18. Table 6.2 Sample APS planning situation

19. Figures 6.5 APS system components
Requirements
Optimization
Demand Management
ERP/Legacy System
Resource Allocation
Resource Management

20. 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.

21. 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.

22. 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

23. 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

24. Basic relationships for CPFR illustrated in a retail situation
Figure 6.6 CPFR in the Retail Information Technology Environment

25. 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

26. Forecasting is influenced by replenishment time and economies of scale
Table 6.3 How Product Characteristics Influence The Need To Forecast

27. 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

28. 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

29. Forecasting model components for time period t= Ft
Base demand ×
Seasonal ×
Trend ×
Cyclic ×
Promotion +
Irregular Bt Bt St T Ct Pt I

30. 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 long-range 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

31. Components of an effective forecast management process
Figure 6.7 Forecast Management Process

32. 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

33. 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

34. 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

35. 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

36. 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

37. Forecast techniques from Table 6.4
Moving average Exponential smoothing Time series Regression Multivariate
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

38. 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

39. Illustration of alternative measures of forecast error
Table 6.5 Monthly Personal Computer Demand and Forecast

40. Figure 6.9 Comparative Forecast Errors
Illustration of how relative forecast error will vary based on the level of measurement
Figure 6.9 Comparative Forecast Errors