426 W3: Case Discussion
CHAPTER 7
Demand Management
Supply Chain Management: A Logistics Perspective (10e)
Coyle, Langley, Novack, and Gibson
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Discussion Outline
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Essence and importance of demand management
Balancing supply and demand
Demand forecasting
Sales and Operations Planning (S&OP)
Collaborative Planning, Forecasting, and Replenishment (CPFR)
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Demand Management The Essence of Demand Management
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To estimate and manage customer demand and use this information to make operating decisions.
To further ability of firms throughout the supply chain to collaborate on activities related to the flow of products, services, information, and capital.
Desired End Result
Greater value for the end user or consumer
Demand Management Importance of Demand Management
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Common Problems in Demand Management
Lack of coordination between departments
Too much emphasis placed on forecasts of demand, with less attention on the collaborative efforts and plans needed to be developed from the forecasts
Non-strategic uses of demand information
Demand Management Importance of Demand Management (continued)
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Effective demand management unifies channel members with the common goals of satisfying customers and solving customer problems.
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Gather & analyze knowledge about consumers, their problems, and their unmet needs.
Identify partners to perform functions needed in demand chain.
Move functions to the channel member that can perform them most effectively and efficiently.
Share with other supply chain members knowledge about customers, technology, and logistics challenges and opportunities.
Developing products and services that solve customers’ problems.
Develop & execute best methods to deliver products & services to consumers in the desired format.
Demand Management Importance of Demand Management (continued)
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Effective demand management supports business strategy.
Source: Table 7.1
Balancing Supply and Demand
Balancing Supply and Demand Problem of Supply-Demand Misalignment
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Source: Figure 7.1
Balancing Supply and Demand Supply-Demand Balancing Methods
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Some combinations of supply-demand balancing methods are used, depending on the nature of the product, the cost of stocking out, and the organization’s ability to properly forecast customer demand.
Change the manner in which the customer orders:
Price
Lead time
External Balancing Methods
Manage gap using internal processes:
Inventory
Production flexibility
Internal Balancing Methods
Demand Forecasting
Types of Forecast Error Measures
Common Forecasting Techniques
Demand Forecasting
Demand forecasting is a major component of demand management. Forecasts serve as a plan for both marketing and operations to set goals and develop execution strategies.
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The demand for the primary item, known as base demand
Independent Demand
The demand directly influenced by demand for independent item
Dependent Demand
Two Types of Demand
Most forecasting techniques focus on independent demand.
Demand Forecasting Factors Affecting Demand
All demand is subject to certain fluctuations.
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Seasonal patterns that will normally repeat themselves during a year for most organizations.
Random fluctuation
A development that cannot be anticipated and is usually the cause to hold safety stocks to avoid stockouts.
Trend fluctuation
Gradual increase or decrease in demand over time for an organization.
Seasonal fluctuation
Demand Forecasting Types of Forecast Error Measures
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CFE
Cumulative sum of forecast errors (CFE) calculates the total forecast error for a set of data, taking into consideration both negative and positive errors.
MSE
Mean squared error (MSE) squares each period error so the negative and positive errors do not cancel each other out.
MAD
Mean absolute deviation (MAD) takes absolute value of each error, so the negative and positive signs are removed.
MAPE
Track signal
Tracking signal can be used to measure forecast error, especially good at identifying if a “bias” exists in the forecast errors.
CFE/ MAD
Demand Forecasting Common Forecasting Techniques
All statistical techniques used to generate forecasts require accurate data and rely on the assumption that the future will repeat the past. The key to good forecasting is to minimize forecast error by utilizing a forecasting technique that best fits the nature of the data. Three common forecasting techniques are:
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Makes forecasts based on recent demand history and allows for the removal of random effects.
Pros: quick and easy to use
Cons: old demand dropped quickly; not accommodate seasonal, trend, or business cycle influences
Simple moving average
Assigns a weight to each previous period with higher weights usually given to more recent demand.
Pros: allows emphasis on more recent demand as a predictor of future demand.
Cons: not easily accommodate seasonal demand patterns.
Weighted moving average
Pros: simplicity and limited requirements for data, good for relatively constant demand
Cons: forecasts will lag actual demand; Not appropriate for highly seasonal demand patterns or patterns with trends
Exponential smoothing
Demand Forecasting Common Forecasting Techniques (continued)
Forecast Accuracy Summary
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Source: Figure 7.6
Sales and Operations Planning (S&OP)
Sales and Operations Planning (S&OP) Arriving at Internal Consensus Forecast
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Preliminary demand forecast
Financial forecast
Manufacturing forecast
Marketing forecast
Distribution forecast
Internal Consensus Forecast
S&OP
It is necessary for an organization to arrive at a forecast internally that all functional areas agree upon and can execute. A process that can be used to arrive at this consensus forecast is called sales and operations planning (S&OP).
Sales and Operations Planning (S&OP) A Five-Step Process
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Source: Figure 7.2
Collaborative Planning, Forecasting, and Replenishment (CPFR)
Collaborative Planning, Forecasting, and Replenishment (CPFR) Arriving at Inter-Organizational Consensus Forecast
Trading partners (retailers, distributors, and manufacturers) use available Internet-based technologies to collaborate on operational planning, allowing them to agree to a single forecast for an item where each partner translates this forecast into a single execution plan.
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Source: CPFR 2.0 GS1 (2014)
Summary
Demand management involves “focused efforts to estimate and manage customers’ demand, with the intention of using this information to shape operating decisions.”
Three common techniques for demand forecast are simple moving average, weighted moving average, and exponential smoothing. Using a forecasting technique that best fits the nature of the data is key to minimize forecast error.
Many forecasts are made across internal functions and throughout the supply chain.
The S&OP process involves participation from sales, operations, and finance to arrive at an internal consensus forecast.
CPFR is a method to allow trading partners in the supply chain to collaboratively develop and agree upon a forecast of sales to enable integrated operational planning and execution.
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