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Demand Management
Basics of Supply Chain Management
Learning Objectives
Upon completion of this session， participants will be able to:
Learning Objectives (cont.)
Basic Forecasting Concepts
Describe three planning levels that are supported by demand forecasts
Explain four major principles of forecasting and three principles of data collection and preparation
Differentiate quantitative from qualitative forecasting techniques
Estimate Demand
Calculate and explain the logic of an exponential smoothing forecast
Explain the logic behind the calculation of a seasonal forecast
Calculate and explain the use of the mean absolute deviation
Demand Management
Session 2
Demand Management Processes
Marketing Management and Mix
Customer Relationship Management
Design assistance: helping in the design of new products or improvement of existing ones
Customer needs: assessing the customer’s business and creating (expanding) product offerings
Information and communications: collecting and analyzing customer data to support marketing， sales， and customer service
Order Management
Demand Planning
Recognition承认 of customer requirements through
Forecasts
Management of orders from
Internal customers
External customers
Demand Management
Session 2
Independent vs. Dependent Demand
Only independent demand needs to be forecasted
Dependent demand should never be forecasted; it should be calculated
Sources of Demand
Forecasts
Customer orders
Replenishment补充 orders from DCs
Interplant transfers
Other
Demand Patterns: Trend
Demand Patterns: Seasonal Demand
Demand Patterns: Random
Stable vs. Dynamic Demand
Stable demand retains same general shape over time
Dynamic demand tends to be erratic
Demand Management
Introduction
Purposes and uses of the forecast
Principles of forecasting
Principles of data collection and preparation
How Forecasting Supports Planning
Principles of Forecasting
Forecasts
Are rarely 100% accurate over time
Should include an estimate of error
Are more accurate for product groups and families
Are more accurate for nearer periods of time
Data Collection and Preparation
Record data in terms needed for the forecast
Record circumstances relating to the data
Record demand separately for different customer groups
Data Collection and Preparation Example
Demand Management
Forecasting Techniques
Qualitative Techniques
Are based on intuition直觉 and informed opinion
Tend to be subjective主观
Are used for business planning and forecasting for new products
Are used for medium-term to long-term forecasting
Quantitative Techniques: Extrinsic
Based on correlation相互关系 and causality因果关系
Rely on external indicators
Useful in forecasting total company demand or demand for families of products
Two types of leading indicators
Economic
Demographic
Quantitative Techniques: Intrinsic
Based on several assumptions
The past helps you understand the future
Time series are available
The past pattern of demand predicts the future pattern of demand
Examples
Moving Averages
Exponential Smoothing指数
Moving Averages: Principles
Best used when demand is stable and there is little trend or seasonality， and demand variations are random
When past demand shows random variation…
Do not second-guess what the effect of random variation will be
It is better to forecast based on average demand
Moving Average Forecast Example
Assume it is the end of December; forecast demand for the next month， January
Moving Average Forecast Logic
Class Problem 2.1
Class Problem 2.1 Solution
Class Problem 2.1 Solution (cont.)
Three-Month Moving Average Forecast
Six-Month Moving Average Forecast
Moving Averages: Lessons Learned
The moving average forecast will lag落后 the development of a rising or falling trend
The farther back the moving average forecast reaches for data， the greater the lag
The three-month moving average forecast may have overreacted if the demand surge猛增 had abated减弱
The moving average forecast works best when demand is stable with random variation; it will “filter out” random variation
Exponential指数 Smoothing Logic
Take the old forecast and the actual demand for the latest (most current) period
Assign a weighting factor or smoothing constant (α， alpha) to the latest period demand vs. the old forecast
Calculate the weighted average of the old forecast and the latest demand
Smoothing Constant (α， Alpha)
Low smoothing constant gives more weight to the old forecast: e.g.，
α = .2 for latest demand (e.g. period X)
1 – α = .8 for old forecast (also period X)
Appropriate if demand is stable， not rising or falling
Run simulations with different α values to see which one best fits the historical demand pattern
Class Problem 2.2
A. Prepare an exponential smoothing forecast for June.
May data: actual demand = 220; forecast = 200.
Calculate the forecast for June using a smoothing constant (α) of .20
B. Prepare an exponential smoothing forecast for July.
June data: actual demand = 240
Calculate the forecast for July also using a smoothing constant (α) of .20
Class Problem 2.2 Solution
A. Prepare an exponential smoothing forecast for June.
= (.2) 220 + (.8) 200 =
= 44 + 160 = 204
B. Prepare an exponential smoothing forecast for July.
= (.2) 240 + (.8) 204 =
= 48 + 163 = 211
Seasonal Demand
Seasonal Forecast Process
Seasonal Demand Indexes (Step 1)
Deseasonalized Forecast (Step 2)
Make the forecast for the next year
Deseasonalize the forecast — distribute it evenly across the four quarters
Seasonal Forecast (Step 3)
Demand Management
Session 2
Tracking the Forecast
Forecasts are rarely 100% correct over time.
Why track the forecast?
To understand why demand differs from the forecast
To plan around error in the future
To improve forecasting methods
Bias vs. Random Variation
Forecast Error Data
Mean Absolute Deviation (MAD)
MAD Analysis: Normal Distribution
Uses of Forecast Measurement
Identify changes and trends in demand
Identify and adjust for forecast error that results from random events
Adjust the period forecast so that it is close to the true forecast average demand to minimize bias
Making decisions on safety stock and service levels based on the degree of random variation (forecast error)
Supply Chain Management Implications
Decrease reliance on long-term forecasts and increase ability to react quickly to demand
Collaborate with customers and suppliers， especially in sharing demand information
Increase manufacturing flexibility internally and operations integration externally with customers and suppliers
Demand Management
Session 2
Learning Objectives
Upon completion of this session， participants will be able to:
Learning Objectives (cont.)
Basic Forecasting Concepts
Describe three planning levels that are supported by demand forecasts
Explain four major principles of forecasting and three principles of data collection and preparation
Differentiate quantitative from qualitative forecasting techniques
Estimate Demand
Calculate and explain the logic of an exponential smoothing forecast
Explain the logic behind the calculation of a seasonal forecast
Calculate and explain the use of the mean absolute deviation
Vocabulary Check
Objective:
Reinforce terminology used in this session
Complete the activity in class， individually or in pairs， or as homework
Vocabulary Check
Problem 2.4
Problem 2.4 (Solution)
Demand Management Summary
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