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### Demand Management

Road mapRoad mapRoad map### Demand ManagementThe End

Processing, Influencing, & Anticipating Demand

Customer

Supply-Demand Management

Relationship

Relationship

"Make, Move, Store"

Management

Management

Plant

"Buy"

"Sell"

Plant

Warehouse

Customers

Suppliers

Plant

Managing the sell side of a businessKey questions

- What is the scope of demand management?
- What does order processing involve; why is it an important area for management attention?
- What is customer profit potential, & how is it relevant for influencing demand?
- What are 5 alternatives for improving forecast accuracy, what do they mean, & how can they be applied?
- How do the tactics of part standardization & postponement of form or place help improve forecast accuracy?
- What is the difference between long term & short term forecasting?
- What are 4 long term forecasting methods; what are the risks of salesperson/customer input?
- What are the components of demand, & which component is not forecasted?
- How do the moving average, Winters, & focus forecasting methods work?
- What is the role of the number of periods in the moving average method, & the smoothing parameters in the Winters method?
- What is the purpose of filtering, & why is it important for computer-based forecasting?
- What do the following principles of nature mean & how are they relevant for demand management? (1) law of large numbers, (2) trumpet of doom, (3) recency effect, (4) hockey stick effect, (5) Pareto phenomenon
- What are the managerial insights from the chapter?

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Summary

Scope of demand management

- So what is demand management?
- Concerned with processing, influencing, and anticipating demand
- We’ll begin with processing demand or, in more common terms, order processing or order fulfillment

Order processing

- Order processing is usually viewed to span order booking to order shipment
- Example steps?
- Customer validation, order entry, credit checking, pricing, design changes, availability checks, delivery time estimation, notification of shipment, notification of delays

ORDER ENTRY AND

CHECKING

Customer Validation

Credit Control Operations…

ERP

ORDER

INTERRUPTION

RETURNS

ORDER

PICKING AND

ASSEMBLY

CUSTOMER SERVICE

SHIPPING

INVOICING

Processing Demand

Characteristics

- Can be a complex & time consuming process dealing largely with information flow
- Susceptible to ad hoc modifications over time in response to problems (e.g., extra credit check added due to expensive nonpaying customer a few years ago)
- A major customer contact point with organization

Can significantly impact customer perceptions

- IT advances & high customer impact

A potential profitable target for improvement

Example 1

Benetton

- Electronic loop linking sales agent, factory, & warehouse
- If not available, measurements transferred to knitting machine for production
- Benetton uses a single warehouse
- Staffed by 8 people & about 230,000 pieces shipped daily

Example 2

K-Mart and MasterLock

- Policy for mistake in shipment or invoice
- Strike 1: $10,000, Strike 2: $50,000, Strike 3: lose business
- MasterLock revamped their order processing function

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Summary

Measure customer profit potential

A simple idea

- Some customers are more profitable than others
- Advancing technologies more practical to estimate profit potential of individual customers
- Can guide efforts/investments for customer retention & acquisition . . . investments to influence demand
- E.g.,
- Electronics manufacturer: reviews historical customer profit before sending service contract renewal
- Wireless phone firm: churn scores & lifetime value estimates influence # of customer contacts & attractiveness of offerings
- Ongoing development of data mining methods

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Summary

Motivating example 1

Sunbeam

Improved forecasting led to 45% reduction in inventory

- Included estimates from top 200 customers

Motivating example 2

Apple

A history of problems forecasting demand

Many components sourced from 1 supplier - accurate forecasts are critical

Over $1 billion in unfilled orders during the crucial holiday season. The CEO (Spindler) ousted a few months later

Motivating example 3

IBM

Badly misjudged demand in PC business in 1996 – went from being profitable in 1995 to a $200 million loss through 1st half of 1996

Motivating example 4

Christmas 1999 & e-commerce takes off

Large unanticipated increase in Internet orders –didn’t ship on time

E.g., Many Toys ‘R Us Christmas orders not delivered until March – “I will never buy online again”

Improvement alternatives

- Change the forecasting method
- Collect more or different data
- Analyze the information differently
- E.g., involve more people, new forecasting software, spend more time manually reviewing, focus groups etc.
- Change operations or operating policies
- Introduce early warning mechanisms
- Take advantage of the law of large numbers
- Reduce information delays & leadtimes (trumpet of doom)
- Reduce demand volatility

Early warning

- Change policies so that some (or more) customers provide earlier commitment of future demand, e.g.,
- Early bird program for builder markets – discount for 60-day advance order
- Invite large buyers to Aspen in February to view next year’s skiwear line, & encourage orders
- “Commitment” asking customers how much they are likely to buy next quarter

Law of large numbers

Principle of Nature

- As volume increases, relative variability decreases
- Postponement in form or place, e.g.,
- Dell – configure your own PC
- From full product line at 12 regional DCs to full product line at a single super DC, with 10% of product line stocked at 11 regional DCs (i.e., fast movers that account for 70% of sales)
- Part standardization, e.g.,
- Arby’s sandwich wrappers; plastic lids with push down drink indicator
- Intel Pentium processors all the same size
- 2.8 GHz tests out below 2.8 spec can be sold as a 2.66 GHz chip (“down-binning”)

As forecast horizon increases, accuracy decreases, e.g.,

Reduce production & delivery leadtimes

Dell pick-to-light system for assembly

Reduce information delays

EDI transmission of daily consumer demand up through multiple levels in the supply chain

Forecasting Alternatives

Trumpet of doomPrinciple of Nature

Reduce demand volatility

2 Principles of Nature

- Beware of product proliferation
- Pareto analysis – separating the important few from the trivial many
- Periodic length of line analysis to critically assess whether to continually offer “slow movers”
- Principle of Nature: Pareto phenomenon – the lion’s share of an aggregate measure is determined by relatively few factors
- E.g., “the 80-20 rule” – 80% of demand is due to 20% of product line
- Beware of perverse cycle of promotions – customers wait for sale before buying, thereby forcing a sale
- A step further – dynamic pricing to stabilize demand & align with supply
- Reduce the hockey stick effect…

Feb

Forecasting Alternatives

Hockey stick effectPrinciple of Nature

- Volume tends to pick up towards the end of a reporting period . . . why?
- Look for ways to lessen the effect – contributes to demand volatility, inefficiency, poor service

Channel stuffing

One contributor to the hockey stick effect

Lots of sales booked near the end of a quarter, then sales drop off at the start of the next quarter

E.g.,

- A large brewer offered a vacation to the salesperson in each region who sold the most beer to stores over a 3 month period
- One winner was able to convince a few stores to free up backroom space and fill it entirely with beer

Improvement alternatives

- We’re about to focus on methods for predicting demand

short pork bellies

- But, important to remember . . . many creative ways to improve forecast accuracy that have nothing to do with method
- E.g., early warning incentives, law of large numbers, trumpet of doom, reduce demand volatility

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Summary

Characteristics of long term forecasts

- Single or multi-year horizon
- Monthly or annual time bucket
- Aggregate units
- Input to “long term” decisions
- Accuracy generally more important than short term forecasts . . . why?
- Tend to use expensive & time consuming methods . . . due to the preceding point & due to a PON . . . which is?

Recency effect

Principle of Nature

Humans tend to overreact to (or be overly influenced by) recent events

E.g.,

Hughes Electronics Corp. developed an artificial intelligence based financial trading system. The developers did this by encoding the wisdom of Christine Downton, a successful portfolio manager. One motivation for creating the system is that it is immune to the recency effect, i.e., humans tend to get overly fixated on the most recent information.

Some alternative methods

- Judgment
- Salesperson & customer input
- Great information source, but beware of bias potential & recency effect = humans tend to be overly influenced by recent events
- Outside services
- Causal methods . . . examples?

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Characteristics
- Components of demand
- Moving average
- Winters method
- Focus forecasting
- Filtering
- Summary

Long term forecasts

Single or multi-year horizon

Monthly or annual time bucket

Aggregate units (e.g., product/ service categories)

Input to “long term” decisions

Expensive & time consuming methods

Accuracy importance

Trumpet of doom

Short term forecasts

Weekly or monthly horizon

Daily & weekly time bucket

Detailed units (e.g., SKU)

Input to “short term” decisions

Inexpensive & quick methods

Accuracy importance

Trumpet of doom

Short Term Forecasting

Long term/short term characteristicsCould argue using 2 different principles of nature that it’s [easier?/harder?] to be accurate with short term forecasting than with long term forecasting

Definition of the Forecasting Process

- The Art and Science of Predicting Future Events
- Forecasting vs. Predicting
- Based on Past Data
- Economic vs. Demand Forecasting

Elements of Demand Forecasting

- Dynamic in Nature
- Consider Uncertainty (Stochastic)
- Rely on Information contained in Past Data
- Applied to various time horizons
- short term
- medium term forecasts
- long term forecasts

Steps in the Forecasting Process

- Determine the Use of the Forecast
- Select the Items to be Forecasted
- Determine a Suitable Time Horizon
- Select an appropriate Set of Forecasting Models
- Gather Relevant Data
- Conduct the Analysis
- Validate the Model - Assess its Accuracy
- Make the Forecast
- Implement the Results

Independent Demand: What a firm can do to manage it?

- Can take an active role to influence demandFORECASTING
- Can take a passive role and simply respond to demand

Types of Forecasts

- Qualitative (Judgmental)
- Quantitative
- Time Series Analysis
- Causal Relationships
- Simulation

Qualitative Methods

Executive Judgment

Grass Roots

Qualitative

Methods

Market Research

Historical analogy

Delphi Method

Panel Consensus

Delphi Method

- Choose the experts to participate representing a variety of knowledgeable people in different areas
- Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants
- Summarize the results and redistribute them to the participants along with appropriate new questions
- Summarize again, refining forecasts and conditions, and again develop new questions
- Repeat Step 4 as necessary and distribute the final results to all participants

Quantitative Forecasting Models

- Both Pattern Based and Correlational Models rest on the assumption that the relationships of the past will continue into the Future
- Both can Mathematically Characterize the Probabilistic Nature of the Forecast
- Both Use Information from Relevant Time Frames

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Characteristics
- Components of demand
- Moving average
- Winters method
- Focus forecasting
- Filtering
- Summary

Components of Demand

- Average demand for a period of time
- Trend
- Seasonal element
- Cyclical elements
- Random variation
- Autocorrelation

Pattern Based Analyses

- Definition
- Identifying an underlying pattern in historical data, describe it in mathematical terms, and then extrapolate it into the future
- Uses a “Time Series” of Past Data

Time Series Variation

- Time Series of Demand Data Typically Contain Four Components of Variation About the Mean or Average
- Pattern Based Forecasting Needs to Mathematically Characterize Each of these

Finding Components of Demand

x

x

x

Linear

Trend

x

x

x

x

x

x

x

x

x

x

x

Sales

x

x

x

x

x

x

x

Average

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

1

2

3

4

Year

Time Series Analysis

- Time series forecasting models try to predict the future based on past data
- You can pick models based on:

1. Time horizon to forecast

2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified personnel

Simple Moving Average Formula

- The simple moving average model assumes an average is a good estimator of future behavior
- The formula for the simple moving average is:

Ft = Forecast for the coming period

n = Number of periods to be averaged

A t-1 = Actual occurrence in the past period for up to “n” periods

Simple Moving Average Problem (1)

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Calculating the moving averages gives us:

F4=(650+678+720)/3

=682.67

F7=(650+678+720

+785+859+920)/6

=768.67

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example

Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother

Simple Moving Average Problem (2) Data

Question: What is the 3 week moving average forecast for this data?

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Weighted Moving Average Formula

While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods

The formula for the moving average is:

wt = weight given to time period “t” occurrence (weights must add to one)

Weighted Moving Average Problem (1) Data

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Weights:

t-1 .5

t-2 .3

t-3 .2

Note that the weights place more emphasis on the most recent data, that is time period “t-1”

Weighted Moving Average Problem (1) Solution

F4 = 0.5(720)+0.3(678)+0.2(650)=693.4

Weighted Moving Average Problem (2) Data

Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week?

Weights:

t-1 .7

t-2 .2

t-3 .1

Weighted Moving Average Problem (2) Solution

F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672

Some pros/cons

Short Term Forecasting – Moving Average and Weighted Moving Average

- Simple (+)
- Designated weights of history (-)
- History cut-off beyond m periods (-)

Exponential Smoothing Model

Ft = Ft-1 + a(At-1 - Ft-1)

- Premise: The most recent observations might have the highest predictive value
- Therefore, we should give more weight to the more recent time periods when forecasting

Exponential Smoothing Problem (1) Data

Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using a=0.10 and a=0.60?

Assume F1=D1

Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

Exponential Smoothing Problem (1) Plotting

Note how that the smaller alpha results in a smoother line in this example

Exponential Smoothing Problem (2) Data

Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5?

Assume F1=D1

Seasonal Adjustments

- Applied to Moving Averages and Time Series Regression
- First, Calculate a Seasonal Index (SI) Factor for Each Relevant Time Period (day, week, month, quarter)
- Each Seasonal Period’s SI is Calculated by Averaging the Ratio of its Actual Demand to the Forecast Demand for all Corresponding Periods

Seasonal Adjustments

- Forecast for Future Periods is Calculated by Multiplying the Unadjusted Moving Average or Time Series Forecast for a given Period by the Corresponding Seasonal Index for that Period
- i.e. if the SMA forecast for the month of March is 27 and the SI for March is 1.125, then
- Emar = 27*1.125 = 30.375

Evaluating Forecast Accuracy

- Use of Residuals Analyses
- Residuals are the Difference Between the Forecast and the Actual Demand for a Given Period
- Assessed by Several Measures
- Mean Absolute Deviation - MAD
- Mean Squared Error - MSE
- Tracking Signal

The MAD Statistic to Determine Forecasting Error

- The ideal MAD is zero which would mean there is no forecasting error
- The larger the MAD, the less the accurate the resulting model

MAD Problem Data

Question: What is the MAD value given the forecast values in the table below?

Month

Sales

Forecast

1

220

n/a

2

250

255

3

210

205

4

300

320

5

325

315

Sales

Forecast

Abs Error

1

220

n/a

2

250

255

5

3

210

205

5

4

300

320

20

5

325

315

10

40

MAD Problem SolutionNote that by itself, the MAD only lets us know the mean error in a set of forecasts

Evaluating Forecast AccuracyMean Absolute Deviation - MAD

- Exponentially Smoothed MAD
- MADt = MAD|Dt - Forecastt| + (1- MAD)MADt-1

Evaluating Forecast AccuracyMean Squared Error - MSE

- MSE = ((Di - Forecasti)2)/n

Tracking Signal Formula

- The Tracking Signal or TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand.
- Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts.
- The TS formula is:

Evaluating Forecast AccuracyTracking Signal

- Tracking Signal = Running Sum of Forecast Error / MAD = RSFE/MAD

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Characteristics
- Components of demand
- Moving average
- Winters method
- Focus forecasting
- Filtering
- Summary

Short Term Forecasting – Winters

Winters method used to forecast one period into the future

See how method detects patterns & adapts to market changes over time

Short Term Forecasting – Winters

Key to Winters method- Winters is an exponential smoothing method
- Smoothing is based on a key idea
- For each component (which are?), a portion of difference between estimate & actual is due to randomness & certain portion due to real change

Short Term Forecasting – Winters

Smoothing in action...- New estimate = old estimate + (some percentage)(error)
- Smoothes out peaks & valleys (i.e., randomness) of actual

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Characteristics
- Components of demand
- Moving average
- Winters method
- Focus forecasting
- Filtering
- Summary

Short Term Forecasting – Focus

Bernie’s insight……or what is focus forecasting?

- An intuitive & successful idea
- Regularly use a # of different methods to generate forecasts
- Maintain historical accuracy information on each method
- Use the most accurate method to generate “official” forecasts

Short Term Forecasting – Focus

Advertisement appearing in APICS The Performance Advantage

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Characteristics
- Components of demand
- Moving average
- Winters method
- Focus forecasting
- Filtering
- Summary

Short Term Forecasting – Filtering

Two types of filters- An important feature of computer-based forecasting systems
- Large amounts of data – impractical to manually review all
- For data input errors (e.g., typos, scanner errors)
- If |“actual” - forecast| > limit, then report
- For unacceptable forecast errors (e.g., warranting management attention)
- If average absolute error > limit, then report
- If average error (i.e., bias) > limit, then report

Road map

- Processing Demand
- Influencing Demand
- How to Improve Forecast Accuracy
- Long Term Forecasting
- Short Term Forecasting
- Dependent Demand
- Correlational Forecasting
- Summary

Finished Goods

Dependent Demand:

Raw Materials,

Component parts,

Sub-assemblies, etc.

C(2)

B(4)

D(2)

E(1)

D(3)

F(2)

Demand ManagementBill of Materials (BOM)A

Web-Based Forecasting: CPFR

- Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners.
- Used to integrate the multi-tier or n-Tier supply chain, including manufacturers, distributors and retailers.
- CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain.
- CPFR uses a cyclic and iterative approach to derive consensus forecasts.

Web-Based Forecasting: Steps in CPFR

- Creation of a front-end partnership agreement
- Joint business planning
- Development of demand forecasts
- Sharing forecasts
- Inventory replenishment

Correlational Forecasting

- Assumes an Outcome is Dependent an Existing Relationship Between the Demand Variable and Some other Independent Variable(s)
- Demand Variable is Dependent Variable
- Other Related Variables are Independent Variables
- Generally Expressed as a Multiple Linear Regression Model
- Y = + X1+ X2+ X2+ . . . nXn+ i

Simple Linear Regression Model

The simple linear regression model seeks to fit a line through various data over time

Y

a

0 1 2 3 4 5 x (Time)

Yt = a + bx

Is the linear regression model

- Yt is the regressed forecast value or dependent variable in the model

- a is the intercept value of the the regression line, and
- b is similar to the slope of the regression line.
- However, since it is calculated with the variability of the data in mind, its formulation is not as straight forward as our usual notion of slope.

Simple Linear Regression Problem Data

Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

Answer: First, using the linear regression formulas, we can compute “a” and “b”

180

175

170

165

Sales

160

155

Sales

Forecast

150

145

140

135

1

2

3

4

5

Period

The resulting regression model is:

Yt = 143.5 + 6.3x

Now if we plot the regression generated forecasts against the actual sales we obtain the following chart:

Statistical Assumptions of Multiple Linear Regression

- The Error Term (the residual i) is Normally Distributed
- There is no Serial Correlation Among Error Terms
- Magnitude of the Error Term is Independent of the Size of Any of the Independent Variables - Xi
- Assumptions Can be Tested Through Analyses of the Residuals - i

Major Statistical Problems of Multiple Linear Regression

- Multicolinarity
- Use of Time-Lagged Independent Variables
- Both of These Problems Result in Models with Potentially Valid Predictions, but the Reliability of the Coefficients is Questionable

Processing, Influencing, & Anticipating Demand

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