Basics of Supply Chain Management
Session 2
Basics of Supply Chain
Management
Introduction to
Supply Chain
Management
Aggregate
Inventory
Management
Demand
Management
Item Inventory
Management
Capacity
Management
andProduction
Activity
Control
Theory of
Constraints
andReview
Activity
Material
Requirements
Planning
Lean/JIT
andQuality
Systems
Master
Planning
Purchasing
andPhysical
Distribution
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Demand Management Processes
• Describe the significance of marketing management and customer
relationship management
• Explain the role and objectives of demand planning (forecasting and
customer order management)
Characteristics of Demand
• Differentiate independent from dependent demand
• Identify at least five sources of independent demand
• Recognize at least four demand patterns
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 Processes
Session 1
Marketing
Management
Customer
Relationship
Management
(CRM)
Demand
Planning
Demand Management Processes
Forecasting &
Other
Demands (e.g.
Internal)
Marketing
Strategy &
Product
Management
Customer
Interaction &
Order
Management
These topics
are covered
in the CSCP
Program
Marketing Mix: The Four Ps
•
Product
•
Price
•
Promotion
•
Place
The four P’s are used to implement marketing strategy via product positioning, product differentiation, and market segmentation. Each attribute should contribute to the creation of Order Qualifiers & Order Winners consistent with strategy.
The design, features, cost, service, etc.., of the product need to be aligned with the market segment requirements and the pricing strategy.
Key decision is whether to compete with a commodity product or provide value that will bring premium pricing.
Must decide what sales promotion and advertising approach is right for the product marketing strategy.
Such decisions as sales channels used, distribution inventory policy, and network design are critical to providing the product where and when the customer wants it.
Order Qualifiers and Winners
•
Order qualifiers—Competitive characteristics that a
firm’s products and services must exhibit in order
for the firm to be a viable competitor in the
marketplace
•
Order winners—Competitive characteristics that
cause customers to prefer a firm’s products and
services over those of its competitors
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
CRM plays a major role in operations efficiency and
customer service through:
Fast and accurate order entry and tracking
Meet promised delivery dates and quantities
Handle customer inquiries and service
complaints, returns, and repair
Accurate and timely shipping documentation,
invoicing, and recording of sales history
Real-time, on-line order confirmation using Available-to-Promise functionality is best.
Measure and improve “Delivery Reliability”
“Perfect Order Fulfillment” is the goal. Firm should be easy to do business with.
Demand Planning
Recognition of customer requirements through
– Forecasts
– Management of orders from
• Internal customers
• External customers
Internal Customer External Customer Forecast Distribution ReplenishmentSample Demand Plan - APO
Characteristics of Demand
Session 2
Independent vs. Dependent Demand
•
Only independent demand needs to be forecasted
•
Dependent demand should never be forecasted; it
should be calculated
In this example,
only the
“arrows” would
be forecasted.
The components
would be
calculated using
MRP.
Sources of Demand
•
Forecasts
•
Customer orders
•
Replenishment orders from DCs
•
Interplant transfers
•
Other
Estimate of future demand based on quantitative or qualitative methods or a combination of the two.
Orders from external customers, represents “actual” demand not estimated demand.
Based on both forecast placed at the DC level and customer orders placed at the DC.
Orders from other divisions or affiliates within the firm.
Demand Patterns: Trend
Quarters
Deman
d
Increasing
Decreasing
Level
Trends can be “linear” or “exponential”Demand Patterns: Seasonal Demand
De
man
d
Quarters
Third Quarter is always high
First quarter is always low
In this case, Seasonal & Trending Upward
Cyclical Pattern
Growth or Expansion
Recession or Contraction
The general economy goes through periods of expansion or growth followed by
contraction or recession.
Stable vs. Dynamic Demand
•
Stable demand retains same general shape
over time
and average demand may yield a
usable forecast
.
•
Dynamic demand tends to be erratic
and more
difficult to forecast.
Stable vs.
Dynamic
Demand
Stable
Dynamic
Average demand
Forecasting
Session 2
Introduction
•
Purposes and uses of the forecast
•
Principles of forecasting
How Forecasting Supports Planning
Planning Level
Forecast
Horizon (up to)
Business Planning
Sales volume ($); new
market and supply
chain initiatives
2 to 10 years
Sales and Operations
Planning
Physical units of
production at the
product family level
1 to 3 years
Master Scheduling
Physical units of
production at the end
item level
3 to 18 months
The business should generate a “one number” forecast at the detailed level which can then be aggregated byPrinciples 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
Data Collection and Preparation
•
Record data in terms needed for the
forecast
Record demand and in similar forecast periods as manufacturing.
•
Record circumstances relating to the data
Weather, price changes, competitors initiatives
•
Record demand separately for different
customer groups
Data Collection and Preparation Example
Customer A’s annual demand:
Customer B’s annual demand:
Total:
Average over 12 months:
12,000
6,000
18,000
1,500 per month
Month
1
2
3
4
5
6
7
8
9
10
11
12
A
6000
6000
B
500
500
500
500
500
500
500
500
500
500
500
500
Average
Forecast
(Produce)
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
PAB 1000 2000 -3000 -2000 -1500 -1000 -0- 1000 -4000 -3000 -2000 -1000
Forecasting Techniques
Session 2
Forecasting
Techniques
Qualitative
Quantitative
Judgment
Mathematics
Intrinsic
(Time Series)
Extrinsic
(Causal)
Forecasting Techniques
• Using housing start forecasts to predict
demand for construction chemicals
• Using weather forecasts to predict demand
for agricultural chemicals
• Based on historical sales • Assumes the past demand
pattern will continue Includes inputs from Sales &
Qualitative Techniques
•
Are based on intuition and informed opinion
•
Tend to be subjective
•
Are used for business planning and forecasting for
new products
•
Are used for medium-term to long-term forecasting
Use such tools as surveys, expert opinions, marketing estimates of changes, etc…
Contain more “bias” (tendency to over or under forecast) than quantitative methods.
Factor in qualitative information about the economy, competitors, trends, etc.. .
Because quantitative forecasts are based on history and longer term changes must be input by marketing and sales.
Quantitative Techniques: Extrinsic
•
Based on correlation and causality
•
Rely on external indicators
•
Useful in forecasting total company demand or
demand for families of products
•
Two types of leading indicators
– Economic
– Demographic
For example, Consumer Price Index, Housing Starts, Auto Build Rates, Unemployment Rates, Interest Rates, Stock Prices, etc….
For example, decrease prices to increase sales; higher unemployment leads to lower consumer spending.
Adjustments based on these type inputs is usually not applied at the article level.
e.g. Housing Starts, Defense Contracts, Consumer Spending, etc…
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
Future buying will be similar to past buying.
Accurate demand data exist in the firm’s software system.
No major change expected in demand components (e.g. trend, seasonality, etc..)
Best used with horizontal demand patterns with only random variation. No good with trends or seasonality.
Provides the ability to place more weight on recent data points which in times of change may be more representative of the demand pattern.
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
Jan Feb Mar Apr
May Jun Jul
Aug Sep Oct
Mo.1
Nov
Mo.2
Dec
Mo.3
Jan
92
83
66
74
75
84
84
81
75
63
91
84
?
60 65 70 75 80 85 90 95 1 2 3 4 5 6 7 8 9 10 11 12 Avg.What forecast would you choose
for January and Why? 79
Moving Average Forecast Logic
Month 4 forecast
Σ demand for months 1 - 3
number of months
=
288
3
=
= 96 units
Moving average forecast = average demand of past periods
Key:
= sum
Moving average forecast for month 4
Month
Demand
Three-month total
Forecast
1
102
2
91
3
95
288
Class Problem 2.1
Month
Demand
Three-month
total
Forecast
1
102
2
91
3
95
4
105
5
94
6
101
7
Month
Demand
Three-month
total
Forecast
1
102
2
91
3
95
4
105
5
94
6
101
7
Class Problem 2.1 Solution
288
96
291
97
294
98
300
100
Class Problem 2.1 Solution (cont
.)
Month
Three-month
total
Forecast
3
288
4
291
96
5
294
97
6
300
98
7
100
90 92 94 96 98 100 102 104 106 0 2 4 6 8 D e m a n d Period Actual ForecastThree-Month Moving-Average Forecast
Month
Demand
Three-month total
Forecast
1
89
2
89
3
94
272
4
91
274
91
5
95
280
91
6
104
290
93
7
106
305
97
8
110
320
102
9
107
Six-Month Moving-Average Forecast
Month
Demand
Six-month total
Forecast
1
89
2
89
3
94
4
91
5
95
6
104
562
7
106
579
94
8
110
600
97
9
100
Moving Averages: Lessons Learned
•
The moving average forecast will lag
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 had abated
•
The moving average forecast works
best when demand is stable with
random variation; it will “filter out”
random variation
80 85 90 95 100 105 110 1 2 3 4 5 6 7 8 Actual Sales 3-Mth MovAvg 6-Mth MovAvgExponential 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
New forecast = (α) (latest demand) + (1 – α) ( old forecast)
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
New forecast = (α) (latest demand) + (1 – α) (previous forecast)
May Actual Demand = 220 Units May’s Forecast = 200 Units
Actual June Demand = 240
Average demand
for all periods
De
man
d
(units
)
Time (quarters)
Seasonal demand
Seasonal Demand
Seasonal Forecast Process
1
2
3
Calculate a seasonal index of demand for
each period to establish seasonality
Develop a deseasonalized demand forecast
spanning all periods
Develop a seasonal forecast for each
period of the year being forecast
Quarter
Average Quarterly Demand/100
Seasonal Index
1
128/100
=
1.28
2
102/100
=
1.02
3
75/100
=
0.75
4
95/100
=
0.95
Total
=
4.00
Seasonal Demand Indexes (Step 1)
Average demand for all quarters = = 100 units
400
4
Demand History
Year
Quarter
Total
1
2
3
4
1
122
108
81
90
401
2
130
100
73
96
399
3
132
98
71
99
400
Average
128
102
75
95
400
Average Period Demand/Average Demand•
Make the forecast for the next year
(The business
expects to sell 420 in Year 4)
•
De-seasonalize the forecast — distribute it evenly
across the four quarters
Annual forecast
No. of periods
=
De-seasonalized demand
(average demand/period)
105 units
420
4
=
=
Seasonal Forecast (Step 3)
Expected quarter demand
=
(seasonal index)
(deseasonalized forecast
demand)
Expected first quarter demand
=
1.28 X 105 = 134 units
Expected second quarter
demand
=
1.02 X 105 = 107 units
Expected third quarter demand =
.75 X 105 = 79 units
Expected fourth quarter
demand
=
.95 X 105 = 100 units
Total forecast demand
=
420 units
Calculation
Tracking the Forecast
Session 2
•
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
never
Random Variations alone ensures some error will occur.
Develop safety stock targets, make contingency plans in case of demand peaks, etc..
Identifying errors and investigating to find root causes will result in improved forecasting methods.
And take actions to eliminate error.
Bias vs. Random Variation
Bias
Random Variation
Cumulative demand may not be the
same as forecast
Demand will vary plus and minus
about the average
Month Forecast Actual Variation Forecast Actual Variation
1
100
90
-10
100
105
+5
2
100
125
+25
100
94
-6
3
100
120
+20
100
98
-2
4
100
125
+25
100
104
+4
5
100
120
+20
100
103
+3
6
100
110
+10
100
96
-4
Cumulative
Total
600
690
+90
600
600
0
Bias exists since cumulative
variation is not zero.
There is no bias since
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Total
Forecast
500
500
500
500
500
500
500
500
500
500
500
500
-
Actual
460
520
530
490
460
500
530
490
530
480
490
520
-
Absolute
deviation
40
20
30
10
40
0
30
10
30
20
10
20
260
Σ Absolute errors
No. of
periods
=
MAD
260
12
= 22 units
=
Key:
= Sum; I I = Absolute Value
n
|
|
MAD =
n
|A - F|
Use “Absolute” error as both over and under forecasting are problems.
MAPE =
A - F
A
[%]
n
MAPE =
A - F
A
[%]
n
A - F
A
[%]
A - F
A
A - F
A
[%]
n
MAD Analysis: Normal Distribution
-3
-2
-1
0
1
2
3
MAD
-66
-44
-22
22
44
66
Units
If the data is normally distributed, 60% of the data points will fall within +or- 1 MAD or 22 Units. Ninety (90%) will fall within +or- 2 MADs.
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)
For example, calculate statistical safety stocks using the standard deviation of error. For example, remove data outliers that vary significantly from average demand. So the forecasting method can be changed to match the new demand pattern.
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
Deal with demand uncertainty through process improvements
Improved manufacturing flexibility and reduced lead times make it possible to react more quickly to changes in demand.
