BUSN 6110 CLASS 4
Download
Report
Transcript BUSN 6110 CLASS 4
Syllabus
•
•
•
•
•
•
•
•
•
•
Class 1 (Jan 5): chap 1; chap 2, case study
Class 2: (Jan 12) No Class
Class 3: (Jan 19) Chap 6, Chap 8
Class 4: (Jan 26) chap 10, chap 11, Chap 17(Take home exam)
Class 5: (Feb 2) Chap 5, Chap 7
Class 6: (Feb 9) Chap 9, Chap 12, 14
Class 7: (Feb 16) Chap 15, Reverse Logistics – need “The Forklifts
Have Nothing To Do!” Available in the Lewis and Clark Bookstore
Class 8: (Feb 23) Cabela’s Tour
Class 9: (Mar 2) Chap 13; Chap 16, Chap 4 (take home exam)
Other requirements:
→visit Harley-Davidson Plant in Kansas City to see operations
management in practice and write a 3-5 page paper comparing the
class slides and readings to the Harley operations
→ Home Work
Supply Chain
Management
Supply Chain
Management
• First appearance – Financial Times
• Importance → Inventory ~ 14% of GDP
→ GDP ~ $12 trillion
→ Warehousing/Trans ~ 9% of GDP
→ Rule of Thumb - $12 increase in sales to = $1 savings in
Supply Chain
• 1982 Peter Drucker – last frontier
• Supply Chain problems can cause ≤ 11% drop in stock
price
• Customer perception of company
SCOR
Reference: www.supply-chain.org
Supply Chain
All activities associated with the flow
and transformation of goods and
services from raw materials to the end
user, the customer
A sequence of business activities
from suppliers through customers
that provide the products, services,
and information to achieve customer
satisfaction
Supply Chain
“The global network used to deliver
products and services from raw
materials to end customers through
an engineered flow of information,
physical distribution, and cash.”
APICS Dictionary, 10th ed.
Supply Chain Management
Synchronization of activities
required to achieve maximum
competitive benefits
Coordination, cooperation, and
communication
Rapid flow of information
Vertical integration
Supply Chain Uncertainty
Forecasting, lead times, batch
ordering, price fluctuations, and
inflated orders contribute to
variability
Inventory is a form of insurance
Distorted information is one of
the main causes of uncertainty
Bullwhip effect
Information in the
Supply Chain
Centralized coordination of
information flows
Integration of transportation,
distribution, ordering, and production
Direct access to domestic and global
transportation and distribution
channels
Locating and tracking the movement
of every item in the supply chain RFID
Information in the
Supply Chain
Consolidation of purchasing from all
suppliers
Intercompany and intracompany
information access
Electronic Data Interchange
Data acquisition at the point of origin
and point of sale
Instantaneous updating of inventory
levels
Visibility
Electronic Business
In Theory:
Replacement of physical processes
with electronic ones
Cost and price reductions
Reduction or elimination of
intermediaries
Shortening transaction times for
ordering and delivery
Wider presence and increased visibility
Electronic Business
Greater choices and more information for
customers
Improved service
Collection and analysis of customer data
and preferences
Virtual companies with lower prices
Leveling the playing field for smaller
companies
Gain global access to markets & customers
Electronic Data Interchange
Computer-to-computer exchange of
business documents in a standard
format
Quick access, better customer service,
less paperwork, better communication,
increased productivity, improved
tracing and expediting, improves billing
and cost efficiency
Bar Codes
Computer readable codes attached to
items flowing through the supply chain
Generates point-of-sale data which is
useful for determining sales trends,
ordering, production scheduling, and
deliver plans
1234
5678
IT Issues
Increased benefits and sophistication
come with increased costs
Efficient web sites do not necessarily
mean the rest of the supply chain will
be as efficient
Security problems are very real –
camera phones, cell phones, thumb
drives
Collaboration and trust are important
elements that may be new to business
relationships
Suppliers
Purchased materials account for about
half of manufacturing costs
Materials, parts, and service must be
delivered on time, of high quality, and
low cost
Suppliers should be integrated into
their customers’ supply chains
Partnerships should be established
On-demand delivery (JIT) is a frequent
requirement - what is JIT and does it
work?
Sourcing
Relationship between customers and
suppliers focuses on collaboration and
cooperation
Outsourcing has become a long-term
strategic decision
Organizations focus on core
competencies
How does
Single-sourcing is
single source
increasingly a part
differ from sole
of supplier relations
source?
Distribution
The actual movement of products
and materials between locations
Handling of materials and products at
receiving docks, storing products,
packaging, and shipping
Often called logistics
Driving force today
is speed
Particularly important
for Internet dot-coms
Distribution Centers
and Warehousing
DCs are some of the largest business
facilities in the United States
Trend is for more frequent orders in
smaller quantities
Flow-through facilities and automated
material handling
Final assembly and product
configuration (postponement) may
be done at the DC
Warehouse Management
Systems
Highly automated systems
A good system will control item
slotting, pick lists, packing, and
shipping
Most newer systems include
transportation management (load
management/configuration), order
management, yard management, labor
management, warehouse optimization
Vendor-Managed Inventory
Not a new concept – same process used by
bread deliveries to stores for decades
Reduces need for warehousing
Increased speed, reduced errors, and
improved service
Onus is on the supplier to keep the shelves
full or assembly lines running
variation of JIT
Proctor&Gamble - Wal-Mart
DLA – moving from a manager of supplies to
a manager of suppliers
Direct Vendor Deliveries – loss of visibility
Collaborative Distribution
and Outsourcing
Collaborative planning, forecasting, and
replenishment (CPFR) started by Nabisco
Allows suppliers to know what is really needed
and when
Electronic-based exchange of data and
information
Significant decrease in inventory levels and
more efficient logistics - maybe not!
Companies work together for benefit of all of
the supply chain
Transportation
Common methods are railroads,
trucking, water, air, intermodal,
package carriers, and pipelines
Railroads
150,000 miles in US
Low cost, high-volume
Improving flexibility
intermodal service
double stacking
Complaints: slow, inflexible, large loads
Advantages: large/bulky loads, intermodal
Award-Winning Service
Recognition
Wal-Mart Stores, Inc.
Carrier of the Year – 5 years in a row
Target
Only rail carrier to receive
the Vice President’s Award
Federal Express
United Parcel Service
99.5% failure free, damage free
and on-time rating from United
Parcel Service every year since
1995
American Honda Motor Company
Premier Partner – 4 consecutive years
Only rail carrier to receive outstanding
supplier award - 2 years in a row
Toyota’s North American Parts
and Logistics Division (NAPLD)
Rail Carrier of the Year –
3 consecutive years
Schneider
KIA
Carrier of the Year – 3 consecutive years
Carrier of the Year
Trucking
Most used mode in US -75% of total
freight (not total weight)
Flexible, small loads
Consolidation,
Internet load match sites
Single sourcing reduces number of
trucking firms serving a company
Truck load (TL) vs. Less Than Truck
Load (LTL)
Air
Rapidly growing segment of
transportation industry
Lightweight, small items
Quick, reliable, expensive
(relatively expensive depending on
costs of not getting item there)
Major airlines and US Postal
Service, UPS, FedEx, DHL
Package Carriers
FedEx, UPS, US Postal Service, DHL
Significant growth driven by
e-businesses and the move to smaller
shipments and consumer desire to have it
NOW
Use several modes
of transportation
Expensive - relative!!
Fast and reliable - relative!!
Innovative use of technologies in some
cases
Online tracking – some better than others
Intermodal
Combination of several modes of
transportation
Most common are truck/rail/truck
and truck/water/rail/truck
Enabled by the use of containers –
the development of the 20 and 40
foot containers significantly
changed the face of shipping
~2% of all US cargo via intermodal
Water
One of oldest means of transport
Low-cost, high-volume, slow
(relative)
Security - sheer volume - millions of
containers annually
Bulky, heavy and/or large items
Standardized shipping containers
improve service
The most common form of
international shipping
Pipelines
Primarily for oil & refined oil
products
Slurry lines carry coal or kaolin
High initial capital investment
Low operating costs
Can cross difficult terrain
Global Supply Chain
Free trade & global opportunities
Nations form trading groups
No tariffs or duties
Freely transport
goods across borders
Security!!
Global Supply Chain
Problems
National and regional differences
Customs, business practices, and
regulations
Foreign markets are
not homogeneous
Quality can be a
major issue
Security
• ~ 10+ million containers annually
• Customs-Trade Partnership Against Terrorism (CTPAT)
• Port Security – SAFE Ports Act; Scanning of all
Containers
• Cost - $2 billion closing of major port
• 66% of all goods into US comes through 20 major
ports
• 44% through LA/Long Beach
• Cost of attack on major port estimated at $20
Billion
Chapter 11
Forecasting
Forecasting Survey
• How far into the future do you
typically project when trying to
forecast the health of your industry?
less than 4 months 3%
4-6 months
12%
7-12 months
28%
> 12 months
57%
Fortune Council survey, Nov 2005
Indices to forecast health
of industry
•
•
•
•
•
•
•
•
•
Consumer price index
51%
Consumer Confidence index 44%
Durable goods orders
20%
Gross Domestic Product
35%
Manufacturing and trade inventories
and sales
27%
Price of oil/barrel
34%
Strength of US $
46%
Unemployment rate
53%
Interest rates/fed funds
59%
Fortune Council survey, Nov 2005
Forecasting Importance
• Improving customer demand forecasting
and sharing the information downstream
will allow more efficient scheduling and
inventory management
• Boeing, 1997: $2.6 billion write down due
to “raw material shortages, internal and
supplier parts shortages” Wall Street
Journal, Oct 23, 1987
Forecasting Importance
• “Second Quarter sales at US Surgical
Corporation decline 25%, resulting in a
$22 mil loss…attributed to larger than
anticipated inventories on shelves of
hospitals.” US Surgical Quarterly, Jul 1993
• “IBM sells out new Aetna PC; shortage
may cost millions in potential revenue.”
Wall Street Journal, Oct 7, 1994
Principles of Forecasting
• Forecasts are usually wrong
• every forecast should include an
estimate of error
• Forecasts are more accurate for
families or groups
• Forecasts are more accurate for
nearer periods.
Important Factors to
Improve Forecasting
• Record Data in the same terms as
needed in the forecast – production
data for production forecasts; time
periods
• Record circumstances related to the
data
• Record the demand separately for
different customer groups
Forecast Techniques
• Extrinsic Techniques – projections
based on indicators that relate to
products – examples
• Intrinsic – historical data used to
forecast (most common)
Forecasting
• Forecasting errors can increase the total
cost of ownership for a product
- inventory carrying costs
- obsolete inventory
- lack of sufficient inventory
- quality of products due to accepting
marginal products to prevent
stockout
Forecasting
• Essential for smooth operations of
business organizations
• Estimates of the occurrence, timing,
or magnitude of uncertain future
events
• Costs of forecasting: excess labor;
excess materials; expediting costs;
lost revenues
Forecasting
Predicting future events
Usually demand behavior
over a time frame
Qualitative methods
Based on subjective methods
Quantitative methods
Based on mathematical formulas
Impact of Just-in-Time
on Forecasting
• Just in time as a inventory method
• Just in time as a Continuous process
improvement program
• Just in time - one on the shelf
• Usage factors
• Single order vs. Case order
Strategic Role of
Forecasting
Focus on supply chain management
Short term role of product demand
Long term role of new products,
processes, and technologies
Focus on Total Quality Management
Satisfy customer demand
Uninterrupted product flow with no
defective items
Necessary for strategic planning
Strategic Role of
Forecasting
Focus on supply chain management
Short term role of product demand
Long term role of new products,
processes, and technologies
Focus on Total Quality Management
Satisfy customer demand
Uninterrupted product flow with no
defective items
Necessary for strategic planning
Total Quality Management
• Management approach to long term
success through customer
satisfaction
• Total Quality Control - process of
creating and producing quality
goods and services that meet the
expectations of the customer
• quality - conformance to
requirements or fitness for use
Trumpet of Doom
• As forecast horizon increases, so does the
forecasting error (i.e., accuracy
decreases) – shorten horizon by
shortening of cycles or flow times
• Law of Large Numbers – as volume
increases, relative variability decreases –
forecasting error is smaller: goal –
forecast at aggregate levels; collaborate;
standardize parts
• Volume and activity increase at end of
reporting periods – Krispy Kreme
Components of
Forecasting Demand
Time Frame
Short-range, mediumrange, long-range
Demand Behavior
Trends, cycles, seasonal
patterns, random
Time Frame
Short-range to medium-range
Daily, weekly monthly forecasts of
sales data
Up to 2 years into the future
Long-range
Strategic planning of goals, products,
markets
Planning beyond 2 years into the future
Demand Behavior
Trend
gradual, long-term up or down
movement
Cycle
up & down movement repeating over
long time frame
Seasonal pattern
periodic oscillation in demand which
repeats
Random movements follow no pattern
Demand
Demand
Forms of Forecast Movement
Random
movement
Demand
Time
(c) Seasonal pattern
Figure 8.1
Time
(b) Cycle
Demand
Time
(a) Trend
Time
(d) Trend with seasonal pattern
Forecasting Methods
Time series
Regression or causal modeling
Qualitative methods
Management judgment, expertise, opinion
Use management, marketing, purchasing,
engineering
Delphi method
Solicit forecasts from experts
Time Series Methods
Statistical methods using historical
data
Moving average
Exponential smoothing
Linear trend line
Assume patterns will repeat
Naive forecasts
Forecast = data from last period
Moving Average
Average several periods of data
Dampen, smooth out changes
Use when demand is stable with no
trend or seasonal pattern
stock market analysis - trend
analysis
Moving Average
Average several
periods of data
Sum of Demand
Dampen, smooth out
In n Periods
changes
n
Use when demand is
stable with no trend
or seasonal pattern
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Example 8.1
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Example 8.1
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
Daug+Dsep+Doct
MAnov =
3
90 + 110 + 130
=
3
= 110 orders for Nov
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Example 8.1
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
–
THREE-MONTH
MOVING AVERAGE
–
–
–
103.3
88.3
95.0
78.3
78.3
85.0
105.0
110.0
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Example 8.1
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
–
THREE-MONTH
MOVING AVERAGE
–
–
–
103.3
88.3
95.0
78.3
78.3
85.0
105.0
110.0
5
Di
i=1
MA5 =
=
5
90 + 110 + 130 + 75 + 50
5
= 91 orders for Nov
Simple Moving Average
MONTH
Jan
Feb
Mar
Apr
May
June
July
Aug
Sept
Oct
Nov
Example 8.1
ORDERS
PER MONTH
120
90
100
75
110
50
75
130
110
90
–
THREE-MONTH
MOVING AVERAGE
–
–
–
103.3
88.3
95.0
78.3
78.3
85.0
105.0
110.0
FIVE-MONTH
MOVING AVERAGE
–
–
–
–
–
99.0
85.0
82.0
88.0
95.0
91.0
Smoothing Effects
150 –
125 –
Orders
100 –
75 –
50 –
25 –
0–
|
Jan
|
Feb
|
Mar
|
|
Apr May
|
|
June July
Month
Figure 8.2
|
|
Aug Sept
|
Oct
|
Nov
Smoothing Effects
150 –
125 –
Orders
100 –
75 –
50 –
Actual
25 –
0–
|
Jan
|
Feb
|
Mar
|
|
Apr May
|
|
June July
Month
Figure 8.2
|
|
Aug Sept
|
Oct
|
Nov
Smoothing Effects
150 –
125 –
Orders
100 –
75 –
50 –
3-month
Actual
25 –
0–
|
Jan
|
Feb
|
Mar
|
|
Apr May
|
|
June July
Month
Figure 8.2
|
|
Aug Sept
|
Oct
|
Nov
Smoothing Effects
150 –
5-month
125 –
Orders
100 –
75 –
50 –
3-month
Actual
25 –
0–
|
Jan
|
Feb
|
Mar
|
|
Apr May
|
|
June July
Month
Figure 8.2
|
|
Aug Sept
|
Oct
|
Nov
Weighted Moving Average
Adjusts moving average
method to more closely
reflect data fluctuations
Weighted Moving Average
WMAn = Wi Di
Adjusts
i=1
moving
where
average
Wi = the weight for period i,
method to
between 0 and 100
more closely
percent
reflect data
fluctuations
W = 1.00
i
Weighted Moving
Average Example
MONTH
August
September
October
Example 8.2
WEIGHT
DATA
17%
33%
50%
130
110
90
Weighted Moving
Average Example
MONTH
August
September
October
WEIGHT
DATA
17%
33%
50%
130
110
90
3
November forecast WMA3 =
Wi Di
i=1
= (0.50)(90) + (0.33)(110) + (0.17)(130)
= 103.4 orders
3 Month = 110
5 month = 91
Linear Trend Line
y = a + bx
where
a
b
x
y
=
=
=
=
intercept (at period 0)
slope of the line
the time period
forecast for demand for period x
Seasonal Adjustments
Repetitive increase/
decrease in demand
Use seasonal factor
to adjust forecast
Seasonal Adjustments
Repetitive increase/
decrease in demand
Use seasonal factor
to adjust forecast
Di
Seasonal factor = Si =
D
= demand for period/sum of demand
Seasonal Adjustment
YEAR
1999
2000
2001
Total
DEMAND (1000’S PER QUARTER)
1
2
3
4
Total
12.6
14.1
15.3
42.0
8.6
10.3
10.6
29.5
6.3
7.5
8.1
21.9
17.5
18.2
19.6
55.3
45.0
50.1
53.6
148.7
Seasonal Adjustment
YEAR
1999
2000
2001
Total
DEMAND (1000’S PER QUARTER)
1
2
3
4
Total
12.6
14.1
15.3
42.0
8.6
10.3
10.6
29.5
6.3
7.5
8.1
21.9
17.5
18.2
19.6
55.3
45.0
50.1
53.6
148.7
D1
42.0
S1 =
=
= 0.28
D 148.7
D3
21.9
S3 =
=
= 0.15
D 148.7
D2
29.5
S2 =
=
= 0.20
D 148.7
D4
55.3
S4 =
=
= 0.37
D 148.7
Seasonal Adjustment
YEAR
DEMAND (1000’S PER QUARTER)
1
2
3
4
Total
1999
2000
2001
Total
12.6
14.1
15.3
42.0
8.6
10.3
10.6
29.5
6.3
7.5
8.1
21.9
17.5
18.2
19.6
55.3
Si
0.28
0.20
0.15
0.37
45.0
50.1
53.6
148.7
Seasonal Adjustment
YEAR
DEMAND (1000’S PER QUARTER)
1
2
3
4
Total
1999
2000
2001
Total
12.6
14.1
15.3
42.0
8.6
10.3
10.6
29.5
6.3
7.5
8.1
21.9
17.5
18.2
19.6
55.3
Si
0.28
0.20
0.15
0.37
45.0
50.1
53.6
148.7
45
Forecast for 1st qtr 2002
50.1
50*.28
14
53.6
148.7
49.56667 Forecast for 2002 using simple 3 year moving ave
Forecast Accuracy
Find a method which minimizes error
Error = Actual - Forecast
Mean Absolute
Deviation (MAD)
MAD Example
PERIOD
1
2
3
4
5
6
7
8
9
10
11
12
DEMAND, Dt
Ft ( =0.3)
37
40
41
37
45
50
43
47
56
52
55
54
37.00
37.00
37.90
38.83
38.28
40.29
43.20
43.14
44.30
47.81
49.06
50.84
557
Forecast Control
Reasons for out-of-control forecasts
Change in trend
Appearance of cycle
Weather changes
Promotions
Competition
Politics
Tracking Signal
• Tracking Signal establishes control limits usually +/- 3 MAD
• The greater the tracking signal the more
the demand exceeds the forecast
• Sum(Demand-Forecast)/Mean Absolute
Deviation
• Sometimes called Running Sum of
Forecasting Error
Next Week
• Chap 9
• Chapter 14