Healthcare Operations
Management
An Integrated Approach to
Improving Quality and Efficiency
Chapter 13. Supply Chain
Management
Daniel B. McLaughlin
Julie M. Hays
Chapter 13
Supply Chain Management
• What is Supply Chain Management (SCM)?
• Why is SCM Important for Healthcare
Organizations?
• Tracking and Managing Inventory
• Forecasting
• Order Amount and Timing
• Inventory Systems
• Procurement and Vendor Relationship
Management
• Strategic SCM
Copyright 2008 Health Administration Press. All rights reserved.
13-2
Supply Chain Management (SCM)
• The management of all activities and
processes related to both upstream vendors
and downstream customers in the value
chain
• Tracking and managing demand, inventory,
and delivery
• Procurement and vendor relationship
management
• Technology enabled
Copyright 2008 Health Administration Press. All rights reserved.
13-3
SCM in Healthcare
• In 2006, the United States will spend over
$2 trillion on healthcare.
• Annual cost/family for health insurance is
forecasted to be $22,000 by 2010.
• By 2016, it is predicted that one dollar of
every five dollars of the U.S. economy will
be devoted to healthcare.
Copyright 2008 Health Administration Press. All rights reserved.
13-4
SCM in Healthcare
• Supply costs in hospitals account for 15–25
percent of operating costs (HFMA 2002;
HFMA 2005).
• Transaction costs are estimated at $150 per
order for buyer and seller (HFMA 2001).
• There is 35 percent inconsistency between
hospital and supplier data, and it costs $15
to $50 to research and correct a single order
discrepancy.
Copyright 2008 Health Administration Press. All rights reserved.
13-5
Inventory
• Inventory is the stock of items held to meet
future demand.
• Inventory management answers three
questions:
- How much to hold
- How much to order
- When to order
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13-6
Functions of Inventory
• To meet anticipated demand
• To level process flow
• To protect against stockouts
• To take advantage of order cycles
• To help hedge against price increases or to
take advantage of quantity discounts
• To decouple process steps
Copyright 2008 Health Administration Press. All rights reserved.
13-7
Effective Inventory Management
•
•
•
•
•
Classification system
Inventory tracking system
Reliable forecast of demand
Knowledge of lead times
Reasonable estimates of:
- Holding or carrying costs
- Ordering or setup costs
- Shortage or stockout costs
Copyright 2008 Health Administration Press. All rights reserved.
13-8
ABC Classification System
Classifying inventory according to some
measure of importance and allocating control
efforts accordingly
Pareto Principle
-A very important
-B moderately
important
-C least important
High (80%)
Annual
$ volume
of items
A
B
C
Low (5%)
Few
(20%)
Many
(50%)
Number of Items
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13-9
Inventory Tracking
• Track additions and removals
- Bar-coding
- Point of use or point of sale (POS)
- RFID
• Physical count of items
- Periodic intervals
- Cycle count
- Find and correct errors
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13-10
Forecasting
•
•
•
•
•
Exercise
Averaging methods
Trend, seasonal, and cyclical models
Model development and evaluation
VVH example
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13-11
Forecasting
Exercise I
•
•
Identify the pattern and construct a formula
that will “predict” successive numbers in
the series.
What is the next number in the series?
(a) 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7
(b) 2.5, 4.5, 6.5, 8.5, 10.5, 12.5, 14.5, 16.5
(c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5
•
What is the formula for the next number in
the series?
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13-12
Exercise I—Graphs
Series b
18
16
Series a
14
12
4.4
10
Series1
4.2
8
4.0
6
4
3.8
2
Series1
3.6
0
1
3.4
2
3
4
5
6
7
8
Series c
3.2
10.0
3.0
9.0
2.8
1
2
3
4
5
6
7
8
8.0
7.0
6.0
Series1
5.0
4.0
3.0
2.0
1.0
0.0
1
Copyright 2008 Health Administration Press. All rights reserved.
2
3
4
5
6
7
8
13-13
Exercise I Solution
a)
3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7
-
Constant
-
Next number is 3.7
b)
2.5, 4.5, 6.5, 8.5, 10.5, 12.5, 14.5, 16.5
-
c)
0.5 + 2x, where x specifies the position (index) of the number in the
series
Next number is 18.5
5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5
-
4.5 + 0.5x + Cs, where x specifies the position (index) of the number in
the series
Cs represents the seasonality factor
C1 = 0, C2 = 2, C3 = 0, C4 = −2
Next numbers: 9, 11.5, 10, 8.5
Copyright 2008 Health Administration Press. All rights reserved.
13-14
Exercise II
• Identify the pattern and construct a formula
that will “predict” successive numbers in the
series.
• What is the next number in the series?
(a) 4.1, 3.3, 4.0, 3.8, 3.9, 3.4, 3.5, 3.7
(b) 2.9, 4.7, 6.8, 8.2, 10.3, 12.7, 14.2, 16.3
(c) 5.3, 7.2, 6.4, 4.5, 6.8, 9.7, 8.2, 6.3
Copyright 2008 Health Administration Press. All rights reserved.
13-15
Exercise II Solution
• Same as series above, but with a random
component generated from normal random
number generator with mean 0
(a) 4.1, 3.3, 4.0, 3.8, 3.9, 3.4, 3.5, 3.7
• 3.7 + 
(b) 2.9, 4.7, 6.8, 8.2, 10.3, 12.7, 14.2, 16.3
• 0.5 + 2x + 
(c) 5.3, 7.2, 6.4, 4.5, 6.8, 9.7, 8.2, 6.3
• 4.5 + 0.5x + Cs + 
Copyright 2008 Health Administration Press. All rights reserved.
13-16
Forecasting Methods
• Qualitative methods
- Based on expert opinion
and intuition; often used
when there are no data available
• Quantitative methods
- Time series methods, causal methods
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13-17
Demand Behavior
• Trend
- Gradual, long-term up or down movement
• Cycle
- Up and down movement repeating over long
time frame
• Seasonal pattern
- Periodic, repeating oscillation in demand
• Random movements follow no pattern
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13-18
Trend
Demand
Demand
Forms of Forecast Movement
Cycle
Random
movement
Time
Seasonal
pattern
Demand
Demand
Time
Trend with
seasonal pattern
Time
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Time
13-19
Forecasting
Averaging Methods
•
•
•
•
Simple moving average
Weighted moving average
Exponential smoothing
Averaging methods all assume that the
dependent variable is relatively constant
over time; no trends or cycles
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13-20
Simple Moving Average
Average over a given number of periods that
is updated by replacing the data in the oldest
period with that in the most recent period
F
t
D




D
Dt  n
t 1
t 2
n
Ft = Forecasted demand for the period
Dt-1 = Actual demand in period t − 1
n = Number of periods in the moving average
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13-21
Weighted Moving Average
Simple moving average where weights are
assigned to each period in the average.
The sum of all the weights must equal
one.
Ft w D
t 1
t 1
 w t  2 D t  2    w t  n Dt  n
Ft = Forecasted demand for the period
Dt-1 = Actual demand in period t − 1
wt-1 = Weight assigned to period t − 1
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13-22
Exponential Smoothing
Times series forecasting technique that does
not require large amounts of historical data
F  1  F
t
Ft
Ft-1
Dt-1

=
=
=
=


Dt 1
t 1
Exponentially smoothed forecast for period t
Exponentially smoothed forecast for prior period
Actual demand in the prior period
Desired response rate, or smoothing constant
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13-23
Forecasting
Trend, Seasonal, and Cyclical Models
• Holt’s trend-adjusted exponential smoothing
technique
• Winter’s triple exponential smoothed model
• ARIMA models
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13-24
Holt’s Trend Adjusted
Exponential Smoothing
Exponentially smoothed forecast that accounts
for a trend in the data
FITt  Ft  Tt
and
Ft  αDt 1  ( 1  α)FITt 1
Tt  Tt 1  δ(Ft 1 -FITt 1 )
FITt = Forecast for period t including the trend
Ft = Smoothed forecast for period t
Tt = Smoothed trend for period t
Dt−1 = Value in the previous period
0  = smoothing constant 1; 0  = smoothing constant 1
Copyright 2008 Health Administration Press. All rights reserved.
13-25
Forecast Accuracy
•
•
•
•
Error = Actual − Forecast
Find a method that minimizes error
Mean absolute deviation (MAD)
Mean squared error
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13-26
Forecasting
Model Development and Evaluation
•
•
•
•
•
•
•
•
Identify purpose of forecast
Determine time horizon of forecast
Collect relevant data
Plot data and identify pattern
Select forecasting model(s)
Make forecast
Evaluate quality of forecast
Adjust forecast and monitor results
Copyright 2008 Health Administration Press. All rights reserved.
13-27
VVH Diaper Example
Week of Period Actual
1-Jan
8-Jan
15-Jan
22-Jan
29-Jan
5-Feb
12-Feb
19-Feb
26-Feb
5-Mar
12-Mar
19-Mar
26-Mar
1
2
3
4
5
6
7
8
9
10
11
12
13
70
42
63
52
56
53
66
61
45
54
53
43
60
Weekly Demand
80
70
60
50
40
30
20
10
0
1
2
3
4
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5
6
7
8
9
10
11
12
13
Period
13-28
VVH Simple Moving Average
F
F
D
t 1
t
 Dt 2  Dt n
n
D
13
14
 D12  D11  D10  D9
5
60  43  53  54  45
 51
F 14 
5
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13-29
VVH Weighted Moving Average
F t  w D  w D  w D
F 14  w D  w D  w D
F 14  0.5  60  0.3  43  0.2  53  53.5
t 1
13
t 1
13
t 2
12
t 2
12
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t n
11
t n
11
13-30
VVH Exponential Smoothing
F
F
F
t
  Dt 1  1    F t 1
14
  D13  1    F 13 
14
 (0.25  60)  (0.75  52)  54
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13-31
VVH Comparison
(from the Excel template)
Weight 3
Weight 2
0.3
Most
Periods
5
Least
Recent
0.2
MAD
MSE
7
86
MAD
MSE
6
75
Period Actual Forecast Error
70
1
42
2
63
3
52
4
56
5
4
57
53
6
13
53
66
7
3
58
61
8
13
58
45
9
2
56
54
10
3
56
53
11
13
56
43
12
9
51
60
13
51
14
Weight 1
0.5 Recent
Period Actual Forecast Error
70
1
42
2
63
3
6
58
52
4
3
53
56
5
3
56
53
6
12
54
66
7
1
60
61
8
16
61
45
9
0
54
54
10
0
53
53
11
9
52
43
12
12
48
60
13
53.5
14
Copyright 2008 Health Administration Press. All rights reserved.
α
MAD
MSE
0.25
8
135
Period Actual Forecast Error
70
1
28
70
42
2
0
63
63
3
11
63
52
4
4
60
56
5
6
59
53
6
8
58
66
7
1
60
61
8
15
60
45
9
2
56
54
10
3
56
53
11
12
55
43
12
8
52
60
13
54
14
13-32
Realities of Forecasting
• Forecasts are seldom perfect.
• Most forecasting methods assume that there
is some underlying stability in the system.
• Service family and aggregated service
forecasts are more accurate I see that you will
get an A this semester.
than individual service
forecasts.
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13-33
Order Amount and Timing
How much to hold
How much to order
When to order
• Basic economic order quantity (EOQ)
• Fixed order quantity with safety stock
• More models
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13-34
Definitions
Lead time—time between placing an order and
receiving the order
Holding (or carrying) costs—costs associated with
keeping goods in storage
Ordering (or setup) costs—costs of ordering and
receiving goods
Shortage costs—costs of not having something in
inventory when it is needed
Back orders—unfilled orders
Stockouts—occur when the desired good is not
available
Copyright 2008 Health Administration Press. All rights reserved.
13-35
Definitions
Independent demand is demand
that is generated by the customer
and is not a result of demand for
another good or service.
Dependent demand is demand that
results from another demand.
Demand for tires and steering
wheels (dependent) is related to
the demand for cars (independent).
Copyright 2008 Health Administration Press. All rights reserved.
13-36
Assumptions of the
Basic EOQ Model
• Demand for the item in question is
independent.
• Demand is known and constant.
• Lead time is known and constant.
• Ordering costs are known and constant.
• Back orders, stockouts, and quantity
discounts are not allowed.
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13-37
Inventory Order Cycle
Demand
rate
Order
quantity, Q
Average
amount of
inventory
held = Q/2
Inventory
Level
Reorder
point, R
0
Time
Lead
time
Lead
time
Order Order
Placed Received
Order
Placed
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Order
Received
13-38
Reorder Point
The point in time by which stock must be ordered
to replenish inventory before a stockout occurs
R  dL
R = Reorder point
d = average demand per period
L = lead time (in the same units as above)
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13-39
EOQ Model Cost Curves
Annual
cost ($)
Minimum
Total Cost
Total Cost
Holding Cost = h*Q/2
Ordering Cost = o*D/Q
Optimal
Order Quantity
Q OPT =
Order
Quantity, Q
2Do
2(Annual Demand)(Or der or Setup Cost)
=
h
Annual Holding Cost
Copyright 2008 Health Administration Press. All rights reserved.
13-40
EOQ Model Insights
• As holding costs increase, the optimal order
quantity decreases. (Order smaller amounts
more often because inventory is expensive
to hold.)
• As ordering costs increase, the optimal
order quantity increases. (Order larger
amounts less often because it is expensive
to order.)
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13-41
EOQ Model Implications
Total Cost
Annual
Cost ($)
Holding Cost
Ordering Cost
Q*
Q*
Order Quantity
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13-42
EOQ Model Implications
Total Cost
Annual
Cost ($)
Holding Cost
Ordering Cost
Q*
Q*
Order Quantity
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13-43
VVH Diaper Example
•
•
•
•
•
Cost $5/case
Holding costs 33% or $1.67/case-year
Ordering costs $100
Lead time 1 week
She calculates annual demand as:
D  d  period
 53.5 cases of diapers
 2,782 cases
week
 52 weeks
year
year
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13-44
VVH Diaper Example
She calculates the reorder point as
Reorder point  R  d L
 53.5 cases
1 week  53.5 cases
week
She calculates the EOQ as:
2 o D
Economic order quantity  Q* 
h
2  $100  2,782 cases

$1.67 case
 333,174 cases 2  577 cases
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13-45
VVH Diaper Example
Annual demand
Ordering cost per order (setup)
Annual carrying cost per unit
Working days per year
Economic order quantity
D=
S=
H=
=
EOQ =
Actual order quantity
Q=
Increment
DQ =
Number of orders per year
D/Q =
Length of order cycle (days)
Q/D =
Average inventory
Q/2 =
Annual carrying cost
(Q/2) * H = $
Annual ordering cost
(D/Q) * S = $
Total annual cost
TC = $
2,782
100
1.67
365
577.21
577
500
4.8
75.7
288.5
481.80
482.15
963.94
Copyright 2008 Health Administration Press. All rights reserved.
units/year
$/order
$/unit-year
days/year
units
orders/year
days
units
13-46
Reorder Point with Safety Stock
Order
quantity (Q)
Inventory
level
Reorder
point (R)
Safety
stock (SS)
0
Lead
time
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Time
Lead
time
13-47
Reorder Point with Safety Stock
Reorder point
R  d L  SS
Safety stock
SS  z   L
where
z is the z-score associated with the desired service
level (number of standard deviations above the
mean)
L= standard deviation of demand during lead time
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13-48
Normal(100, 20)
2.5
Safety Stock
2.0
Normal(100, 20)
2.5
1.5
BestFit Student Version
Probability of
meeting demand during
lead time = service level = 84%
Reorder
point
For Academic Use Only
2.0
1.0
1.5
BestFit Student Version
0.5
For Academic Use Only
1.0
0.5
<
-Infinity
84.1%
Probability of
a stockout = 16%
160
140
120
100
80
60
40
0.0
15.9%
>
120.0
<
-Infinity
84.1%
15.9%
160
120
140
100
120
100
80
Example
units
60
40
0.0
>
120.0
Average demand during
Lead time = dL
Z
0
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1
13-49
Model Insights
• As the desired service level increases, the
amount of safety stock increases. (If fewer
stockouts are desired, more inventory must
be carried.)
• As the variation in demand during lead time
increases, the amount of safety stock
increases. (If demand variation or lead time
can be decreased, less safety stock is
needed.)
Copyright 2008 Health Administration Press. All rights reserved.
13-50
VVH Diaper Example
• Desired service level = 95 percent
- With five orders/year, this means that the hospital would
experience one stockout every four years
• Standard deviation of demand during lead = σL =
11.5 cases of diapers
• Amount of safety stock needed:
SS  z  L  1.64 11.5  18.9 cases
• New reorder point:

R  d L  SS  53.5 cases
week

1 week  18.9 cases  72.4 cases
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13-51
VVH Diaper Example
d=
7.64
units
Average lead time
L=
7
days
Std dev demand during lead
time
L =
11.5
units
Service level
SL =
0.95
Increment
DSL =
Stock out risk
Reorder Point
Probability
Average daily
demand
0.05
0.0
z associated with service level
40.0
60.0
80.0
100.0
Daliy Demand
1.64
Average demand during lead
time
dL =
53.48
units
Safety
stock
SS =
18.9
units
ROP =
72.4
units
Reorder point
20.0
daily demand
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ROP
13-52
VVH Diaper Example
Average demand
= 53.5
cases/week
Order
quantity (577)
Inventory
level
Safety
stock (19)
Reorder
point (72)
0
Lead
time =
1 week
Copyright 2008 Health Administration Press. All rights reserved.
Time
Lead
time
13-53
More Inventory Models
• Fixed period with safety stock
- Orders are bundled and/or vendors deliver
according to a set schedule
• Quantity discounts
• Price breaks
• Etc.
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13-54
Inventory Systems
•
•
•
•
Simple
JIT
MRP
ERP
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13-55
Two-Bin System
When the first bin is empty,
stock is taken from the
second bin and an order is
placed. There should be
enough stock in the second
bin to last until more stock
is delivered.
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13-56
JIT—Kanbans
Empty
Kanban
Empty
Kanban
Full
Kanban
Task 1
Workstation
1
Full
Kanban
Task 2
Workstation
2
Customer
Order
Microsoft Visio® screen shots reprinted with permission from Microsoft Corporation.
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13-57
Flow and Pull
• Continuous or single piece flow—move items
(jobs, patients, products) through the steps of the
process one at a time without interuptions or
waiting.
• Pull or just-in-time (JIT)—products or services are
not produced until the downstream customer
demands them.
• Heijunka (i.e., “make flat and level”)—eliminate
variation in volume and variety of production.
- Level patient demand
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13-58
Enterprise Information
Technology Trends
E-Commerce
E-Business
Automation
Collaborative
Engineering
Data Processing
Computer
Integrated
Manufacturing
Concurrent
Engineering
Business Webs
ERP
SCM
Appliances
MRP II
Handheld
MRP I
Microcomputer
CAD/CAM
Minicomputer
Mainframe
1960
1970
Networks TCP/IP
1980
1990
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Mobile Networks
2000
2010
13-59
MRP Product Structure
Table
(end item)
Lead time = 1 week
Table top
(1)
Lead time = 2 weeks
Leg
(4)
Lead time = 3 weeks
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13-60
MRP Logic
Order
table tops
Order
table legs
Week
1
2
3
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4
5
13-61
ERP Systems Link Functional
Areas
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13-62
Procurement and Vendor
Relationship Management
•
•
•
•
•
•
•
E-procurement
Value-based standardization
Outsourcing
Vendor managed inventory (VMI)
Automated supply carts
Group purchasing organizations (GPO)
Disintermediation
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13-63
Strategic Supply Chain
Management
Many are the same as any other
improvement/change initiative:
• Top management support
• Employee buy-in
• Structure and staffing need to support the desired
improvements
• Process analysis and improvement
• Need relevant, accurate data and metrics
• Training
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13-64
Strategic Supply Chain
Management
• Need to evaluate cost and benefits of
technology-enabled solutions
• Need to highlight the necessity and benefits
of strategic supply chain management
• Improved inventory management through
better understanding of the systems
- Consequences of unofficial inventory
- Just-in-time systems
- Improved inventory tracking systems
Copyright 2008 Health Administration Press. All rights reserved.
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Strategic Supply Chain
Management
• Vendor partnerships
- Information sharing
- Investigation and determination of mutually
beneficial solutions
- Performance tracking
• Continually educate and support a systemwide view of the supply chain and seek
improvement for the system rather than for
individual departments or organizations in
that system.
Copyright 2008 Health Administration Press. All rights reserved.
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