chapter 13. supply chain management an integrated approach to improving quality and efficiency...
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Chapter 13. Supply Chain Management
An Integrated Approach to Improving Quality and Efficiency
Daniel B. McLaughlinJulie M. Hays
Healthcare Operations Management
Copyright 2008 Health Administration Press. All rights reserved. 13-2
Chapter 13Supply 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
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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
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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.
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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.
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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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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
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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
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ABC Classification System
Classifying inventory according to some measure of importance and allocating control efforts accordingly
Pareto Principle
-AA very important
-BB moderately
important
-CC least important
Annual $ volume of items
AA
BB
CC
High (80%)
Low (5%)
Few(20%)
Many(50%)
Number of Items
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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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Forecasting
• Exercise
• Averaging methods
• Trend, seasonal, and cyclical models
• Model development and evaluation
• VVH example
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ForecastingExercise 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?
Copyright 2008 Health Administration Press. All rights reserved. 13-13
Exercise I—Graphs
2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
1 2 3 4 5 6 7 8
Series1
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8
Series1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
1 2 3 4 5 6 7 8
Series1
Series a
Series b
Series c
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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- 0.5 + 2x, where x specifies the position (index) of the number in the
series
- Next number is 18.5
c) 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
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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
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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 +
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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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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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Forms of Forecast MovementD
eman
d
Time
Trend
Randommovement
Dem
and
Time
Seasonalpattern
Dem
and
Time
Dem
and
Time
Cycle
Trend with seasonal pattern
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ForecastingAveraging 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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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
nDDDF nttt
t
21
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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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.
DwDwDwF ntntttttt
2211
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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Exponential Smoothing
Times series forecasting technique that does not require large amounts of historical data
DFF ttt 111
Ft = Exponentially smoothed forecast for period t
Ft-1 = Exponentially smoothed forecast for prior period
Dt-1 = Actual demand in the prior period
= Desired response rate, or smoothing constant
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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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Holt’s Trend Adjusted Exponential Smoothing
Exponentially smoothed forecast that accounts for a trend in the data
) -FITδ(FTT
α)FIT(αDF
TFFIT
tttt
ttt
ttt
111
11 1
and
FITt = Forecast for period t including the trendFt = Smoothed forecast for period tTt = Smoothed trend for period tDt−1 = Value in the previous period0 = smoothing constant 1; 0 = smoothing constant 1
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Forecast Accuracy
• Error = Actual − Forecast
• Find a method that minimizes error
• Mean absolute deviation (MAD)
• Mean squared error
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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
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VVH Diaper Example
Week of Period Actual1-Jan 1 708-Jan 2 4215-Jan 3 6322-Jan 4 5229-Jan 5 565-Feb 6 53
12-Feb 7 6619-Feb 8 6126-Feb 9 455-Mar 10 5412-Mar 11 5319-Mar 12 4326-Mar 13 60
Weekly Demand
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13
Period
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VVH Simple Moving Average
515
45545343605
14
91011121314
21
F
DDDDDF
DDDF nnttt
t
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VVH Weighted Moving Average
5.53532.0433.0605.014
14 111112121313
2211
FDwDwDwF
DwDwDwF ntntttttt
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VVH Exponential Smoothing
54)5275.0()6025.0(
1
1
14
131314
11
FFDFFDF ttt
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VVH Comparison (from the Excel template)
Weight 3 Weight 2 Weight 1
Periods 5Least Recent 0.2 0.3 0.5
Most Recent α 0.25
MAD 7 MAD 6 MAD 8MSE 86 MSE 75 MSE 135
Period Actual Forecast Error Period Actual Forecast Error Period Actual Forecast Error1 70 1 70 1 702 42 2 42 2 42 70 283 63 3 63 3 63 63 04 52 4 52 58 6 4 52 63 115 56 5 56 53 3 5 56 60 46 53 57 4 6 53 56 3 6 53 59 67 66 53 13 7 66 54 12 7 66 58 88 61 58 3 8 61 60 1 8 61 60 19 45 58 13 9 45 61 16 9 45 60 15
10 54 56 2 10 54 54 0 10 54 56 211 53 56 3 11 53 53 0 11 53 56 312 43 56 13 12 43 52 9 12 43 55 1213 60 51 9 13 60 48 12 13 60 52 814 51 14 53.5 14 54
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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 accuratethan individual service forecasts.
I see that you willget an A this semester.
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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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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
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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. 13-37
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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Inventory Order Cycle
Demand rate
0
TimeLead time
Lead time
Order Placed
Order Placed
Order Received
Order Received
InventoryLevel
Reorderpoint, R
Orderquantity, Q
Average amount of inventory
held = Q/2
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Reorder Point
The point in time by which stock must be ordered to replenish inventory before a stockout occurs
LdR R = Reorder point
d = average demand per period
L = lead time (in the same units as above)
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EOQ Model Cost Curves
MinimumTotal Cost
Total Cost
Ordering Cost = o*D/Q
OrderQuantity, Q
Annualcost ($)
OptimalOrder Quantity
Holding Cost = h*Q/2
Cost Holding Annual
Cost) Setupor der Demand)(Or 2(Annual =
h
2Do = QOPT
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EOQ Model Insights
• As holding costs increase, the optimal order quantity decreases. (Order smaller amounts more often because inventory is expensive to hold.)
• As ordering costs increase, the optimal order quantity increases. (Order larger amounts less often because it is expensive to order.)
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EOQ Model Implications
Total Cost
Ordering Cost
AnnualCost ($)
Order Quantity
Holding Cost
Q* Q*
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EOQ Model Implications
Total Cost
Ordering Cost
AnnualCost ($)
Order Quantity
Holding Cost
Q* Q*
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VVH Diaper Example
• Cost $5/case
• Holding costs 33% or $1.67/case-year
• Ordering costs $100
• Lead time 1 week
• She calculates annual demand as:
yearcases 782,2
year weeks52
weekdiapers of cases 5.53
period
dD
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VVH Diaper Example
She calculates the reorder point as
She calculates the EOQ as:
cases 577cases 174,333
case67.1$
cases 782,2100$2
2*quantityorder Economic
2
h
DoQ
cases 5.53 week1weekcases 5.53
pointReorder
LdR
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VVH Diaper Example
Annual demand D = 2,782 units/yearOrdering cost per order (setup) S = 100 $/orderAnnual carrying cost per unit H = 1.67 $/unit-yearWorking days per year = 365 days/yearEconomic order quantity EOQ = 577.21 units
Actual order quantity Q = 577Increment DQ = 500Number of orders per year D/Q = 4.8 orders/yearLength of order cycle (days) Q/D = 75.7 daysAverage inventory Q/2 = 288.5 unitsAnnual carrying cost (Q/2) * H = $ 481.80 Annual ordering cost (D/Q) * S = $ 482.15 Total annual cost TC = $ 963.94
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Reorder Point with Safety Stock
Reorder point (R)
Order quantity (Q)
Inventory level
0Lead time Time
Safety stock (SS)
Lead time
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Reorder Point with Safety Stock
Reorder point
Safety stock
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
SSLdR
LzSS
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Safety StockNormal(100, 20)
0.0
0.5
1.0
1.5
2.0
2.5
40
60
80
100
120
140
160
< >15.9%84.1%-Infinity 120.0
BestFit Student VersionFor Academic Use Only
Normal(100, 20)
0.0
0.5
1.0
1.5
2.0
2.5
40
60
80
100
120
140
160
< >15.9%84.1%-Infinity 120.0
BestFit Student VersionFor Academic Use Only Reorder
point
Probability of a stockout = 16%
Probability of meeting demand during lead time = service level = 84%
Example units
Z
100
Average demand duringLead time = dL
0
120
1
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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.)
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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:
• New reorder point:
cases 9.185.1164.1 LzSS
cases 4.72cases 9.18 week1weekcases 5.53 SSLdR
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VVH Diaper Example
Average daily demand 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 SL =
Stock out risk 0.05
z associated with service level 1.64
Average demand during lead time dL = 53.48 units
Safety stock SS = 18.9 units
Reorder point ROP = 72.4 units
Reorder Point
0.0 20.0 40.0 60.0 80.0 100.0
Daliy Demand
Pro
bab
ility
daily demand ROP
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VVH Diaper Example
Reorder point (72)
Order quantity (577)
Inventory level
0Lead time =1 week
Time
Safety stock (19)
Lead time
Average demand = 53.5
cases/week
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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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Inventory Systems
• Simple
• JIT
• MRP
• ERP
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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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JIT—Kanbans
Task 1Workstation
1
Task 2Workstation
2
Full Kanban
Customer Order
Full Kanban
Empty Kanban
Empty Kanban
Microsoft Visio® screen shots reprinted with permission from Microsoft Corporation.
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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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Enterprise Information Technology Trends
1960 1970 1980 1990 2000 2010
Computer Integrated
Manufacturing
ConcurrentEngineering
CollaborativeEngineering
SCM
MRP II
MRP ICAD/CAM
ERP
Business Webs
Networks TCP/IPMobile Networks
Automation
E-BusinessE-Commerce
Data Processing
MainframeMinicomputer
Microcomputer
HandheldAppliances
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MRP Product Structure
Table top(1)
Lead time = 2 weeks
Leg(4)
Lead time = 3 weeks
Table(end item)
Lead time = 1 week
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MRP Logic
Ordertable tops
Week 1 2 3 4 5
Ordertable legs
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ERP Systems Link Functional Areas
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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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Strategic Supply Chain Management
Many are the same as any otherimprovement/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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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
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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 system-wide view of the supply chain and seek improvement for the system rather than for individual departments or organizations in that system.