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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


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


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.


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.


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


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


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


ABC Classification SystemClassifying 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

AABB

CC

High (80%)

Low (5%)

Few(20%)

Many(50%)

Number of Items


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


Forecasting

• Exercise• Averaging methods• Trend, seasonal, and cyclical models• Model development and evaluation• VVH example


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?


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


Exercise I Solutiona) 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


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


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 +


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


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


Forms of Forecast MovementD

eman

d

Time

Trend

Randommovement

Dem

and

Time

Seasonalpattern

Dem

and

Time

Dem

and

Time

Cycle

Trend with seasonal pattern


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


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 periodDt-1 = Actual demand in period t − 1

n = Number of periods in the moving average


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 periodDt-1 = Actual demand in period t − 1wt-1 = Weight assigned to period t − 1


Exponential SmoothingTimes series forecasting technique that does not require large amounts of historical data

DFF ttt 111

Ft = Exponentially smoothed forecast for period tFt-1 = Exponentially smoothed forecast for prior periodDt-1 = Actual demand in the prior period

= Desired response rate, or smoothing constant


Forecasting Trend, Seasonal, and Cyclical Models

• Holt’s trend-adjusted exponential smoothing technique

• Winter’s triple exponential smoothed model• ARIMA models


Holt’s Trend Adjusted Exponential Smoothing

Exponentially smoothed forecast that accounts for a trend in the data

) -FITδ(FTTα)FIT(αDF

TFFIT

tttt

ttt

ttt

111

11 1and

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


Forecast Accuracy• Error = Actual − Forecast• Find a method that minimizes error• Mean absolute deviation (MAD)• Mean squared error


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


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

01020

30405060

7080

1 2 3 4 5 6 7 8 9 10 11 12 13Period


VVH Simple Moving Average

515

45545343605

14

91011121314

21

F

DDDDDF

DDDF nnttt

t


VVH Weighted Moving Average

5.53532.0433.0605.014

14 111112121313

2211

FDwDwDwF

DwDwDwF ntntttttt


VVH Exponential Smoothing

54)5275.0()6025.0(

1

1

14

131314

11

FFDFFDF ttt


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


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.


Order Amount and Timing

How much to holdHow much to order

When to order

• Basic economic order quantity (EOQ)• Fixed order quantity with safety stock• More models


DefinitionsLead time—time between placing an order and receiving

the orderHolding (or carrying) costs—costs associated with

keeping goods in storageOrdering (or setup) costs—costs of ordering and

receiving goodsShortage costs—costs of not having something in

inventory when it is neededBack orders—unfilled ordersStockouts—occur when the desired good is not

available


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).


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.


Inventory Order Cycle

Demand rate

0Time

Lead time

Lead time

Order Placed

Order Placed

Order Received

Order Received

InventoryLevel

Reorderpoint, R

Orderquantity, Q

Average amount of inventory

held = Q/2


Reorder PointThe 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)


EOQ Model Cost CurvesMinimumTotal Cost

Total Cost

Ordering Cost = o*D/Q

OrderQuantity, Q

Annualcost ($)

OptimalOrder Quantity

Holding Cost = h*Q/2

Cost Holding AnnualCost) Setupor der Demand)(Or 2(Annual =

h2Do = QOPT


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.)


EOQ Model ImplicationsTotal Cost

Ordering Cost

AnnualCost ($)

Order Quantity

Holding Cost

Q* Q*


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


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

hDoQ

cases 5.53 week1weekcases 5.53

pointReorder

LdR


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


Reorder Point with Safety Stock

Reorder point (R)

Order quantity (Q)

Inventory level

0Lead time Time

Safety stock (SS)

Lead time


Reorder Point with Safety Stock

Reorder point

Safety stock

wherez 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


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


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.)


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


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

Prob

abili

ty

daily demand ROP


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


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.


Inventory Systems

• Simple• JIT• MRP• ERP


Two-Bin SystemWhen 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.


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.


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


Enterprise Information Technology Trends

1960 1970 1980 1990 2000 2010

Computer Integrated

Manufacturing

ConcurrentEngineering

CollaborativeEngineering

SCM

MRP IIMRP I

CAD/CAM

ERP

Business Webs

Networks TCP/IPMobile Networks

Automation

E-BusinessE-Commerce

Data Processing

MainframeMinicomputer

MicrocomputerHandheld

Appliances


MRP Product Structure

Table top(1)

Lead time = 2 weeks

Leg(4)

Lead time = 3 weeks

Table(end item)

Lead time = 1 week


MRP Logic

Ordertable tops

Week 1 2 3 4 5

Ordertable legs


ERP Systems Link Functional Areas


Procurement and Vendor Relationship Management

• E-procurement• Value-based standardization• Outsourcing• Vendor managed inventory (VMI)• Automated supply carts• Group purchasing organizations (GPO)• Disintermediation


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



• 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



• 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.

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